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Imtiaz MA, Melas K, Tin A, Talevi V, Chen H, Fornage M, Shrestha S, Gögele M, Emmert D, Pattaro C, Pramstaller P, Förster F, Horn K, Mosley TH, Fuchsberger C, Scholz M, Breteler MM, Aziz NA. Genome-Wide Association Study Meta-Analysis Uncovers Novel Genetic Variants Associated with Olfactory Dysfunction. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.08.09.24311665. [PMID: 39148842 PMCID: PMC11326328 DOI: 10.1101/2024.08.09.24311665] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
IMPORTANCE Olfactory dysfunction is among the earliest signs of many age-related neurodegenerative diseases and has been associated with increased mortality in older adults; however, its genetic basis remains largely unknown. OBJECTIVE To identify the genetic loci associated with olfactory dysfunction in the general population. DESIGN SETTING AND PARTIICIPANTS This genome-wide association study meta-analysis (GWMA) included participants of European ancestry (N = 22,730) enrolled in four different large population-based studies, followed by a multi-ancestry GWMA including participants of African ancestry (N = 1,030). The data analysis was performed from March 2023 through June 2024. EXPOSURES Genome-wide single nucleotide polymorphisms. MAIN OUTCOMES AND MEASURES Olfactory dysfunction was the outcome and assessed using a 12-item smell identification test. RESULTS GWMA revealed a novel genome-wide significant locus (tagged by rs11228623 at 11q12) associated with olfactory dysfunction. Gene-based analysis revealed a high enrichment for olfactory receptor genes in this region. Phenome-wide association studies demonstrated associations between genetic variants related to olfactory dysfunction and blood cell counts, kidney function, skeletal muscle mass, cholesterol levels and cardiovascular disease. Using individual-level data, we also confirmed and quantified the strength of these associations on a phenotypic level. Moreover, employing two-sample Mendelian Randomization analyses, we found evidence for causal associations between olfactory dysfunction and these phenotypes. CONCLUSIONS These findings provide novel insights into the genetic architecture of the sense of smell and highlight its importance for many aspects of human health.
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Affiliation(s)
- Mohammed Aslam Imtiaz
- Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Konstantinos Melas
- Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Adrienne Tin
- Department of Medicine, University of Mississippi Medical Center, Jackson, 39216, MS, USA
| | - Valentina Talevi
- Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Honglei Chen
- Department of Epidemiology and Biostatistics, Michigan State University, Michigan, USA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science at Houston, Houston, TX 77030, USA
- Human Genetics Center, School of Public Health, University of Texas Health Science at Houston, Houston, TX 77030, USA
| | - Srishti Shrestha
- The Memory Impairment Neurodegenerative Dementia (MIND) Research Center, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | - Martin Gögele
- Eurac Research, Institute for Biomedicine, Via Volta 21, 39100, Bolzano, Italy
| | - David Emmert
- Eurac Research, Institute for Biomedicine, Via Volta 21, 39100, Bolzano, Italy
| | - Cristian Pattaro
- Eurac Research, Institute for Biomedicine, Via Volta 21, 39100, Bolzano, Italy
| | - Peter Pramstaller
- Eurac Research, Institute for Biomedicine, Via Volta 21, 39100, Bolzano, Italy
| | - Franz Förster
- Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Medical Faculty, Leipzig University, Leipzig, Germany
| | - Katrin Horn
- Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Medical Faculty, Leipzig University, Leipzig, Germany
| | - Thomas H. Mosley
- The Memory Impairment Neurodegenerative Dementia (MIND) Research Center, University of Mississippi Medical Center, Jackson, MS 39216, USA
| | | | - Markus Scholz
- Institute for Medical Informatics, Statistics and Epidemiology (IMISE), Medical Faculty, Leipzig University, Leipzig, Germany
- LIFE Research Center for Civilization Diseases, Medical Faculty, Leipzig University, Leipzig, Germany
| | - Monique M.B. Breteler
- Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, Germany
| | - N. Ahmad Aziz
- Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurology, Faculty of Medicine, University of Bonn, Bonn, Germany
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Liu D, Aziz NA, Imtiaz MA, Pehlivan G, Breteler MMB. Associations of measured and genetically predicted leukocyte telomere length with vascular phenotypes: a population-based study. GeroScience 2024; 46:1947-1970. [PMID: 37782440 PMCID: PMC10828293 DOI: 10.1007/s11357-023-00914-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 08/15/2023] [Indexed: 10/03/2023] Open
Abstract
Shorter leukocyte telomere length (LTL) is associated with cardiovascular dysfunction. Whether this association differs between measured and genetically predicted LTL is still unclear. Moreover, the molecular processes underlying the association remain largely unknown. We used baseline data of the Rhineland Study, an ongoing population-based cohort study in Bonn, Germany [56.2% women, age: 55.5 ± 14.0 years (range 30 - 95 years)]. We calculated genetically predicted LTL in 4180 participants and measured LTL in a subset of 1828 participants with qPCR. Using multivariable regression, we examined the association of measured and genetically predicted LTL, and the difference between measured and genetically predicted LTL (ΔLTL), with four vascular functional domains and the overall vascular health. Moreover, we performed epigenome-wide association studies of three LTL measures. Longer measured LTL was associated with better microvascular and cardiac function. Longer predicted LTL was associated with better cardiac function. Larger ΔLTL was associated with better microvascular and cardiac function and overall vascular health, independent of genetically predicted LTL. Several CpGs were associated (p < 1e-05) with measured LTL (n = 5), genetically predicted LTL (n = 8), and ΔLTL (n = 27). Genes whose methylation status was associated with ΔLTL were enriched in vascular endothelial signaling pathways and have been linked to environmental exposures, cardiovascular diseases, and mortality. Our findings suggest that non-genetic causes of LTL contribute to microvascular and cardiac function and overall vascular health, through an effect on the vascular endothelial signaling pathway. Interventions that counteract LTL may thus improve vascular function.
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Affiliation(s)
- Dan Liu
- German Center for Neurodegenerative Diseases (DZNE), Population Health Sciences, Bonn, Germany
| | - N Ahmad Aziz
- German Center for Neurodegenerative Diseases (DZNE), Population Health Sciences, Bonn, Germany
- Department of Neurology, Faculty of Medicine, University of Bonn, Bonn, Germany
| | - Mohammed Aslam Imtiaz
- German Center for Neurodegenerative Diseases (DZNE), Population Health Sciences, Bonn, Germany
| | - Gökhan Pehlivan
- German Center for Neurodegenerative Diseases (DZNE), Population Health Sciences, Bonn, Germany
| | - Monique M B Breteler
- German Center for Neurodegenerative Diseases (DZNE), Population Health Sciences, Bonn, Germany.
- Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, Bonn, Germany.
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Verma SS, Gudiseva HV, Chavali VRM, Salowe RJ, Bradford Y, Guare L, Lucas A, Collins DW, Vrathasha V, Nair RM, Rathi S, Zhao B, He J, Lee R, Zenebe-Gete S, Bowman AS, McHugh CP, Zody MC, Pistilli M, Khachatryan N, Daniel E, Murphy W, Henderer J, Kinzy TG, Iyengar SK, Peachey NS, Taylor KD, Guo X, Chen YDI, Zangwill L, Girkin C, Ayyagari R, Liebmann J, Chuka-Okosa CM, Williams SE, Akafo S, Budenz DL, Olawoye OO, Ramsay M, Ashaye A, Akpa OM, Aung T, Wiggs JL, Ross AG, Cui QN, Addis V, Lehman A, Miller-Ellis E, Sankar PS, Williams SM, Ying GS, Cooke Bailey J, Rotter JI, Weinreb R, Khor CC, Hauser MA, Ritchie MD, O'Brien JM. A multi-cohort genome-wide association study in African ancestry individuals reveals risk loci for primary open-angle glaucoma. Cell 2024; 187:464-480.e10. [PMID: 38242088 DOI: 10.1016/j.cell.2023.12.006] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 07/24/2023] [Accepted: 12/04/2023] [Indexed: 01/21/2024]
Abstract
Primary open-angle glaucoma (POAG), the leading cause of irreversible blindness worldwide, disproportionately affects individuals of African ancestry. We conducted a genome-wide association study (GWAS) for POAG in 11,275 individuals of African ancestry (6,003 cases; 5,272 controls). We detected 46 risk loci associated with POAG at genome-wide significance. Replication and post-GWAS analyses, including functionally informed fine-mapping, multiple trait co-localization, and in silico validation, implicated two previously undescribed variants (rs1666698 mapping to DBF4P2; rs34957764 mapping to ROCK1P1) and one previously associated variant (rs11824032 mapping to ARHGEF12) as likely causal. For individuals of African ancestry, a polygenic risk score (PRS) for POAG from our mega-analysis (African ancestry individuals) outperformed a PRS from summary statistics of a much larger GWAS derived from European ancestry individuals. This study quantifies the genetic architecture similarities and differences between African and non-African ancestry populations for this blinding disease.
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Affiliation(s)
- Shefali S Verma
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Harini V Gudiseva
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Venkata R M Chavali
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rebecca J Salowe
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yuki Bradford
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Lindsay Guare
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anastasia Lucas
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - David W Collins
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Vrathasha Vrathasha
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rohini M Nair
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Sonika Rathi
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Bingxin Zhao
- Department of Statistics and Data Science, The Wharton School, University of Pennsylvania, Philadelphia, PA, USA
| | - Jie He
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Roy Lee
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Selam Zenebe-Gete
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anita S Bowman
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | - Maxwell Pistilli
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Naira Khachatryan
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ebenezer Daniel
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Jeffrey Henderer
- Department of Ophthalmology, Lewis Katz School of Medicine, Temple University, Philadelphia, PA, USA
| | - Tyler G Kinzy
- Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA; Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA
| | - Sudha K Iyengar
- Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA; Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA
| | - Neal S Peachey
- Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA; Cole Eye Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Kent D Taylor
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Xiuqing Guo
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Yii-Der Ida Chen
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Linda Zangwill
- Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA
| | - Christopher Girkin
- Department of Ophthalmology and Visual Sciences, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Radha Ayyagari
- Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA
| | - Jeffrey Liebmann
- Department of Ophthalmology, Columbia University Medical Center, Columbia University, New York, NY, USA
| | | | - Susan E Williams
- Division of Ophthalmology, Department of Neurosciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Stephen Akafo
- Unit of Ophthalmology, Department of Surgery, University of Ghana Medical School, Accra, Ghana
| | - Donald L Budenz
- Department of Ophthalmology, University of North Carolina, Chapel Hill, NC, USA
| | | | - Michele Ramsay
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Adeyinka Ashaye
- Department of Ophthalmology, University of Ibadan, Ibadan, Nigeria
| | - Onoja M Akpa
- Department of Epidemiology and Medical Statistics, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Tin Aung
- Singapore Eye Research Institute, Singapore, Singapore
| | - Janey L Wiggs
- Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Ahmara G Ross
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Qi N Cui
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Victoria Addis
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Amanda Lehman
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Eydie Miller-Ellis
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Prithvi S Sankar
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Scott M Williams
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, USA
| | - Gui-Shuang Ying
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jessica Cooke Bailey
- Department of Population and Quantitative Health Sciences, Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH, USA; Louis Stokes Cleveland VA Medical Center, Cleveland, OH, USA; Department of Pharmacology and Toxicology, Center for Health Disparities, Brody School of Medicine. East Carolina University, Greenville, NC, 27834, USA
| | - Jerome I Rotter
- Department of Pediatrics, The Institute for Translational Genomics and Population Sciences, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Robert Weinreb
- Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California, San Diego, La Jolla, CA, USA
| | | | | | - Marylyn D Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Joan M O'Brien
- Scheie Eye Institute, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. joan.o'
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Li R, Benz L, Duan R, Denny JC, Hakonarson H, Mosley JD, Smoller JW, Wei WQ, Ritchie MD, Moore JH, Chen Y. mixWAS: An efficient distributed algorithm for mixed-outcomes genome-wide association studies. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.09.24301073. [PMID: 38260403 PMCID: PMC10802662 DOI: 10.1101/2024.01.09.24301073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Genome-wide association studies (GWAS) have been instrumental in identifying genetic associations for various diseases and traits. However, uncovering genetic underpinnings among traits beyond univariate phenotype associations remains a challenge. Multi-phenotype associations (MPA), or genetic pleiotropy, offer important insights into shared genes and pathways among traits, enhancing our understanding of genetic architectures of complex diseases. GWAS of biobank-linked electronic health record (EHR) data are increasingly being utilized to identify MPA among various traits and diseases. However, methodologies that can efficiently take advantage of distributed EHR to detect MPA are still lacking. Here, we introduce mixWAS, a novel algorithm that efficiently and losslessly integrates multiple EHRs via summary statistics, allowing the detection of MPA among mixed phenotypes while accounting for heterogeneities across EHRs. Simulations demonstrate that mixWAS outperforms the widely used MPA detection method, Phenome-wide association study (PheWAS), across diverse scenarios. Applying mixWAS to data from seven EHRs in the US, we identified 4,534 MPA among blood lipids, BMI, and circulatory diseases. Validation in an independent EHR data from UK confirmed 97.7% of the associations. mixWAS fundamentally improves the detection of MPA and is available as a free, open-source software.
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Affiliation(s)
- Ruowang Li
- Department of Computational Biomedicine, Cedars-Sinai Medical Center
| | - Luke Benz
- Department of Biostatistics, Harvard T.H. Chan School of Public Health
| | - Rui Duan
- Department of Biostatistics, Harvard T.H. Chan School of Public Health
| | - Joshua C Denny
- National Human Genome Research Institute, National Institutes of Health
| | - Hakon Hakonarson
- Division of Human Genetics, Children's Hospital of Philadelphia
- Center for Applied Genomics, Children's Hospital of Philadelphia
- Department of Pediatrics, University of Pennsylvania, Perelman School of Medicine
| | - Jonathan D Mosley
- Department of Medicine, Vanderbilt University Medical Center
- Department of Biomedical Informatics, Vanderbilt University Medical Center
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center
| | - Marylyn D Ritchie
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center
| | - Yong Chen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania
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Yang G, González P, Moneró M, Carrasquillo K, Renta JY, Hernandez-Suarez DF, Botton MR, Melin K, Scott SA, Ruaño G, Roche-Lima A, Alarcon C, Ritchie MD, Perera MA, Duconge J. Discovery of Ancestry-specific Variants Associated with Clopidogrel Response among Caribbean Hispanics. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.29.23296372. [PMID: 37873439 PMCID: PMC10593031 DOI: 10.1101/2023.09.29.23296372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Background High on-treatment platelet reactivity (HTPR) with clopidogrel is predictive of ischemic events in adults with coronary artery disease. Despite strong data suggesting HTPR varies with ethnicity, including clinical and genetic variables, no genome-wide association study (GWAS) of clopidogrel response has been performed among Caribbean Hispanics. This study aimed to identify genetic predictors of HTPR in a cohort of Caribbean Hispanic cardiovascular patients from Puerto Rico. Methods Local Ancestry inference (LAI) and traditional GWASs were performed on a cohort of 511 clopidogrel-treated patients, stratified based on their P2Y12 reaction units (PRU) into responders and non-responders (HTPR). Results The LAI GWAS identified variants within the CYP2C19 region associated with HTPR, predominantly driven by individuals of European ancestry and absent in those with native ancestry. Incorporating local ancestry adjustment notably enhanced our ability to detect associations. While no loci reached traditional GWAS significance, three variants showed suggestive significance at chromosomes 3, 14 and 22 (OSBPL10 rs1376606, DERL3 rs5030613, and RGS6 rs9323567). In addition, a variant in the UNC5C gene on chromosome 4 was associated with an increased risk of HTPR. These findings were not identified in other cohorts, highlighting the unique genetic landscape of Caribbean Hispanics. Conclusion This is the first GWAS of clopidogrel response in Hispanics, confirming the relevance of the CYP2C19 cluster, particularly among those with European ancestry, and also identifying novel markers in a diverse patient population. Further studies are warranted to replicate our findings in other diverse cohorts and meta-analyses.
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Affiliation(s)
- Guang Yang
- Department of Pharmacology, Center for Pharmacogenomics, Feinberg School of Medicine, Northwestern University, Chicago IL 60611, United States
| | - Pablo González
- Department of Pharmacology, School of Medicine, University of Puerto Rico - Medical Sciences Campus, San Juan, Puerto Rico, 00936, United States
| | - Mariangeli Moneró
- Department of Pharmacology, School of Medicine, University of Puerto Rico - Medical Sciences Campus, San Juan, Puerto Rico, 00936, United States
| | - Kelvin Carrasquillo
- Research Centers in Minority Institutions (RCMI) Program, Center for Collaborative Research in Health Disparities (CCRHD), Academic Affairs Deanship, University of Puerto Rico - Medical Sciences Campus, San Juan, Puerto Rico, 00936, United States
| | - Jessicca Y. Renta
- Research Centers in Minority Institutions (RCMI) Program, Center for Collaborative Research in Health Disparities (CCRHD), Academic Affairs Deanship, University of Puerto Rico - Medical Sciences Campus, San Juan, Puerto Rico, 00936, United States
| | - Dagmar F. Hernandez-Suarez
- Department of Translational Medicine, Herbert Wertheim College of Medicine, Florida International University, Miami, FL 33199, United States
| | - Mariana R. Botton
- Transplant Immunology and Personalized Medicine Unit, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | - Kyle Melin
- Department of Pharmacy Practice, School of Pharmacy, University of Puerto Rico - Medical Sciences Campus, San Juan, Puerto Rico, 00936, United States
| | - Stuart A. Scott
- Department of Pathology, Stanford University, Palo Alto, CA 94304, United States
| | - Gualberto Ruaño
- Institute of Living at Hartford Hospital, Hartford, CT 06102, United States
| | - Abiel Roche-Lima
- Research Centers in Minority Institutions (RCMI) Program, Center for Collaborative Research in Health Disparities (CCRHD), Academic Affairs Deanship, University of Puerto Rico - Medical Sciences Campus, San Juan, Puerto Rico, 00936, United States
| | - Cristina Alarcon
- Department of Pharmacology, Center for Pharmacogenomics, Feinberg School of Medicine, Northwestern University, Chicago IL 60611, United States
| | - Marylyn D. Ritchie
- Department of Genetics and Institute for Biomedical Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, United States
| | - Minoli A. Perera
- Department of Pharmacology, Center for Pharmacogenomics, Feinberg School of Medicine, Northwestern University, Chicago IL 60611, United States
| | - Jorge Duconge
- Research Centers in Minority Institutions (RCMI) Program, Center for Collaborative Research in Health Disparities (CCRHD), Academic Affairs Deanship, University of Puerto Rico - Medical Sciences Campus, San Juan, Puerto Rico, 00936, United States
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Puerto Rico - Medical Sciences Campus, San Juan, Puerto Rico, 00936, United States
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Semagn K, Henriquez MA, Iqbal M, Brûlé-Babel AL, Strenzke K, Ciechanowska I, Navabi A, N’Diaye A, Pozniak C, Spaner D. Identification of Fusarium head blight sources of resistance and associated QTLs in historical and modern Canadian spring wheat. FRONTIERS IN PLANT SCIENCE 2023; 14:1190358. [PMID: 37680355 PMCID: PMC10482112 DOI: 10.3389/fpls.2023.1190358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 07/18/2023] [Indexed: 09/09/2023]
Abstract
Fusarium head blight (FHB) is one the most globally destructive fungal diseases in wheat and other small grains, causing a reduction in grain yield by 10-70%. The present study was conducted in a panel of historical and modern Canadian spring wheat (Triticum aestivum L.) varieties and lines to identify new sources of FHB resistance and map associated quantitative trait loci (QTLs). We evaluated 249 varieties and lines for reaction to disease incidence, severity, and visual rating index (VRI) in seven environments by artificially spraying a mixture of four Fusarium graminearum isolates. A subset of 198 them were genotyped with the Wheat 90K iSelect single nucleotide polymorphisms (SNPs) array. Genome-wide association mapping performed on the overall best linear unbiased estimators (BLUE) computed from all seven environments and the International Wheat Genome Sequencing Consortium (IWGSC) RefSeq v2.0 physical map of 26,449 polymorphic SNPs out of the 90K identified sixteen FHB resistance QTLs that individually accounted for 5.7-10.2% of the phenotypic variance. The positions of two of the FHB resistance QTLs overlapped with plant height and flowering time QTLs. Four of the QTLs (QFhb.dms-3B.1, QFhb.dms-5A.5, QFhb.dms-5A.7, and QFhb.dms-6A.4) were simultaneously associated with disease incidence, severity, and VRI, which accounted for 27.0-33.2% of the total phenotypic variance in the combined environments. Three of the QTLs (QFhb.dms-2A.2, QFhb.dms-2D.2, and QFhb.dms-5B.8) were associated with both incidence and VRI and accounted for 20.5-22.1% of the total phenotypic variance. In comparison with the VRI of the checks, we identified four highly resistant and thirty-three moderately resistant lines and varieties. The new FHB sources of resistance and the physical map of the associated QTLs would provide wheat breeders valuable information towards their efforts in developing improved varieties in western Canada.
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Affiliation(s)
- Kassa Semagn
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, Canada
| | - Maria Antonia Henriquez
- Morden Research and Development Centre, Agriculture and Agri-Food Canada, Morden, MB, Canada
| | - Muhammad Iqbal
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, Canada
| | | | - Klaus Strenzke
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, Canada
| | - Izabela Ciechanowska
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, Canada
| | - Alireza Navabi
- Department of Plant Agriculture, Crop Science Building, University of Guelph, Guelph, ON, Canada
| | - Amidou N’Diaye
- Crop Development Centre and Department of Plant Sciences, University of Saskatchewan, Saskatoon, SK, Canada
| | - Curtis Pozniak
- Crop Development Centre and Department of Plant Sciences, University of Saskatchewan, Saskatoon, SK, Canada
| | - Dean Spaner
- Department of Agricultural, Food, and Nutritional Science, 4-10 Agriculture-Forestry Centre, University of Alberta, Edmonton, AB, Canada
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Benca-Bachman CE, Bubier J, Syed RA, Romero Villela PN, Palmer RHC. Polygenic influences on the behavioral effects of alcohol withdrawal in a mixed-ancestry population from the collaborative study on the genetics of alcoholism (COGA). Mol Cell Neurosci 2023; 125:103851. [PMID: 37031923 PMCID: PMC10315187 DOI: 10.1016/j.mcn.2023.103851] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 03/29/2023] [Accepted: 04/02/2023] [Indexed: 04/11/2023] Open
Abstract
Alcohol withdrawal (AW) is a feature of alcohol use disorder that may occur in up to half of individuals with chronic, heavy alcohol consumption whenever alcohol use is abruptly stopped or significantly reduced. To date, few genes have been robustly associated with AW; this may be partly due to most studies defining AW as a binary construct despite the multiple symptoms and their range in severity from mild to severe. The current study examined the effects of genome-wide loci on a factor score for AW in high risk and community family samples in the Collaborative Study for the Genetics of Alcoholism (COGA). In addition, we tested whether differentially expressed genes associated with alcohol withdrawal in model organisms are enriched in human genome-wide association study (GWAS) effects. Analyses employed roughly equal numbers of males and females (mean age 35, standard deviation = 15; total N = 8009) and included individuals from multiple ancestral backgrounds. Genomic data were imputed to the HRC reference panel and underwent strict quality control procedures using Plink2. Analyses controlled for age, sex, and population stratification effects using ancestral principal components. We found support that AW is a polygenic disease (SNP-heritability = 0.08 [95 % CI = 0.01, 0.15; pedigree-based heritability = 0.12 [0.08,0.16]. We identified five single nucleotide variants that met genomewide significance, some of which have previously been associated with alcohol phenotypes. Gene-level analyses suggest a role for COL19A1 in AW; H-MAGMA analyses implicated 12 genes associated with AW. Cross-species enrichment analyses indicated that variation within genes identified in model organism studies explained <1 % of the phenotypic variability in human AW. Notably, the surrounding regulatory regions of model organism genes explained more variance than expected by chance, indicating that these regulatory regions and gene sets may be important for human AW. Lastly, when comparing the overlap in genes identified from the human GWAS and H-MAGMA analyses with the genes identified from the animal studies, there was modest overlap, indicating some convergence between the methods and organisms.
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Affiliation(s)
- Chelsie E Benca-Bachman
- Behavioral Genetics of Addiction Laboratory, Department of Psychology, Emory University, Atlanta, GA 30322, USA; Providence Veterans Affairs Medical Center, Providence, RI 02908, USA
| | | | - Rameez A Syed
- Behavioral Genetics of Addiction Laboratory, Department of Psychology, Emory University, Atlanta, GA 30322, USA
| | - Pamela N Romero Villela
- Behavioral Genetics of Addiction Laboratory, Department of Psychology, Emory University, Atlanta, GA 30322, USA; Institute for Behavior Genetics, University of Colorado of Boulder, Boulder, CO 80309, USA; Department of Psychology and Neuroscience, University of Colorado, Boulder, CO 80309, USA
| | - Rohan H C Palmer
- Behavioral Genetics of Addiction Laboratory, Department of Psychology, Emory University, Atlanta, GA 30322, USA; Providence Veterans Affairs Medical Center, Providence, RI 02908, USA; Jackson Laboratory, Bar Harbor, ME 04609, USA.
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8
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Sanguesa J, Marquez S, Bustamante M, Sunyer J, Iniguez C, Vioque J, Rodriguez LSM, Jimeno-Romero A, Torrent M, Casas M, Vrijheid M. Prenatal Vitamin D Levels Influence Growth and Body Composition until 11 Years in Boys. Nutrients 2023; 15:2033. [PMID: 37432159 DOI: 10.3390/nu15092033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 04/12/2023] [Accepted: 04/19/2023] [Indexed: 07/12/2023] Open
Abstract
BACKGROUND Gestational vitamin D levels may influence offspring growth and modulate adipogenesis. Findings from prospective studies are inconsistent, and few have evaluated the persistence of these associations into late childhood. OBJECTIVE To examine the association between prenatal vitamin D levels and growth and adiposity in late childhood. METHODS We included 2027 mother-child pairs from the INMA birth cohort. 25-hydroxyvitamin D3 (vitamin D3) levels were measured in serum at 13 weeks of pregnancy. Sex- and age-specific body mass index z-scores were calculated at 7 and 11 years, overweight was defined as z-score ≥ 85th percentile, and body fat mass was measured at 11 years. Z-score body mass index (zBMI) trajectories from birth to 11 years were identified using latent class growth analysis. RESULTS The prevalence of vitamin D3 deficiency (<20 ng/mL) was 17.5%, and around 40% of the children had overweight at both ages. Associations between vitamin D levels and outcomes differed by sex. In boys, maternal vitamin D3 deficient status was associated with higher zBMI, higher fat mass percentage, higher odds of being overweight, and with an increased risk of belonging to lower birth size followed by accelerated BMI gain trajectory. In girls no associations were observed. CONCLUSION Our results support a sex-specific programming effect of early pregnancy vitamin D3 levels on offspring body composition into late childhood observed in boys.
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Affiliation(s)
- Julia Sanguesa
- ISGlobal, 08036 Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain
- Universitat Pompeu Fabra (UPF), 08002 Barcelona, Spain
| | - Sandra Marquez
- ISGlobal, 08036 Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain
- Universitat Pompeu Fabra (UPF), 08002 Barcelona, Spain
| | - Mariona Bustamante
- ISGlobal, 08036 Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain
- Universitat Pompeu Fabra (UPF), 08002 Barcelona, Spain
| | - Jordi Sunyer
- ISGlobal, 08036 Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain
- Universitat Pompeu Fabra (UPF), 08002 Barcelona, Spain
| | - Carmen Iniguez
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain
- Department of Statistics and Operational Research, Universitat de València, 46010 València, Spain
| | - Jesus Vioque
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain
- Alicante Institute for Health and Biomedical Research (ISABIAL-FISABIO Foundation), 03010 Alicante, Spain
- Nutritional Epidemiology Unit, University Miguel Hernandez, 03202 Elche, Spain
| | - Loreto Santa-Marina Rodriguez
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain
- Biodonostia Health Research Institute, Group of Environmental Epidemiology and Child Development, 20014 San Sebastian, Spain
- Ministry of Health of the Basque Government, SubDirectorate for Public Health and Addictions of Gipuzkoa, 20010 San Sebastian, Spain
| | - Alba Jimeno-Romero
- Biodonostia Health Research Institute, Group of Environmental Epidemiology and Child Development, 20014 San Sebastian, Spain
- Preventive Medicine and Public Health Department, University of the Basque Country, 48940 Leioa, Spain
| | - Matias Torrent
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain
- IB-SALUT, Area de Salud de Menorca, Menorca, Spain
| | - Maribel Casas
- ISGlobal, 08036 Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain
- Universitat Pompeu Fabra (UPF), 08002 Barcelona, Spain
| | - Martine Vrijheid
- ISGlobal, 08036 Barcelona, Spain
- Spanish Consortium for Research on Epidemiology and Public Health (CIBERESP), 28029 Madrid, Spain
- Universitat Pompeu Fabra (UPF), 08002 Barcelona, Spain
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9
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Cammann D, Lu Y, Cummings MJ, Zhang ML, Cue JM, Do J, Ebersole J, Chen X, Oh EC, Cummings JL, Chen J. Genetic correlations between Alzheimer's disease and gut microbiome genera. Sci Rep 2023; 13:5258. [PMID: 37002253 PMCID: PMC10066300 DOI: 10.1038/s41598-023-31730-5] [Citation(s) in RCA: 38] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 03/16/2023] [Indexed: 04/03/2023] Open
Abstract
A growing body of evidence suggests that dysbiosis of the human gut microbiota is associated with neurodegenerative diseases like Alzheimer's disease (AD) via neuroinflammatory processes across the microbiota-gut-brain axis. The gut microbiota affects brain health through the secretion of toxins and short-chain fatty acids, which modulates gut permeability and numerous immune functions. Observational studies indicate that AD patients have reduced microbiome diversity, which could contribute to the pathogenesis of the disease. Uncovering the genetic basis of microbial abundance and its effect on AD could suggest lifestyle changes that may reduce an individual's risk for the disease. Using the largest genome-wide association study of gut microbiota genera from the MiBioGen consortium, we used polygenic risk score (PRS) analyses with the "best-fit" model implemented in PRSice-2 and determined the genetic correlation between 119 genera and AD in a discovery sample (ADc12 case/control: 1278/1293). To confirm the results from the discovery sample, we next repeated the PRS analysis in a replication sample (GenADA case/control: 799/778) and then performed a meta-analysis with the PRS results from both samples. Finally, we conducted a linear regression analysis to assess the correlation between the PRSs for the significant genera and the APOE genotypes. In the discovery sample, 20 gut microbiota genera were initially identified as genetically associated with AD case/control status. Of these 20, three genera (Eubacterium fissicatena as a protective factor, Collinsella, and Veillonella as a risk factor) were independently significant in the replication sample. Meta-analysis with discovery and replication samples confirmed that ten genera had a significant correlation with AD, four of which were significantly associated with the APOE rs429358 risk allele in a direction consistent with their protective/risk designation in AD association. Notably, the proinflammatory genus Collinsella, identified as a risk factor for AD, was positively correlated with the APOE rs429358 risk allele in both samples. Overall, the host genetic factors influencing the abundance of ten genera are significantly associated with AD, suggesting that these genera may serve as biomarkers and targets for AD treatment and intervention. Our results highlight that proinflammatory gut microbiota might promote AD development through interaction with APOE. Larger datasets and functional studies are required to understand their causal relationships.
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Affiliation(s)
- Davis Cammann
- Nevada Institute of Personalized Medicine, University of Nevada, Las Vegas 4505 S. Maryland Parkway, Las Vegas, NV, 89154, USA
| | - Yimei Lu
- Nevada Institute of Personalized Medicine, University of Nevada, Las Vegas 4505 S. Maryland Parkway, Las Vegas, NV, 89154, USA
| | - Melika J Cummings
- Nevada Institute of Personalized Medicine, University of Nevada, Las Vegas 4505 S. Maryland Parkway, Las Vegas, NV, 89154, USA
| | - Mark L Zhang
- Columbia University, West 116 St and Broadway, New York, NY, 10027, USA
| | - Joan Manuel Cue
- Nevada Institute of Personalized Medicine, University of Nevada, Las Vegas 4505 S. Maryland Parkway, Las Vegas, NV, 89154, USA
| | - Jenifer Do
- Nevada Institute of Personalized Medicine, University of Nevada, Las Vegas 4505 S. Maryland Parkway, Las Vegas, NV, 89154, USA
| | - Jeffrey Ebersole
- Department of Biomedical Sciences, University of Nevada, Las Vegas, NV, 89154, USA
| | - Xiangning Chen
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Edwin C Oh
- Nevada Institute of Personalized Medicine, University of Nevada, Las Vegas 4505 S. Maryland Parkway, Las Vegas, NV, 89154, USA
- Laboratory of Neurogenetics and Precision Medicine, University of Nevada Las Vegas, Las Vegas, NV, 89154, USA
- Department of Internal Medicine, UNLV School of Medicine, University of Nevada Las Vegas, Las Vegas, NV, 89154, USA
| | - Jeffrey L Cummings
- Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas, Las Vegas, NV, USA
| | - Jingchun Chen
- Nevada Institute of Personalized Medicine, University of Nevada, Las Vegas 4505 S. Maryland Parkway, Las Vegas, NV, 89154, USA.
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10
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Coors A, Imtiaz MA, Boenniger MM, Aziz NA, Breteler MMB, Ettinger U. Polygenic risk scores for schizophrenia are associated with oculomotor endophenotypes. Psychol Med 2023; 53:1611-1619. [PMID: 34412712 PMCID: PMC10009390 DOI: 10.1017/s0033291721003251] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 06/15/2021] [Accepted: 07/20/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND Schizophrenia is a heterogeneous disorder with substantial heritability. The use of endophenotypes may help clarify its aetiology. Measures from the smooth pursuit and antisaccade eye movement tasks have been identified as endophenotypes for schizophrenia in twin and family studies. However, the genetic basis of the overlap between schizophrenia and these oculomotor markers is largely unknown. Here, we tested whether schizophrenia polygenic risk scores (PRS) were associated with oculomotor performance in the general population. METHODS Analyses were based on the data of 2956 participants (aged 30-95) of the Rhineland Study, a community-based cohort study in Bonn, Germany. Genotyping was performed on Omni-2.5 exome arrays. Using summary statistics from a recent meta-analysis based on the two largest schizophrenia genome-wide association studies to date, we quantified genetic risk for schizophrenia by creating PRS at different p value thresholds for genetic markers. We examined associations between PRS and oculomotor performance using multivariable regression models. RESULTS Higher PRS were associated with higher antisaccade error rate and latency, and lower antisaccade amplitude gain. PRS showed inconsistent patterns of association with smooth pursuit velocity gain and were not associated with saccade rate during smooth pursuit or performance on a prosaccade control task. CONCLUSIONS There is an overlap between genetic determinants of schizophrenia and oculomotor endophenotypes. Our findings suggest that the mechanisms that underlie schizophrenia also affect oculomotor function in the general population.
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Affiliation(s)
- Annabell Coors
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Mohammed-Aslam Imtiaz
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Meta M. Boenniger
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - N. Ahmad Aziz
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Neurology, Faculty of Medicine, University of Bonn, Bonn, Germany
| | - Monique M. B. Breteler
- Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Institute for Medical Biometry, Informatics and Epidemiology (IMBIE), Faculty of Medicine, University of Bonn, Bonn, Germany
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11
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Truong VQ, Woerner JA, Cherlin TA, Bradford Y, Lucas AM, Okeh CC, Shivakumar MK, Hui DH, Kumar R, Pividori M, Jones SC, Bossa AC, Turner SD, Ritchie MD, Verma SS. Quality Control Procedures for Genome-Wide Association Studies. Curr Protoc 2022; 2:e603. [PMID: 36441943 DOI: 10.1002/cpz1.603] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Genome-wide association studies (GWAS) are being conducted at an unprecedented rate in population-based cohorts and have increased our understanding of the pathophysiology of many complex diseases. Regardless of the context, the practical utility of this information ultimately depends upon the quality of the data used for statistical analyses. Quality control (QC) procedures for GWAS are constantly evolving. Here, we enumerate some of the challenges in QC of genotyped GWAS data and describe the approaches involving genotype imputation of a sample dataset along with post-imputation quality assurance, thereby minimizing potential bias and error in GWAS results. We discuss common issues associated with QC of the GWAS data (genotyped and imputed), including data file formats, software packages for data manipulation and analysis, sex chromosome anomalies, sample identity, sample relatedness, population substructure, batch effects, and marker quality. We provide detailed guidelines along with a sample dataset to suggest current best practices and discuss areas of ongoing and future research. © 2022 Wiley Periodicals LLC.
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Affiliation(s)
- Van Q Truong
- Genomics and Computational Biology Graduate Group, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Jakob A Woerner
- Genomics and Computational Biology Graduate Group, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Tess A Cherlin
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Yuki Bradford
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Anastasia M Lucas
- Genomics and Computational Biology Graduate Group, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Chelsea C Okeh
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Manu K Shivakumar
- Genomics and Computational Biology Graduate Group, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Daniel H Hui
- Genomics and Computational Biology Graduate Group, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Rachit Kumar
- Genomics and Computational Biology Graduate Group, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Milton Pividori
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - S Chris Jones
- Genomics and Computational Biology Graduate Group, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Abigail C Bossa
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | | | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Shefali S Verma
- Department of Pathology and Laboratory Medicine, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania, USA
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12
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Associations of genetic liability for Alzheimer's disease with cognition and eye movements in a large, population-based cohort study. Transl Psychiatry 2022; 12:337. [PMID: 35982049 PMCID: PMC9388528 DOI: 10.1038/s41398-022-02093-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2021] [Revised: 07/14/2022] [Accepted: 07/22/2022] [Indexed: 11/08/2022] Open
Abstract
To identify cognitive measures that may be particularly sensitive to early cognitive decline in preclinical Alzheimer's disease (AD), we investigated the relation between genetic risk for AD and cognitive task performance in a large population-based cohort study. We measured performance on memory, processing speed, executive function, crystallized intelligence and eye movement tasks in 5182 participants of the Rhineland Study, aged 30 to 95 years. We quantified genetic risk for AD by creating three weighted polygenic risk scores (PRS) based on the genome-wide significant single-nucleotide polymorphisms coming from three different genetic association studies. We assessed the relation of AD PRS with cognitive performance using generalized linear models. Three PRS were associated with lower performance on the Corsi forward task, and two PRS were associated with a lower probability of correcting antisaccade errors, but none of these associations remained significant after correction for multiple testing. Associations between age and trail-making test A (TMT-A) performance were modified by AD genetic risk, with individuals at high genetic risk showing the strongest association. We conclude that no single measure of our cognitive test battery robustly captures genetic liability for AD as quantified by current PRS. However, Corsi forward performance and the probability of correcting antisaccade errors may represent promising candidates whose ability to capture genetic liability for AD should be investigated further. Additionally, our finding on TMT-A performance suggests that processing speed represents a sensitive marker of AD genetic risk in old age and supports the processing speed theory of age-related cognitive decline.
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13
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Chen G, Shriner D, Zhang J, Zhou J, Adikaram P, Doumatey AP, Bentley AR, Adeyemo A, Rotimi CN. Additive genetic effect of GCKR, G6PC2, and SLC30A8 variants on fasting glucose levels and risk of type 2 diabetes. PLoS One 2022; 17:e0269378. [PMID: 35657990 PMCID: PMC9165855 DOI: 10.1371/journal.pone.0269378] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 05/19/2022] [Indexed: 01/11/2023] Open
Abstract
Impaired glucose tolerance is a major risk factor for type 2 diabetes (T2D) and several cardiometabolic disorders. To identify genetic loci underlying fasting glucose levels, we conducted an analysis of 9,232 individuals of European ancestry who at enrollment were either nondiabetic or had untreated type 2 diabetes. Multivariable linear mixed models were used to test for associations between fasting glucose and 7.9 million SNPs, with adjustment for age, body mass index (BMI), sex, significant principal components of the genotypes, and cryptic relatedness. Three previously discovered loci were genome-wide significant, with the lead SNPs being rs1260326, a missense variant in GCKR (p = 1.06×10−8); rs560887, an intronic variant in G6PC2 (p = 3.39×10−11); and rs13266634, a missense variant in SLC30A8 (p = 4.28×10−10). Fine mapping, genome-wide conditional analysis, and functional annotation indicated that the three loci were independently associated with fasting glucose. Each copy of an alternate allele at any of these three SNPs was associated with a reduction of 0.012 mmol/L in fasting glucose levels (p = 8.0×10−28), and this association was replicated in trans-ethnic analysis of 14,303 individuals (p = 2.2×10−16). The three SNPs were jointly associated with significantly reduced T2D risk, with an odds ratio (95% CI) of 0.93 (0.88, 0.98) per protective allele. Our findings implicate additive effects across pathophysiological pathways involved in type 2 diabetes, including glycolysis, gluconeogenesis, and insulin secretion. Since none of the individuals homozygous for the alternate alleles at all three loci has T2D, it might be possible to use a genetic predictor of fasting glucose levels to identify individuals at low vs. high risk of developing type 2 diabetes.
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Affiliation(s)
- Guanjie Chen
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
| | - Daniel Shriner
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
| | - Jianhua Zhang
- Metabolic Diseases Branch, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, Maryland, United States of America
| | - Jie Zhou
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
| | - Poorni Adikaram
- Advanced BioScience Laboratories, Rockville, Maryland, United States of America
| | - Ayo P. Doumatey
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
| | - Amy R. Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
| | - Adebowale Adeyemo
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
| | - Charles N. Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, Bethesda, Maryland, United States of America
- * E-mail:
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14
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High heritability of ascending aortic diameter and trans-ancestry prediction of thoracic aortic disease. Nat Genet 2022; 54:772-782. [PMID: 35637384 DOI: 10.1038/s41588-022-01070-7] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 03/31/2022] [Indexed: 12/24/2022]
Abstract
Enlargement of the aorta is an important risk factor for aortic aneurysm and dissection, a leading cause of morbidity in the developed world. Here we performed automated extraction of ascending aortic diameter from cardiac magnetic resonance images of 36,021 individuals from the UK Biobank, followed by genome-wide association. We identified lead variants across 41 loci, including genes related to cardiovascular development (HAND2, TBX20) and Mendelian forms of thoracic aortic disease (ELN, FBN1). A polygenic score significantly predicted prevalent risk of thoracic aortic aneurysm and the need for surgical intervention for patients with thoracic aneurysm across multiple ancestries within the UK Biobank, FinnGen, the Penn Medicine Biobank and the Million Veterans Program (MVP). Additionally, we highlight the primary causal role of blood pressure in reducing aortic dilation using Mendelian randomization. Overall, our findings provide a roadmap for using genetic determinants of human anatomy to understand cardiovascular development while improving prediction of diseases of the thoracic aorta.
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15
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Bray D, Hook H, Zhao R, Keenan JL, Penvose A, Osayame Y, Mohaghegh N, Chen X, Parameswaran S, Kottyan LC, Weirauch MT, Siggers T. CASCADE: high-throughput characterization of regulatory complex binding altered by non-coding variants. CELL GENOMICS 2022; 2. [PMID: 35252945 PMCID: PMC8896503 DOI: 10.1016/j.xgen.2022.100098] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Non-coding DNA variants (NCVs) impact gene expression by altering binding sites for regulatory complexes. New high-throughput methods are needed to characterize the impact of NCVs on regulatory complexes. We developed CASCADE (Customizable Approach to Survey Complex Assembly at DNA Elements), an array-based high-throughput method to profile cofactor (COF) recruitment. CASCADE identifies DNA-bound transcription factor-cofactor (TF-COF) complexes in nuclear extracts and quantifies the impact of NCVs on their binding. We demonstrate CASCADE sensitivity in characterizing condition-specific recruitment of COFs p300 and RBBP5 (MLL subunit) to the CXCL10 promoter in lipopolysaccharide (LPS)-stimulated human macrophages and quantify the impact of all possible NCVs. To demonstrate applicability to NCV screens, we profile TF-COF binding to ~1,700 single-nucleotide polymorphism quantitative trait loci (SNP-QTLs) in human macrophages and identify perturbed ETS domain-containing complexes. CASCADE will facilitate high-throughput testing of molecular mechanisms of NCVs for diverse biological applications. Bray et al. develop CASCADE, a method to profile transcription factor (TF)-cofactor (COF) complexes binding to DNA. They demonstrate the approach by profiling complex binding across the CXCL10 cytokine promoter and to ~1,700 single-nucleotide polymorphisms (SNPs). They anticipate that CASCADE can be applied to diverse biological systems to examine regulatory complex binding to DNA.
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Affiliation(s)
- David Bray
- Department of Biology, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
- Bioinformatics Program, Boston University, Boston, MA, USA
| | - Heather Hook
- Department of Biology, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
| | - Rose Zhao
- Department of Biology, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
| | - Jessica L. Keenan
- Department of Biology, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
- Bioinformatics Program, Boston University, Boston, MA, USA
| | - Ashley Penvose
- Department of Biology, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
| | - Yemi Osayame
- Department of Biology, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
| | - Nima Mohaghegh
- Department of Biology, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
| | - Xiaoting Chen
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
| | - Sreeja Parameswaran
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
| | - Leah C. Kottyan
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
- Division of Allergy and Immunology, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio 45229, USA
| | - Matthew T. Weirauch
- Center for Autoimmune Genomics and Etiology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Division of Developmental Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Trevor Siggers
- Department of Biology, Boston University, Boston, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA
- Corresponding author
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16
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Yu Y, Werdyani S, Carey M, Parfrey P, Yilmaz YE, Savas S. A comprehensive analysis of SNPs and CNVs identifies novel markers associated with disease outcomes in colorectal cancer. Mol Oncol 2021; 15:3329-3347. [PMID: 34309201 PMCID: PMC8637572 DOI: 10.1002/1878-0261.13067] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 05/29/2021] [Accepted: 07/24/2021] [Indexed: 12/15/2022] Open
Abstract
We aimed to examine the associations of a genome-wide set of single nucleotide polymorphisms (SNPs) and 254 copy number variations (CNVs) and/or insertion/deletions (INDELs) with clinical outcomes in colorectal cancer patients (n = 505). We also aimed to investigate whether their associations changed (e.g., appeared, diminished) over time. Multivariable Cox proportional hazards and piece-wise Cox regression models were used to examine the associations. The Cancer Genome Atlas (TCGA) datasets were used for replication purposes and to examine the gene expression differences between tumor and nontumor tissue samples. A common SNP (WBP11-rs7314075) was associated with disease-specific survival with P-value of 3.2 × 10-8 . Association of this region with disease-specific survival was also detected in the TCGA patient cohort. Two expression quantitative trait loci (eQTLs) were identified in this locus that were implicated in the regulation of ERP27 expression. Interestingly, expression levels of ERP27 and WBP11 were significantly different between colorectal tumors and nontumor tissues. Three SNPs predicted the risk of recurrent disease only after 5 years postdiagnosis. Overall, our study identified novel variants, one of which also showed an association in the TCGA dataset, but no CNVs/INDELs, that associated with outcomes in colorectal cancer. Three SNPs were candidate predictors of long-term recurrence/metastasis risk.
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Affiliation(s)
- Yajun Yu
- Discipline of GeneticsFaculty of MedicineMemorial UniversitySt. John’sNLCanada
- Present address:
Division of Biomedical SciencesFaculty of MedicineMemorial UniversitySt. John’sNLCanada
| | - Salem Werdyani
- Discipline of GeneticsFaculty of MedicineMemorial UniversitySt. John’sNLCanada
- Present address:
Division of Biomedical SciencesFaculty of MedicineMemorial UniversitySt. John’sNLCanada
| | - Megan Carey
- Discipline of GeneticsFaculty of MedicineMemorial UniversitySt. John’sNLCanada
| | - Patrick Parfrey
- Discipline of MedicineFaculty of MedicineMemorial UniversitySt. John’sNLCanada
| | - Yildiz E. Yilmaz
- Discipline of GeneticsFaculty of MedicineMemorial UniversitySt. John’sNLCanada
- Discipline of MedicineFaculty of MedicineMemorial UniversitySt. John’sNLCanada
- Department of Mathematics and StatisticsFaculty of ScienceMemorial UniversitySt. John’sNLCanada
| | - Sevtap Savas
- Discipline of GeneticsFaculty of MedicineMemorial UniversitySt. John’sNLCanada
- Discipline of OncologyFaculty of MedicineMemorial UniversitySt. John’sNLCanada
- Present address:
Division of Biomedical SciencesFaculty of MedicineMemorial UniversitySt. John’sNLCanada
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17
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Nilsson NIV, Picard C, Labonté A, Köbe T, Meyer PF, Villeneuve S, Auld D, Poirier J. Association of a Total Cholesterol Polygenic Score with Cholesterol Levels and Pathological Biomarkers across the Alzheimer's Disease Spectrum. Genes (Basel) 2021; 12:genes12111805. [PMID: 34828411 PMCID: PMC8623969 DOI: 10.3390/genes12111805] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/08/2021] [Accepted: 11/10/2021] [Indexed: 01/16/2023] Open
Abstract
Midlife hypercholesterolemia is a well-known risk factor for sporadic Alzheimer's disease (AD), and like AD, it is highly influenced by genetics with heritability estimates of 32-63%. We thus hypothesized that genetics underlying peripheral blood total cholesterol (TC) levels could influence the risk of developing AD. We created a weighted polygenic score (TC-PGS) using summary data from a meta-analysis of TC genome-wide association studies for evaluation in three independent AD-related cohorts spanning pre-clinical, clinical, and pathophysiologically proved AD. APOE-ε4 variant was purposely included in the analysis as it represents an already well-established genetic risk factor for both AD and circulating TC. We could vastly improve the performance of the score when considering p-value thresholds for inclusion in the score, sex, and statin use. This optimized score (p-value threshold of 1 × 10-6 for inclusion in the score) explained 18.2% of the variance in TC levels in statin free females compared to 6.9% in the entire sample and improved prediction of hypercholesterolemia (receiver operator characteristics analysis revealed area under the curve increase from 70.8% to 80.5%). The TC-PGS was further evaluated for association with AD risk and pathology. We found no association between the TC-PGS and either of the AD hallmark pathologies, assessed by cerebrospinal fluid levels of Aβ-42, p-Tau, and t-Tau, and 18F-NAV4694 and 18F-AV-1451 positron emission tomography. Similarly, we found no association with the risk of developing amyloid pathology or becoming cognitively impaired in individuals with amyloid pathology.
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Affiliation(s)
- Nathalie I. V. Nilsson
- Department of Psychiatry, McGill University, Montreal, QC H3A 0G4, Canada; (N.I.V.N.); (T.K.); (P.-F.M.); (S.V.)
- Douglas Research Centre, Montreal, QC H4H 1R3, Canada; (C.P.); (A.L.)
- Centre for the Studies in the Prevention of Alzheimer’s Disease, Montreal, QC H4H 1R3, Canada
| | - Cynthia Picard
- Douglas Research Centre, Montreal, QC H4H 1R3, Canada; (C.P.); (A.L.)
- Centre for the Studies in the Prevention of Alzheimer’s Disease, Montreal, QC H4H 1R3, Canada
| | - Anne Labonté
- Douglas Research Centre, Montreal, QC H4H 1R3, Canada; (C.P.); (A.L.)
- Centre for the Studies in the Prevention of Alzheimer’s Disease, Montreal, QC H4H 1R3, Canada
| | - Theresa Köbe
- Department of Psychiatry, McGill University, Montreal, QC H3A 0G4, Canada; (N.I.V.N.); (T.K.); (P.-F.M.); (S.V.)
- Centre for the Studies in the Prevention of Alzheimer’s Disease, Montreal, QC H4H 1R3, Canada
| | - Pierre-François Meyer
- Department of Psychiatry, McGill University, Montreal, QC H3A 0G4, Canada; (N.I.V.N.); (T.K.); (P.-F.M.); (S.V.)
- Centre for the Studies in the Prevention of Alzheimer’s Disease, Montreal, QC H4H 1R3, Canada
| | - Sylvia Villeneuve
- Department of Psychiatry, McGill University, Montreal, QC H3A 0G4, Canada; (N.I.V.N.); (T.K.); (P.-F.M.); (S.V.)
- Douglas Research Centre, Montreal, QC H4H 1R3, Canada; (C.P.); (A.L.)
- Centre for the Studies in the Prevention of Alzheimer’s Disease, Montreal, QC H4H 1R3, Canada
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, QC H3A 0G4, Canada
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, QC H3A 0G4, Canada
| | - Daniel Auld
- Genome Centre, McGill University, Montreal, QC H3A 0G4, Canada;
| | - Judes Poirier
- Department of Psychiatry, McGill University, Montreal, QC H3A 0G4, Canada; (N.I.V.N.); (T.K.); (P.-F.M.); (S.V.)
- Douglas Research Centre, Montreal, QC H4H 1R3, Canada; (C.P.); (A.L.)
- Centre for the Studies in the Prevention of Alzheimer’s Disease, Montreal, QC H4H 1R3, Canada
- Department of Medicine, McGill University, Montreal, QC H3A 0G4, Canada
- Correspondence:
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18
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Vasilopoulou C, Wingfield B, Morris AP, Duddy W. snpQT: flexible, reproducible, and comprehensive quality control and imputation of genomic data. F1000Res 2021; 10:567. [PMID: 34900230 PMCID: PMC8637247 DOI: 10.12688/f1000research.53821.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/17/2021] [Indexed: 11/20/2022] Open
Abstract
Quality control of genomic data is an essential but complicated multi-step procedure, often requiring separate installation and expert familiarity with a combination of different bioinformatics tools. Software incompatibilities, and inconsistencies across computing environments, are recurrent challenges, leading to poor reproducibility. Existing semi-automated or automated solutions lack comprehensive quality checks, flexible workflow architecture, and user control. To address these challenges, we have developed snpQT: a scalable, stand-alone software pipeline using nextflow and BioContainers, for comprehensive, reproducible and interactive quality control of human genomic data. snpQT offers some 36 discrete quality filters or correction steps in a complete standardised pipeline, producing graphical reports to demonstrate the state of data before and after each quality control procedure. This includes human genome build conversion, population stratification against data from the 1,000 Genomes Project, automated population outlier removal, and built-in imputation with its own pre- and post- quality controls. Common input formats are used, and a synthetic dataset and comprehensive online tutorial are provided for testing, educational purposes, and demonstration. The snpQT pipeline is designed to run with minimal user input and coding experience; quality control steps are implemented with numerous user-modifiable thresholds, and workflows can be flexibly combined in custom combinations. snpQT is open source and freely available at https://github.com/nebfield/snpQT. A comprehensive online tutorial and installation guide is provided through to GWAS (https://snpqt.readthedocs.io/en/latest/), introducing snpQT using a synthetic demonstration dataset and a real-world Amyotrophic Lateral Sclerosis SNP-array dataset.
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Affiliation(s)
- Christina Vasilopoulou
- Northern Ireland Centre for Stratified Medicine, University of Ulster, Derry/Londonderry, BT47 6SB, UK
| | - Benjamin Wingfield
- Centre for Personalised Medicine, University of Ulster, Derry/Londonderry, BT47 6SB, UK
| | - Andrew P. Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, University of Manchester, Manchester, UK
| | - William Duddy
- Northern Ireland Centre for Stratified Medicine, University of Ulster, Derry/Londonderry, BT47 6SB, UK
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19
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Vasilopoulou C, Wingfield B, Morris AP, Duddy W. snpQT: flexible, reproducible, and comprehensive quality control and imputation of genomic data. F1000Res 2021; 10:567. [PMID: 34900230 PMCID: PMC8637247 DOI: 10.12688/f1000research.53821.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/17/2021] [Indexed: 11/09/2023] Open
Abstract
Quality control of genomic data is an essential but complicated multi-step procedure, often requiring separate installation and expert familiarity with a combination of different bioinformatics tools. Dependency hell and reproducibility are recurrent challenges. Existing semi-automated or automated solutions lack comprehensive quality checks, flexible workflow architecture, and user control. To address these challenges, we have developed snpQT: a scalable, stand-alone software pipeline using nextflow and BioContainers, for comprehensive, reproducible and interactive quality control of human genomic data. snpQT offers some 36 discrete quality filters or correction steps in a complete standardised pipeline, producing graphical reports to demonstrate the state of data before and after each quality control procedure. This includes human genome build conversion, population stratification against data from the 1,000 Genomes Project, automated population outlier removal, and built-in imputation with its own pre- and post- quality controls. Common input formats are used, and a synthetic dataset and comprehensive online tutorial are provided for testing, educational purposes, and demonstration. The snpQT pipeline is designed to run with minimal user input and coding experience; quality control steps are implemented with default thresholds which can be modified by the user, and workflows can be flexibly combined in custom combinations. snpQT is open source and freely available at https://github.com/nebfield/snpQT. A comprehensive online tutorial and installation guide is provided through to GWAS (https://snpqt.readthedocs.io/en/latest/), introducing snpQT using a synthetic demonstration dataset and a real-world Amyotrophic Lateral Sclerosis SNP-array dataset.
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Affiliation(s)
- Christina Vasilopoulou
- Northern Ireland Centre for Stratified Medicine, University of Ulster, Derry/Londonderry, BT47 6SB, UK
| | - Benjamin Wingfield
- Centre for Personalised Medicine, University of Ulster, Derry/Londonderry, BT47 6SB, UK
| | - Andrew P. Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, University of Manchester, Manchester, UK
| | - William Duddy
- Northern Ireland Centre for Stratified Medicine, University of Ulster, Derry/Londonderry, BT47 6SB, UK
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20
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Veturi Y, Lucas A, Bradford Y, Hui D, Dudek S, Theusch E, Verma A, Miller JE, Kullo I, Hakonarson H, Sleiman P, Schaid D, Stein CM, Edwards DRV, Feng Q, Wei WQ, Medina MW, Krauss R, Hoffmann TJ, Risch N, Voight BF, Rader DJ, Ritchie MD. A unified framework identifies new links between plasma lipids and diseases from electronic medical records across large-scale cohorts. Nat Genet 2021; 53:972-981. [PMID: 34140684 PMCID: PMC8555954 DOI: 10.1038/s41588-021-00879-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Accepted: 05/05/2021] [Indexed: 02/05/2023]
Abstract
Plasma lipids are known heritable risk factors for cardiovascular disease, but increasing evidence also supports shared genetics with diseases of other organ systems. We devised a comprehensive three-phase framework to identify new lipid-associated genes and study the relationships among lipids, genotypes, gene expression and hundreds of complex human diseases from the Electronic Medical Records and Genomics (347 traits) and the UK Biobank (549 traits). Aside from 67 new lipid-associated genes with strong replication, we found evidence for pleiotropic SNPs/genes between lipids and diseases across the phenome. These include discordant pleiotropy in the HLA region between lipids and multiple sclerosis and putative causal paths between triglycerides and gout, among several others. Our findings give insights into the genetic basis of the relationship between plasma lipids and diseases on a phenome-wide scale and can provide context for future prevention and treatment strategies.
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Affiliation(s)
- Yogasudha Veturi
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anastasia Lucas
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yuki Bradford
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Daniel Hui
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Scott Dudek
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth Theusch
- Department of Pediatrics, University of California San Francisco, Oakland, CA, USA
| | - Anurag Verma
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jason E. Miller
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Iftikhar Kullo
- Division of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, USA
| | - Hakon Hakonarson
- Center for Applied Genomics, Children’s Hospital of Philadelphia, PA, USA
| | - Patrick Sleiman
- Center for Applied Genomics, Children’s Hospital of Philadelphia, PA, USA
| | - Daniel Schaid
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Charles M. Stein
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Digna R. Velez Edwards
- Department of Biomedical Informatics in School of Medicine, Vanderbilt University, Nashville, TN, USA.,Vanderbilt Genetics Institute, Vanderbilt University, Nashville, TN, USA.,Division of Quantitative Science, Department of Obstetrics and Gynecology, Vanderbilt University Medical Center, Nashville, TN, USA
| | - QiPing Feng
- Division of Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Wei-Qi Wei
- Department of Biomedical Informatics in School of Medicine, Vanderbilt University, Nashville, TN, USA
| | - Marisa W. Medina
- Department of Pediatrics, University of California San Francisco, Oakland, CA, USA
| | - Ronald Krauss
- Department of Pediatrics, University of California San Francisco, Oakland, CA, USA
| | - Thomas J. Hoffmann
- Institute for Human Genetics, and Department of Epidemiology & Biostatistics, University of California and San Francisco, San Francisco, CA, USA
| | - Neil Risch
- Institute for Human Genetics, and Department of Epidemiology & Biostatistics, University of California and San Francisco, San Francisco, CA, USA
| | - Benjamin F. Voight
- Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Institute of Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Daniel J. Rader
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Marylyn D. Ritchie
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.,
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21
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Chung RH, Chiu YF, Wang WC, Hwu CM, Hung YJ, Lee IT, Chuang LM, Quertermous T, Rotter JI, Chen YDI, Chang IS, Hsiung CA. Multi-omics analysis identifies CpGs near G6PC2 mediating the effects of genetic variants on fasting glucose. Diabetologia 2021; 64:1613-1625. [PMID: 33842983 DOI: 10.1007/s00125-021-05449-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 02/08/2021] [Indexed: 10/21/2022]
Abstract
AIMS/HYPOTHESIS An elevated fasting glucose level in non-diabetic individuals is a key predictor of type 2 diabetes. Genome-wide association studies (GWAS) have identified hundreds of SNPs for fasting glucose but most of their functional roles in influencing the trait are unclear. This study aimed to identify the mediation effects of DNA methylation between SNPs identified as significant from GWAS and fasting glucose using Mendelian randomisation (MR) analyses. METHODS We first performed GWAS analyses for three cohorts (Taiwan Biobank with 18,122 individuals, the Healthy Aging Longitudinal Study in Taiwan with 1989 individuals and the Stanford Asia-Pacific Program for Hypertension and Insulin Resistance with 416 individuals) with individuals of Han Chinese ancestry in Taiwan, followed by a meta-analysis for combining the three GWAS analysis results to identify significant and independent SNPs for fasting glucose. We determined whether these SNPs were methylation quantitative trait loci (meQTLs) by testing their associations with DNA methylation levels at nearby CpG sites using a subsample of 1775 individuals from the Taiwan Biobank. The MR analysis was performed to identify DNA methylation with causal effects on fasting glucose using meQTLs as instrumental variables based on the 1775 individuals. We also used a two-sample MR strategy to perform replication analysis for CpG sites with significant MR effects based on literature data. RESULTS Our meta-analysis identified 18 significant (p < 5 × 10-8) and independent SNPs for fasting glucose. Interestingly, all 18 SNPs were meQTLs. The MR analysis identified seven CpGs near the G6PC2 gene that mediated the effects of a significant SNP (rs2232326) in the gene on fasting glucose. The MR effects for two CpGs were replicated using summary data based on the European population, using an exonic SNP rs2232328 in G6PC2 as the instrument. CONCLUSIONS/INTERPRETATION Our analysis results suggest that rs2232326 and rs2232328 in G6PC2 may affect DNA methylation at CpGs near the gene and that the methylation may have downstream effects on fasting glucose. Therefore, SNPs in G6PC2 and CpGs near G6PC2 may reside along the pathway that influences fasting glucose levels. This is the first study to report CpGs near G6PC2, an important gene for regulating insulin secretion, mediating the effects of GWAS-significant SNPs on fasting glucose.
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Affiliation(s)
- Ren-Hua Chung
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan.
| | - Yen-Feng Chiu
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan
| | - Wen-Chang Wang
- The Ph.D. Program for Translational Medicine, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan
| | - Chii-Min Hwu
- Section of Endocrinology and Metabolism, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- School of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Yi-Jen Hung
- Division of Endocrine and Metabolism, Tri-Service General Hospital, Taipei, Taiwan
- Institute of Preventive Medicine, National Defense Medical Center, Taipei, Taiwan
| | - I-Te Lee
- School of Medicine, National Yang-Ming University, Taipei, Taiwan
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung, Taiwan
- School of Medicine, Chung Shan Medical University, Taichung, Taiwan
| | - Lee-Ming Chuang
- Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan
- Institutes of Molecular Medicine, Collage of Medicine, National Taiwan University, Taipei, Taiwan
| | - Thomas Quertermous
- Division of Cardiovascular Medicine and Stanford Cardiovascular Institute, Falk Cardiovascular Research Center, Stanford University, Stanford, CA, USA
| | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences, the Lundquist Institute, Harbor-UCLA Medical Center, Torrance, CA, USA
- Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Yii-Der I Chen
- Institute for Translational Genomics and Population Sciences, the Lundquist Institute, Harbor-UCLA Medical Center, Torrance, CA, USA
- Department of Pediatrics, Harbor-UCLA Medical Center, Torrance, CA, USA
| | - I-Shou Chang
- National Institute of Cancer Research, National Health Research Institutes, Zhunan, Taiwan
| | - Chao A Hsiung
- Division of Biostatistics and Bioinformatics, Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan.
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22
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Hall MA, Wallace J, Lucas AM, Bradford Y, Verma SS, Müller-Myhsok B, Passero K, Zhou J, McGuigan J, Jiang B, Pendergrass SA, Zhang Y, Peissig P, Brilliant M, Sleiman P, Hakonarson H, Harley JB, Kiryluk K, Van Steen K, Moore JH, Ritchie MD. Novel EDGE encoding method enhances ability to identify genetic interactions. PLoS Genet 2021; 17:e1009534. [PMID: 34086673 PMCID: PMC8208534 DOI: 10.1371/journal.pgen.1009534] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 06/16/2021] [Accepted: 04/06/2021] [Indexed: 11/26/2022] Open
Abstract
Assumptions are made about the genetic model of single nucleotide polymorphisms (SNPs) when choosing a traditional genetic encoding: additive, dominant, and recessive. Furthermore, SNPs across the genome are unlikely to demonstrate identical genetic models. However, running SNP-SNP interaction analyses with every combination of encodings raises the multiple testing burden. Here, we present a novel and flexible encoding for genetic interactions, the elastic data-driven genetic encoding (EDGE), in which SNPs are assigned a heterozygous value based on the genetic model they demonstrate in a dataset prior to interaction testing. We assessed the power of EDGE to detect genetic interactions using 29 combinations of simulated genetic models and found it outperformed the traditional encoding methods across 10%, 30%, and 50% minor allele frequencies (MAFs). Further, EDGE maintained a low false-positive rate, while additive and dominant encodings demonstrated inflation. We evaluated EDGE and the traditional encodings with genetic data from the Electronic Medical Records and Genomics (eMERGE) Network for five phenotypes: age-related macular degeneration (AMD), age-related cataract, glaucoma, type 2 diabetes (T2D), and resistant hypertension. A multi-encoding genome-wide association study (GWAS) for each phenotype was performed using the traditional encodings, and the top results of the multi-encoding GWAS were considered for SNP-SNP interaction using the traditional encodings and EDGE. EDGE identified a novel SNP-SNP interaction for age-related cataract that no other method identified: rs7787286 (MAF: 0.041; intergenic region of chromosome 7)–rs4695885 (MAF: 0.34; intergenic region of chromosome 4) with a Bonferroni LRT p of 0.018. A SNP-SNP interaction was found in data from the UK Biobank within 25 kb of these SNPs using the recessive encoding: rs60374751 (MAF: 0.030) and rs6843594 (MAF: 0.34) (Bonferroni LRT p: 0.026). We recommend using EDGE to flexibly detect interactions between SNPs exhibiting diverse action. Although traditional genetic encodings are widely implemented in genetics research, including in genome-wide association studies (GWAS) and epistasis, each method makes assumptions that may not reflect the underlying etiology. Here, we introduce a novel encoding method that estimates and assigns an individualized data-driven encoding for each single nucleotide polymorphism (SNP): the elastic data-driven genetic encoding (EDGE). With simulations, we demonstrate that this novel method is more accurate and robust than traditional encoding methods in estimating heterozygous genotype values, reducing the type I error, and detecting SNP-SNP interactions. We further applied the traditional encodings and EDGE to biomedical data from the Electronic Medical Records and Genomics (eMERGE) Network for five phenotypes, and EDGE identified a novel interaction for age-related cataract not detected by traditional methods, which replicated in data from the UK Biobank. EDGE provides an alternative approach to understanding and modeling diverse SNP models and is recommended for studying complex genetics in common human phenotypes.
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Affiliation(s)
- Molly A. Hall
- Department of Veterinary and Biomedical Sciences, College of Agricultural Sciences, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- Penn State Cancer Institute, The Pennsylvania State University, University Park, Pennsylvania, United States of America
- * E-mail:
| | - John Wallace
- Department of Veterinary and Biomedical Sciences, College of Agricultural Sciences, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Anastasia M. Lucas
- Department of Genetics, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Yuki Bradford
- Department of Genetics, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Shefali S. Verma
- Department of Genetics, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Bertram Müller-Myhsok
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom
| | - Kristin Passero
- Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Jiayan Zhou
- Department of Veterinary and Biomedical Sciences, College of Agricultural Sciences, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - John McGuigan
- Department of Veterinary and Biomedical Sciences, College of Agricultural Sciences, The Pennsylvania State University, University Park, Pennsylvania, United States of America
| | - Beibei Jiang
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Institute of Translational Medicine, University of Liverpool, Liverpool, United Kingdom
| | | | - Yanfei Zhang
- Genomic Medicine Institute, Geisinger Health System, Danville, Pennsylvania, United States of America
| | - Peggy Peissig
- Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, Wisconsin, United States of America
| | - Murray Brilliant
- Center for Precision Medicine Research, Marshfield Clinic Research Institute, Marshfield, Wisconsin, United States of America
| | - Patrick Sleiman
- Department of Pediatrics, Center for Applied Genomics, Children’s Hospital of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Hakon Hakonarson
- Department of Pediatrics, Center for Applied Genomics, Children’s Hospital of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - John B. Harley
- Center for Autoimmune Genomics and Etiology (CAGE), Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, United States of America
- United States Department of Veterans Affairs Medical Center, Cincinnati, Ohio, United States of America
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, New York, United States of America
| | - Kristel Van Steen
- WELBIO, GIGA-R Medical Genomics-BIO3, University of Liège, Liège, Belgium
- Department of Human Genetics, University of Leuven, Leuven, Belgium
| | - Jason H. Moore
- Department of Genetics, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Marylyn D. Ritchie
- Department of Genetics, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
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Riboflavin intake, MTRR genetic polymorphism (rs1532268) and gastric cancer risk in a Korean population: a case-control study. Br J Nutr 2021; 127:1026-1033. [PMID: 34078503 DOI: 10.1017/s0007114521001811] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The vitamin B group, including riboflavin, plays paramount roles in one-carbon metabolism (OCM), and disorders related to this pathway have been linked to cancer development. The variants of genes encoding OCM enzymes and the insufficiency of B vitamins could contribute to carcinogenesis. Very few observational studies have revealed a relationship between riboflavin and gastric cancer (GC), especially under conditions of modified genetic factors. We carried out a study examining the association of riboflavin intake and its interaction with MTRR (rs1532268) genetic variants with GC risk among 756 controls and 377 cases. The OR and 95 % CI were evaluated using unconditional logistic regression models. We observed protective effects of riboflavin intake against GC, particularly in the female subgroup (OR = 0·52, 95 % CI 0·28, 0·97, Ptrend = 0·031). In the MTRR (rs1532268) genotypes analysis, the dominant model showed that the effects of riboflavin differed between the CC and CT + TT genotypes. Compared with CC carriers, low riboflavin intake in T+ carriers was significantly associated with a 93 % higher GC risk (OR = 1·93, 95 % CI 1·09, 3·42, Pinteraction = 0·037). In general, higher riboflavin intake might help reduce the risk of GC in both CC and TC + TT carriers, particularly the T+ carriers, with marginal significance (OR = 0·54, 95 % CI 0·28, 1·02, Pinteraction = 0·037). Our study indicates a protective effect of riboflavin intake against GC. Those who carry at least one minor allele and have low riboflavin intake could modify this association to increase GC risk in the Korean population.
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Li W, Klein RJ. Genome-wide association study identifies a role for the progesterone receptor in benign prostatic hyperplasia risk. Prostate Cancer Prostatic Dis 2021; 24:492-498. [PMID: 33219367 DOI: 10.1038/s41391-020-00303-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Revised: 10/26/2020] [Accepted: 11/05/2020] [Indexed: 11/08/2022]
Abstract
BACKGROUND Benign prostatic hyperplasia (BPH) is common noncancerous prostate enlargement, which is usually associated with lower urinary tract symptoms (LUTS) and can lead to complex urinary, bladder, or kidney diseases. The majority of elderly men will be affected by BPH as age increases. METHODS Here, we conducted a genome-wide association study (GWAS) of BPH using 1942 cases and 4730 controls from the Electronic Medical Records and Genomics network (eMERGE) as discovery cohort. We then used 5109 cases and 161,911 controls from UK Biobank as validation cohort. RESULTS This GWAS discovered 35 genome-wide significant variants (P < 5 × 10-8), located at 22 different loci in discovery cohort. We validated four significant variants located at four different loci in validation cohort: rs8027714 at 15q11.2, rs8136152 at 22q13.2, rs10192133 at 2q24.2, and rs1237696 at 11q22.1. rs1237696 is an intronic variant on chromosome 11 in the progesterone receptor (PGR) gene (P = 4.21 ×10-8, OR [95% CI] = 1.36 [1.22-1.52]). PGR is a known drug target for BPH as the PGR agonist gestonorone caproate has been used to treat BPH in multiple countries. CONCLUSIONS Our results suggest that genetic variants identified from BPH GWAS can identify pharmacologic targets for BPH treatment.
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Affiliation(s)
- Weiqiang Li
- Icahn Institute for Data Science and Genomic Technology and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Robert J Klein
- Icahn Institute for Data Science and Genomic Technology and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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25
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Stanislawski MA, Litkowski E, Fore R, Rifas-Shiman SL, Oken E, Hivert MF, Lange EM, Lange LA, Dabelea D, Raghavan S. Genetic Interactions with Intrauterine Diabetes Exposure in Relation to Obesity: The EPOCH and Project Viva Studies. Pediatr Rep 2021; 13:279-288. [PMID: 34205853 PMCID: PMC8293453 DOI: 10.3390/pediatric13020036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 05/18/2021] [Accepted: 05/21/2021] [Indexed: 11/16/2022] Open
Abstract
To examine whether BMI-associated genetic risk variants modify the association of intrauterine diabetes exposure with childhood BMI z-scores, we assessed the interaction between 95 BMI-associated genetic variants and in utero exposure to maternal diabetes among 459 children in the Exploring Perinatal Outcomes among Children historical prospective cohort study (n = 86 exposed; 373 unexposed) in relation to age- and sex-standardized childhood BMI z-scores (mean age = 10.3 years, standard deviation = 1.5 years). For the genetic variants showing a nominally significant interaction, we assessed the relationship in an additional 621 children in Project Viva, which is an independent longitudinal cohort study, and used meta-analysis to combine the results for the two studies. Seven of the ninety-five genetic variants tested exhibited a nominally significant interaction with in utero exposure to maternal diabetes in relation to the offspring BMI z-score in EPOCH. Five of the seven variants exhibited a consistent direction of interaction effect across both EPOCH and Project Viva. While none achieved statistical significance in the meta-analysis after accounting for multiple testing, three variants exhibited a nominally significant interaction with in utero exposure to maternal diabetes in relation to offspring BMI z-score: rs10733682 near LMX1B (interaction β = 0.39; standard error (SE) = 0.17), rs17001654 near SCARB2 (β = 0.53; SE = 0.22), and rs16951275 near MAP2K5 (β = 0.37; SE = 0.17). BMI-associated genetic variants may enhance the association between exposure to in utero diabetes and higher childhood BMI, but larger studies of in utero exposures are necessary to confirm the observed nominally significant relationships.
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Affiliation(s)
- Maggie A. Stanislawski
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado School of Medicine, Aurora, CO 80045, USA; (E.L.); (E.M.L.); (L.A.L.); (S.R.)
- Correspondence:
| | - Elizabeth Litkowski
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado School of Medicine, Aurora, CO 80045, USA; (E.L.); (E.M.L.); (L.A.L.); (S.R.)
- Department of Epidemiology, University of Colorado School of Public Health, Aurora, CO 80045, USA;
| | - Ruby Fore
- Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA 02215, USA; (R.F.); (S.L.R.-S.); (E.O.); (M.-F.H.)
| | - Sheryl L. Rifas-Shiman
- Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA 02215, USA; (R.F.); (S.L.R.-S.); (E.O.); (M.-F.H.)
| | - Emily Oken
- Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA 02215, USA; (R.F.); (S.L.R.-S.); (E.O.); (M.-F.H.)
| | - Marie-France Hivert
- Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA 02215, USA; (R.F.); (S.L.R.-S.); (E.O.); (M.-F.H.)
- Diabetes Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Ethan M. Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado School of Medicine, Aurora, CO 80045, USA; (E.L.); (E.M.L.); (L.A.L.); (S.R.)
- Department of Biostatistics and Informatics, University of Colorado School of Public Health, Aurora, CO 80045, USA
| | - Leslie A. Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado School of Medicine, Aurora, CO 80045, USA; (E.L.); (E.M.L.); (L.A.L.); (S.R.)
- Department of Epidemiology, University of Colorado School of Public Health, Aurora, CO 80045, USA;
| | - Dana Dabelea
- Department of Epidemiology, University of Colorado School of Public Health, Aurora, CO 80045, USA;
- Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, Aurora, CO 80045, USA
- Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Sridharan Raghavan
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado School of Medicine, Aurora, CO 80045, USA; (E.L.); (E.M.L.); (L.A.L.); (S.R.)
- Veterans Affairs Eastern Colorado Healthcare System, Aurora, CO 80045, USA
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26
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Aleknonytė-Resch M, Szymczak S, Freitag-Wolf S, Dempfle A, Krawczak M. Genotype imputation in case-only studies of gene-environment interaction: validity and power. Hum Genet 2021; 140:1217-1228. [PMID: 34041609 PMCID: PMC8263402 DOI: 10.1007/s00439-021-02294-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Accepted: 05/10/2021] [Indexed: 11/26/2022]
Abstract
Case-only (CO) studies are a powerful means to uncover gene-environment (G × E) interactions for complex human diseases. Moreover, such studies may in principle also draw upon genotype imputation to increase statistical power even further. However, genotype imputation usually employs healthy controls such as the Haplotype Reference Consortium (HRC) data as an imputation base, which may systematically perturb CO studies in genomic regions with main effects upon disease risk. Using genotype data from 719 German Crohn Disease (CD) patients, we investigated the level of imputation accuracy achievable for single nucleotide polymorphisms (SNPs) with or without a genetic main effect, and with varying minor allele frequency (MAF). Genotypes were imputed from neighbouring SNPs at different levels of linkage disequilibrium (LD) to the target SNP using the HRC data as an imputation base. Comparison of the true and imputed genotypes revealed lower imputation accuracy for SNPs with strong main effects. We also simulated different levels of G × E interaction to evaluate the potential loss of statistical validity and power incurred by the use of imputed genotypes. Simulations under the null hypothesis revealed that genotype imputation does not inflate the type I error rate of CO studies of G × E. However, the statistical power was found to be reduced by imputation, particularly for SNPs with low MAF, and a gradual loss of statistical power resulted when the level of LD to the SNPs driving the imputation decreased. Our study thus highlights that genotype imputation should be employed with great care in CO studies of G × E interaction.
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Affiliation(s)
| | - Silke Szymczak
- Institute of Medical Informatics and Statistics, Kiel University, Kiel, Germany
- Institute of Medical Biometry and Statistics, University of Lübeck, Lübeck, Germany
| | - Sandra Freitag-Wolf
- Institute of Medical Informatics and Statistics, Kiel University, Kiel, Germany
| | - Astrid Dempfle
- Institute of Medical Informatics and Statistics, Kiel University, Kiel, Germany
| | - Michael Krawczak
- Institute of Medical Informatics and Statistics, Kiel University, Kiel, Germany.
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Charon C, Allodji R, Meyer V, Deleuze JF. Impact of pre- and post-variant filtration strategies on imputation. Sci Rep 2021; 11:6214. [PMID: 33737531 PMCID: PMC7973508 DOI: 10.1038/s41598-021-85333-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2020] [Accepted: 02/22/2021] [Indexed: 01/04/2023] Open
Abstract
Quality control (QC) methods for genome-wide association studies and fine mapping are commonly used for imputation, however they result in loss of many single nucleotide polymorphisms (SNPs). To investigate the consequences of filtration on imputation, we studied the direct effects on the number of markers, their allele frequencies, imputation quality scores and post-filtration events. We pre-phrased 1031 genotyped individuals from diverse ethnicities and compared the imputed variants to 1089 NCBI recorded individuals for additional validation. Without QC-based variant pre-filtration, we observed no impairment in the imputation of SNPs that failed QC whereas with pre-filtration there was an overall loss of information. Significant differences between frequencies with and without pre-filtration were found only in the range of very rare (5E-04-1E-03) and rare variants (1E-03-5E-03) (p < 1E-04). Increasing the post-filtration imputation quality score from 0.3 to 0.8 reduced the number of single nucleotide variants (SNVs) < 0.001 2.5 fold with or without QC pre-filtration and halved the number of very rare variants (5E-04). Thus, to maintain confidence and enough SNVs, we propose here a two-step filtering procedure which allows less stringent filtering prior to imputation and post-imputation in order to increase the number of very rare and rare variants compared to conservative filtration methods.
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Affiliation(s)
- Céline Charon
- CEA Paris-Saclay, Institut François Jacob, Centre National de Recherche en Génomique Humaine, 2 rue Gaston Crémieux, Evry, 91057, France.
| | - Rodrigue Allodji
- Radiation Epidemiology Group CESP, Inserm Unit 1018, Gustave Roussy Université Paris Saclay, 114 rue Edouard Vaillant, Villejuif, 94805, France
| | - Vincent Meyer
- CEA Paris-Saclay, Institut François Jacob, Centre National de Recherche en Génomique Humaine, 2 rue Gaston Crémieux, Evry, 91057, France
| | - Jean-François Deleuze
- CEA Paris-Saclay, Institut François Jacob, Centre National de Recherche en Génomique Humaine, 2 rue Gaston Crémieux, Evry, 91057, France
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Indencleef K, Hoskens H, Lee MK, White JD, Liu C, Eller RJ, Naqvi S, Wehby GL, Moreno Uribe LM, Hecht JT, Long RE, Christensen K, Deleyiannis FW, Walsh S, Shriver MD, Richmond S, Wysocka J, Peeters H, Shaffer JR, Marazita ML, Hens G, Weinberg SM, Claes P. The Intersection of the Genetic Architectures of Orofacial Clefts and Normal Facial Variation. Front Genet 2021; 12:626403. [PMID: 33692830 PMCID: PMC7937973 DOI: 10.3389/fgene.2021.626403] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 01/26/2021] [Indexed: 01/06/2023] Open
Abstract
Unaffected relatives of individuals with non-syndromic cleft lip with or without cleft palate (NSCL/P) show distinctive facial features. The presence of this facial endophenotype is potentially an expression of underlying genetic susceptibility to NSCL/P in the larger unselected population. To explore this hypothesis, we first partitioned the face into 63 partially overlapping regions representing global-to-local facial morphology and then defined endophenotypic traits by contrasting the 3D facial images from 264 unaffected parents of individuals with NSCL/P versus 3,171 controls. We observed distinct facial features between parents and controls across 59 global-to-local facial segments at nominal significance (p ≤ 0.05) and 52 segments at Bonferroni corrected significance (p < 1.2 × 10-3), respectively. Next, we quantified these distinct facial features as univariate traits in another dataset of 8,246 unaffected European individuals and performed a genome-wide association study. We identified 29 independent genetic loci that were associated (p < 5 × 10-8) with at least one of the tested endophenotypic traits, and nine genetic loci also passed the study-wide threshold (p < 8.47 × 10-10). Of the 29 loci, 22 were in proximity of loci previously associated with normal facial variation, 18 were near genes that show strong evidence in orofacial clefting (OFC), and another 10 showed some evidence in OFC. Additionally, polygenic risk scores for NSCL/P showed associations with the endophenotypic traits. This study thus supports the hypothesis of a shared genetic architecture of normal facial development and OFC.
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Affiliation(s)
- Karlijne Indencleef
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
- Medical Imaging Research Center, UZ Leuven, Leuven, Belgium
| | - Hanne Hoskens
- Medical Imaging Research Center, UZ Leuven, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Myoung Keun Lee
- Department of Oral Biology, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, United States
| | - Julie D. White
- Department of Anthropology, Pennsylvania State University, State College, PA, United States
| | - Chenxing Liu
- Department of Oral Biology, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, United States
| | - Ryan J. Eller
- Department of Biology, Indiana University Purdue University Indianapolis, Indianapolis, IN, United States
| | - Sahin Naqvi
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, United States
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, United States
| | - George L. Wehby
- Department of Health Management and Policy, College of Public Health, University of Iowa, Iowa City, IA, United States
| | - Lina M. Moreno Uribe
- Department of Orthodontics & The Iowa Institute for Oral Health Research, College of Dentistry, University of Iowa, Iowa City, IA, United States
| | - Jacqueline T. Hecht
- Department of Pediatrics, McGovern Medical School and School of Dentistry, UT Health at Houston, Houston, TX, United States
| | - Ross E. Long
- Lancaster Cleft Palate Clinic, Lancaster, PA, United States
| | - Kaare Christensen
- Department of Epidemiology, Institute of Public Health, University of Southern Denmark, Odense, Denmark
| | | | - Susan Walsh
- Department of Biology, Indiana University Purdue University Indianapolis, Indianapolis, IN, United States
| | - Mark D. Shriver
- Department of Anthropology, Pennsylvania State University, State College, PA, United States
| | - Stephen Richmond
- Applied Clinical Research and Public Health, School of Dentistry, Cardiff University, Cardiff, United Kingdom
| | - Joanna Wysocka
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, United States
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, United States
| | - Hilde Peeters
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - John R. Shaffer
- Department of Oral Biology, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, United States
| | - Mary L. Marazita
- Department of Oral Biology, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, United States
| | - Greet Hens
- Department of Otorhinolaryngology, KU Leuven, Leuven, Belgium
| | - Seth M. Weinberg
- Department of Oral Biology, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, United States
- Department of Anthropology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Peter Claes
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
- Medical Imaging Research Center, UZ Leuven, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
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Li R, Chen Y, Moore JH. Integration of genetic and clinical information to improve imputation of data missing from electronic health records. J Am Med Inform Assoc 2021; 26:1056-1063. [PMID: 31329892 DOI: 10.1093/jamia/ocz041] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2018] [Revised: 03/12/2019] [Accepted: 03/18/2019] [Indexed: 01/29/2023] Open
Abstract
OBJECTIVE Clinical data of patients' measurements and treatment history stored in electronic health record (EHR) systems are starting to be mined for better treatment options and disease associations. A primary challenge associated with utilizing EHR data is the considerable amount of missing data. Failure to address this issue can introduce significant bias in EHR-based research. Currently, imputation methods rely on correlations among the structured phenotype variables in the EHR. However, genetic studies have shown that many EHR-based phenotypes have a heritable component, suggesting that measured genetic variants might be useful for imputing missing data. In this article, we developed a computational model that incorporates patients' genetic information to perform EHR data imputation. MATERIALS AND METHODS We used the individual single nucleotide polymorphism's association with phenotype variables in the EHR as input to construct a genetic risk score that quantifies the genetic contribution to the phenotype. Multiple approaches to constructing the genetic risk score were evaluated for optimal performance. The genetic score, along with phenotype correlation, is then used as a predictor to impute the missing values. RESULTS To demonstrate the method performance, we applied our model to impute missing cardiovascular related measurements including low-density lipoprotein, heart failure, and aortic aneurysm disease in the electronic Medical Records and Genomics data. The integration method improved imputation's area-under-the-curve for binary phenotypes and decreased root-mean-square error for continuous phenotypes. CONCLUSION Compared with standard imputation approaches, incorporating genetic information offers a novel approach that can utilize more of the EHR data for better performance in missing data imputation.
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Affiliation(s)
- Ruowang Li
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Yong Chen
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Center for Evidence-based Practice, The University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Applied Mathematics & Computational Science, Penn Arts & Sciences, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Jason H Moore
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Martin LJ, Murrison LB, Butsch Kovacic M. Building a Population Representative Pediatric Biobank: Lessons Learned From the Greater Cincinnati Childhood Cohort. Front Public Health 2021; 8:535116. [PMID: 33520904 PMCID: PMC7841396 DOI: 10.3389/fpubh.2020.535116] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Accepted: 12/15/2020] [Indexed: 01/07/2023] Open
Abstract
Background: Biobanks can accelerate research by providing researchers with samples and data. However, hospital-based recruitment as a source for controls may create bias as who comes to the hospital may be different from the broader population. Methods: In an effort to broadly improve the quality of research studies and reduce costs and challenges associated with recruitment and sample collection, a group of diverse researchers at Cincinnati Children's Hospital Medical Center led an institution-supported initiative to create a population representative pediatric "Greater Cincinnati Childhood Cohort (GCC)." Participants completed a detailed survey, underwent a brief physician-led physical exam, and provided blood, urine, and hair samples. DNA underwent high-throughput genotyping. Results: In total, 1,020 children ages 3-18 years living in the 7 county Greater Cincinnati Metropolitan region were recruited. Racial composition of the cohort was 84% non-Hispanic white, 15% non-Hispanic black, and 2% other race or Hispanic. Participants exhibited marked demographic and disease burden differences by race. Overall, the cohort was broadly used resulting in publications, grants and patents; yet, it did not meet the needs of all potential researchers. Conclusions: Learning from both the strengths and weaknesses, we propose leveraging a community-based participatory research framework for future broad use biobanking efforts.
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Affiliation(s)
- Lisa J. Martin
- Division of Human Genetics, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati School of Medicine, Cincinnati, OH, United States
| | - Liza Bronner Murrison
- Division of Asthma Research, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati School of Medicine, Cincinnati, OH, United States
| | - Melinda Butsch Kovacic
- Division of Asthma Research, Department of Pediatrics, Cincinnati Children's Hospital Medical Center, University of Cincinnati School of Medicine, Cincinnati, OH, United States
- Department of Rehabilitation, Exercise and Nutrition, Sciences, College of Allied Health Sciences, University of Cincinnati, Cincinnati, OH, United States
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31
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Li R, Duan R, Zhang X, Lumley T, Pendergrass S, Bauer C, Hakonarson H, Carrell DS, Smoller JW, Wei WQ, Carroll R, Velez Edwards DR, Wiesner G, Sleiman P, Denny JC, Mosley JD, Ritchie MD, Chen Y, Moore JH. Lossless integration of multiple electronic health records for identifying pleiotropy using summary statistics. Nat Commun 2021; 12:168. [PMID: 33420026 PMCID: PMC7794298 DOI: 10.1038/s41467-020-20211-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 11/13/2020] [Indexed: 11/22/2022] Open
Abstract
Increasingly, clinical phenotypes with matched genetic data from bio-bank linked electronic health records (EHRs) have been used for pleiotropy analyses. Thus far, pleiotropy analysis using individual-level EHR data has been limited to data from one site. However, it is desirable to integrate EHR data from multiple sites to improve the detection power and generalizability of the results. Due to privacy concerns, individual-level patients' data are not easily shared across institutions. As a result, we introduce Sum-Share, a method designed to efficiently integrate EHR and genetic data from multiple sites to perform pleiotropy analysis. Sum-Share requires only summary-level data and one round of communication from each site, yet it produces identical test statistics compared with that of pooled individual-level data. Consequently, Sum-Share can achieve lossless integration of multiple datasets. Using real EHR data from eMERGE, Sum-Share is able to identify 1734 potential pleiotropic SNPs for five cardiovascular diseases.
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Affiliation(s)
- Ruowang Li
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Rui Duan
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Xinyuan Zhang
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Thomas Lumley
- Department of Statistics, University of Auckland, Auckland, New Zealand
| | - Sarah Pendergrass
- Biomedical and Translational Informatics Institute, Geisinger, Danville, PA, USA
| | - Christopher Bauer
- Biomedical and Translational Informatics Institute, Geisinger, Danville, PA, USA
| | - Hakon Hakonarson
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - David S Carrell
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Centre, Nashville, TN, USA
| | - Robert Carroll
- Department of Biomedical Informatics, Vanderbilt University Medical Centre, Nashville, TN, USA
| | - Digna R Velez Edwards
- Clinical and Translational Hereditary Cancer Program, Division of Genetic Medicine, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA
| | - Georgia Wiesner
- Clinical and Translational Hereditary Cancer Program, Division of Genetic Medicine, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA
| | - Patrick Sleiman
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Josh C Denny
- Department of Biomedical Informatics, Vanderbilt University Medical Centre, Nashville, TN, USA
| | - Jonathan D Mosley
- Department of Biomedical Informatics, Vanderbilt University Medical Centre, Nashville, TN, USA
| | - Marylyn D Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yong Chen
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA, USA.
| | - Jason H Moore
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA, USA.
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White JD, Indencleef K, Naqvi S, Eller RJ, Hoskens H, Roosenboom J, Lee MK, Li J, Mohammed J, Richmond S, Quillen EE, Norton HL, Feingold E, Swigut T, Marazita ML, Peeters H, Hens G, Shaffer JR, Wysocka J, Walsh S, Weinberg SM, Shriver MD, Claes P. Insights into the genetic architecture of the human face. Nat Genet 2021; 53:45-53. [PMID: 33288918 PMCID: PMC7796995 DOI: 10.1038/s41588-020-00741-7] [Citation(s) in RCA: 70] [Impact Index Per Article: 23.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Accepted: 10/23/2020] [Indexed: 01/28/2023]
Abstract
The human face is complex and multipartite, and characterization of its genetic architecture remains challenging. Using a multivariate genome-wide association study meta-analysis of 8,246 European individuals, we identified 203 genome-wide-significant signals (120 also study-wide significant) associated with normal-range facial variation. Follow-up analyses indicate that the regions surrounding these signals are enriched for enhancer activity in cranial neural crest cells and craniofacial tissues, several regions harbor multiple signals with associations to different facial phenotypes, and there is evidence for potential coordinated actions of variants. In summary, our analyses provide insights into the understanding of how complex morphological traits are shaped by both individual and coordinated genetic actions.
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Affiliation(s)
- Julie D White
- Department of Anthropology, Pennsylvania State University, State College, PA, USA.
| | - Karlijne Indencleef
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium.
- Medical Imaging Research Center, UZ Leuven, Leuven, Belgium.
| | - Sahin Naqvi
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Ryan J Eller
- Department of Biology, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA
| | - Hanne Hoskens
- Medical Imaging Research Center, UZ Leuven, Leuven, Belgium
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Jasmien Roosenboom
- Department of Oral Biology, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Myoung Keun Lee
- Department of Oral Biology, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jiarui Li
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
- Medical Imaging Research Center, UZ Leuven, Leuven, Belgium
| | - Jaaved Mohammed
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Stephen Richmond
- Applied Clinical Research and Public Health, School of Dentistry, Cardiff University, Cardiff, UK
| | - Ellen E Quillen
- Department of Internal Medicine, Section of Molecular Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
- Center for Precision Medicine, Wake Forest School of Medicine, Winston-Salem, NC, USA
| | - Heather L Norton
- Department of Anthropology, University of Cincinnati, Cincinnati, OH, USA
| | - Eleanor Feingold
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Tomek Swigut
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Mary L Marazita
- Department of Oral Biology, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Hilde Peeters
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Greet Hens
- Department of Neurosciences, Experimental Oto-Rhino-Laryngology, KU Leuven, Leuven, Belgium
| | - John R Shaffer
- Department of Oral Biology, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Joanna Wysocka
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, USA
- Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Susan Walsh
- Department of Biology, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA
| | - Seth M Weinberg
- Department of Oral Biology, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Anthropology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mark D Shriver
- Department of Anthropology, Pennsylvania State University, State College, PA, USA
| | - Peter Claes
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium.
- Medical Imaging Research Center, UZ Leuven, Leuven, Belgium.
- Department of Human Genetics, KU Leuven, Leuven, Belgium.
- Murdoch Children's Research Institute, Melbourne, Victoria, Australia.
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Li W, Bicak M, Sjoberg DD, Vertosick E, Dahlin A, Melander O, Ulmert D, Lilja H, Klein RJ. Genome-wide association study identifies novel single nucleotide polymorphisms having age-specific effect on prostate-specific antigen levels. Prostate 2020; 80:1405-1412. [PMID: 32914890 PMCID: PMC7606728 DOI: 10.1002/pros.24070] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Revised: 08/17/2020] [Accepted: 08/24/2020] [Indexed: 01/17/2023]
Abstract
BACKGROUND Testing for prostate-specific antigen (PSA) levels in blood are widely used and associated with prostate cancer risk and outcome. After puberty, PSA levels increase by age and multiple single nucleotide polymorphisms (SNPs) have been found to be associated with PSA levels. However, the relationship between the effects of SNPs and age on PSA remains unknown. METHODS To test for SNP × age interaction, we conducted a genome-wide association study using 2394 men without prostate cancer diagnosis from Malmö, Sweden as a discovery set and 2137 men from the eMERGE study (USA) for validation. Linear regression was used to identify significant interactions between SNP and age (p < 1 × 10-4 for discovery, p < .05 for validation). RESULTS The 15 SNPs from three different loci (8p11.22, 8p12, 3q25.31) are found to have age-specific effect on PSA levels. Expression quantitative trait loci (eQTLs) analysis shows that 12 SNPs from 3q25.31 locus affect the expression level of three genes: KCNAB1, SLC33A1, PLCH1. CONCLUSIONS Our results suggest that SNPs may have age-specific effect on PSA levels, which provides new direction to study genetic markers for PSA.
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Affiliation(s)
- Weiqiang Li
- Icahn Institute for Genomics and Multiscale Biology and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Mesude Bicak
- Icahn Institute for Genomics and Multiscale Biology and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Daniel D. Sjoberg
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Emily Vertosick
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Anders Dahlin
- Department of Clinical Sciences, Malmö, Lund University, Malmö, Sweden
| | - Olle Melander
- Department of Clinical Sciences, Malmö, Lund University, Malmö, Sweden
| | - David Ulmert
- Molecular pharmacology program, Sloan Kettering Institute, New York, NY USA
| | - Hans Lilja
- Departments of Laboratory Medicine, Surgery, and Medicine, Memorial Sloan Kettering Cancer Center, New York, NY USA; Department of Translational Medicine, Lund University, Malmö, Sweden
| | - Robert J. Klein
- Icahn Institute for Genomics and Multiscale Biology and Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
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35
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Vasilopoulou C, Morris AP, Giannakopoulos G, Duguez S, Duddy W. What Can Machine Learning Approaches in Genomics Tell Us about the Molecular Basis of Amyotrophic Lateral Sclerosis? J Pers Med 2020; 10:E247. [PMID: 33256133 PMCID: PMC7712791 DOI: 10.3390/jpm10040247] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 11/21/2020] [Accepted: 11/23/2020] [Indexed: 02/07/2023] Open
Abstract
Amyotrophic Lateral Sclerosis (ALS) is the most common late-onset motor neuron disorder, but our current knowledge of the molecular mechanisms and pathways underlying this disease remain elusive. This review (1) systematically identifies machine learning studies aimed at the understanding of the genetic architecture of ALS, (2) outlines the main challenges faced and compares the different approaches that have been used to confront them, and (3) compares the experimental designs and results produced by those approaches and describes their reproducibility in terms of biological results and the performances of the machine learning models. The majority of the collected studies incorporated prior knowledge of ALS into their feature selection approaches, and trained their machine learning models using genomic data combined with other types of mined knowledge including functional associations, protein-protein interactions, disease/tissue-specific information, epigenetic data, and known ALS phenotype-genotype associations. The importance of incorporating gene-gene interactions and cis-regulatory elements into the experimental design of future ALS machine learning studies is highlighted. Lastly, it is suggested that future advances in the genomic and machine learning fields will bring about a better understanding of ALS genetic architecture, and enable improved personalized approaches to this and other devastating and complex diseases.
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Affiliation(s)
- Christina Vasilopoulou
- Northern Ireland Centre for Stratified Medicine, Altnagelvin Hospital Campus, Ulster University, Londonderry BT47 6SB, UK; (C.V.); (S.D.)
| | - Andrew P. Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, University of Manchester, Manchester M13 9PT, UK;
| | - George Giannakopoulos
- Institute of Informatics and Telecommunications, NCSR Demokritos, 153 10 Aghia Paraskevi, Greece;
- Science For You (SciFY) PNPC, TEPA Lefkippos-NCSR Demokritos, 27, Neapoleos, 153 41 Ag. Paraskevi, Greece
| | - Stephanie Duguez
- Northern Ireland Centre for Stratified Medicine, Altnagelvin Hospital Campus, Ulster University, Londonderry BT47 6SB, UK; (C.V.); (S.D.)
| | - William Duddy
- Northern Ireland Centre for Stratified Medicine, Altnagelvin Hospital Campus, Ulster University, Londonderry BT47 6SB, UK; (C.V.); (S.D.)
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Verma SS, Bergmeijer TO, Gong L, Reny JL, Lewis JP, Mitchell BD, Alexopoulos D, Aradi D, Altman RB, Bliden K, Bradford Y, Campo G, Chang K, Cleator JH, Déry JP, Dridi NP, Fernandez-Cadenas I, Fontana P, Gawaz M, Geisler T, Gensini GF, Giusti B, Gurbel PA, Hochholzer W, Holmvang L, Kim EY, Kim HS, Marcucci R, Montaner J, Backman JD, Pakyz RE, Roden DM, Schaeffeler E, Schwab M, Shin JG, Siller-Matula JM, Ten Berg JM, Trenk D, Valgimigli M, Wallace J, Wen MS, Kubo M, Lee MTM, Whaley R, Winter S, Klein TE, Shuldiner AR, Ritchie MD. Genomewide Association Study of Platelet Reactivity and Cardiovascular Response in Patients Treated With Clopidogrel: A Study by the International Clopidogrel Pharmacogenomics Consortium. Clin Pharmacol Ther 2020; 108:1067-1077. [PMID: 32472697 PMCID: PMC7689744 DOI: 10.1002/cpt.1911] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Accepted: 05/08/2020] [Indexed: 01/07/2023]
Abstract
Antiplatelet response to clopidogrel shows wide variation, and poor response is correlated with adverse clinical outcomes. CYP2C19 loss‐of‐function alleles play an important role in this response, but account for only a small proportion of variability in response to clopidogrel. An aim of the International Clopidogrel Pharmacogenomics Consortium (ICPC) is to identify other genetic determinants of clopidogrel pharmacodynamics and clinical response. A genomewide association study (GWAS) was performed using DNA from 2,750 European ancestry individuals, using adenosine diphosphate‐induced platelet reactivity and major cardiovascular and cerebrovascular events as outcome parameters. GWAS for platelet reactivity revealed a strong signal for CYP2C19*2 (P value = 1.67e−33). After correction for CYP2C19*2 no other single‐nucleotide polymorphism reached genomewide significance. GWAS for a combined clinical end point of cardiovascular death, myocardial infarction, or stroke (5.0% event rate), or a combined end point of cardiovascular death or myocardial infarction (4.7% event rate) showed no significant results, although in coronary artery disease, percutaneous coronary intervention, and acute coronary syndrome subgroups, mutations in SCOS5P1, CDC42BPA, and CTRAC1 showed genomewide significance (lowest P values: 1.07e−09, 4.53e−08, and 2.60e−10, respectively). CYP2C19*2 is the strongest genetic determinant of on‐clopidogrel platelet reactivity. We identified three novel associations in clinical outcome subgroups, suggestive for each of these outcomes.
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Affiliation(s)
- Shefali Setia Verma
- Department of Genetics and Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Thomas O Bergmeijer
- Department of Cardiology, St. Antonius Center for Platelet Function Research, Nieuwegein, The Netherlands
| | - Li Gong
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA
| | - Jean-Luc Reny
- Internal Medicine, Béziers Hospital, Béziers, France.,Geneva Platelet Group, School of Medicine, University of Geneva, Geneva, Switzerland.,Department of Internal Medicine, Rehabilitation and Geriatrics, University Hospitals of Geneva, Geneva, Switzerland.,Geneva Platelet Group and Division of Angiology and Haemostasis, University Hospitals of Geneva, Geneva, Switzerland
| | - Joshua P Lewis
- Department of Medicine and Program for Personalized and Genomic Medicine, University of Maryland, Baltimore, Maryland, USA
| | - Braxton D Mitchell
- Department of Medicine and Program for Personalized and Genomic Medicine, University of Maryland, Baltimore, Maryland, USA.,Geriatrics Research and Education Clinical Center, Baltimore Veterans Administration Medical Center, Baltimore, Maryland, USA
| | - Dimitrios Alexopoulos
- National and Kapodistrian University of Athens Medical School, Attikon University Hospital, Athens, Greece
| | - Daniel Aradi
- Department of Cardiology, Heart Center Balatonfüred, Balatonfüred, Hungary
| | - Russ B Altman
- Department of Bioengineering, Genetics and Medicine, Stanford University, Stanford, California, USA
| | - Kevin Bliden
- Sinai Center for Thrombosis Research and Drug Development, Baltimore, Maryland, USA
| | - Yuki Bradford
- Department of Genetics and Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Gianluca Campo
- Cardiology Unit, Azienda Ospedaliero-Universitaria di Ferrara, Ferrara and Maria Cecilia Hospital, GVM Care and Research, Cotignola, Italy
| | - Kiyuk Chang
- Department of Internal Medicine, Cardiology Division, Seoul St. Mary's Hospital, The Catholic University of Korea, Seoul, South Korea
| | - John H Cleator
- Division of Cardiology and Department of Pharmacology, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jean-Pierre Déry
- Quebec Heart and Lung Institute, University Laval, Quebec City, QC, Canada
| | - Nadia P Dridi
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Israel Fernandez-Cadenas
- Neurology, Stroke Pharmacogenomics and Genetics Group, Sant Pau Institute of Research, Barcelona, Spain
| | - Pierre Fontana
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA.,Geneva Platelet Group and Division of Angiology and Haemostasis, University Hospitals of Geneva, Geneva, Switzerland
| | - Meinrad Gawaz
- Department of Cardiology and Angiology, University of Tübingen, Tübingen, Germany
| | - Tobias Geisler
- Department of Cardiology and Angiology, Medizinische Klinik III, University Hospital Tübingen, Tübingen, Germany
| | - Gian Franco Gensini
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Betti Giusti
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Paul A Gurbel
- Sinai Center for Thrombosis Research and Drug Development, Baltimore, Maryland, USA
| | - Willibald Hochholzer
- Department of Cardiology and Angiology II, University Heart Center Freiburg Bad Krozingen, Bad Krozingen, Germany
| | - Lene Holmvang
- Department of Cardiology, Rigshospitalet, Copenhagen University Hospital, Copenhagen, Denmark
| | - Eun-Young Kim
- Department of Clinical Pharmacology, Inje University, Busan Paik Hospital, Busan, South Korea
| | - Ho-Sook Kim
- Department of Clinical Pharmacology, Inje University, Busan Paik Hospital, Busan, South Korea
| | - Rossella Marcucci
- Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Joan Montaner
- Neurovascular Research Laboratory, Vall d'Hebron Institute of Research, Barcelona, Spain
| | - Joshua D Backman
- Department of Medicine and Program for Personalized and Genomic Medicine, University of Maryland, Baltimore, Maryland, USA
| | - Ruth E Pakyz
- Department of Medicine and Program for Personalized and Genomic Medicine, University of Maryland, Baltimore, Maryland, USA
| | - Dan M Roden
- Medicine, Pharmacology, and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Elke Schaeffeler
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology and University of Tübingen, Tübingen, Germany
| | - Matthias Schwab
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology and University of Tübingen, Tübingen, Germany.,Department of Clinical Pharmacology, and Pharmacy and Biochemistry, University of Tübingen, Tübingen, Germany
| | - Jae Gook Shin
- Department of Clinical Pharmacology, Inje University, Busan Paik Hospital, Busan, South Korea.,Department of Pharmacology and Pharmacogenomics Research Center, Inje University, Busan Paik Hospital, Busan, South Korea
| | - Jolanta M Siller-Matula
- Department of Internal Medicine II, Division of Cardiology, Medical University of Vienna, Vienna, Austria.,Department of Experimental and Clinical Pharmacology, Centre for Preclinical Research and Technology (CEPT), Medical University of Warsaw, Warsaw, Poland
| | - Jurriën M Ten Berg
- Department of Cardiology, St. Antonius Center for Platelet Function Research, Nieuwegein, The Netherlands
| | - Dietmar Trenk
- Department of Cardiology and Angiology II, University Heart Center Freiburg Bad Krozingen, Bad Krozingen, Germany.,Department of Clinical Pharmacology, University Heart Centre Freiburg, Bad Krozingen, Germany
| | - Marco Valgimigli
- Department of Cardiology, Swiss Cardiovascular Center Bern, Bern University Hospital, Bern, Switzerland
| | - John Wallace
- Department of Biochemistry and Molecular Biology, Penn State University, University Park, Pennsylvania, USA
| | - Ming-Shien Wen
- Division of Cardiology, Department of Internal Medicine, Chang Gung Memorial Hospital, Linkou and School of Medicine, Chang Gung University, Taoyuan City, Taiwan
| | - Michiaki Kubo
- Center for Integrative Medical Sciences, RIKEN, Yokohama, Japan
| | | | - Ryan Whaley
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA
| | - Stefan Winter
- Dr. Margarete Fischer-Bosch Institute of Clinical Pharmacology and University of Tübingen, Tübingen, Germany
| | - Teri E Klein
- Department of Biomedical Data Science, Stanford University, Stanford, California, USA.,Department of Medicine, Stanford University, Stanford, California, USA
| | - Alan R Shuldiner
- Department of Medicine and Program for Personalized and Genomic Medicine, University of Maryland, Baltimore, Maryland, USA
| | - Marylyn D Ritchie
- Department of Genetics and Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA
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Melhuish Beaupre LM, Gonçalves VF, Zai CC, Tiwari AK, Harripaul RS, Herbert D, Freeman N, Müller DJ, Kennedy JL. Genome-Wide Association Study of Sleep Disturbances in Depressive Disorders. MOLECULAR NEUROPSYCHIATRY 2020; 5:34-43. [PMID: 32399468 DOI: 10.1159/000505804] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2019] [Accepted: 12/21/2019] [Indexed: 11/19/2022]
Abstract
Sleep disturbance affects about 75% of depressed individuals and is associated with poorer patient outcomes. The genetics in this field is an emerging area of research. Thus far, only core circadian genes have been examined in this context. We expanded on this by performing a genome-wide association study (GWAS) followed by a preplanned hypothesis-driven analysis with 27 genes associated with the biology of sleep. All participants were diagnosed by their referring physician, completed the Beck Depression Inventory (BDI), and the Udvalg for Kliniske Undersogelser Side Effect Rating Scale at baseline. Our phenotype consisted of replies to 3 questions from these questionnaires. From standard GWAS chip data, imputations were performed. Baseline total BDI scores (n = 364) differed significantly between those with and those without sleep problems. We were unable to find any significant GWAS hits although our top hit was for changes in sleep and an intergenic marker near SNX18 (p = 1.06 × 10<sup>-6</sup>). None of the markers in our hypothesis-driven analysis remained significant after applying Bonferroni corrections. Our top finding among these genes was for rs13019460 of Neuronal PAS Domain Protein 2 with changes in sleep (p = 0.0009). Overall, both analyses were unable to detect any significant associations in our modest sample though we did find some interesting preliminary associations worth further exploration.
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Affiliation(s)
- Lindsay M Melhuish Beaupre
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada.,Molecular Brain Science Research Department, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Vanessa F Gonçalves
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.,Molecular Brain Science Research Department, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Clement C Zai
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.,Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada.,Molecular Brain Science Research Department, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Arun K Tiwari
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.,Molecular Brain Science Research Department, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Ricardo S Harripaul
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada.,Molecular Neuropsychiatry and Development Lab, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Deanna Herbert
- Molecular Brain Science Research Department, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Natalie Freeman
- Molecular Brain Science Research Department, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Daniel J Müller
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.,Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - James L Kennedy
- Institute of Medical Sciences, University of Toronto, Toronto, Ontario, Canada.,Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.,Molecular Brain Science Research Department, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
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Stanislawski MA, Shaw J, Litkowski E, Lange EM, Perng W, Dabelea D, Lange LA. Genetic Risk for Hepatic Fat among an Ethnically Diverse Cohort of Youth: The Exploring Perinatal Outcomes among Children Study. J Pediatr 2020; 220:146-153.e2. [PMID: 32143931 PMCID: PMC8148653 DOI: 10.1016/j.jpeds.2020.01.031] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Revised: 12/18/2019] [Accepted: 01/13/2020] [Indexed: 12/15/2022]
Abstract
OBJECTIVE To assess the importance of genetic and nongenetic risk factors contributing to hepatic fat accumulation in a multiethnic population of youth. STUDY DESIGN We investigated the relationship between genetic factors and hepatic fat fraction (HFF) in 347 children aged 12.5-19.5 years. We examined 5 single nucleotide polymorphisms previously associated with HFF and a weighted genetic risk score (GRS) and examined how these associations varied with ethnicity (Hispanic vs non-Hispanic white) and body mass index (BMI) category. We also compared how much variation in HFF was explained by genetic factors vs cardiometabolic factors (BMI z-score and the Homeostasis Model of Insulin Resistance) or diet. RESULTS PNPLA3 rs738409 and the GRS were each associated with HFF among Hispanic (β = 0.39; 95% CI, 0.16-0.62; P = .001; and β = 0.20; 95% CI, 0.05-0.34; P = .007, respectively) but not non-Hispanic white (β = 0.04; 95% CI, -0.18 to 0.26; P = .696; and β = 0.03; 95% CI, -0.09 to 0.14; P = .651, respectively) youth. Cardiometabolic risk factors explained more of the variation in HFF than genetic risk factors among non-lean Hispanic individuals (27.2% for cardiometabolic markers vs 6.4% for rs738409 and 4.3% for the GRS), and genetic risk factors were more important among lean individuals (2.7% for cardiometabolic markers vs 12.6% for rs738409 and 4.4% for the GRS). CONCLUSIONS Poor cardiometabolic health may be more important than genetic factors when predicting HFF in overweight and obese young populations. Genetic risk is an important contributor to pediatric HFF among lean Hispanics, but further studies are necessary to elucidate the strength of the association between genetic risk and HFF in non-Hispanic white youth.
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Affiliation(s)
- Maggie A Stanislawski
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO; Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus, Aurora, CO.
| | - Jessica Shaw
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO; Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Elizabeth Litkowski
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO; Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Ethan M Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO; Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Wei Perng
- Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus, Aurora, CO; Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Dana Dabelea
- Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus, Aurora, CO; Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO; Department of Pediatrics, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Leslie A Lange
- Division of Biomedical Informatics and Personalized Medicine, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO; Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus, Aurora, CO; Department of Epidemiology, Colorado School of Public Health, University of Colorado Anschutz Medical Campus, Aurora, CO
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39
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Genome-wide association and multi-omic analyses reveal ACTN2 as a gene linked to heart failure. Nat Commun 2020; 11:1122. [PMID: 32111823 PMCID: PMC7048760 DOI: 10.1038/s41467-020-14843-7] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Accepted: 01/27/2020] [Indexed: 12/02/2022] Open
Abstract
Heart failure is a major public health problem affecting over 23 million people worldwide. In this study, we present the results of a large scale meta-analysis of heart failure GWAS and replication in a comparable sized cohort to identify one known and two novel loci associated with heart failure. Heart failure sub-phenotyping shows that a new locus in chromosome 1 is associated with left ventricular adverse remodeling and clinical heart failure, in response to different initial cardiac muscle insults. Functional characterization and fine-mapping of that locus reveal a putative causal variant in a cardiac muscle specific regulatory region activated during cardiomyocyte differentiation that binds to the ACTN2 gene, a crucial structural protein inside the cardiac sarcolemma (Hi-C interaction p-value = 0.00002). Genome-editing in human embryonic stem cell-derived cardiomyocytes confirms the influence of the identified regulatory region in the expression of ACTN2. Our findings extend our understanding of biological mechanisms underlying heart failure. Heart failure has a heterogeneous etiology and the genetic underpinnings are not well understood. Here, Arvanitis et al. perform GWAS meta-analysis including 10,976 heart failure cases and 437,573 controls, identify new loci near ABO and ACTN2 and show that deletion of a ACTN2 enhancer leads to reduced ACTN2 expression in differentiating cardiomyocytes.
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Potential of Genome-Wide Association Studies and Genomic Selection to Improve Productivity and Quality of Commercial Timber Species in Tropical Rainforest, a Case Study of Shorea platyclados. FORESTS 2020. [DOI: 10.3390/f11020239] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Shorea platyclados (Dark Red Meranti) is a commercially important timber tree species in Southeast Asia. However, its stocks have dramatically declined due, inter alia, to excessive logging, insufficient natural regeneration and a slow recovery rate. Thus, there is a need to promote enrichment planting and develop effective technique to support its rehabilitation and improve timber production through implementation of Genome-Wide Association Studies (GWAS) and Genomic Selection (GS). To assist such efforts, plant materials were collected from a half-sib progeny population in Sari Bumi Kusuma forest concession, Kalimantan, Indonesia. Using 5900 markers in sequences obtained from 356 individuals, we detected high linkage disequilibrium (LD) extending up to >145 kb, suggesting that associations between phenotypic traits and markers in LD can be more easily and feasibly detected with GWAS than with analysis of quantitative trait loci (QTLs). However, the detection power of GWAS seems low, since few single nucleotide polymorphisms linked to any focal traits were detected with a stringent false discovery rate, indicating that the species’ phenotypic traits are mostly under polygenic quantitative control. Furthermore, Machine Learning provided higher prediction accuracies than Bayesian methods. We also found that stem diameter, branch diameter ratio and wood density were more predictable than height, clear bole, branch angle and wood stiffness traits. Our study suggests that GS has potential for improving the productivity and quality of S. platyclados, and our genomic heritability estimates may improve the selection of traits to target in future breeding of this species.
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Genomic and transcriptomic association studies identify 16 novel susceptibility loci for venous thromboembolism. Blood 2020; 134:1645-1657. [PMID: 31420334 DOI: 10.1182/blood.2019000435] [Citation(s) in RCA: 132] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2019] [Accepted: 07/17/2019] [Indexed: 12/29/2022] Open
Abstract
Venous thromboembolism (VTE) is a significant contributor to morbidity and mortality. To advance our understanding of the biology contributing to VTE, we conducted a genome-wide association study (GWAS) of VTE and a transcriptome-wide association study (TWAS) based on imputed gene expression from whole blood and liver. We meta-analyzed GWAS data from 18 studies for 30 234 VTE cases and 172 122 controls and assessed the association between 12 923 718 genetic variants and VTE. We generated variant prediction scores of gene expression from whole blood and liver tissue and assessed them for association with VTE. Mendelian randomization analyses were conducted for traits genetically associated with novel VTE loci. We identified 34 independent genetic signals for VTE risk from GWAS meta-analysis, of which 14 are newly reported associations. This included 11 newly associated genetic loci (C1orf198, PLEK, OSMR-AS1, NUGGC/SCARA5, GRK5, MPHOSPH9, ARID4A, PLCG2, SMG6, EIF5A, and STX10) of which 6 replicated, and 3 new independent signals in 3 known genes. Further, TWAS identified 5 additional genetic loci with imputed gene expression levels differing between cases and controls in whole blood (SH2B3, SPSB1, RP11-747H7.3, RP4-737E23.2) and in liver (ERAP1). At some GWAS loci, we found suggestive evidence that the VTE association signal for novel and previously known regions colocalized with expression quantitative trait locus signals. Mendelian randomization analyses suggested that blood traits may contribute to the underlying risk of VTE. To conclude, we identified 16 novel susceptibility loci for VTE; for some loci, the association signals are likely mediated through gene expression of nearby genes.
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WHEELER NICHOLASR, BENCHEK PENELOPE, KUNKLE BRIANW, HAMILTON-NELSON KARAL, WARFE MIKE, FONDRAN JEREMYR, HAINES JONATHANL, BUSH WILLIAMS. Hadoop and PySpark for reproducibility and scalability of genomic sequencing studies. PACIFIC SYMPOSIUM ON BIOCOMPUTING. PACIFIC SYMPOSIUM ON BIOCOMPUTING 2020; 25:523-534. [PMID: 31797624 PMCID: PMC6956992] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Modern genomic studies are rapidly growing in scale, and the analytical approaches used to analyze genomic data are increasing in complexity. Genomic data management poses logistic and computational challenges, and analyses are increasingly reliant on genomic annotation resources that create their own data management and versioning issues. As a result, genomic datasets are increasingly handled in ways that limit the rigor and reproducibility of many analyses. In this work, we examine the use of the Spark infrastructure for the management, access, and analysis of genomic data in comparison to traditional genomic workflows on typical cluster environments. We validate the framework by reproducing previously published results from the Alzheimer's Disease Sequencing Project. Using the framework and analyses designed using Jupyter notebooks, Spark provides improved workflows, reduces user-driven data partitioning, and enhances the portability and reproducibility of distributed analyses required for large-scale genomic studies.
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Affiliation(s)
- NICHOLAS R. WHEELER
- Cleveland Institute for Computational Biology, Department of Population and Quantitative Health Sciences, Case Western Reserve University, Wolstein Research Building, 2103 Cornell Road Cleveland OH 44106, USA
| | - PENELOPE BENCHEK
- Cleveland Institute for Computational Biology, Department of Population and Quantitative Health Sciences, Case Western Reserve University, Wolstein Research Building, 2103 Cornell Road Cleveland OH 44106, USA
| | - BRIAN W. KUNKLE
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, 1501 NW 10th Ave, Miami, FL 33136, USA
| | - KARA L. HAMILTON-NELSON
- John P. Hussman Institute for Human Genomics, Miller School of Medicine, University of Miami, 1501 NW 10th Ave, Miami, FL 33136, USA
| | - MIKE WARFE
- Cleveland Institute for Computational Biology, Center for Advanced Research Computing, University Technology, Case Western Reserve University, Wolstein Research Building, 2103 Cornell Road Cleveland OH 44106, USA
| | - JEREMY R. FONDRAN
- Cleveland Institute for Computational Biology, Center for Advanced Research Computing, University Technology, Case Western Reserve University, Wolstein Research Building, 2103 Cornell Road Cleveland OH 44106, USA
| | - JONATHAN L. HAINES
- Cleveland Institute for Computational Biology, Department of Population and Quantitative Health Sciences, Case Western Reserve University, Wolstein Research Building, 2103 Cornell Road Cleveland OH 44106, USA
| | - WILLIAM S. BUSH
- Cleveland Institute for Computational Biology, Department of Population and Quantitative Health Sciences, Case Western Reserve University, Wolstein Research Building, 2103 Cornell Road Cleveland OH 44106, USA
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Lucas AM, Palmiero NE, McGuigan J, Passero K, Zhou J, Orie D, Ritchie MD, Hall MA. CLARITE Facilitates the Quality Control and Analysis Process for EWAS of Metabolic-Related Traits. Front Genet 2019; 10:1240. [PMID: 31921293 PMCID: PMC6930237 DOI: 10.3389/fgene.2019.01240] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Accepted: 11/08/2019] [Indexed: 02/03/2023] Open
Abstract
While genome-wide association studies are an established method of identifying genetic variants associated with disease, environment-wide association studies (EWAS) highlight the contribution of nongenetic components to complex phenotypes. However, the lack of high-throughput quality control (QC) pipelines for EWAS data lends itself to analysis plans where the data are cleaned after a first-pass analysis, which can lead to bias, or are cleaned manually, which is arduous and susceptible to user error. We offer a novel software, CLeaning to Analysis: Reproducibility-based Interface for Traits and Exposures (CLARITE), as a tool to efficiently clean environmental data, perform regression analysis, and visualize results on a single platform through user-guided automation. It exists as both an R package and a Python package. Though CLARITE focuses on EWAS, it is intended to also improve the QC process for phenotypes and clinical lab measures for a variety of downstream analyses, including phenome-wide association studies and gene-environment interaction studies. With the goal of demonstrating the utility of CLARITE, we performed a novel EWAS in the National Health and Nutrition Examination Survey (NHANES) (N overall Discovery=9063, N overall Replication=9874) for body mass index (BMI) and over 300 environment variables post-QC, adjusting for sex, age, race, socioeconomic status, and survey year. The analysis used survey weights along with cluster and strata information in order to account for the complex survey design. Sixteen BMI results replicated at a Bonferroni corrected p < 0.05. The top replicating results were serum levels of g-tocopherol (vitamin E) (Discovery Bonferroni p: 8.67x10-12, Replication Bonferroni p: 2.70x10-9) and iron (Discovery Bonferroni p: 1.09x10-8, Replication Bonferroni p: 1.73x10-10). Results of this EWAS are important to consider for metabolic trait analysis, as BMI is tightly associated with these phenotypes. As such, exposures predictive of BMI may be useful for covariate and/or interaction assessment of metabolic-related traits. CLARITE allows improved data quality for EWAS, gene-environment interactions, and phenome-wide association studies by establishing a high-throughput quality control infrastructure. Thus, CLARITE is recommended for studying the environmental factors underlying complex disease.
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Affiliation(s)
- Anastasia M Lucas
- Department of Genetics, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, United States
| | - Nicole E Palmiero
- Department of Veterinary and Biomedical Sciences, College of Agricultural Sciences, The Pennsylvania State University, University Park, PA, United States
| | - John McGuigan
- Department of Veterinary and Biomedical Sciences, College of Agricultural Sciences, The Pennsylvania State University, University Park, PA, United States
| | - Kristin Passero
- Department of Veterinary and Biomedical Sciences, College of Agricultural Sciences, The Pennsylvania State University, University Park, PA, United States.,Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, United States
| | - Jiayan Zhou
- Department of Veterinary and Biomedical Sciences, College of Agricultural Sciences, The Pennsylvania State University, University Park, PA, United States
| | - Deven Orie
- Department of Veterinary and Biomedical Sciences, College of Agricultural Sciences, The Pennsylvania State University, University Park, PA, United States
| | - Marylyn D Ritchie
- Department of Genetics, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, United States
| | - Molly A Hall
- Department of Veterinary and Biomedical Sciences, College of Agricultural Sciences, The Pennsylvania State University, University Park, PA, United States.,Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA, United States
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Viljakainen H, Dahlström E, Figueiredo R, Sandholm N, Rounge TB, Weiderpass E. Genetic risk score predicts risk for overweight and obesity in Finnish preadolescents. Clin Obes 2019; 9:e12342. [PMID: 31595703 PMCID: PMC6900004 DOI: 10.1111/cob.12342] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2019] [Accepted: 09/04/2019] [Indexed: 12/12/2022]
Abstract
Common genetic variants predispose to obesity with varying contribution by age. We incorporated known genetic variants into genetic risk scores (GRSs) and investigated their associations with overweight/obesity and central obesity in preadolescents. Furthermore, we compared GRSs with lifestyle factors, and tested if they predict the change in body size and shape in a 4-year follow-up. We utilized 1142 subjects from the Finnish Health in Teens (Fin-HIT) cohort. Overweight and obesity were defined with age- and gender-specific body mass index (BMI) z-score (BMIz), while central obesity by the waist-to-height ratio (WHtR). Background data on parental language, eating habits, leisure-time physical activity (LTPA) and sleep duration were included. Genotyping was performed with the Metabochip platform. Weighted, standardized GRSs were derived. Of the11-year-old children, 25.5% were at least overweight and 90.8% had Finnish speaking background. BMI-GRS was associated with higher risk for overweight with odds ratio (95% confidence interval) of 1.39 (1.20; 1.60) and obesity 1.41 (1.08; 1.83), but not with central obesity. BMI-GRS was weakly and inversely associated with the changes in BMIz and WHtR in the 4-year follow-up. Waist-to-hip ratio-GRS was not related to any obesity measures at baseline nor in the follow-up. The effect of BMI-GRS is similar to that of low LTPA on overweight. An interaction between parental language and BMI-GRS was noted (P = .019): BMI-GRS associated more strongly with overweight in Swedish than in Finnish speakers. We further identified two suggestive genetic variants near LOC101926977 and LOC105369677 associated with BMIz in preadolescents which were replicated in the adult population. In preadolescents, known genetic predisposing factors induce a risk for overweight comparable to low LTPA. However, the GRS was poor in predicting short-term changes in BMI or WHtR.
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Affiliation(s)
- Heli Viljakainen
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Department of Food and Nutrition, University of Helsinki, Helsinki, Finland
| | - Emma Dahlström
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Department of Nephrology, Abdominal Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Rejane Figueiredo
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Niina Sandholm
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Department of Nephrology, Abdominal Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Research Program for Clinical and Molecular Metabolism, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Trine B Rounge
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Department of Research, Cancer Registry of Norway, Institute of Population-based Cancer Research, Oslo, Norway
| | - Elisabete Weiderpass
- Folkhälsan Institute of Genetics, Folkhälsan Research Center, Helsinki, Finland
- Department of Research, Cancer Registry of Norway, Institute of Population-based Cancer Research, Oslo, Norway
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, Stockholm, Sweden
- Department of Community Medicine, Faculty of Health Sciences, University of Tromsø, The Arctic University of Norway, Tromsø, Norway
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Holmes JB, Speed D, Balding DJ. Summary statistic analyses can mistake confounding bias for heritability. Genet Epidemiol 2019; 43:930-940. [PMID: 31541496 DOI: 10.1002/gepi.22259] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 07/28/2019] [Accepted: 08/09/2019] [Indexed: 11/11/2022]
Abstract
Linkage disequilibrium SCore regression (LDSC) has become a popular approach to estimate confounding bias, heritability, and genetic correlation using only genome-wide association study (GWAS) test statistics. SumHer is a newly introduced alternative with similar aims. We show using theory and simulations that both approaches fail to adequately account for confounding bias, even when the assumed heritability model is correct. Consequently, these methods may estimate heritability poorly if there was an inadequate adjustment for confounding in the original GWAS analysis. We also show that the choice of a summary statistic for use in LDSC or SumHer can have a large impact on resulting inferences. Further, covariate adjustments in the original GWAS can alter the target of heritability estimation, which can be problematic for test statistics from a meta-analysis of GWAS with different covariate adjustments.
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Affiliation(s)
- John B Holmes
- Melbourne Integrative Genomics, School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia
| | - Doug Speed
- Aarhus Institute of Advanced Studies (AIAS), Aarhus University, Aarhus, Denmark.,UCL Genetics Institute, University College London, London, UK
| | - David J Balding
- Melbourne Integrative Genomics, School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia.,UCL Genetics Institute, University College London, London, UK
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Liu D, Baskett W, Beversdorf D, Shyu CR. Exploratory Data Mining for Subgroup Cohort Discoveries and Prioritization. IEEE J Biomed Health Inform 2019; 24:1456-1468. [PMID: 31494566 PMCID: PMC9341221 DOI: 10.1109/jbhi.2019.2939149] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Finding small homogeneous subgroup cohorts in large heterogeneous populations is a critical process for hypothesis development in biomedical research. Concurrent computational approaches are still lacking in robust answers to the question "what hypotheses are likely to be novel and to produce clinically relevant results with well thought-out study designs?" We have developed a novel subgroup discovery method which employs a deep exploratory mining process to slice and dice thousands of potential subpopulations and prioritize potential cohorts based on their explainable contrast patterns and which may provide interventionable insights. We conducted computational experiments on both synthesized data and a clinical autism data set to assess performance quantitatively for coverage of pre-defined cohorts and qualitatively for novel knowledge discovery, respectively. We also conducted a scaling analysis using a distributed computing environment to suggest computational resource needs for when the subpopulation number increases. This work will provide a robust data-driven framework to automatically tailor potential interventions for precision health.
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Fitzgerald KC, Kim K, Smith MD, Aston SA, Fioravante N, Rothman AM, Krieger S, Cofield SS, Kimbrough DJ, Bhargava P, Saidha S, Whartenby KA, Green AJ, Mowry EM, Cutter GR, Lublin FD, Baranzini SE, De Jager PL, Calabresi PA. Early complement genes are associated with visual system degeneration in multiple sclerosis. Brain 2019; 142:2722-2736. [PMID: 31289819 PMCID: PMC6776113 DOI: 10.1093/brain/awz188] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2018] [Revised: 04/17/2019] [Accepted: 04/28/2019] [Indexed: 11/12/2022] Open
Abstract
Multiple sclerosis is a heterogeneous disease with an unpredictable course and a wide range of severity; some individuals rapidly progress to a disabled state whereas others experience only mild symptoms. Though genetic studies have identified variants that are associated with an increased risk of developing multiple sclerosis, no variants have been consistently associated with multiple sclerosis severity. In part, the lack of findings is related to inherent limitations of clinical rating scales; these scales are insensitive to early degenerative changes that underlie disease progression. Optical coherence tomography imaging of the retina and low-contrast letter acuity correlate with and predict clinical and imaging-based outcomes in multiple sclerosis. Therefore, they may serve as sensitive phenotypes to discover genetic predictors of disease course. We conducted a set of genome-wide association studies of longitudinal structural and functional visual pathway phenotypes in multiple sclerosis. First, we assessed genetic predictors of ganglion cell/inner plexiform layer atrophy in a discovery cohort of 374 patients with multiple sclerosis using mixed-effects models adjusting for age, sex, disease duration, optic neuritis and genetic ancestry and using a combination of single-variant and network-based analyses. For candidate variants identified in discovery, we conducted a similar set of analyses of ganglion cell/inner plexiform layer thinning in a replication cohort (n = 376). Second, we assessed genetic predictors of sustained loss of 5-letters in low-contrast letter acuity in discovery (n = 582) using multivariable-adjusted Cox proportional hazards models. We then evaluated candidate variants/pathways in a replication cohort. (n = 253). Results of both studies revealed novel subnetworks highly enriched for connected genes in early complement activation linked to measures of disease severity. Within these networks, C3 was the gene most strongly associated with ganglion cell/inner plexiform layer atrophy (P = 0.004) and C1QA and CR1 were top results in analysis of sustained low-contrast letter acuity loss. Namely, variant rs158772, linked to C1QA, and rs61822967, linked to CR1, were associated with 71% and 40% increases in risk of sustained LCLA loss, respectively, in meta-analysis pooling discovery and replication cohorts (rs158772: hazard ratio: 1.71; 95% confidence interval 1.30-2.25; P = 1.3 × 10-4; rs61822967: hazard ratio: 1.40; 95% confidence interval: 1.16-1.68; P = 4.1 × 10-4). In conclusion, early complement pathway gene variants were consistently associated with structural and functional measures of multiple sclerosis severity. These results from unbiased analyses are strongly supported by several prior reports that mechanistically implicated early complement factors in neurodegeneration.
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Affiliation(s)
| | - Kicheol Kim
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Matthew D Smith
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Sean A Aston
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Nicholas Fioravante
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Alissa M Rothman
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Stephen Krieger
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stacey S Cofield
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA
| | | | - Pavan Bhargava
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Shiv Saidha
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Katharine A Whartenby
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Ari J Green
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
- Department of Ophthalmology, University of California San Francisco, San Francisco, CA, USA
| | - Ellen M Mowry
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Gary R Cutter
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Fred D Lublin
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sergio E Baranzini
- Department of Neurology, University of California San Francisco, San Francisco, CA, USA
| | - Philip L De Jager
- Center for Translational and Computational Neuroimmunology, Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, NY, USA
- Cell Circuits Program, Broad Institute, Cambridge, MA, USA
| | - Peter A Calabresi
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Solomon Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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Carrera C, Cullell N, Torres-Águila N, Muiño E, Bustamante A, Dávalos A, López-Cancio E, Ribó M, Molina CA, Giralt-Steinhauer E, Soriano-Tárraga C, Mola-Caminal M, Jiménez-Conde J, Roquer J, Vives-Bauza C, Navarro RD, Obach V, Arenillas JF, Segura T, Serrano-Heras G, Martí-Fàbregas J, Freijo M, Cabezas JA, Tatlisumak T, Heitsch L, Ibañez L, Cruchaga C, Lee JM, Strbian D, Montaner J, Fernández-Cadenas I. Validation of a clinical-genetics score to predict hemorrhagic transformations after rtPA. Neurology 2019; 93:e851-e863. [PMID: 31366724 DOI: 10.1212/wnl.0000000000007997] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Accepted: 04/02/2019] [Indexed: 01/01/2023] Open
Abstract
OBJECTIVE To validate the Genot-PA score, a clinical-genetic logistic regression score that stratifies the thrombolytic therapy safety, in a new cohort of patients with stroke. METHODS We enrolled 1,482 recombinant tissue plasminogen activator (rtPA)-treated patients with stroke in Spain and Finland from 2003 to 2016. Cohorts were analyzed on the basis of ethnicity and therapy: Spanish patients treated with IV rtPA within 4.5 hours of onset (cohort A and B) or rtPA in combination with mechanical thrombectomy within 6 hours of onset (cohort C) and Finnish participants treated with IV rtPA within 4.5 hours of onset (cohort D). The Genot-PA score was calculated, and hemorrhagic transformation (HT) and parenchymal hematoma (PH) risks were determined for each score stratum. RESULTS Genot-PA score was tested in 1,324 (cohort A, n = 726; B, n = 334; C, n = 54; and D, n = 210) patients who had enough information to complete the score. Of these, 213 (16.1%) participants developed HT and 85 (6.4%) developed PH. In cohorts A, B, and D, HT occurrence was predicted by the score (p = 2.02 × 10-6, p = 0.023, p = 0.033); PH prediction was associated in cohorts A through C (p = 0.012, p = 0.034, p = 5.32 × 10-4). Increased frequency of PH events from the lowest to the highest risk group was found (cohort A 4%-15.7%, cohort B 1.5%-18.2%, cohort C 0%-100%). The best odds ratio for PH prediction in the highest-risk group was obtained in cohort A (odds ratio 5.16, 95% confidence interval 1.46-18.08, p = 0.009). CONCLUSION The Genot-PA score predicts HT in patients with stroke treated with IV rtPA. Moreover, in an exploratory study, the score was associated with PH risk in mechanical thrombectomy-treated patients.
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Affiliation(s)
- Caty Carrera
- From the Neurovascular Research Laboratory (C.C., A.B.), Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; Stroke Pharmacogenomics and Genetics (N.C., N.-T.A.) and Stroke Genomics and Genetics (E.M.), Fundació Docència i Recerca Mútua Terrassa; Department of Neuroscience (A.D.), Hospital Germans Trias i Pujol; Stroke Unit (E.L.-C.), Hospital Universitario Central de Asturias; Stroke Unit (M.R., C.A.M.), Hospital Universitari Vall d'Hebron; Stroke Unit, Hospital Universitari Vall d'Hebron; Department of Neurology (E.G.-S., C.S.-T., M.M.-C., J.J.-C., J.R.), Neurovascular Research Group, IMIM-Hospital del Mar; Neurobiology Laboratory (C.V.-B.), Institut d'Investigacio Sanitaria de Palma; Department of Neurology (R.D.N.), Hospital Universitari Son Espases; Department of Neurology (V.O.), Hospital Clínic i Provincial de Barcelona; Department of Neurology (J.F.A.), Hospital Clínico Universitario, University of Valladolid; Department of Neurology (T.S.) and Experimental Research Unit (G.S.-H.), Hospital Universitario de Albacete; Department of Neurology (J.M.-F.), Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau; Department of Neurology (M.F.), Hospital de Basurto; Department of Neurology (J.A.C.), Virgen del Rocío & Macarena Hospitals, IBIS, Spain; Department of Clinical Neuroscience/Neurology (T.T.), Sahlgrenska Academy at University of Gothenburg and Sahlgrenska University Hospital, Sweden; Division of Emergency Medicine (L.H.), Department of Psychiatry (L.I., C.C.), and Department of Neurology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; Department of Neurology (D.S.), Helsinki University Hospital, Finland; Department of Neurology (J.M.), Virgen del Rocío & Macarena Hospitals, IBIS. Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; and Stroke Pharmacogenomics and Genetics (I.F.-C.), Fundació Docència i Recerca Mútua Terrassa, Hospital Universitari Mútua de Terrassa, Stroke Pharmacogenomics and Genetics, IIB-Sant Pau, Spain
| | - Natalia Cullell
- From the Neurovascular Research Laboratory (C.C., A.B.), Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; Stroke Pharmacogenomics and Genetics (N.C., N.-T.A.) and Stroke Genomics and Genetics (E.M.), Fundació Docència i Recerca Mútua Terrassa; Department of Neuroscience (A.D.), Hospital Germans Trias i Pujol; Stroke Unit (E.L.-C.), Hospital Universitario Central de Asturias; Stroke Unit (M.R., C.A.M.), Hospital Universitari Vall d'Hebron; Stroke Unit, Hospital Universitari Vall d'Hebron; Department of Neurology (E.G.-S., C.S.-T., M.M.-C., J.J.-C., J.R.), Neurovascular Research Group, IMIM-Hospital del Mar; Neurobiology Laboratory (C.V.-B.), Institut d'Investigacio Sanitaria de Palma; Department of Neurology (R.D.N.), Hospital Universitari Son Espases; Department of Neurology (V.O.), Hospital Clínic i Provincial de Barcelona; Department of Neurology (J.F.A.), Hospital Clínico Universitario, University of Valladolid; Department of Neurology (T.S.) and Experimental Research Unit (G.S.-H.), Hospital Universitario de Albacete; Department of Neurology (J.M.-F.), Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau; Department of Neurology (M.F.), Hospital de Basurto; Department of Neurology (J.A.C.), Virgen del Rocío & Macarena Hospitals, IBIS, Spain; Department of Clinical Neuroscience/Neurology (T.T.), Sahlgrenska Academy at University of Gothenburg and Sahlgrenska University Hospital, Sweden; Division of Emergency Medicine (L.H.), Department of Psychiatry (L.I., C.C.), and Department of Neurology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; Department of Neurology (D.S.), Helsinki University Hospital, Finland; Department of Neurology (J.M.), Virgen del Rocío & Macarena Hospitals, IBIS. Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; and Stroke Pharmacogenomics and Genetics (I.F.-C.), Fundació Docència i Recerca Mútua Terrassa, Hospital Universitari Mútua de Terrassa, Stroke Pharmacogenomics and Genetics, IIB-Sant Pau, Spain
| | - Nuria Torres-Águila
- From the Neurovascular Research Laboratory (C.C., A.B.), Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; Stroke Pharmacogenomics and Genetics (N.C., N.-T.A.) and Stroke Genomics and Genetics (E.M.), Fundació Docència i Recerca Mútua Terrassa; Department of Neuroscience (A.D.), Hospital Germans Trias i Pujol; Stroke Unit (E.L.-C.), Hospital Universitario Central de Asturias; Stroke Unit (M.R., C.A.M.), Hospital Universitari Vall d'Hebron; Stroke Unit, Hospital Universitari Vall d'Hebron; Department of Neurology (E.G.-S., C.S.-T., M.M.-C., J.J.-C., J.R.), Neurovascular Research Group, IMIM-Hospital del Mar; Neurobiology Laboratory (C.V.-B.), Institut d'Investigacio Sanitaria de Palma; Department of Neurology (R.D.N.), Hospital Universitari Son Espases; Department of Neurology (V.O.), Hospital Clínic i Provincial de Barcelona; Department of Neurology (J.F.A.), Hospital Clínico Universitario, University of Valladolid; Department of Neurology (T.S.) and Experimental Research Unit (G.S.-H.), Hospital Universitario de Albacete; Department of Neurology (J.M.-F.), Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau; Department of Neurology (M.F.), Hospital de Basurto; Department of Neurology (J.A.C.), Virgen del Rocío & Macarena Hospitals, IBIS, Spain; Department of Clinical Neuroscience/Neurology (T.T.), Sahlgrenska Academy at University of Gothenburg and Sahlgrenska University Hospital, Sweden; Division of Emergency Medicine (L.H.), Department of Psychiatry (L.I., C.C.), and Department of Neurology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; Department of Neurology (D.S.), Helsinki University Hospital, Finland; Department of Neurology (J.M.), Virgen del Rocío & Macarena Hospitals, IBIS. Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; and Stroke Pharmacogenomics and Genetics (I.F.-C.), Fundació Docència i Recerca Mútua Terrassa, Hospital Universitari Mútua de Terrassa, Stroke Pharmacogenomics and Genetics, IIB-Sant Pau, Spain
| | - Elena Muiño
- From the Neurovascular Research Laboratory (C.C., A.B.), Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; Stroke Pharmacogenomics and Genetics (N.C., N.-T.A.) and Stroke Genomics and Genetics (E.M.), Fundació Docència i Recerca Mútua Terrassa; Department of Neuroscience (A.D.), Hospital Germans Trias i Pujol; Stroke Unit (E.L.-C.), Hospital Universitario Central de Asturias; Stroke Unit (M.R., C.A.M.), Hospital Universitari Vall d'Hebron; Stroke Unit, Hospital Universitari Vall d'Hebron; Department of Neurology (E.G.-S., C.S.-T., M.M.-C., J.J.-C., J.R.), Neurovascular Research Group, IMIM-Hospital del Mar; Neurobiology Laboratory (C.V.-B.), Institut d'Investigacio Sanitaria de Palma; Department of Neurology (R.D.N.), Hospital Universitari Son Espases; Department of Neurology (V.O.), Hospital Clínic i Provincial de Barcelona; Department of Neurology (J.F.A.), Hospital Clínico Universitario, University of Valladolid; Department of Neurology (T.S.) and Experimental Research Unit (G.S.-H.), Hospital Universitario de Albacete; Department of Neurology (J.M.-F.), Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau; Department of Neurology (M.F.), Hospital de Basurto; Department of Neurology (J.A.C.), Virgen del Rocío & Macarena Hospitals, IBIS, Spain; Department of Clinical Neuroscience/Neurology (T.T.), Sahlgrenska Academy at University of Gothenburg and Sahlgrenska University Hospital, Sweden; Division of Emergency Medicine (L.H.), Department of Psychiatry (L.I., C.C.), and Department of Neurology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; Department of Neurology (D.S.), Helsinki University Hospital, Finland; Department of Neurology (J.M.), Virgen del Rocío & Macarena Hospitals, IBIS. Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; and Stroke Pharmacogenomics and Genetics (I.F.-C.), Fundació Docència i Recerca Mútua Terrassa, Hospital Universitari Mútua de Terrassa, Stroke Pharmacogenomics and Genetics, IIB-Sant Pau, Spain
| | - Alejandro Bustamante
- From the Neurovascular Research Laboratory (C.C., A.B.), Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; Stroke Pharmacogenomics and Genetics (N.C., N.-T.A.) and Stroke Genomics and Genetics (E.M.), Fundació Docència i Recerca Mútua Terrassa; Department of Neuroscience (A.D.), Hospital Germans Trias i Pujol; Stroke Unit (E.L.-C.), Hospital Universitario Central de Asturias; Stroke Unit (M.R., C.A.M.), Hospital Universitari Vall d'Hebron; Stroke Unit, Hospital Universitari Vall d'Hebron; Department of Neurology (E.G.-S., C.S.-T., M.M.-C., J.J.-C., J.R.), Neurovascular Research Group, IMIM-Hospital del Mar; Neurobiology Laboratory (C.V.-B.), Institut d'Investigacio Sanitaria de Palma; Department of Neurology (R.D.N.), Hospital Universitari Son Espases; Department of Neurology (V.O.), Hospital Clínic i Provincial de Barcelona; Department of Neurology (J.F.A.), Hospital Clínico Universitario, University of Valladolid; Department of Neurology (T.S.) and Experimental Research Unit (G.S.-H.), Hospital Universitario de Albacete; Department of Neurology (J.M.-F.), Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau; Department of Neurology (M.F.), Hospital de Basurto; Department of Neurology (J.A.C.), Virgen del Rocío & Macarena Hospitals, IBIS, Spain; Department of Clinical Neuroscience/Neurology (T.T.), Sahlgrenska Academy at University of Gothenburg and Sahlgrenska University Hospital, Sweden; Division of Emergency Medicine (L.H.), Department of Psychiatry (L.I., C.C.), and Department of Neurology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; Department of Neurology (D.S.), Helsinki University Hospital, Finland; Department of Neurology (J.M.), Virgen del Rocío & Macarena Hospitals, IBIS. Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; and Stroke Pharmacogenomics and Genetics (I.F.-C.), Fundació Docència i Recerca Mútua Terrassa, Hospital Universitari Mútua de Terrassa, Stroke Pharmacogenomics and Genetics, IIB-Sant Pau, Spain
| | - Antonio Dávalos
- From the Neurovascular Research Laboratory (C.C., A.B.), Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; Stroke Pharmacogenomics and Genetics (N.C., N.-T.A.) and Stroke Genomics and Genetics (E.M.), Fundació Docència i Recerca Mútua Terrassa; Department of Neuroscience (A.D.), Hospital Germans Trias i Pujol; Stroke Unit (E.L.-C.), Hospital Universitario Central de Asturias; Stroke Unit (M.R., C.A.M.), Hospital Universitari Vall d'Hebron; Stroke Unit, Hospital Universitari Vall d'Hebron; Department of Neurology (E.G.-S., C.S.-T., M.M.-C., J.J.-C., J.R.), Neurovascular Research Group, IMIM-Hospital del Mar; Neurobiology Laboratory (C.V.-B.), Institut d'Investigacio Sanitaria de Palma; Department of Neurology (R.D.N.), Hospital Universitari Son Espases; Department of Neurology (V.O.), Hospital Clínic i Provincial de Barcelona; Department of Neurology (J.F.A.), Hospital Clínico Universitario, University of Valladolid; Department of Neurology (T.S.) and Experimental Research Unit (G.S.-H.), Hospital Universitario de Albacete; Department of Neurology (J.M.-F.), Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau; Department of Neurology (M.F.), Hospital de Basurto; Department of Neurology (J.A.C.), Virgen del Rocío & Macarena Hospitals, IBIS, Spain; Department of Clinical Neuroscience/Neurology (T.T.), Sahlgrenska Academy at University of Gothenburg and Sahlgrenska University Hospital, Sweden; Division of Emergency Medicine (L.H.), Department of Psychiatry (L.I., C.C.), and Department of Neurology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; Department of Neurology (D.S.), Helsinki University Hospital, Finland; Department of Neurology (J.M.), Virgen del Rocío & Macarena Hospitals, IBIS. Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; and Stroke Pharmacogenomics and Genetics (I.F.-C.), Fundació Docència i Recerca Mútua Terrassa, Hospital Universitari Mútua de Terrassa, Stroke Pharmacogenomics and Genetics, IIB-Sant Pau, Spain
| | - Elena López-Cancio
- From the Neurovascular Research Laboratory (C.C., A.B.), Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; Stroke Pharmacogenomics and Genetics (N.C., N.-T.A.) and Stroke Genomics and Genetics (E.M.), Fundació Docència i Recerca Mútua Terrassa; Department of Neuroscience (A.D.), Hospital Germans Trias i Pujol; Stroke Unit (E.L.-C.), Hospital Universitario Central de Asturias; Stroke Unit (M.R., C.A.M.), Hospital Universitari Vall d'Hebron; Stroke Unit, Hospital Universitari Vall d'Hebron; Department of Neurology (E.G.-S., C.S.-T., M.M.-C., J.J.-C., J.R.), Neurovascular Research Group, IMIM-Hospital del Mar; Neurobiology Laboratory (C.V.-B.), Institut d'Investigacio Sanitaria de Palma; Department of Neurology (R.D.N.), Hospital Universitari Son Espases; Department of Neurology (V.O.), Hospital Clínic i Provincial de Barcelona; Department of Neurology (J.F.A.), Hospital Clínico Universitario, University of Valladolid; Department of Neurology (T.S.) and Experimental Research Unit (G.S.-H.), Hospital Universitario de Albacete; Department of Neurology (J.M.-F.), Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau; Department of Neurology (M.F.), Hospital de Basurto; Department of Neurology (J.A.C.), Virgen del Rocío & Macarena Hospitals, IBIS, Spain; Department of Clinical Neuroscience/Neurology (T.T.), Sahlgrenska Academy at University of Gothenburg and Sahlgrenska University Hospital, Sweden; Division of Emergency Medicine (L.H.), Department of Psychiatry (L.I., C.C.), and Department of Neurology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; Department of Neurology (D.S.), Helsinki University Hospital, Finland; Department of Neurology (J.M.), Virgen del Rocío & Macarena Hospitals, IBIS. Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; and Stroke Pharmacogenomics and Genetics (I.F.-C.), Fundació Docència i Recerca Mútua Terrassa, Hospital Universitari Mútua de Terrassa, Stroke Pharmacogenomics and Genetics, IIB-Sant Pau, Spain
| | - Marc Ribó
- From the Neurovascular Research Laboratory (C.C., A.B.), Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; Stroke Pharmacogenomics and Genetics (N.C., N.-T.A.) and Stroke Genomics and Genetics (E.M.), Fundació Docència i Recerca Mútua Terrassa; Department of Neuroscience (A.D.), Hospital Germans Trias i Pujol; Stroke Unit (E.L.-C.), Hospital Universitario Central de Asturias; Stroke Unit (M.R., C.A.M.), Hospital Universitari Vall d'Hebron; Stroke Unit, Hospital Universitari Vall d'Hebron; Department of Neurology (E.G.-S., C.S.-T., M.M.-C., J.J.-C., J.R.), Neurovascular Research Group, IMIM-Hospital del Mar; Neurobiology Laboratory (C.V.-B.), Institut d'Investigacio Sanitaria de Palma; Department of Neurology (R.D.N.), Hospital Universitari Son Espases; Department of Neurology (V.O.), Hospital Clínic i Provincial de Barcelona; Department of Neurology (J.F.A.), Hospital Clínico Universitario, University of Valladolid; Department of Neurology (T.S.) and Experimental Research Unit (G.S.-H.), Hospital Universitario de Albacete; Department of Neurology (J.M.-F.), Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau; Department of Neurology (M.F.), Hospital de Basurto; Department of Neurology (J.A.C.), Virgen del Rocío & Macarena Hospitals, IBIS, Spain; Department of Clinical Neuroscience/Neurology (T.T.), Sahlgrenska Academy at University of Gothenburg and Sahlgrenska University Hospital, Sweden; Division of Emergency Medicine (L.H.), Department of Psychiatry (L.I., C.C.), and Department of Neurology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; Department of Neurology (D.S.), Helsinki University Hospital, Finland; Department of Neurology (J.M.), Virgen del Rocío & Macarena Hospitals, IBIS. Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; and Stroke Pharmacogenomics and Genetics (I.F.-C.), Fundació Docència i Recerca Mútua Terrassa, Hospital Universitari Mútua de Terrassa, Stroke Pharmacogenomics and Genetics, IIB-Sant Pau, Spain
| | - Carlos A Molina
- From the Neurovascular Research Laboratory (C.C., A.B.), Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; Stroke Pharmacogenomics and Genetics (N.C., N.-T.A.) and Stroke Genomics and Genetics (E.M.), Fundació Docència i Recerca Mútua Terrassa; Department of Neuroscience (A.D.), Hospital Germans Trias i Pujol; Stroke Unit (E.L.-C.), Hospital Universitario Central de Asturias; Stroke Unit (M.R., C.A.M.), Hospital Universitari Vall d'Hebron; Stroke Unit, Hospital Universitari Vall d'Hebron; Department of Neurology (E.G.-S., C.S.-T., M.M.-C., J.J.-C., J.R.), Neurovascular Research Group, IMIM-Hospital del Mar; Neurobiology Laboratory (C.V.-B.), Institut d'Investigacio Sanitaria de Palma; Department of Neurology (R.D.N.), Hospital Universitari Son Espases; Department of Neurology (V.O.), Hospital Clínic i Provincial de Barcelona; Department of Neurology (J.F.A.), Hospital Clínico Universitario, University of Valladolid; Department of Neurology (T.S.) and Experimental Research Unit (G.S.-H.), Hospital Universitario de Albacete; Department of Neurology (J.M.-F.), Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau; Department of Neurology (M.F.), Hospital de Basurto; Department of Neurology (J.A.C.), Virgen del Rocío & Macarena Hospitals, IBIS, Spain; Department of Clinical Neuroscience/Neurology (T.T.), Sahlgrenska Academy at University of Gothenburg and Sahlgrenska University Hospital, Sweden; Division of Emergency Medicine (L.H.), Department of Psychiatry (L.I., C.C.), and Department of Neurology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; Department of Neurology (D.S.), Helsinki University Hospital, Finland; Department of Neurology (J.M.), Virgen del Rocío & Macarena Hospitals, IBIS. Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; and Stroke Pharmacogenomics and Genetics (I.F.-C.), Fundació Docència i Recerca Mútua Terrassa, Hospital Universitari Mútua de Terrassa, Stroke Pharmacogenomics and Genetics, IIB-Sant Pau, Spain
| | - Eva Giralt-Steinhauer
- From the Neurovascular Research Laboratory (C.C., A.B.), Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; Stroke Pharmacogenomics and Genetics (N.C., N.-T.A.) and Stroke Genomics and Genetics (E.M.), Fundació Docència i Recerca Mútua Terrassa; Department of Neuroscience (A.D.), Hospital Germans Trias i Pujol; Stroke Unit (E.L.-C.), Hospital Universitario Central de Asturias; Stroke Unit (M.R., C.A.M.), Hospital Universitari Vall d'Hebron; Stroke Unit, Hospital Universitari Vall d'Hebron; Department of Neurology (E.G.-S., C.S.-T., M.M.-C., J.J.-C., J.R.), Neurovascular Research Group, IMIM-Hospital del Mar; Neurobiology Laboratory (C.V.-B.), Institut d'Investigacio Sanitaria de Palma; Department of Neurology (R.D.N.), Hospital Universitari Son Espases; Department of Neurology (V.O.), Hospital Clínic i Provincial de Barcelona; Department of Neurology (J.F.A.), Hospital Clínico Universitario, University of Valladolid; Department of Neurology (T.S.) and Experimental Research Unit (G.S.-H.), Hospital Universitario de Albacete; Department of Neurology (J.M.-F.), Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau; Department of Neurology (M.F.), Hospital de Basurto; Department of Neurology (J.A.C.), Virgen del Rocío & Macarena Hospitals, IBIS, Spain; Department of Clinical Neuroscience/Neurology (T.T.), Sahlgrenska Academy at University of Gothenburg and Sahlgrenska University Hospital, Sweden; Division of Emergency Medicine (L.H.), Department of Psychiatry (L.I., C.C.), and Department of Neurology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; Department of Neurology (D.S.), Helsinki University Hospital, Finland; Department of Neurology (J.M.), Virgen del Rocío & Macarena Hospitals, IBIS. Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; and Stroke Pharmacogenomics and Genetics (I.F.-C.), Fundació Docència i Recerca Mútua Terrassa, Hospital Universitari Mútua de Terrassa, Stroke Pharmacogenomics and Genetics, IIB-Sant Pau, Spain
| | - Carolina Soriano-Tárraga
- From the Neurovascular Research Laboratory (C.C., A.B.), Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; Stroke Pharmacogenomics and Genetics (N.C., N.-T.A.) and Stroke Genomics and Genetics (E.M.), Fundació Docència i Recerca Mútua Terrassa; Department of Neuroscience (A.D.), Hospital Germans Trias i Pujol; Stroke Unit (E.L.-C.), Hospital Universitario Central de Asturias; Stroke Unit (M.R., C.A.M.), Hospital Universitari Vall d'Hebron; Stroke Unit, Hospital Universitari Vall d'Hebron; Department of Neurology (E.G.-S., C.S.-T., M.M.-C., J.J.-C., J.R.), Neurovascular Research Group, IMIM-Hospital del Mar; Neurobiology Laboratory (C.V.-B.), Institut d'Investigacio Sanitaria de Palma; Department of Neurology (R.D.N.), Hospital Universitari Son Espases; Department of Neurology (V.O.), Hospital Clínic i Provincial de Barcelona; Department of Neurology (J.F.A.), Hospital Clínico Universitario, University of Valladolid; Department of Neurology (T.S.) and Experimental Research Unit (G.S.-H.), Hospital Universitario de Albacete; Department of Neurology (J.M.-F.), Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau; Department of Neurology (M.F.), Hospital de Basurto; Department of Neurology (J.A.C.), Virgen del Rocío & Macarena Hospitals, IBIS, Spain; Department of Clinical Neuroscience/Neurology (T.T.), Sahlgrenska Academy at University of Gothenburg and Sahlgrenska University Hospital, Sweden; Division of Emergency Medicine (L.H.), Department of Psychiatry (L.I., C.C.), and Department of Neurology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; Department of Neurology (D.S.), Helsinki University Hospital, Finland; Department of Neurology (J.M.), Virgen del Rocío & Macarena Hospitals, IBIS. Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; and Stroke Pharmacogenomics and Genetics (I.F.-C.), Fundació Docència i Recerca Mútua Terrassa, Hospital Universitari Mútua de Terrassa, Stroke Pharmacogenomics and Genetics, IIB-Sant Pau, Spain
| | - Marina Mola-Caminal
- From the Neurovascular Research Laboratory (C.C., A.B.), Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; Stroke Pharmacogenomics and Genetics (N.C., N.-T.A.) and Stroke Genomics and Genetics (E.M.), Fundació Docència i Recerca Mútua Terrassa; Department of Neuroscience (A.D.), Hospital Germans Trias i Pujol; Stroke Unit (E.L.-C.), Hospital Universitario Central de Asturias; Stroke Unit (M.R., C.A.M.), Hospital Universitari Vall d'Hebron; Stroke Unit, Hospital Universitari Vall d'Hebron; Department of Neurology (E.G.-S., C.S.-T., M.M.-C., J.J.-C., J.R.), Neurovascular Research Group, IMIM-Hospital del Mar; Neurobiology Laboratory (C.V.-B.), Institut d'Investigacio Sanitaria de Palma; Department of Neurology (R.D.N.), Hospital Universitari Son Espases; Department of Neurology (V.O.), Hospital Clínic i Provincial de Barcelona; Department of Neurology (J.F.A.), Hospital Clínico Universitario, University of Valladolid; Department of Neurology (T.S.) and Experimental Research Unit (G.S.-H.), Hospital Universitario de Albacete; Department of Neurology (J.M.-F.), Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau; Department of Neurology (M.F.), Hospital de Basurto; Department of Neurology (J.A.C.), Virgen del Rocío & Macarena Hospitals, IBIS, Spain; Department of Clinical Neuroscience/Neurology (T.T.), Sahlgrenska Academy at University of Gothenburg and Sahlgrenska University Hospital, Sweden; Division of Emergency Medicine (L.H.), Department of Psychiatry (L.I., C.C.), and Department of Neurology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; Department of Neurology (D.S.), Helsinki University Hospital, Finland; Department of Neurology (J.M.), Virgen del Rocío & Macarena Hospitals, IBIS. Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; and Stroke Pharmacogenomics and Genetics (I.F.-C.), Fundació Docència i Recerca Mútua Terrassa, Hospital Universitari Mútua de Terrassa, Stroke Pharmacogenomics and Genetics, IIB-Sant Pau, Spain
| | - Jordi Jiménez-Conde
- From the Neurovascular Research Laboratory (C.C., A.B.), Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; Stroke Pharmacogenomics and Genetics (N.C., N.-T.A.) and Stroke Genomics and Genetics (E.M.), Fundació Docència i Recerca Mútua Terrassa; Department of Neuroscience (A.D.), Hospital Germans Trias i Pujol; Stroke Unit (E.L.-C.), Hospital Universitario Central de Asturias; Stroke Unit (M.R., C.A.M.), Hospital Universitari Vall d'Hebron; Stroke Unit, Hospital Universitari Vall d'Hebron; Department of Neurology (E.G.-S., C.S.-T., M.M.-C., J.J.-C., J.R.), Neurovascular Research Group, IMIM-Hospital del Mar; Neurobiology Laboratory (C.V.-B.), Institut d'Investigacio Sanitaria de Palma; Department of Neurology (R.D.N.), Hospital Universitari Son Espases; Department of Neurology (V.O.), Hospital Clínic i Provincial de Barcelona; Department of Neurology (J.F.A.), Hospital Clínico Universitario, University of Valladolid; Department of Neurology (T.S.) and Experimental Research Unit (G.S.-H.), Hospital Universitario de Albacete; Department of Neurology (J.M.-F.), Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau; Department of Neurology (M.F.), Hospital de Basurto; Department of Neurology (J.A.C.), Virgen del Rocío & Macarena Hospitals, IBIS, Spain; Department of Clinical Neuroscience/Neurology (T.T.), Sahlgrenska Academy at University of Gothenburg and Sahlgrenska University Hospital, Sweden; Division of Emergency Medicine (L.H.), Department of Psychiatry (L.I., C.C.), and Department of Neurology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; Department of Neurology (D.S.), Helsinki University Hospital, Finland; Department of Neurology (J.M.), Virgen del Rocío & Macarena Hospitals, IBIS. Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; and Stroke Pharmacogenomics and Genetics (I.F.-C.), Fundació Docència i Recerca Mútua Terrassa, Hospital Universitari Mútua de Terrassa, Stroke Pharmacogenomics and Genetics, IIB-Sant Pau, Spain
| | - Jaume Roquer
- From the Neurovascular Research Laboratory (C.C., A.B.), Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; Stroke Pharmacogenomics and Genetics (N.C., N.-T.A.) and Stroke Genomics and Genetics (E.M.), Fundació Docència i Recerca Mútua Terrassa; Department of Neuroscience (A.D.), Hospital Germans Trias i Pujol; Stroke Unit (E.L.-C.), Hospital Universitario Central de Asturias; Stroke Unit (M.R., C.A.M.), Hospital Universitari Vall d'Hebron; Stroke Unit, Hospital Universitari Vall d'Hebron; Department of Neurology (E.G.-S., C.S.-T., M.M.-C., J.J.-C., J.R.), Neurovascular Research Group, IMIM-Hospital del Mar; Neurobiology Laboratory (C.V.-B.), Institut d'Investigacio Sanitaria de Palma; Department of Neurology (R.D.N.), Hospital Universitari Son Espases; Department of Neurology (V.O.), Hospital Clínic i Provincial de Barcelona; Department of Neurology (J.F.A.), Hospital Clínico Universitario, University of Valladolid; Department of Neurology (T.S.) and Experimental Research Unit (G.S.-H.), Hospital Universitario de Albacete; Department of Neurology (J.M.-F.), Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau; Department of Neurology (M.F.), Hospital de Basurto; Department of Neurology (J.A.C.), Virgen del Rocío & Macarena Hospitals, IBIS, Spain; Department of Clinical Neuroscience/Neurology (T.T.), Sahlgrenska Academy at University of Gothenburg and Sahlgrenska University Hospital, Sweden; Division of Emergency Medicine (L.H.), Department of Psychiatry (L.I., C.C.), and Department of Neurology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; Department of Neurology (D.S.), Helsinki University Hospital, Finland; Department of Neurology (J.M.), Virgen del Rocío & Macarena Hospitals, IBIS. Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; and Stroke Pharmacogenomics and Genetics (I.F.-C.), Fundació Docència i Recerca Mútua Terrassa, Hospital Universitari Mútua de Terrassa, Stroke Pharmacogenomics and Genetics, IIB-Sant Pau, Spain
| | - Cristófol Vives-Bauza
- From the Neurovascular Research Laboratory (C.C., A.B.), Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; Stroke Pharmacogenomics and Genetics (N.C., N.-T.A.) and Stroke Genomics and Genetics (E.M.), Fundació Docència i Recerca Mútua Terrassa; Department of Neuroscience (A.D.), Hospital Germans Trias i Pujol; Stroke Unit (E.L.-C.), Hospital Universitario Central de Asturias; Stroke Unit (M.R., C.A.M.), Hospital Universitari Vall d'Hebron; Stroke Unit, Hospital Universitari Vall d'Hebron; Department of Neurology (E.G.-S., C.S.-T., M.M.-C., J.J.-C., J.R.), Neurovascular Research Group, IMIM-Hospital del Mar; Neurobiology Laboratory (C.V.-B.), Institut d'Investigacio Sanitaria de Palma; Department of Neurology (R.D.N.), Hospital Universitari Son Espases; Department of Neurology (V.O.), Hospital Clínic i Provincial de Barcelona; Department of Neurology (J.F.A.), Hospital Clínico Universitario, University of Valladolid; Department of Neurology (T.S.) and Experimental Research Unit (G.S.-H.), Hospital Universitario de Albacete; Department of Neurology (J.M.-F.), Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau; Department of Neurology (M.F.), Hospital de Basurto; Department of Neurology (J.A.C.), Virgen del Rocío & Macarena Hospitals, IBIS, Spain; Department of Clinical Neuroscience/Neurology (T.T.), Sahlgrenska Academy at University of Gothenburg and Sahlgrenska University Hospital, Sweden; Division of Emergency Medicine (L.H.), Department of Psychiatry (L.I., C.C.), and Department of Neurology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; Department of Neurology (D.S.), Helsinki University Hospital, Finland; Department of Neurology (J.M.), Virgen del Rocío & Macarena Hospitals, IBIS. Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; and Stroke Pharmacogenomics and Genetics (I.F.-C.), Fundació Docència i Recerca Mútua Terrassa, Hospital Universitari Mútua de Terrassa, Stroke Pharmacogenomics and Genetics, IIB-Sant Pau, Spain
| | - Rosa Díaz Navarro
- From the Neurovascular Research Laboratory (C.C., A.B.), Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; Stroke Pharmacogenomics and Genetics (N.C., N.-T.A.) and Stroke Genomics and Genetics (E.M.), Fundació Docència i Recerca Mútua Terrassa; Department of Neuroscience (A.D.), Hospital Germans Trias i Pujol; Stroke Unit (E.L.-C.), Hospital Universitario Central de Asturias; Stroke Unit (M.R., C.A.M.), Hospital Universitari Vall d'Hebron; Stroke Unit, Hospital Universitari Vall d'Hebron; Department of Neurology (E.G.-S., C.S.-T., M.M.-C., J.J.-C., J.R.), Neurovascular Research Group, IMIM-Hospital del Mar; Neurobiology Laboratory (C.V.-B.), Institut d'Investigacio Sanitaria de Palma; Department of Neurology (R.D.N.), Hospital Universitari Son Espases; Department of Neurology (V.O.), Hospital Clínic i Provincial de Barcelona; Department of Neurology (J.F.A.), Hospital Clínico Universitario, University of Valladolid; Department of Neurology (T.S.) and Experimental Research Unit (G.S.-H.), Hospital Universitario de Albacete; Department of Neurology (J.M.-F.), Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau; Department of Neurology (M.F.), Hospital de Basurto; Department of Neurology (J.A.C.), Virgen del Rocío & Macarena Hospitals, IBIS, Spain; Department of Clinical Neuroscience/Neurology (T.T.), Sahlgrenska Academy at University of Gothenburg and Sahlgrenska University Hospital, Sweden; Division of Emergency Medicine (L.H.), Department of Psychiatry (L.I., C.C.), and Department of Neurology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; Department of Neurology (D.S.), Helsinki University Hospital, Finland; Department of Neurology (J.M.), Virgen del Rocío & Macarena Hospitals, IBIS. Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; and Stroke Pharmacogenomics and Genetics (I.F.-C.), Fundació Docència i Recerca Mútua Terrassa, Hospital Universitari Mútua de Terrassa, Stroke Pharmacogenomics and Genetics, IIB-Sant Pau, Spain
| | - Victor Obach
- From the Neurovascular Research Laboratory (C.C., A.B.), Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; Stroke Pharmacogenomics and Genetics (N.C., N.-T.A.) and Stroke Genomics and Genetics (E.M.), Fundació Docència i Recerca Mútua Terrassa; Department of Neuroscience (A.D.), Hospital Germans Trias i Pujol; Stroke Unit (E.L.-C.), Hospital Universitario Central de Asturias; Stroke Unit (M.R., C.A.M.), Hospital Universitari Vall d'Hebron; Stroke Unit, Hospital Universitari Vall d'Hebron; Department of Neurology (E.G.-S., C.S.-T., M.M.-C., J.J.-C., J.R.), Neurovascular Research Group, IMIM-Hospital del Mar; Neurobiology Laboratory (C.V.-B.), Institut d'Investigacio Sanitaria de Palma; Department of Neurology (R.D.N.), Hospital Universitari Son Espases; Department of Neurology (V.O.), Hospital Clínic i Provincial de Barcelona; Department of Neurology (J.F.A.), Hospital Clínico Universitario, University of Valladolid; Department of Neurology (T.S.) and Experimental Research Unit (G.S.-H.), Hospital Universitario de Albacete; Department of Neurology (J.M.-F.), Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau; Department of Neurology (M.F.), Hospital de Basurto; Department of Neurology (J.A.C.), Virgen del Rocío & Macarena Hospitals, IBIS, Spain; Department of Clinical Neuroscience/Neurology (T.T.), Sahlgrenska Academy at University of Gothenburg and Sahlgrenska University Hospital, Sweden; Division of Emergency Medicine (L.H.), Department of Psychiatry (L.I., C.C.), and Department of Neurology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; Department of Neurology (D.S.), Helsinki University Hospital, Finland; Department of Neurology (J.M.), Virgen del Rocío & Macarena Hospitals, IBIS. Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; and Stroke Pharmacogenomics and Genetics (I.F.-C.), Fundació Docència i Recerca Mútua Terrassa, Hospital Universitari Mútua de Terrassa, Stroke Pharmacogenomics and Genetics, IIB-Sant Pau, Spain
| | - Juan Francisco Arenillas
- From the Neurovascular Research Laboratory (C.C., A.B.), Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; Stroke Pharmacogenomics and Genetics (N.C., N.-T.A.) and Stroke Genomics and Genetics (E.M.), Fundació Docència i Recerca Mútua Terrassa; Department of Neuroscience (A.D.), Hospital Germans Trias i Pujol; Stroke Unit (E.L.-C.), Hospital Universitario Central de Asturias; Stroke Unit (M.R., C.A.M.), Hospital Universitari Vall d'Hebron; Stroke Unit, Hospital Universitari Vall d'Hebron; Department of Neurology (E.G.-S., C.S.-T., M.M.-C., J.J.-C., J.R.), Neurovascular Research Group, IMIM-Hospital del Mar; Neurobiology Laboratory (C.V.-B.), Institut d'Investigacio Sanitaria de Palma; Department of Neurology (R.D.N.), Hospital Universitari Son Espases; Department of Neurology (V.O.), Hospital Clínic i Provincial de Barcelona; Department of Neurology (J.F.A.), Hospital Clínico Universitario, University of Valladolid; Department of Neurology (T.S.) and Experimental Research Unit (G.S.-H.), Hospital Universitario de Albacete; Department of Neurology (J.M.-F.), Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau; Department of Neurology (M.F.), Hospital de Basurto; Department of Neurology (J.A.C.), Virgen del Rocío & Macarena Hospitals, IBIS, Spain; Department of Clinical Neuroscience/Neurology (T.T.), Sahlgrenska Academy at University of Gothenburg and Sahlgrenska University Hospital, Sweden; Division of Emergency Medicine (L.H.), Department of Psychiatry (L.I., C.C.), and Department of Neurology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; Department of Neurology (D.S.), Helsinki University Hospital, Finland; Department of Neurology (J.M.), Virgen del Rocío & Macarena Hospitals, IBIS. Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; and Stroke Pharmacogenomics and Genetics (I.F.-C.), Fundació Docència i Recerca Mútua Terrassa, Hospital Universitari Mútua de Terrassa, Stroke Pharmacogenomics and Genetics, IIB-Sant Pau, Spain
| | - Tomás Segura
- From the Neurovascular Research Laboratory (C.C., A.B.), Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; Stroke Pharmacogenomics and Genetics (N.C., N.-T.A.) and Stroke Genomics and Genetics (E.M.), Fundació Docència i Recerca Mútua Terrassa; Department of Neuroscience (A.D.), Hospital Germans Trias i Pujol; Stroke Unit (E.L.-C.), Hospital Universitario Central de Asturias; Stroke Unit (M.R., C.A.M.), Hospital Universitari Vall d'Hebron; Stroke Unit, Hospital Universitari Vall d'Hebron; Department of Neurology (E.G.-S., C.S.-T., M.M.-C., J.J.-C., J.R.), Neurovascular Research Group, IMIM-Hospital del Mar; Neurobiology Laboratory (C.V.-B.), Institut d'Investigacio Sanitaria de Palma; Department of Neurology (R.D.N.), Hospital Universitari Son Espases; Department of Neurology (V.O.), Hospital Clínic i Provincial de Barcelona; Department of Neurology (J.F.A.), Hospital Clínico Universitario, University of Valladolid; Department of Neurology (T.S.) and Experimental Research Unit (G.S.-H.), Hospital Universitario de Albacete; Department of Neurology (J.M.-F.), Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau; Department of Neurology (M.F.), Hospital de Basurto; Department of Neurology (J.A.C.), Virgen del Rocío & Macarena Hospitals, IBIS, Spain; Department of Clinical Neuroscience/Neurology (T.T.), Sahlgrenska Academy at University of Gothenburg and Sahlgrenska University Hospital, Sweden; Division of Emergency Medicine (L.H.), Department of Psychiatry (L.I., C.C.), and Department of Neurology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; Department of Neurology (D.S.), Helsinki University Hospital, Finland; Department of Neurology (J.M.), Virgen del Rocío & Macarena Hospitals, IBIS. Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; and Stroke Pharmacogenomics and Genetics (I.F.-C.), Fundació Docència i Recerca Mútua Terrassa, Hospital Universitari Mútua de Terrassa, Stroke Pharmacogenomics and Genetics, IIB-Sant Pau, Spain
| | - Gemma Serrano-Heras
- From the Neurovascular Research Laboratory (C.C., A.B.), Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; Stroke Pharmacogenomics and Genetics (N.C., N.-T.A.) and Stroke Genomics and Genetics (E.M.), Fundació Docència i Recerca Mútua Terrassa; Department of Neuroscience (A.D.), Hospital Germans Trias i Pujol; Stroke Unit (E.L.-C.), Hospital Universitario Central de Asturias; Stroke Unit (M.R., C.A.M.), Hospital Universitari Vall d'Hebron; Stroke Unit, Hospital Universitari Vall d'Hebron; Department of Neurology (E.G.-S., C.S.-T., M.M.-C., J.J.-C., J.R.), Neurovascular Research Group, IMIM-Hospital del Mar; Neurobiology Laboratory (C.V.-B.), Institut d'Investigacio Sanitaria de Palma; Department of Neurology (R.D.N.), Hospital Universitari Son Espases; Department of Neurology (V.O.), Hospital Clínic i Provincial de Barcelona; Department of Neurology (J.F.A.), Hospital Clínico Universitario, University of Valladolid; Department of Neurology (T.S.) and Experimental Research Unit (G.S.-H.), Hospital Universitario de Albacete; Department of Neurology (J.M.-F.), Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau; Department of Neurology (M.F.), Hospital de Basurto; Department of Neurology (J.A.C.), Virgen del Rocío & Macarena Hospitals, IBIS, Spain; Department of Clinical Neuroscience/Neurology (T.T.), Sahlgrenska Academy at University of Gothenburg and Sahlgrenska University Hospital, Sweden; Division of Emergency Medicine (L.H.), Department of Psychiatry (L.I., C.C.), and Department of Neurology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; Department of Neurology (D.S.), Helsinki University Hospital, Finland; Department of Neurology (J.M.), Virgen del Rocío & Macarena Hospitals, IBIS. Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; and Stroke Pharmacogenomics and Genetics (I.F.-C.), Fundació Docència i Recerca Mútua Terrassa, Hospital Universitari Mútua de Terrassa, Stroke Pharmacogenomics and Genetics, IIB-Sant Pau, Spain
| | - Joan Martí-Fàbregas
- From the Neurovascular Research Laboratory (C.C., A.B.), Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; Stroke Pharmacogenomics and Genetics (N.C., N.-T.A.) and Stroke Genomics and Genetics (E.M.), Fundació Docència i Recerca Mútua Terrassa; Department of Neuroscience (A.D.), Hospital Germans Trias i Pujol; Stroke Unit (E.L.-C.), Hospital Universitario Central de Asturias; Stroke Unit (M.R., C.A.M.), Hospital Universitari Vall d'Hebron; Stroke Unit, Hospital Universitari Vall d'Hebron; Department of Neurology (E.G.-S., C.S.-T., M.M.-C., J.J.-C., J.R.), Neurovascular Research Group, IMIM-Hospital del Mar; Neurobiology Laboratory (C.V.-B.), Institut d'Investigacio Sanitaria de Palma; Department of Neurology (R.D.N.), Hospital Universitari Son Espases; Department of Neurology (V.O.), Hospital Clínic i Provincial de Barcelona; Department of Neurology (J.F.A.), Hospital Clínico Universitario, University of Valladolid; Department of Neurology (T.S.) and Experimental Research Unit (G.S.-H.), Hospital Universitario de Albacete; Department of Neurology (J.M.-F.), Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau; Department of Neurology (M.F.), Hospital de Basurto; Department of Neurology (J.A.C.), Virgen del Rocío & Macarena Hospitals, IBIS, Spain; Department of Clinical Neuroscience/Neurology (T.T.), Sahlgrenska Academy at University of Gothenburg and Sahlgrenska University Hospital, Sweden; Division of Emergency Medicine (L.H.), Department of Psychiatry (L.I., C.C.), and Department of Neurology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; Department of Neurology (D.S.), Helsinki University Hospital, Finland; Department of Neurology (J.M.), Virgen del Rocío & Macarena Hospitals, IBIS. Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; and Stroke Pharmacogenomics and Genetics (I.F.-C.), Fundació Docència i Recerca Mútua Terrassa, Hospital Universitari Mútua de Terrassa, Stroke Pharmacogenomics and Genetics, IIB-Sant Pau, Spain
| | - Marimar Freijo
- From the Neurovascular Research Laboratory (C.C., A.B.), Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; Stroke Pharmacogenomics and Genetics (N.C., N.-T.A.) and Stroke Genomics and Genetics (E.M.), Fundació Docència i Recerca Mútua Terrassa; Department of Neuroscience (A.D.), Hospital Germans Trias i Pujol; Stroke Unit (E.L.-C.), Hospital Universitario Central de Asturias; Stroke Unit (M.R., C.A.M.), Hospital Universitari Vall d'Hebron; Stroke Unit, Hospital Universitari Vall d'Hebron; Department of Neurology (E.G.-S., C.S.-T., M.M.-C., J.J.-C., J.R.), Neurovascular Research Group, IMIM-Hospital del Mar; Neurobiology Laboratory (C.V.-B.), Institut d'Investigacio Sanitaria de Palma; Department of Neurology (R.D.N.), Hospital Universitari Son Espases; Department of Neurology (V.O.), Hospital Clínic i Provincial de Barcelona; Department of Neurology (J.F.A.), Hospital Clínico Universitario, University of Valladolid; Department of Neurology (T.S.) and Experimental Research Unit (G.S.-H.), Hospital Universitario de Albacete; Department of Neurology (J.M.-F.), Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau; Department of Neurology (M.F.), Hospital de Basurto; Department of Neurology (J.A.C.), Virgen del Rocío & Macarena Hospitals, IBIS, Spain; Department of Clinical Neuroscience/Neurology (T.T.), Sahlgrenska Academy at University of Gothenburg and Sahlgrenska University Hospital, Sweden; Division of Emergency Medicine (L.H.), Department of Psychiatry (L.I., C.C.), and Department of Neurology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; Department of Neurology (D.S.), Helsinki University Hospital, Finland; Department of Neurology (J.M.), Virgen del Rocío & Macarena Hospitals, IBIS. Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; and Stroke Pharmacogenomics and Genetics (I.F.-C.), Fundació Docència i Recerca Mútua Terrassa, Hospital Universitari Mútua de Terrassa, Stroke Pharmacogenomics and Genetics, IIB-Sant Pau, Spain
| | - Juan Antonio Cabezas
- From the Neurovascular Research Laboratory (C.C., A.B.), Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; Stroke Pharmacogenomics and Genetics (N.C., N.-T.A.) and Stroke Genomics and Genetics (E.M.), Fundació Docència i Recerca Mútua Terrassa; Department of Neuroscience (A.D.), Hospital Germans Trias i Pujol; Stroke Unit (E.L.-C.), Hospital Universitario Central de Asturias; Stroke Unit (M.R., C.A.M.), Hospital Universitari Vall d'Hebron; Stroke Unit, Hospital Universitari Vall d'Hebron; Department of Neurology (E.G.-S., C.S.-T., M.M.-C., J.J.-C., J.R.), Neurovascular Research Group, IMIM-Hospital del Mar; Neurobiology Laboratory (C.V.-B.), Institut d'Investigacio Sanitaria de Palma; Department of Neurology (R.D.N.), Hospital Universitari Son Espases; Department of Neurology (V.O.), Hospital Clínic i Provincial de Barcelona; Department of Neurology (J.F.A.), Hospital Clínico Universitario, University of Valladolid; Department of Neurology (T.S.) and Experimental Research Unit (G.S.-H.), Hospital Universitario de Albacete; Department of Neurology (J.M.-F.), Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau; Department of Neurology (M.F.), Hospital de Basurto; Department of Neurology (J.A.C.), Virgen del Rocío & Macarena Hospitals, IBIS, Spain; Department of Clinical Neuroscience/Neurology (T.T.), Sahlgrenska Academy at University of Gothenburg and Sahlgrenska University Hospital, Sweden; Division of Emergency Medicine (L.H.), Department of Psychiatry (L.I., C.C.), and Department of Neurology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; Department of Neurology (D.S.), Helsinki University Hospital, Finland; Department of Neurology (J.M.), Virgen del Rocío & Macarena Hospitals, IBIS. Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; and Stroke Pharmacogenomics and Genetics (I.F.-C.), Fundació Docència i Recerca Mútua Terrassa, Hospital Universitari Mútua de Terrassa, Stroke Pharmacogenomics and Genetics, IIB-Sant Pau, Spain
| | - Turgut Tatlisumak
- From the Neurovascular Research Laboratory (C.C., A.B.), Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; Stroke Pharmacogenomics and Genetics (N.C., N.-T.A.) and Stroke Genomics and Genetics (E.M.), Fundació Docència i Recerca Mútua Terrassa; Department of Neuroscience (A.D.), Hospital Germans Trias i Pujol; Stroke Unit (E.L.-C.), Hospital Universitario Central de Asturias; Stroke Unit (M.R., C.A.M.), Hospital Universitari Vall d'Hebron; Stroke Unit, Hospital Universitari Vall d'Hebron; Department of Neurology (E.G.-S., C.S.-T., M.M.-C., J.J.-C., J.R.), Neurovascular Research Group, IMIM-Hospital del Mar; Neurobiology Laboratory (C.V.-B.), Institut d'Investigacio Sanitaria de Palma; Department of Neurology (R.D.N.), Hospital Universitari Son Espases; Department of Neurology (V.O.), Hospital Clínic i Provincial de Barcelona; Department of Neurology (J.F.A.), Hospital Clínico Universitario, University of Valladolid; Department of Neurology (T.S.) and Experimental Research Unit (G.S.-H.), Hospital Universitario de Albacete; Department of Neurology (J.M.-F.), Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau; Department of Neurology (M.F.), Hospital de Basurto; Department of Neurology (J.A.C.), Virgen del Rocío & Macarena Hospitals, IBIS, Spain; Department of Clinical Neuroscience/Neurology (T.T.), Sahlgrenska Academy at University of Gothenburg and Sahlgrenska University Hospital, Sweden; Division of Emergency Medicine (L.H.), Department of Psychiatry (L.I., C.C.), and Department of Neurology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; Department of Neurology (D.S.), Helsinki University Hospital, Finland; Department of Neurology (J.M.), Virgen del Rocío & Macarena Hospitals, IBIS. Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; and Stroke Pharmacogenomics and Genetics (I.F.-C.), Fundació Docència i Recerca Mútua Terrassa, Hospital Universitari Mútua de Terrassa, Stroke Pharmacogenomics and Genetics, IIB-Sant Pau, Spain
| | - Laura Heitsch
- From the Neurovascular Research Laboratory (C.C., A.B.), Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; Stroke Pharmacogenomics and Genetics (N.C., N.-T.A.) and Stroke Genomics and Genetics (E.M.), Fundació Docència i Recerca Mútua Terrassa; Department of Neuroscience (A.D.), Hospital Germans Trias i Pujol; Stroke Unit (E.L.-C.), Hospital Universitario Central de Asturias; Stroke Unit (M.R., C.A.M.), Hospital Universitari Vall d'Hebron; Stroke Unit, Hospital Universitari Vall d'Hebron; Department of Neurology (E.G.-S., C.S.-T., M.M.-C., J.J.-C., J.R.), Neurovascular Research Group, IMIM-Hospital del Mar; Neurobiology Laboratory (C.V.-B.), Institut d'Investigacio Sanitaria de Palma; Department of Neurology (R.D.N.), Hospital Universitari Son Espases; Department of Neurology (V.O.), Hospital Clínic i Provincial de Barcelona; Department of Neurology (J.F.A.), Hospital Clínico Universitario, University of Valladolid; Department of Neurology (T.S.) and Experimental Research Unit (G.S.-H.), Hospital Universitario de Albacete; Department of Neurology (J.M.-F.), Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau; Department of Neurology (M.F.), Hospital de Basurto; Department of Neurology (J.A.C.), Virgen del Rocío & Macarena Hospitals, IBIS, Spain; Department of Clinical Neuroscience/Neurology (T.T.), Sahlgrenska Academy at University of Gothenburg and Sahlgrenska University Hospital, Sweden; Division of Emergency Medicine (L.H.), Department of Psychiatry (L.I., C.C.), and Department of Neurology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; Department of Neurology (D.S.), Helsinki University Hospital, Finland; Department of Neurology (J.M.), Virgen del Rocío & Macarena Hospitals, IBIS. Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; and Stroke Pharmacogenomics and Genetics (I.F.-C.), Fundació Docència i Recerca Mútua Terrassa, Hospital Universitari Mútua de Terrassa, Stroke Pharmacogenomics and Genetics, IIB-Sant Pau, Spain
| | - Laura Ibañez
- From the Neurovascular Research Laboratory (C.C., A.B.), Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; Stroke Pharmacogenomics and Genetics (N.C., N.-T.A.) and Stroke Genomics and Genetics (E.M.), Fundació Docència i Recerca Mútua Terrassa; Department of Neuroscience (A.D.), Hospital Germans Trias i Pujol; Stroke Unit (E.L.-C.), Hospital Universitario Central de Asturias; Stroke Unit (M.R., C.A.M.), Hospital Universitari Vall d'Hebron; Stroke Unit, Hospital Universitari Vall d'Hebron; Department of Neurology (E.G.-S., C.S.-T., M.M.-C., J.J.-C., J.R.), Neurovascular Research Group, IMIM-Hospital del Mar; Neurobiology Laboratory (C.V.-B.), Institut d'Investigacio Sanitaria de Palma; Department of Neurology (R.D.N.), Hospital Universitari Son Espases; Department of Neurology (V.O.), Hospital Clínic i Provincial de Barcelona; Department of Neurology (J.F.A.), Hospital Clínico Universitario, University of Valladolid; Department of Neurology (T.S.) and Experimental Research Unit (G.S.-H.), Hospital Universitario de Albacete; Department of Neurology (J.M.-F.), Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau; Department of Neurology (M.F.), Hospital de Basurto; Department of Neurology (J.A.C.), Virgen del Rocío & Macarena Hospitals, IBIS, Spain; Department of Clinical Neuroscience/Neurology (T.T.), Sahlgrenska Academy at University of Gothenburg and Sahlgrenska University Hospital, Sweden; Division of Emergency Medicine (L.H.), Department of Psychiatry (L.I., C.C.), and Department of Neurology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; Department of Neurology (D.S.), Helsinki University Hospital, Finland; Department of Neurology (J.M.), Virgen del Rocío & Macarena Hospitals, IBIS. Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; and Stroke Pharmacogenomics and Genetics (I.F.-C.), Fundació Docència i Recerca Mútua Terrassa, Hospital Universitari Mútua de Terrassa, Stroke Pharmacogenomics and Genetics, IIB-Sant Pau, Spain
| | - Carlos Cruchaga
- From the Neurovascular Research Laboratory (C.C., A.B.), Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; Stroke Pharmacogenomics and Genetics (N.C., N.-T.A.) and Stroke Genomics and Genetics (E.M.), Fundació Docència i Recerca Mútua Terrassa; Department of Neuroscience (A.D.), Hospital Germans Trias i Pujol; Stroke Unit (E.L.-C.), Hospital Universitario Central de Asturias; Stroke Unit (M.R., C.A.M.), Hospital Universitari Vall d'Hebron; Stroke Unit, Hospital Universitari Vall d'Hebron; Department of Neurology (E.G.-S., C.S.-T., M.M.-C., J.J.-C., J.R.), Neurovascular Research Group, IMIM-Hospital del Mar; Neurobiology Laboratory (C.V.-B.), Institut d'Investigacio Sanitaria de Palma; Department of Neurology (R.D.N.), Hospital Universitari Son Espases; Department of Neurology (V.O.), Hospital Clínic i Provincial de Barcelona; Department of Neurology (J.F.A.), Hospital Clínico Universitario, University of Valladolid; Department of Neurology (T.S.) and Experimental Research Unit (G.S.-H.), Hospital Universitario de Albacete; Department of Neurology (J.M.-F.), Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau; Department of Neurology (M.F.), Hospital de Basurto; Department of Neurology (J.A.C.), Virgen del Rocío & Macarena Hospitals, IBIS, Spain; Department of Clinical Neuroscience/Neurology (T.T.), Sahlgrenska Academy at University of Gothenburg and Sahlgrenska University Hospital, Sweden; Division of Emergency Medicine (L.H.), Department of Psychiatry (L.I., C.C.), and Department of Neurology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; Department of Neurology (D.S.), Helsinki University Hospital, Finland; Department of Neurology (J.M.), Virgen del Rocío & Macarena Hospitals, IBIS. Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; and Stroke Pharmacogenomics and Genetics (I.F.-C.), Fundació Docència i Recerca Mútua Terrassa, Hospital Universitari Mútua de Terrassa, Stroke Pharmacogenomics and Genetics, IIB-Sant Pau, Spain
| | - Jin-Moo Lee
- From the Neurovascular Research Laboratory (C.C., A.B.), Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; Stroke Pharmacogenomics and Genetics (N.C., N.-T.A.) and Stroke Genomics and Genetics (E.M.), Fundació Docència i Recerca Mútua Terrassa; Department of Neuroscience (A.D.), Hospital Germans Trias i Pujol; Stroke Unit (E.L.-C.), Hospital Universitario Central de Asturias; Stroke Unit (M.R., C.A.M.), Hospital Universitari Vall d'Hebron; Stroke Unit, Hospital Universitari Vall d'Hebron; Department of Neurology (E.G.-S., C.S.-T., M.M.-C., J.J.-C., J.R.), Neurovascular Research Group, IMIM-Hospital del Mar; Neurobiology Laboratory (C.V.-B.), Institut d'Investigacio Sanitaria de Palma; Department of Neurology (R.D.N.), Hospital Universitari Son Espases; Department of Neurology (V.O.), Hospital Clínic i Provincial de Barcelona; Department of Neurology (J.F.A.), Hospital Clínico Universitario, University of Valladolid; Department of Neurology (T.S.) and Experimental Research Unit (G.S.-H.), Hospital Universitario de Albacete; Department of Neurology (J.M.-F.), Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau; Department of Neurology (M.F.), Hospital de Basurto; Department of Neurology (J.A.C.), Virgen del Rocío & Macarena Hospitals, IBIS, Spain; Department of Clinical Neuroscience/Neurology (T.T.), Sahlgrenska Academy at University of Gothenburg and Sahlgrenska University Hospital, Sweden; Division of Emergency Medicine (L.H.), Department of Psychiatry (L.I., C.C.), and Department of Neurology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; Department of Neurology (D.S.), Helsinki University Hospital, Finland; Department of Neurology (J.M.), Virgen del Rocío & Macarena Hospitals, IBIS. Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; and Stroke Pharmacogenomics and Genetics (I.F.-C.), Fundació Docència i Recerca Mútua Terrassa, Hospital Universitari Mútua de Terrassa, Stroke Pharmacogenomics and Genetics, IIB-Sant Pau, Spain
| | - Daniel Strbian
- From the Neurovascular Research Laboratory (C.C., A.B.), Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; Stroke Pharmacogenomics and Genetics (N.C., N.-T.A.) and Stroke Genomics and Genetics (E.M.), Fundació Docència i Recerca Mútua Terrassa; Department of Neuroscience (A.D.), Hospital Germans Trias i Pujol; Stroke Unit (E.L.-C.), Hospital Universitario Central de Asturias; Stroke Unit (M.R., C.A.M.), Hospital Universitari Vall d'Hebron; Stroke Unit, Hospital Universitari Vall d'Hebron; Department of Neurology (E.G.-S., C.S.-T., M.M.-C., J.J.-C., J.R.), Neurovascular Research Group, IMIM-Hospital del Mar; Neurobiology Laboratory (C.V.-B.), Institut d'Investigacio Sanitaria de Palma; Department of Neurology (R.D.N.), Hospital Universitari Son Espases; Department of Neurology (V.O.), Hospital Clínic i Provincial de Barcelona; Department of Neurology (J.F.A.), Hospital Clínico Universitario, University of Valladolid; Department of Neurology (T.S.) and Experimental Research Unit (G.S.-H.), Hospital Universitario de Albacete; Department of Neurology (J.M.-F.), Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau; Department of Neurology (M.F.), Hospital de Basurto; Department of Neurology (J.A.C.), Virgen del Rocío & Macarena Hospitals, IBIS, Spain; Department of Clinical Neuroscience/Neurology (T.T.), Sahlgrenska Academy at University of Gothenburg and Sahlgrenska University Hospital, Sweden; Division of Emergency Medicine (L.H.), Department of Psychiatry (L.I., C.C.), and Department of Neurology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; Department of Neurology (D.S.), Helsinki University Hospital, Finland; Department of Neurology (J.M.), Virgen del Rocío & Macarena Hospitals, IBIS. Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; and Stroke Pharmacogenomics and Genetics (I.F.-C.), Fundació Docència i Recerca Mútua Terrassa, Hospital Universitari Mútua de Terrassa, Stroke Pharmacogenomics and Genetics, IIB-Sant Pau, Spain
| | - Joan Montaner
- From the Neurovascular Research Laboratory (C.C., A.B.), Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; Stroke Pharmacogenomics and Genetics (N.C., N.-T.A.) and Stroke Genomics and Genetics (E.M.), Fundació Docència i Recerca Mútua Terrassa; Department of Neuroscience (A.D.), Hospital Germans Trias i Pujol; Stroke Unit (E.L.-C.), Hospital Universitario Central de Asturias; Stroke Unit (M.R., C.A.M.), Hospital Universitari Vall d'Hebron; Stroke Unit, Hospital Universitari Vall d'Hebron; Department of Neurology (E.G.-S., C.S.-T., M.M.-C., J.J.-C., J.R.), Neurovascular Research Group, IMIM-Hospital del Mar; Neurobiology Laboratory (C.V.-B.), Institut d'Investigacio Sanitaria de Palma; Department of Neurology (R.D.N.), Hospital Universitari Son Espases; Department of Neurology (V.O.), Hospital Clínic i Provincial de Barcelona; Department of Neurology (J.F.A.), Hospital Clínico Universitario, University of Valladolid; Department of Neurology (T.S.) and Experimental Research Unit (G.S.-H.), Hospital Universitario de Albacete; Department of Neurology (J.M.-F.), Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau; Department of Neurology (M.F.), Hospital de Basurto; Department of Neurology (J.A.C.), Virgen del Rocío & Macarena Hospitals, IBIS, Spain; Department of Clinical Neuroscience/Neurology (T.T.), Sahlgrenska Academy at University of Gothenburg and Sahlgrenska University Hospital, Sweden; Division of Emergency Medicine (L.H.), Department of Psychiatry (L.I., C.C.), and Department of Neurology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; Department of Neurology (D.S.), Helsinki University Hospital, Finland; Department of Neurology (J.M.), Virgen del Rocío & Macarena Hospitals, IBIS. Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; and Stroke Pharmacogenomics and Genetics (I.F.-C.), Fundació Docència i Recerca Mútua Terrassa, Hospital Universitari Mútua de Terrassa, Stroke Pharmacogenomics and Genetics, IIB-Sant Pau, Spain
| | - Israel Fernández-Cadenas
- From the Neurovascular Research Laboratory (C.C., A.B.), Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; Stroke Pharmacogenomics and Genetics (N.C., N.-T.A.) and Stroke Genomics and Genetics (E.M.), Fundació Docència i Recerca Mútua Terrassa; Department of Neuroscience (A.D.), Hospital Germans Trias i Pujol; Stroke Unit (E.L.-C.), Hospital Universitario Central de Asturias; Stroke Unit (M.R., C.A.M.), Hospital Universitari Vall d'Hebron; Stroke Unit, Hospital Universitari Vall d'Hebron; Department of Neurology (E.G.-S., C.S.-T., M.M.-C., J.J.-C., J.R.), Neurovascular Research Group, IMIM-Hospital del Mar; Neurobiology Laboratory (C.V.-B.), Institut d'Investigacio Sanitaria de Palma; Department of Neurology (R.D.N.), Hospital Universitari Son Espases; Department of Neurology (V.O.), Hospital Clínic i Provincial de Barcelona; Department of Neurology (J.F.A.), Hospital Clínico Universitario, University of Valladolid; Department of Neurology (T.S.) and Experimental Research Unit (G.S.-H.), Hospital Universitario de Albacete; Department of Neurology (J.M.-F.), Hospital de la Santa Creu i Sant Pau, IIB-Sant Pau; Department of Neurology (M.F.), Hospital de Basurto; Department of Neurology (J.A.C.), Virgen del Rocío & Macarena Hospitals, IBIS, Spain; Department of Clinical Neuroscience/Neurology (T.T.), Sahlgrenska Academy at University of Gothenburg and Sahlgrenska University Hospital, Sweden; Division of Emergency Medicine (L.H.), Department of Psychiatry (L.I., C.C.), and Department of Neurology (J.-M.L.), Washington University School of Medicine, St. Louis, MO; Department of Neurology (D.S.), Helsinki University Hospital, Finland; Department of Neurology (J.M.), Virgen del Rocío & Macarena Hospitals, IBIS. Vall d'Hebron Research Institute, Universitat Autònoma de Barcelona; and Stroke Pharmacogenomics and Genetics (I.F.-C.), Fundació Docència i Recerca Mútua Terrassa, Hospital Universitari Mútua de Terrassa, Stroke Pharmacogenomics and Genetics, IIB-Sant Pau, Spain.
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49
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Edwards TL, Giri A, Hellwege JN, Hartmann KE, Stewart EA, Jeff JM, Bray MJ, Pendergrass SA, Torstenson ES, Keaton JM, Jones SH, Gogoi RP, Kuivaniemi H, Jackson KL, Kho AN, Kullo IJ, McCarty CA, Im HK, Pacheco JA, Pathak J, Williams MS, Tromp G, Kenny EE, Peissig PL, Denny JC, Roden DM, Velez Edwards DR. A Trans-Ethnic Genome-Wide Association Study of Uterine Fibroids. Front Genet 2019; 10:511. [PMID: 31249589 PMCID: PMC6582231 DOI: 10.3389/fgene.2019.00511] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Accepted: 05/10/2019] [Indexed: 01/02/2023] Open
Abstract
Uterine fibroids affect up to 77% of women by menopause and account for up to $34 billion in healthcare costs each year. Although fibroid risk is heritable, genetic risk for fibroids is not well understood. We conducted a two-stage case-control meta-analysis of genetic variants in European and African ancestry women with and without fibroids classified by a previously published algorithm requiring pelvic imaging or confirmed diagnosis. Women from seven electronic Medical Records and Genomics (eMERGE) network sites (3,704 imaging-confirmed cases and 5,591 imaging-confirmed controls) and women of African and European ancestry from UK Biobank (UKB, 5,772 cases and 61,457 controls) were included in the discovery genome-wide association study (GWAS) meta-analysis. Variants showing evidence of association in Stage I GWAS (P < 1 × 10-5) were targeted in an independent replication sample of African and European ancestry individuals from the UKB (Stage II) (12,358 cases and 138,477 controls). Logistic regression models were fit with genetic markers imputed to a 1000 Genomes reference and adjusted for principal components for each race- and site-specific dataset, followed by fixed-effects meta-analysis. Final analysis with 21,804 cases and 205,525 controls identified 326 genome-wide significant variants in 11 loci, with three novel loci at chromosome 1q24 (sentinel-SNP rs14361789; P = 4.7 × 10-8), chromosome 16q12.1 (sentinel-SNP rs4785384; P = 1.5 × 10-9) and chromosome 20q13.1 (sentinel-SNP rs6094982; P = 2.6 × 10-8). Our statistically significant findings further support previously reported loci including SNPs near WT1, TNRC6B, SYNE1, BET1L, and CDC42/WNT4. We report evidence of ancestry-specific findings for sentinel-SNP rs10917151 in the CDC42/WNT4 locus (P = 1.76 × 10-24). Ancestry-specific effect-estimates for rs10917151 were in opposite directions (P-Het-between-groups = 0.04) for predominantly African (OR = 0.84) and predominantly European women (OR = 1.16). Genetically-predicted gene expression of several genes including LUZP1 in vagina (P = 4.6 × 10-8), OBFC1 in esophageal mucosa (P = 8.7 × 10-8), NUDT13 in multiple tissues including subcutaneous adipose tissue (P = 3.3 × 10-6), and HEATR3 in skeletal muscle tissue (P = 5.8 × 10-6) were associated with fibroids. The finding for HEATR3 was supported by SNP-based summary Mendelian randomization analysis. Our study suggests that fibroid risk variants act through regulatory mechanisms affecting gene expression and are comprised of alleles that are both ancestry-specific and shared across continental ancestries.
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Affiliation(s)
- Todd L Edwards
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States.,Vanderbilt Epidemiology Center, Institute for Medicine and Public Health, Vanderbilt University Medical Center, Nashville, TN, United States.,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Ayush Giri
- Vanderbilt Epidemiology Center, Institute for Medicine and Public Health, Vanderbilt University Medical Center, Nashville, TN, United States.,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, United States.,Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University School of Medicine, Nashville, TN, United States
| | - Jacklyn N Hellwege
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States.,Vanderbilt Epidemiology Center, Institute for Medicine and Public Health, Vanderbilt University Medical Center, Nashville, TN, United States.,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Katherine E Hartmann
- Vanderbilt Epidemiology Center, Institute for Medicine and Public Health, Vanderbilt University Medical Center, Nashville, TN, United States.,Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University School of Medicine, Nashville, TN, United States
| | - Elizabeth A Stewart
- Division of Reproductive Endocrinology and Infertility, Departments of Obstetrics and Gynecology and Surgery, Mayo Clinic, Rochester, MN, United States
| | - Janina M Jeff
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Michael J Bray
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Sarah A Pendergrass
- Biomedical and Translational Informatics Institute, Geisinger Health System, Danville, PA, United States
| | - Eric S Torstenson
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States.,Vanderbilt Epidemiology Center, Institute for Medicine and Public Health, Vanderbilt University Medical Center, Nashville, TN, United States.,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Jacob M Keaton
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States.,Vanderbilt Epidemiology Center, Institute for Medicine and Public Health, Vanderbilt University Medical Center, Nashville, TN, United States.,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Sarah H Jones
- Division of Epidemiology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States.,Vanderbilt Epidemiology Center, Institute for Medicine and Public Health, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Radhika P Gogoi
- Sigfried and Janet Weis Center for Research, Geisinger Health System, Danville, PA, United States
| | - Helena Kuivaniemi
- Sigfried and Janet Weis Center for Research, Geisinger Health System, Danville, PA, United States.,SAMRC-SHIP South African Tuberculosis Bioinformatics Initiative, Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Stellenbosch University, Stellenbosch, South Africa
| | - Kathryn L Jackson
- Center for Health Information Partnerships, Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Abel N Kho
- Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Iftikhar J Kullo
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, MN, United States
| | - Catherine A McCarty
- Department of Family Medicine and Behavioral Health, University of Minnesota Medical School, Duluth, MN, United States
| | - Hae Kyung Im
- Department of Medicine, University of Chicago, Chicago, IL, United States
| | - Jennifer A Pacheco
- Center for Genetic Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, United States
| | - Jyotishman Pathak
- Division of Health Informatics, Department of Healthcare Policy and Research, Weill Cornell Medicine, New York, NY, United States
| | - Marc S Williams
- Genomic Medicine Institute, Geisinger, Danville, PA, United States
| | - Gerard Tromp
- Sigfried and Janet Weis Center for Research, Geisinger Health System, Danville, PA, United States.,SAMRC-SHIP South African Tuberculosis Bioinformatics Initiative, Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, Stellenbosch University, Stellenbosch, South Africa
| | - Eimear E Kenny
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States.,Center for Statistical Genetics, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Peggy L Peissig
- Biomedical Informatics Research Center, Marshfield Clinic Research Institute, Marshfield, WI, United States
| | - Joshua C Denny
- Department of Biomedical Informatics and Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Dan M Roden
- Departments of Medicine, Pharmacology, and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Digna R Velez Edwards
- Vanderbilt Epidemiology Center, Institute for Medicine and Public Health, Vanderbilt University Medical Center, Nashville, TN, United States.,Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, United States.,Division of Quantitative Sciences, Department of Obstetrics and Gynecology, Vanderbilt University School of Medicine, Nashville, TN, United States
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50
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Gorski M, Günther F, Winkler TW, Weber BHF, Heid IM. On the differences between mega- and meta-imputation and analysis exemplified on the genetics of age-related macular degeneration. Genet Epidemiol 2019; 43:559-576. [PMID: 31016765 PMCID: PMC6619271 DOI: 10.1002/gepi.22204] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 03/05/2019] [Accepted: 03/18/2019] [Indexed: 01/20/2023]
Abstract
While current genome‐wide association analyses often rely on meta‐analysis of study‐specific summary statistics, individual participant data (IPD) from multiple studies increase options for modeling. When multistudy IPD is available, however, it is unclear whether this data is to be imputed and modeled across all participants (mega‐imputation and mega‐analysis) or study‐specifically (meta‐imputation and meta‐analysis). Here, we investigated different approaches toward imputation and analysis using 52,189 subjects from 25 studies of the International Age‐related Macular Degeneration (AMD) Genomics Consortium including, 16,144 AMD cases and 17,832 controls for association analysis. From 27,448,454 genetic variants after 1,000‐Genomes‐based imputation, mega‐imputation yielded ~400,000 more variants with high imputation quality (mostly rare variants) compared to meta‐imputation. For AMD signal detection (P < 5 × 10−8) in mega‐imputed data, most loci were detected with mega‐analysis without adjusting for study membership (40 loci, including 34 known); we considered these loci genuine, since genetic effects and P‐values were comparable across analyses. In meta‐imputed data, we found 31 additional signals, mostly near chromosome tails or reference panel gaps, which disappeared after accounting for interaction of whole‐genome amplification (WGA) with study membership or after excluding studies with WGA‐participants. For signal detection with multistudy IPD, we recommend mega‐imputation and mega‐analysis, with meta‐imputation followed by meta‐analysis being a computationally appealing alternative.
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Affiliation(s)
- Mathias Gorski
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Felix Günther
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany.,Statistical Consulting Unit StaBLab, Department of Statistics, LMU Munich, München, Germany
| | - Thomas W Winkler
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Bernhard H F Weber
- Institute of Human Genetics, University of Regensburg, Regensburg, Germany
| | - Iris M Heid
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
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