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Ferreira T, Rodriguez S. Mitochondrial DNA: Inherent Complexities Relevant to Genetic Analyses. Genes (Basel) 2024; 15:617. [PMID: 38790246 PMCID: PMC11121663 DOI: 10.3390/genes15050617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Revised: 05/09/2024] [Accepted: 05/10/2024] [Indexed: 05/26/2024] Open
Abstract
Mitochondrial DNA (mtDNA) exhibits distinct characteristics distinguishing it from the nuclear genome, necessitating specific analytical methods in genetic studies. This comprehensive review explores the complex role of mtDNA in a variety of genetic studies, including genome-wide, epigenome-wide, and phenome-wide association studies, with a focus on its implications for human traits and diseases. Here, we discuss the structure and gene-encoding properties of mtDNA, along with the influence of environmental factors and epigenetic modifications on its function and variability. Particularly significant are the challenges posed by mtDNA's high mutation rate, heteroplasmy, and copy number variations, and their impact on disease susceptibility and population genetic analyses. The review also highlights recent advances in methodological approaches that enhance our understanding of mtDNA associations, advocating for refined genetic research techniques that accommodate its complexities. By providing a comprehensive overview of the intricacies of mtDNA, this paper underscores the need for an integrated approach to genetic studies that considers the unique properties of mitochondrial genetics. Our findings aim to inform future research and encourage the development of innovative methodologies to better interpret the broad implications of mtDNA in human health and disease.
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Affiliation(s)
- Tomas Ferreira
- Bristol Medical School, University of Bristol, Bristol BS8 1UD, UK
- Department of Clinical Neurosciences, School of Clinical Medicine, University of Cambridge, Cambridge CB2 0SL, UK
| | - Santiago Rodriguez
- Bristol Medical School, University of Bristol, Bristol BS8 1UD, UK
- MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 1QU, UK
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2
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Auwerx C, Jõeloo M, Sadler MC, Tesio N, Ojavee S, Clark CJ, Mägi R, Reymond A, Kutalik Z. Rare copy-number variants as modulators of common disease susceptibility. Genome Med 2024; 16:5. [PMID: 38185688 PMCID: PMC10773105 DOI: 10.1186/s13073-023-01265-5] [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: 07/17/2023] [Accepted: 11/27/2023] [Indexed: 01/09/2024] Open
Abstract
BACKGROUND Copy-number variations (CNVs) have been associated with rare and debilitating genomic disorders (GDs) but their impact on health later in life in the general population remains poorly described. METHODS Assessing four modes of CNV action, we performed genome-wide association scans (GWASs) between the copy-number of CNV-proxy probes and 60 curated ICD-10 based clinical diagnoses in 331,522 unrelated white British UK Biobank (UKBB) participants with replication in the Estonian Biobank. RESULTS We identified 73 signals involving 40 diseases, all of which indicating that CNVs increased disease risk and caused earlier onset. We estimated that 16% of these associations are indirect, acting by increasing body mass index (BMI). Signals mapped to 45 unique, non-overlapping regions, nine of which being linked to known GDs. Number and identity of genes affected by CNVs modulated their pathogenicity, with many associations being supported by colocalization with both common and rare single-nucleotide variant association signals. Dissection of association signals provided insights into the epidemiology of known gene-disease pairs (e.g., deletions in BRCA1 and LDLR increased risk for ovarian cancer and ischemic heart disease, respectively), clarified dosage mechanisms of action (e.g., both increased and decreased dosage of 17q12 impacted renal health), and identified putative causal genes (e.g., ABCC6 for kidney stones). Characterization of the pleiotropic pathological consequences of recurrent CNVs at 15q13, 16p13.11, 16p12.2, and 22q11.2 in adulthood indicated variable expressivity of these regions and the involvement of multiple genes. Finally, we show that while the total burden of rare CNVs-and especially deletions-strongly associated with disease risk, it only accounted for ~ 0.02% of the UKBB disease burden. These associations are mainly driven by CNVs at known GD CNV regions, whose pleiotropic effect on common diseases was broader than anticipated by our CNV-GWAS. CONCLUSIONS Our results shed light on the prominent role of rare CNVs in determining common disease susceptibility within the general population and provide actionable insights for anticipating later-onset comorbidities in carriers of recurrent CNVs.
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Affiliation(s)
- Chiara Auwerx
- Center for Integrative Genomics, University of Lausanne, Genopode building, 1015, Lausanne, Switzerland.
- Department of Computational Biology, University of Lausanne, Genopode building, 1015, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland.
- University Center for Primary Care and Public Health, 1005, Lausanne, Switzerland.
| | - Maarja Jõeloo
- Institute of Molecular and Cell Biology, University of Tartu, 51010, Tartu, Estonia
- Estonian Genome Centre, Institute of Genomics, University of Tartu, 51010, Tartu, Estonia
| | - Marie C Sadler
- Department of Computational Biology, University of Lausanne, Genopode building, 1015, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland
- University Center for Primary Care and Public Health, 1005, Lausanne, Switzerland
| | - Nicolò Tesio
- Center for Integrative Genomics, University of Lausanne, Genopode building, 1015, Lausanne, Switzerland
| | - Sven Ojavee
- Department of Computational Biology, University of Lausanne, Genopode building, 1015, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland
| | - Charlie J Clark
- Center for Integrative Genomics, University of Lausanne, Genopode building, 1015, Lausanne, Switzerland
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, 51010, Tartu, Estonia
| | - Alexandre Reymond
- Center for Integrative Genomics, University of Lausanne, Genopode building, 1015, Lausanne, Switzerland.
| | - Zoltán Kutalik
- Department of Computational Biology, University of Lausanne, Genopode building, 1015, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland.
- University Center for Primary Care and Public Health, 1005, Lausanne, Switzerland.
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Hanssen R, Auwerx C, Jõeloo M, Sadler MC, Henning E, Keogh J, Bounds R, Smith M, Firth HV, Kutalik Z, Farooqi IS, Reymond A, Lawler K. Chromosomal deletions on 16p11.2 encompassing SH2B1 are associated with accelerated metabolic disease. Cell Rep Med 2023; 4:101155. [PMID: 37586323 PMCID: PMC10439272 DOI: 10.1016/j.xcrm.2023.101155] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 06/08/2023] [Accepted: 07/18/2023] [Indexed: 08/18/2023]
Abstract
New approaches are needed to treat people whose obesity and type 2 diabetes (T2D) are driven by specific mechanisms. We investigate a deletion on chromosome 16p11.2 (breakpoint 2-3 [BP2-3]) encompassing SH2B1, a mediator of leptin and insulin signaling. Phenome-wide association scans in the UK (N = 502,399) and Estonian (N = 208,360) biobanks show that deletion carriers have increased body mass index (BMI; p = 1.3 × 10-10) and increased rates of T2D. Compared with BMI-matched controls, deletion carriers have an earlier onset of T2D, with poorer glycemic control despite higher medication usage. Cystatin C, a biomarker of kidney function, is significantly elevated in deletion carriers, suggesting increased risk of renal impairment. In a Mendelian randomization study, decreased SH2B1 expression increases T2D risk (p = 8.1 × 10-6). We conclude that people with 16p11.2 BP2-3 deletions have early, complex obesity and T2D and may benefit from therapies that enhance leptin and insulin signaling.
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Affiliation(s)
- Ruth Hanssen
- University of Cambridge Metabolic Research Laboratories, Wellcome-MRC Institute of Metabolic Science and NIHR Cambridge Biomedical Research Centre, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK
| | - Chiara Auwerx
- Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland; Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland; University Center for Primary Care and Public Health, 1010 Lausanne, Switzerland
| | - Maarja Jõeloo
- Institute of Molecular and Cell Biology, University of Tartu, 51010 Tartu, Estonia; Estonian Genome Centre, Institute of Genomics, University of Tartu, 51010 Tartu, Estonia
| | - Marie C Sadler
- Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland; University Center for Primary Care and Public Health, 1010 Lausanne, Switzerland
| | - Elana Henning
- University of Cambridge Metabolic Research Laboratories, Wellcome-MRC Institute of Metabolic Science and NIHR Cambridge Biomedical Research Centre, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK
| | - Julia Keogh
- University of Cambridge Metabolic Research Laboratories, Wellcome-MRC Institute of Metabolic Science and NIHR Cambridge Biomedical Research Centre, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK
| | - Rebecca Bounds
- University of Cambridge Metabolic Research Laboratories, Wellcome-MRC Institute of Metabolic Science and NIHR Cambridge Biomedical Research Centre, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK
| | - Miriam Smith
- University of Cambridge Metabolic Research Laboratories, Wellcome-MRC Institute of Metabolic Science and NIHR Cambridge Biomedical Research Centre, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK
| | - Helen V Firth
- Department of Clinical Genetics, Cambridge University Hospitals NHS Foundation Trust & Wellcome Sanger Institute, Cambridge, UK
| | - Zoltán Kutalik
- Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland; University Center for Primary Care and Public Health, 1010 Lausanne, Switzerland
| | - I Sadaf Farooqi
- University of Cambridge Metabolic Research Laboratories, Wellcome-MRC Institute of Metabolic Science and NIHR Cambridge Biomedical Research Centre, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK.
| | - Alexandre Reymond
- Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland.
| | - Katherine Lawler
- University of Cambridge Metabolic Research Laboratories, Wellcome-MRC Institute of Metabolic Science and NIHR Cambridge Biomedical Research Centre, Addenbrooke's Hospital, Cambridge CB2 0QQ, UK.
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Soto DC, Uribe-Salazar JM, Shew CJ, Sekar A, McGinty S, Dennis MY. Genomic structural variation: A complex but important driver of human evolution. AMERICAN JOURNAL OF BIOLOGICAL ANTHROPOLOGY 2023; 181 Suppl 76:118-144. [PMID: 36794631 PMCID: PMC10329998 DOI: 10.1002/ajpa.24713] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 01/21/2023] [Accepted: 02/05/2023] [Indexed: 02/17/2023]
Abstract
Structural variants (SVs)-including duplications, deletions, and inversions of DNA-can have significant genomic and functional impacts but are technically difficult to identify and assay compared with single-nucleotide variants. With the aid of new genomic technologies, it has become clear that SVs account for significant differences across and within species. This phenomenon is particularly well-documented for humans and other primates due to the wealth of sequence data available. In great apes, SVs affect a larger number of nucleotides than single-nucleotide variants, with many identified SVs exhibiting population and species specificity. In this review, we highlight the importance of SVs in human evolution by (1) how they have shaped great ape genomes resulting in sensitized regions associated with traits and diseases, (2) their impact on gene functions and regulation, which subsequently has played a role in natural selection, and (3) the role of gene duplications in human brain evolution. We further discuss how to incorporate SVs in research, including the strengths and limitations of various genomic approaches. Finally, we propose future considerations in integrating existing data and biospecimens with the ever-expanding SV compendium propelled by biotechnology advancements.
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Affiliation(s)
- Daniela C. Soto
- Genome Center, MIND Institute, and Department of Biochemistry & Molecular Medicine, University of California, Davis, CA, USA
- Integrative Genetics and Genomics Graduate Group, University of California, Davis, CA, USA
| | - José M. Uribe-Salazar
- Genome Center, MIND Institute, and Department of Biochemistry & Molecular Medicine, University of California, Davis, CA, USA
- Integrative Genetics and Genomics Graduate Group, University of California, Davis, CA, USA
| | - Colin J. Shew
- Genome Center, MIND Institute, and Department of Biochemistry & Molecular Medicine, University of California, Davis, CA, USA
- Integrative Genetics and Genomics Graduate Group, University of California, Davis, CA, USA
| | - Aarthi Sekar
- Genome Center, MIND Institute, and Department of Biochemistry & Molecular Medicine, University of California, Davis, CA, USA
- Integrative Genetics and Genomics Graduate Group, University of California, Davis, CA, USA
| | - Sean McGinty
- Genome Center, MIND Institute, and Department of Biochemistry & Molecular Medicine, University of California, Davis, CA, USA
- Integrative Genetics and Genomics Graduate Group, University of California, Davis, CA, USA
| | - Megan Y. Dennis
- Genome Center, MIND Institute, and Department of Biochemistry & Molecular Medicine, University of California, Davis, CA, USA
- Integrative Genetics and Genomics Graduate Group, University of California, Davis, CA, USA
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Yu M, Harper AR, Aguirre M, Pittman M, Tcheandjieu C, Amgalan D, Grace C, Goel A, Farrall M, Xiao K, Engreitz J, Pollard KS, Watkins H, Priest JR. Genetic Determinants of the Interventricular Septum Are Linked to Ventricular Septal Defects and Hypertrophic Cardiomyopathy. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2023; 16:207-215. [PMID: 37017090 PMCID: PMC10293084 DOI: 10.1161/circgen.122.003708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 01/06/2023] [Indexed: 04/06/2023]
Abstract
BACKGROUND A large proportion of genetic risk remains unexplained for structural heart disease involving the interventricular septum (IVS) including hypertrophic cardiomyopathy and ventricular septal defects. This study sought to develop a reproducible proxy of IVS structure from standard medical imaging, discover novel genetic determinants of IVS structure, and relate these loci to diseases of the IVS, hypertrophic cardiomyopathy, and ventricular septal defect. METHODS We estimated the cross-sectional area of the IVS from the 4-chamber view of cardiac magnetic resonance imaging in 32 219 individuals from the UK Biobank which was used as the basis of genome wide association studies and Mendelian randomization. RESULTS Measures of IVS cross-sectional area at diastole were a strong proxy for the 3-dimensional volume of the IVS (Pearson r=0.814, P=0.004), and correlated with anthropometric measures, blood pressure, and diagnostic codes related to cardiovascular physiology. Seven loci with clear genomic consequence and relevance to cardiovascular biology were uncovered by genome wide association studies, most notably a single nucleotide polymorphism in an intron of CDKN1A (rs2376620; β, 7.7 mm2 [95% CI, 5.8-11.0]; P=6.0×10-10), and a common inversion incorporating KANSL1 predicted to disrupt local chromatin structure (β, 8.4 mm2 [95% CI, 6.3-10.9]; P=4.2×10-14). Mendelian randomization suggested that inheritance of larger IVS cross-sectional area at diastole was strongly associated with hypertrophic cardiomyopathy risk (pIVW=4.6×10-10) while inheritance of smaller IVS cross-sectional area at diastole was associated with risk for ventricular septal defect (pIVW=0.007). CONCLUSIONS Automated estimates of cross-sectional area of the IVS supports discovery of novel loci related to cardiac development and Mendelian disease. Inheritance of genetic liability for either small or large IVS, appears to confer risk for ventricular septal defect or hypertrophic cardiomyopathy, respectively. These data suggest that a proportion of risk for structural and congenital heart disease can be localized to the common genetic determinants of size and shape of cardiovascular anatomy.
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Affiliation(s)
- Mengyao Yu
- Dept of Pediatrics, Division of Pediatric Cardiology, Division of Cardiovascular Medicine, Stanford Univ School of Medicine
- Stanford Cardiovascular Institute, Stanford Univ, Stanford, CA
| | - Andrew R. Harper
- Radcliffe Dept of Medicine, Univ of Oxford, Division of Cardiovascular Medicine, John Radcliffe Hospital
- Wellcome Centre for Human Genetics, Roosevelt Drive, Oxford
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, United Kingdom
| | - Matthew Aguirre
- Dept of Pediatrics, Division of Pediatric Cardiology, Division of Cardiovascular Medicine, Stanford Univ School of Medicine
- Dept of Biomedical Data Science, Stanford Medical School, Stanford
| | - Maureen Pittman
- Univ of California, San Francisco, San Francisco
- Gladstone Institute of Data Science & Biotechnology, San Francisco
| | - Catherine Tcheandjieu
- Dept of Pediatrics, Division of Pediatric Cardiology, Division of Cardiovascular Medicine, Stanford Univ School of Medicine
- Stanford Cardiovascular Institute, Stanford Univ, Stanford, CA
- Dept of Medicine, Division of Cardiovascular Medicine, Stanford Univ School of Medicine
| | - Dulguun Amgalan
- Dept of Genetics, Stanford Univ, Stanford, CA
- Basic Sciences and Engineering Initiative, Betty Irene Moore Children’s Heart Center, Lucile Packard Children’s Hospital, Stanford, CA
| | - Christopher Grace
- Radcliffe Dept of Medicine, Univ of Oxford, Division of Cardiovascular Medicine, John Radcliffe Hospital
- Wellcome Centre for Human Genetics, Roosevelt Drive, Oxford
| | - Anuj Goel
- Radcliffe Dept of Medicine, Univ of Oxford, Division of Cardiovascular Medicine, John Radcliffe Hospital
- Wellcome Centre for Human Genetics, Roosevelt Drive, Oxford
| | - Martin Farrall
- Radcliffe Dept of Medicine, Univ of Oxford, Division of Cardiovascular Medicine, John Radcliffe Hospital
- Wellcome Centre for Human Genetics, Roosevelt Drive, Oxford
| | - Ke Xiao
- College of Information & Computer Sciences at Univ of Massachusetts Amherst, Amherst, MA
| | - Jesse Engreitz
- Dept of Genetics, Stanford Univ, Stanford, CA
- Basic Sciences and Engineering Initiative, Betty Irene Moore Children’s Heart Center, Lucile Packard Children’s Hospital, Stanford, CA
| | - Katherine S. Pollard
- Univ of California, San Francisco, San Francisco
- Gladstone Institute of Data Science & Biotechnology, San Francisco
- Chan-Zuckerberg Biohub
| | - Hugh Watkins
- Radcliffe Dept of Medicine, Univ of Oxford, Division of Cardiovascular Medicine, John Radcliffe Hospital
- Wellcome Centre for Human Genetics, Roosevelt Drive, Oxford
| | - James R. Priest
- Dept of Pediatrics, Division of Pediatric Cardiology, Division of Cardiovascular Medicine, Stanford Univ School of Medicine
- Stanford Cardiovascular Institute, Stanford Univ, Stanford, CA
- Chan-Zuckerberg Biohub
- Current affiliation: Tenaya Therapeutics, South San Francisco, CA
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Macnee M, Pérez-Palma E, Brünger T, Klöckner C, Platzer K, Stefanski A, Montanucci L, Bayat A, Radtke M, Collins RL, Talkowski M, Blankenberg D, Møller RS, Lemke JR, Nothnagel M, May P, Lal D. CNV-ClinViewer: enhancing the clinical interpretation of large copy-number variants online. Bioinformatics 2023; 39:btad290. [PMID: 37104749 PMCID: PMC10174702 DOI: 10.1093/bioinformatics/btad290] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 01/17/2023] [Accepted: 04/26/2023] [Indexed: 04/29/2023] Open
Abstract
MOTIVATION Pathogenic copy-number variants (CNVs) can cause a heterogeneous spectrum of rare and severe disorders. However, most CNVs are benign and are part of natural variation in human genomes. CNV pathogenicity classification, genotype-phenotype analyses, and therapeutic target identification are challenging and time-consuming tasks that require the integration and analysis of information from multiple scattered sources by experts. RESULTS Here, we introduce the CNV-ClinViewer, an open-source web application for clinical evaluation and visual exploration of CNVs. The application enables real-time interactive exploration of large CNV datasets in a user-friendly designed interface and facilitates semi-automated clinical CNV interpretation following the ACMG guidelines by integrating the ClassifCNV tool. In combination with clinical judgment, the application enables clinicians and researchers to formulate novel hypotheses and guide their decision-making process. Subsequently, the CNV-ClinViewer enhances for clinical investigators' patient care and for basic scientists' translational genomic research. AVAILABILITY AND IMPLEMENTATION The web application is freely available at https://cnv-ClinViewer.broadinstitute.org and the open-source code can be found at https://github.com/LalResearchGroup/CNV-clinviewer.
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Affiliation(s)
- Marie Macnee
- Cologne Center for Genomics (CCG), University of Cologne, Cologne, Germany
| | - Eduardo Pérez-Palma
- Universidad del Desarrollo, Centro de Genética y Genómica, Facultad de Medicina Clínica Alemana, Santiago, Chile
| | - Tobias Brünger
- Cologne Center for Genomics (CCG), University of Cologne, Cologne, Germany
| | - Chiara Klöckner
- Institute of Human Genetics, University of Leipzig Medical Center, Leipzig, Germany
| | - Konrad Platzer
- Institute of Human Genetics, University of Leipzig Medical Center, Leipzig, Germany
| | - Arthur Stefanski
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Ludovica Montanucci
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Allan Bayat
- Department of Epilepsy Genetics and Personalized Medicine, Member of ERN Epicare, Danish Epilepsy Centre, Dianalund, Denmark
- Department of Regional Health Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Maximilian Radtke
- Institute of Human Genetics, University of Leipzig Medical Center, Leipzig, Germany
| | - Ryan L Collins
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael Talkowski
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Daniel Blankenberg
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Rikke S Møller
- Department of Epilepsy Genetics and Personalized Medicine, Member of ERN Epicare, Danish Epilepsy Centre, Dianalund, Denmark
- Department of Regional Health Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Johannes R Lemke
- Institute of Human Genetics, University of Leipzig Medical Center, Leipzig, Germany
| | - Michael Nothnagel
- Cologne Center for Genomics (CCG), University of Cologne, Cologne, Germany
- University Hospital Cologne, Cologne, Germany
| | - Patrick May
- Luxembourg Centre for Systems Biomedicine, University Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Dennis Lal
- Cologne Center for Genomics (CCG), University of Cologne, Cologne, Germany
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, USA
- Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
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7
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Verbitsky M, Krishnamurthy S, Krithivasan P, Hughes D, Khan A, Marasà M, Vena N, Khosla P, Zhang J, Lim TY, Glessner JT, Weng C, Shang N, Shen Y, Hripcsak G, Hakonarson H, Ionita-Laza I, Levy B, Kenny EE, Loos RJ, Kiryluk K, Sanna-Cherchi S, Crosslin DR, Furth S, Warady BA, Igo RP, Iyengar SK, Wong CS, Parsa A, Feldman HI, Gharavi AG. Genomic Disorders in CKD across the Lifespan. J Am Soc Nephrol 2023; 34:607-618. [PMID: 36302597 PMCID: PMC10103259 DOI: 10.1681/asn.2022060725] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Accepted: 09/15/2022] [Indexed: 01/24/2023] Open
Abstract
SIGNIFICANCE STATEMENT Pathogenic structural genetic variants, also known as genomic disorders, have been associated with pediatric CKD. This study extends those results across the lifespan, with genomic disorders enriched in both pediatric and adult patients compared with controls. In the Chronic Renal Insufficiency Cohort study, genomic disorders were also associated with lower serum Mg, lower educational performance, and a higher risk of death. A phenome-wide association study confirmed the link between kidney disease and genomic disorders in an unbiased way. Systematic detection of genomic disorders can provide a molecular diagnosis and refine prediction of risk and prognosis. BACKGROUND Genomic disorders (GDs) are associated with many comorbid outcomes, including CKD. Identification of GDs has diagnostic utility. METHODS We examined the prevalence of GDs among participants in the Chronic Kidney Disease in Children (CKiD) cohort II ( n =248), Chronic Renal Insufficiency Cohort (CRIC) study ( n =3375), Columbia University CKD Biobank (CU-CKD; n =1986), and the Family Investigation of Nephropathy and Diabetes (FIND; n =1318) compared with 30,746 controls. We also performed a phenome-wide association analysis (PheWAS) of GDs in the electronic MEdical Records and GEnomics (eMERGE; n =11,146) cohort. RESULTS We found nine out of 248 (3.6%) CKiD II participants carried a GD, replicating prior findings in pediatric CKD. We also identified GDs in 72 out of 6679 (1.1%) adult patients with CKD in the CRIC, CU-CKD, and FIND cohorts, compared with 199 out of 30,746 (0.65%) GDs in controls (OR, 1.7; 95% CI, 1.3 to 2.2). Among adults with CKD, we found recurrent GDs at the 1q21.1, 16p11.2, 17q12, and 22q11.2 loci. The 17q12 GD (diagnostic of renal cyst and diabetes syndrome) was most frequent, present in 1:252 patients with CKD and diabetes. In the PheWAS, dialysis and neuropsychiatric phenotypes were the top associations with GDs. In CRIC participants, GDs were associated with lower serum magnesium, lower educational achievement, and higher mortality risk. CONCLUSION Undiagnosed GDs are detected both in children and adults with CKD. Identification of GDs in these patients can enable a precise genetic diagnosis, inform prognosis, and help stratify risk in clinical studies. GDs could also provide a molecular explanation for nephropathy and comorbidities, such as poorer neurocognition for a subset of patients.
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Affiliation(s)
- Miguel Verbitsky
- Division of Nephrology, Department of Medicine, Columbia University, New York, New York
| | | | - Priya Krithivasan
- Division of Nephrology, Department of Medicine, Columbia University, New York, New York
| | - Daniel Hughes
- Institute for Genomic Medicine, Columbia University Medical Center, New York, New York
| | - Atlas Khan
- Division of Nephrology, Department of Medicine, Columbia University, New York, New York
| | - Maddalena Marasà
- Division of Nephrology, Department of Medicine, Columbia University, New York, New York
| | - Natalie Vena
- Division of Nephrology, Department of Medicine, Columbia University, New York, New York
| | - Pavan Khosla
- Division of Nephrology, Department of Medicine, Columbia University, New York, New York
| | - Junying Zhang
- Division of Nephrology, Department of Medicine, Columbia University, New York, New York
| | - Tze Y. Lim
- Division of Nephrology, Department of Medicine, Columbia University, New York, New York
| | - Joseph T. Glessner
- Center for Applied Genomics and Department of Pediatrics, Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University, New York, New York
| | - Ning Shang
- Division of Nephrology, Department of Medicine, Columbia University, New York, New York
- Department of Biomedical Informatics, Columbia University, New York, New York
| | - Yufeng Shen
- Department of Systems Biology and Columbia Genome Center, Columbia University, New York, New York
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University, New York, New York
| | - Hakon Hakonarson
- Center for Applied Genomics and Department of Pediatrics, Perelman School of Medicine, Philadelphia, Pennsylvania
| | | | - Brynn Levy
- Department of Pathology and Cell Biology, Columbia University, New York, New York
| | - Eimear E. Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Ruth J.F. Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, New York
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Columbia University, New York, New York
| | - Simone Sanna-Cherchi
- Division of Nephrology, Department of Medicine, Columbia University, New York, New York
| | - David R. Crosslin
- Division of Biomedical Informatics and Genomics, Tulane University School of Medicine, New Orleans, Louisiana
| | - Susan Furth
- Departments of Pediatrics and Epidemiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania
| | - Bradley A. Warady
- Department of Pediatrics, University of Missouri-Kansas City School of Medicine, Kansas City, Missouri
| | - Robert P. Igo
- Department of Population and Quantitative Health Sciences, Case Western Reserve University and Louis Stoke, Cleveland, Ohio
| | - Sudha K. Iyengar
- Department of Population and Quantitative Health Sciences, Case Western Reserve University and Louis Stoke, Cleveland, Ohio
| | - Craig S. Wong
- Division of Pediatric Nephrology, University of New Mexico Children’s Hospital, Albuquerque, New Mexico
| | - Afshin Parsa
- Division of Kidney, Urologic, and Hematologic Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, Maryland
| | - Harold I. Feldman
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, Philadelphia, Pennsylvania
- Department of Medicine, Perelman School of Medicine, Philadelphia, Pennsylvania
- Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, Philadelphia, Pennsylvania
| | - Ali G. Gharavi
- Division of Nephrology, Department of Medicine, Columbia University, New York, New York
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8
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Glessner JT, Li J, Liu Y, Khan M, Chang X, Sleiman PMA, Hakonarson H. ParseCNV2: efficient sequencing tool for copy number variation genome-wide association studies. Eur J Hum Genet 2023; 31:304-312. [PMID: 36316489 PMCID: PMC9995309 DOI: 10.1038/s41431-022-01222-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 10/01/2022] [Accepted: 10/14/2022] [Indexed: 11/06/2022] Open
Abstract
Improved copy number variation (CNV) detection remains an area of heavy emphasis for algorithm development; however, both CNV curation and disease association approaches remain in its infancy. The current practice of focusing on candidate CNVs, where researchers study specific CNVs they believe to be pathological while discarding others, refrains from considering the full spectrum of CNVs in a hypothesis-free GWAS. To address this, we present a next-generation approach to CNV association by natively supporting the popular VCF specification for sequencing-derived variants as well as SNP array calls using a PennCNV format. The code is fast and efficient, allowing for the analysis of large (>100,000 sample) cohorts without dividing up the data on a compute cluster. The scripts are condensed into a single tool to promote simplicity and best practices. CNV curation pre and post-association is rigorously supported and emphasized to yield reliable results of highest quality. We benchmarked two large datasets, including the UK Biobank (n > 450,000) and CAG Biobank (n > 350,000) both of which are genotyped at >0.5 M probes, for our input files. ParseCNV has been actively supported and developed since 2008. ParseCNV2 presents a critical addition to formalizing CNV association for inclusion with SNP associations in GWAS Catalog. Clinical CNV prioritization, interactive quality control (QC), and adjustment for covariates are revolutionary new features of ParseCNV2 vs. ParseCNV. The software is freely available at: https://github.com/CAG-CNV/ParseCNV2 .
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Affiliation(s)
- Joseph T Glessner
- Department of Pediatrics, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19104, USA.
- Department of Pediatrics, Perelman School of Medicine, 3400 Civic Center Blvd, Philadelphia, PA, 19104, USA.
| | - Jin Li
- Department of Cell Biology, the Province and Ministry Co-sponsored Collaborative Innovation Center for Medical Epigenetics, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Yichuan Liu
- Department of Pediatrics, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19104, USA
- Department of Pediatrics, Perelman School of Medicine, 3400 Civic Center Blvd, Philadelphia, PA, 19104, USA
| | - Munir Khan
- Department of Pediatrics, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19104, USA
- Department of Pediatrics, Perelman School of Medicine, 3400 Civic Center Blvd, Philadelphia, PA, 19104, USA
| | - Xiao Chang
- Department of Pediatrics, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19104, USA
- Department of Pediatrics, Perelman School of Medicine, 3400 Civic Center Blvd, Philadelphia, PA, 19104, USA
| | - Patrick M A Sleiman
- Department of Pediatrics, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19104, USA
- Department of Pediatrics, Perelman School of Medicine, 3400 Civic Center Blvd, Philadelphia, PA, 19104, USA
| | - Hakon Hakonarson
- Department of Pediatrics, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19104, USA
- Department of Pediatrics, Perelman School of Medicine, 3400 Civic Center Blvd, Philadelphia, PA, 19104, USA
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9
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Zamariolli M, Auwerx C, Sadler MC, van der Graaf A, Lepik K, Schoeler T, Moysés-Oliveira M, Dantas AG, Melaragno MI, Kutalik Z. The impact of 22q11.2 copy-number variants on human traits in the general population. Am J Hum Genet 2023; 110:300-313. [PMID: 36706759 PMCID: PMC9943723 DOI: 10.1016/j.ajhg.2023.01.005] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 01/03/2023] [Indexed: 01/27/2023] Open
Abstract
While extensively studied in clinical cohorts, the phenotypic consequences of 22q11.2 copy-number variants (CNVs) in the general population remain understudied. To address this gap, we performed a phenome-wide association scan in 405,324 unrelated UK Biobank (UKBB) participants by using CNV calls from genotyping array. We mapped 236 Human Phenotype Ontology terms linked to any of the 90 genes encompassed by the region to 170 UKBB traits and assessed the association between these traits and the copy-number state of 504 genotyping array probes in the region. We found significant associations for eight continuous and nine binary traits associated under different models (duplication-only, deletion-only, U-shape, and mirror models). The causal effect of the expression level of 22q11.2 genes on associated traits was assessed through transcriptome-wide Mendelian randomization (TWMR), revealing that increased expression of ARVCF increased BMI. Similarly, increased DGCR6 expression causally reduced mean platelet volume, in line with the corresponding CNV effect. Furthermore, cross-trait multivariable Mendelian randomization (MVMR) suggested a predominant role of genuine (horizontal) pleiotropy in the CNV region. Our findings show that within the general population, 22q11.2 CNVs are associated with traits previously linked to genes in the region, and duplications and deletions act upon traits in different fashions. We also showed that gain or loss of distinct segments within 22q11.2 may impact a trait under different association models. Our results have provided new insights to help further the understanding of the complex 22q11.2 region.
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Affiliation(s)
- Malú Zamariolli
- Genetics Division, Universidade Federal de São Paulo, São Paulo, Brazil; Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Chiara Auwerx
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland; University Center for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland; Center for Integrative Genomics, University of Lausanne, Lausanne, Switzerland
| | - Marie C Sadler
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland; University Center for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland
| | | | - Kaido Lepik
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Tabea Schoeler
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Department of Clinical, Educational and Health Psychology, University College London, London, UK
| | | | - Anelisa G Dantas
- Genetics Division, Universidade Federal de São Paulo, São Paulo, Brazil
| | | | - Zoltán Kutalik
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland; Swiss Institute of Bioinformatics, Lausanne, Switzerland; University Center for Primary Care and Public Health, University of Lausanne, Lausanne, Switzerland.
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10
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Wheeler MM, Stilp AM, Rao S, Halldórsson BV, Beyter D, Wen J, Mihkaylova AV, McHugh CP, Lane J, Jiang MZ, Raffield LM, Jun G, Sedlazeck FJ, Metcalf G, Yao Y, Bis JB, Chami N, de Vries PS, Desai P, Floyd JS, Gao Y, Kammers K, Kim W, Moon JY, Ratan A, Yanek LR, Almasy L, Becker LC, Blangero J, Cho MH, Curran JE, Fornage M, Kaplan RC, Lewis JP, Loos RJF, Mitchell BD, Morrison AC, Preuss M, Psaty BM, Rich SS, Rotter JI, Tang H, Tracy RP, Boerwinkle E, Abecasis GR, Blackwell TW, Smith AV, Johnson AD, Mathias RA, Nickerson DA, Conomos MP, Li Y, Þorsteinsdóttir U, Magnússon MK, Stefansson K, Pankratz ND, Bauer DE, Auer PL, Reiner AP. Whole genome sequencing identifies structural variants contributing to hematologic traits in the NHLBI TOPMed program. Nat Commun 2022; 13:7592. [PMID: 36481753 PMCID: PMC9732337 DOI: 10.1038/s41467-022-35354-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 11/29/2022] [Indexed: 12/13/2022] Open
Abstract
Genome-wide association studies have identified thousands of single nucleotide variants and small indels that contribute to variation in hematologic traits. While structural variants are known to cause rare blood or hematopoietic disorders, the genome-wide contribution of structural variants to quantitative blood cell trait variation is unknown. Here we utilized whole genome sequencing data in ancestrally diverse participants of the NHLBI Trans Omics for Precision Medicine program (N = 50,675) to detect structural variants associated with hematologic traits. Using single variant tests, we assessed the association of common and rare structural variants with red cell-, white cell-, and platelet-related quantitative traits and observed 21 independent signals (12 common and 9 rare) reaching genome-wide significance. The majority of these associations (N = 18) replicated in independent datasets. In genome-editing experiments, we provide evidence that a deletion associated with lower monocyte counts leads to disruption of an S1PR3 monocyte enhancer and decreased S1PR3 expression.
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Affiliation(s)
- Marsha M Wheeler
- Department of Genome Sciences, University of Washington, Seattle, WA, 98105, USA
| | - Adrienne M Stilp
- Department of Biostatistics, University of Washington, Seattle, WA, 98105, USA
| | - Shuquan Rao
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, 02115, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, 02115, USA
- Harvard Stem Cell Institute, Boston, MA, 02138, USA
- Broad Institute, Cambridge, MA, 02142, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, 02115, USA
- State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
| | - Bjarni V Halldórsson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- School of Technology, Reykjavik University, Reykjavík, Iceland
| | | | - Jia Wen
- Departments of Biostatistics, Genetics, Computer Science, Applied Physical Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Anna V Mihkaylova
- Department of Biostatistics, University of Washington, Seattle, WA, 98105, USA
| | - Caitlin P McHugh
- Department of Biostatistics, University of Washington, Seattle, WA, 98105, USA
| | - John Lane
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN, 55455, USA
| | - Min-Zhi Jiang
- Departments of Biostatistics, Genetics, Computer Science, Applied Physical Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Laura M Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Goo Jun
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Fritz J Sedlazeck
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Ginger Metcalf
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Yao Yao
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, 02115, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, 02115, USA
- Harvard Stem Cell Institute, Boston, MA, 02138, USA
- Broad Institute, Cambridge, MA, 02142, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, 02115, USA
| | - Joshua B Bis
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, 98101, USA
| | - Nathalie Chami
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Paul S de Vries
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Pinkal Desai
- Division of Hematology and Oncology, Weill Cornell Medical College, New York, NY, 10065, USA
| | - James S Floyd
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, 98101, USA
| | - Yan Gao
- Jackson Heart Study, Department of Medicine, University of Mississippi, Jackson, MS, 39216, USA
| | - Kai Kammers
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Wonji Kim
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, 2115, USA
| | - Jee-Young Moon
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Aakrosh Ratan
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA
| | - Lisa R Yanek
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Laura Almasy
- Children's Hospital of Philadelphia and University of Pennsylvania School of Medicine, Philadelphia, PA, 19104, USA
| | - Lewis C Becker
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - John Blangero
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, 78520, USA
| | - Michael H Cho
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, 2115, USA
| | - Joanne E Curran
- Department of Human Genetics and South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley School of Medicine, Brownsville, TX, 78520, USA
| | - Myriam Fornage
- Brown Foundation Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Robert C Kaplan
- Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Joshua P Lewis
- Department of Medicine, Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Ruth J F Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- The Mindich Child Health and Development Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Braxton D Mitchell
- Department of Medicine, Division of Endocrinology, Diabetes, and Nutrition, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Alanna C Morrison
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Michael Preuss
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Bruce M Psaty
- Cardiovascular Health Research Unit, Department of Medicine, University of Washington, Seattle, WA, 98101, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, 22908, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, 90502, USA
| | - Hua Tang
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Russell P Tracy
- Departments of Pathology & Laboratory Medicine and Biochemistry, Larner College of Medicine at the University of Vermont, Colchester, VT, 5446, USA
| | - Eric Boerwinkle
- Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, 77030, USA
| | - Goncalo R Abecasis
- TOPMed Informatics Research Center, University of Michigan, Department of Biostatistics, Ann Arbor, MI, 48109, USA
| | - Thomas W Blackwell
- TOPMed Informatics Research Center, University of Michigan, Department of Biostatistics, Ann Arbor, MI, 48109, USA
| | - Albert V Smith
- TOPMed Informatics Research Center, University of Michigan, Department of Biostatistics, Ann Arbor, MI, 48109, USA
| | - Andrew D Johnson
- Population Sciences Branch, Division of Intramural Research, National Heart, Lung and Blood Institute, Framingham, MA, 1702, USA
| | - Rasika A Mathias
- GeneSTAR Research Program, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21287, USA
| | - Deborah A Nickerson
- Department of Genome Sciences, University of Washington, Seattle, WA, 98105, USA
| | - Matthew P Conomos
- Department of Biostatistics, University of Washington, Seattle, WA, 98105, USA
| | - Yun Li
- Departments of Biostatistics, Genetics, Computer Science, Applied Physical Sciences, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Unnur Þorsteinsdóttir
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, 101, Reykjavik, Iceland
| | - Magnús K Magnússon
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, 101, Reykjavik, Iceland
| | - Kari Stefansson
- deCODE genetics/Amgen Inc., Reykjavik, Iceland
- Faculty of Medicine, University of Iceland, 101, Reykjavik, Iceland
| | - Nathan D Pankratz
- Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, MN, 55455, USA
| | - Daniel E Bauer
- Division of Hematology/Oncology, Boston Children's Hospital, Boston, MA, 02115, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, 02115, USA
- Harvard Stem Cell Institute, Boston, MA, 02138, USA
- Broad Institute, Cambridge, MA, 02142, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, 02115, USA
| | - Paul L Auer
- Division of Biostatistics, Institute for Health and Equity, and Cancer Center, Medical College of Wisconsin, Milwaukee, WI, 53226, USA.
| | - Alex P Reiner
- Department of Epidemiology, University of Washington, Seattle, WA, 98105, USA.
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11
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Hujoel MLA, Sherman MA, Barton AR, Mukamel RE, Sankaran VG, Terao C, Loh PR. Influences of rare copy-number variation on human complex traits. Cell 2022; 185:4233-4248.e27. [PMID: 36306736 PMCID: PMC9800003 DOI: 10.1016/j.cell.2022.09.028] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 07/22/2022] [Accepted: 09/19/2022] [Indexed: 11/06/2022]
Abstract
The human genome contains hundreds of thousands of regions harboring copy-number variants (CNV). However, the phenotypic effects of most such polymorphisms are unknown because only larger CNVs have been ascertainable from SNP-array data generated by large biobanks. We developed a computational approach leveraging haplotype sharing in biobank cohorts to more sensitively detect CNVs. Applied to UK Biobank, this approach accounted for approximately half of all rare gene inactivation events produced by genomic structural variation. This CNV call set enabled a detailed analysis of associations between CNVs and 56 quantitative traits, identifying 269 independent associations (p < 5 × 10-8) likely to be causally driven by CNVs. Putative target genes were identifiable for nearly half of the loci, enabling insights into dosage sensitivity of these genes and uncovering several gene-trait relationships. These results demonstrate the ability of haplotype-informed analysis to provide insights into the genetic basis of human complex traits.
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Affiliation(s)
- Margaux L A Hujoel
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Maxwell A Sherman
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Alison R Barton
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Bioinformatics and Integrative Genomics Program, Harvard Medical School, Boston, MA, USA
| | - Ronen E Mukamel
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Vijay G Sankaran
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan; Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan; Department of Applied Genetics, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Po-Ru Loh
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Center for Data Sciences, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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12
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Tziastoudi M, Cholevas C, Stefanidis I, Theoharides TC. Genetics of COVID-19 and myalgic encephalomyelitis/chronic fatigue syndrome: a systematic review. Ann Clin Transl Neurol 2022; 9:1838-1857. [PMID: 36204816 PMCID: PMC9639636 DOI: 10.1002/acn3.51631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 07/06/2022] [Accepted: 07/07/2022] [Indexed: 01/08/2023] Open
Abstract
COVID‐19 and ME/CFS present with some similar symptoms, especially physical and mental fatigue. In order to understand the basis of these similarities and the possibility of underlying common genetic components, we performed a systematic review of all published genetic association and cohort studies regarding COVID‐19 and ME/CFS and extracted the genes along with the genetic variants investigated. We then performed gene ontology and pathway analysis of those genes that gave significant results in the individual studies to yield functional annotations of the studied genes using protein analysis through evolutionary relationships (PANTHER) VERSION 17.0 software. Finally, we identified the common genetic components of these two conditions. Seventy‐one studies for COVID‐19 and 26 studies for ME/CFS were included in the systematic review in which the expression of 97 genes for COVID‐19 and 429 genes for ME/CFS were significantly affected. We found that ACE, HLA‐A, HLA‐C, HLA‐DQA1, HLA‐DRB1, and TYK2 are the common genes that gave significant results. The findings of the pathway analysis highlight the contribution of inflammation mediated by chemokine and cytokine signaling pathways, and the T cell activation and Toll receptor signaling pathways. Protein class analysis revealed the contribution of defense/immunity proteins, as well as protein‐modifying enzymes. Our results suggest that the pathogenesis of both syndromes could involve some immune dysfunction.
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Affiliation(s)
- Maria Tziastoudi
- Department of Nephrology, Faculty of Medicine, School of Health Sciences, University of Thessaly, Larissa, Greece
| | - Christos Cholevas
- First Department of Ophthalmology, Faculty of Health Sciences, Aristotle University, AHEPA Hospital, Thessaloniki, Greece
| | - Ioannis Stefanidis
- Department of Nephrology, Faculty of Medicine, School of Health Sciences, University of Thessaly, Larissa, Greece
| | - Theoharis C Theoharides
- Institute of Neuro-Immune Medicine, Nova Southeastern University, Clearwater, FL, USA.,Laboratory of Molecular Immunopharmacology and Drug Discovery, Department of Immunology, Tufts University School of Medicine, Boston, Massachusetts, USA.,School of Graduate Biomedical Sciences, Tufts University School of Medicine, Boston, Massachusetts, USA.,Departments of Internal Medicine and Psychiatry, Tufts University School of Medicine and Tufts Medical Center, Boston, Massachusetts, USA
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13
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Lepamets M, Auwerx C, Nõukas M, Claringbould A, Porcu E, Kals M, Jürgenson T, Morris AP, Võsa U, Bochud M, Stringhini S, Wijmenga C, Franke L, Peterson H, Vilo J, Lepik K, Mägi R, Kutalik Z. Omics-informed CNV calls reduce false-positive rates and improve power for CNV-trait associations. HGG ADVANCES 2022; 3:100133. [PMID: 36035246 PMCID: PMC9399386 DOI: 10.1016/j.xhgg.2022.100133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 07/26/2022] [Indexed: 11/29/2022] Open
Abstract
Copy-number variations (CNV) are believed to play an important role in a wide range of complex traits, but discovering such associations remains challenging. While whole-genome sequencing (WGS) is the gold-standard approach for CNV detection, there are several orders of magnitude more samples with available genotyping microarray data. Such array data can be exploited for CNV detection using dedicated software (e.g., PennCNV); however, these calls suffer from elevated false-positive and -negative rates. In this study, we developed a CNV quality score that weights PennCNV calls (pCNVs) based on their likelihood of being true positive. First, we established a measure of pCNV reliability by leveraging evidence from multiple omics data (WGS, transcriptomics, and methylomics) obtained from the same samples. Next, we built a predictor of omics-confirmed pCNVs, termed omics-informed quality score (OQS), using only PennCNV software output parameters. Promisingly, OQS assigned to pCNVs detected in close family members was up to 35% higher than the OQS of pCNVs not carried by other relatives (p < 3.0 × 10−90), outperforming other scores. Finally, in an association study of four anthropometric traits in 89,516 Estonian Biobank samples, the use of OQS led to a relative increase in the trait variance explained by CNVs of up to 56% compared with published quality filtering methods or scores. Overall, we put forward a flexible framework to improve any CNV detection method leveraging multi-omics evidence, applied it to improve PennCNV calls, and demonstrated its utility by improving the statistical power for downstream association analyses.
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Affiliation(s)
- Maarja Lepamets
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu 51010, Estonia
- Corresponding author
| | - Chiara Auwerx
- Center for Integrative Genomics, University of Lausanne, Lausanne 1015, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland
- Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
- Center for Primary Care and Public Health (Unisanté), Department of Epidemiology and Health Systems, University of Lausanne, Lausanne 1010, Switzerland
| | - Margit Nõukas
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu 51010, Estonia
| | | | - Eleonora Porcu
- Center for Integrative Genomics, University of Lausanne, Lausanne 1015, Switzerland
- Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
- Center for Primary Care and Public Health (Unisanté), Department of Epidemiology and Health Systems, University of Lausanne, Lausanne 1010, Switzerland
| | - Mart Kals
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki 00014, Finland
| | - Tuuli Jürgenson
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
- Institute of Mathematics and Statistics, University of Tartu, Tartu 51009, Estonia
| | | | - Andrew Paul Morris
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
- Centre for Genetics and Genomics Versus Arthritis, Division of Musculoskeletal and Dermatological Sciences, The University of Manchester, Manchester M13 9PL, UK
| | - Urmo Võsa
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
| | - Murielle Bochud
- Center for Primary Care and Public Health (Unisanté), Department of Epidemiology and Health Systems, University of Lausanne, Lausanne 1010, Switzerland
| | - Silvia Stringhini
- Unit of Population Epidemiology, Division of Primary Care, Geneva 1205, Switzerland
| | - Cisca Wijmenga
- University of Groningen, University Medical Center Groningen, Department of Genetics, 9713 AV Groningen, the Netherlands
| | - Lude Franke
- University of Groningen, University Medical Center Groningen, Department of Genetics, 9713 AV Groningen, the Netherlands
- Oncode Institute, 3521 AL Utrecht, the Netherlands
| | - Hedi Peterson
- Institute of Computer Science, University of Tartu, Tartu 51009, Estonia
| | - Jaak Vilo
- Institute of Computer Science, University of Tartu, Tartu 51009, Estonia
| | - Kaido Lepik
- Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland
- Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
- Center for Primary Care and Public Health (Unisanté), Department of Epidemiology and Health Systems, University of Lausanne, Lausanne 1010, Switzerland
- Institute of Computer Science, University of Tartu, Tartu 51009, Estonia
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
| | - Zoltán Kutalik
- Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland
- Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland
- Center for Primary Care and Public Health (Unisanté), Department of Epidemiology and Health Systems, University of Lausanne, Lausanne 1010, Switzerland
- Corresponding author
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14
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Fitzgerald T, Birney E. CNest: A novel copy number association discovery method uncovers 862 new associations from 200,629 whole-exome sequence datasets in the UK Biobank. CELL GENOMICS 2022; 2:100167. [PMID: 36779085 PMCID: PMC9903682 DOI: 10.1016/j.xgen.2022.100167] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Revised: 04/11/2022] [Accepted: 07/13/2022] [Indexed: 10/15/2022]
Abstract
Copy number variation (CNV) is known to influence human traits, having a rich history of research into common and rare genetic disease, and although CNV is accepted as an important class of genomic variation, progress on copy-number-based genome-wide association studies (GWASs) from next-generation sequencing (NGS) data has been limited. Here we present a novel method for large-scale copy number analysis from NGS data generating robust copy number estimates and allowing copy number GWASs (CN-GWASs) to be performed genome-wide in discovery mode. We provide a detailed analysis in the UK Biobank resource and a specifically designed software package. We use these methods to perform CN-GWAS analysis across 78 human traits, discovering over 800 genetic associations that are likely to contribute strongly to trait distributions. Finally, we compare CNV and SNP association signals across the same traits and samples, defining specific CNV association classes.
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Affiliation(s)
- Tomas Fitzgerald
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge CB10 1SD, UK
| | - Ewan Birney
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Cambridge CB10 1SD, UK
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15
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Collins RL, Glessner JT, Porcu E, Lepamets M, Brandon R, Lauricella C, Han L, Morley T, Niestroj LM, Ulirsch J, Everett S, Howrigan DP, Boone PM, Fu J, Karczewski KJ, Kellaris G, Lowther C, Lucente D, Mohajeri K, Nõukas M, Nuttle X, Samocha KE, Trinh M, Ullah F, Võsa U, Hurles ME, Aradhya S, Davis EE, Finucane H, Gusella JF, Janze A, Katsanis N, Matyakhina L, Neale BM, Sanders D, Warren S, Hodge JC, Lal D, Ruderfer DM, Meck J, Mägi R, Esko T, Reymond A, Kutalik Z, Hakonarson H, Sunyaev S, Brand H, Talkowski ME. A cross-disorder dosage sensitivity map of the human genome. Cell 2022; 185:3041-3055.e25. [PMID: 35917817 PMCID: PMC9742861 DOI: 10.1016/j.cell.2022.06.036] [Citation(s) in RCA: 128] [Impact Index Per Article: 64.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Revised: 03/17/2022] [Accepted: 06/20/2022] [Indexed: 02/06/2023]
Abstract
Rare copy-number variants (rCNVs) include deletions and duplications that occur infrequently in the global human population and can confer substantial risk for disease. In this study, we aimed to quantify the properties of haploinsufficiency (i.e., deletion intolerance) and triplosensitivity (i.e., duplication intolerance) throughout the human genome. We harmonized and meta-analyzed rCNVs from nearly one million individuals to construct a genome-wide catalog of dosage sensitivity across 54 disorders, which defined 163 dosage sensitive segments associated with at least one disorder. These segments were typically gene dense and often harbored dominant dosage sensitive driver genes, which we were able to prioritize using statistical fine-mapping. Finally, we designed an ensemble machine-learning model to predict probabilities of dosage sensitivity (pHaplo & pTriplo) for all autosomal genes, which identified 2,987 haploinsufficient and 1,559 triplosensitive genes, including 648 that were uniquely triplosensitive. This dosage sensitivity resource will provide broad utility for human disease research and clinical genetics.
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Affiliation(s)
- Ryan L Collins
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Division of Medical Sciences and Department of Medicine, Harvard Medical School, Boston, MA 02115, USA.
| | - Joseph T Glessner
- Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pediatrics, Division of Human Genetics, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Eleonora Porcu
- Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland; Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Maarja Lepamets
- Estonian Genome Centre, Institute of Genomics, University of Tartu, 51010 Tartu, Estonia; Institute of Molecular and Cell Biology, University of Tartu, 51010 Tartu, Estonia
| | | | | | - Lide Han
- Division of Genetic Medicine, Department of Medicine, and Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Theodore Morley
- Division of Genetic Medicine, Department of Medicine, and Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | | | - Jacob Ulirsch
- Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Division of Medical Sciences and Department of Medicine, Harvard Medical School, Boston, MA 02115, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Selin Everett
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Daniel P Howrigan
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Philip M Boone
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA 02115, USA
| | - Jack Fu
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Konrad J Karczewski
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Georgios Kellaris
- Advanced Center for Translational and Genetic Medicine, Stanley Manne Children's Research Institute, Lurie Children's Hospital, Chicago, IL 60611, USA; Departments of Pediatrics and Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Chelsea Lowther
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Diane Lucente
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Kiana Mohajeri
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Division of Medical Sciences and Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Margit Nõukas
- Estonian Genome Centre, Institute of Genomics, University of Tartu, 51010 Tartu, Estonia; Institute of Molecular and Cell Biology, University of Tartu, 51010 Tartu, Estonia
| | - Xander Nuttle
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Kaitlin E Samocha
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Division of Medical Sciences and Department of Medicine, Harvard Medical School, Boston, MA 02115, USA; Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10, UK
| | - Mi Trinh
- Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10, UK
| | - Farid Ullah
- Advanced Center for Translational and Genetic Medicine, Stanley Manne Children's Research Institute, Lurie Children's Hospital, Chicago, IL 60611, USA; Departments of Pediatrics and Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Urmo Võsa
- Estonian Genome Centre, Institute of Genomics, University of Tartu, 51010 Tartu, Estonia
| | | | | | - Matthew E Hurles
- Human Genetics Programme, Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10, UK
| | | | - Erica E Davis
- Advanced Center for Translational and Genetic Medicine, Stanley Manne Children's Research Institute, Lurie Children's Hospital, Chicago, IL 60611, USA; Departments of Pediatrics and Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Hilary Finucane
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - James F Gusella
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | | | - Nicholas Katsanis
- Advanced Center for Translational and Genetic Medicine, Stanley Manne Children's Research Institute, Lurie Children's Hospital, Chicago, IL 60611, USA; Departments of Pediatrics and Cell and Developmental Biology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | | | - Benjamin M Neale
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | | | | | - Jennelle C Hodge
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Dennis Lal
- Cologne Center for Genomics, University of Cologne, 51149 Cologne, Germany; Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Douglas M Ruderfer
- Division of Genetic Medicine, Department of Medicine, and Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN 37232, USA; Center for Precision Medicine, Department of Biomedical Informatics, and Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | | | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, 51010 Tartu, Estonia
| | - Tõnu Esko
- Estonian Genome Centre, Institute of Genomics, University of Tartu, 51010 Tartu, Estonia
| | - Alexandre Reymond
- Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland
| | - Zoltán Kutalik
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland; Center for Primary Care and Public Health, University of Lausanne, 1015 Lausanne, Switzerland; Department of Computational Biology, University of Lausanne, 1015 Lausanne, Switzerland
| | - Hakon Hakonarson
- Department of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Pediatrics, Division of Human Genetics, Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Shamil Sunyaev
- Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Division of Medical Sciences and Department of Medicine, Harvard Medical School, Boston, MA 02115, USA; Division of Genetics, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Harrison Brand
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Pediatric Surgical Research Laboratories, Massachusetts General Hospital, Boston, MA 02114, USA.
| | - Michael E Talkowski
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA; Program in Medical and Population Genetics, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA; Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA.
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16
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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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17
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Auwerx C, Lepamets M, Sadler MC, Patxot M, Stojanov M, Baud D, Mägi R, Porcu E, Reymond A, Kutalik Z, Metspalu A, Milani L, Mägi R, Nelis M. The individual and global impact of copy-number variants on complex human traits. Am J Hum Genet 2022; 109:647-668. [PMID: 35240056 PMCID: PMC9069145 DOI: 10.1016/j.ajhg.2022.02.010] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 02/09/2022] [Indexed: 12/25/2022] Open
Abstract
The impact of copy-number variations (CNVs) on complex human traits remains understudied. We called CNVs in 331,522 UK Biobank participants and performed genome-wide association studies (GWASs) between the copy number of CNV-proxy probes and 57 continuous traits, revealing 131 signals spanning 47 phenotypes. Our analysis recapitulated well-known associations (e.g., 1q21 and height), revealed the pleiotropy of recurrent CNVs (e.g., 26 and 16 traits for 16p11.2-BP4-BP5 and 22q11.21, respectively), and suggested gene functionalities (e.g., MARF1 in female reproduction). Forty-eight CNV signals (38%) overlapped with single-nucleotide polymorphism (SNP)-GWASs signals for the same trait. For instance, deletion of PDZK1, which encodes a urate transporter scaffold protein, decreased serum urate levels, while deletion of RHD, which encodes the Rhesus blood group D antigen, associated with hematological traits. Other signals overlapped Mendelian disorder regions, suggesting variable expressivity and broad impact of these loci, as illustrated by signals mapping to Rotor syndrome (SLCO1B1/3), renal cysts and diabetes syndrome (HNF1B), or Charcot-Marie-Tooth (PMP22) loci. Total CNV burden negatively impacted 35 traits, leading to increased adiposity, liver/kidney damage, and decreased intelligence and physical capacity. Thirty traits remained burden associated after correcting for CNV-GWAS signals, pointing to a polygenic CNV architecture. The burden negatively correlated with socio-economic indicators, parental lifespan, and age (survivorship proxy), suggesting a contribution to decreased longevity. Together, our results showcase how studying CNVs can expand biological insights, emphasizing the critical role of this mutational class in shaping human traits and arguing in favor of a continuum between Mendelian and complex diseases.
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Affiliation(s)
- Chiara Auwerx
- Center for Integrative Genomics, University of Lausanne, Lausanne 1015, Switzerland; Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland; Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland; University Center for Primary Care and Public Health, Lausanne 1010, Switzerland
| | - Maarja Lepamets
- Institute of Molecular and Cell Biology, University of Tartu, Tartu 51010, Estonia; Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
| | - Marie C Sadler
- Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland; University Center for Primary Care and Public Health, Lausanne 1010, Switzerland
| | - Marion Patxot
- Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland
| | - Miloš Stojanov
- Materno-fetal and Obstetrics Research Unit, Department Woman-Mother-Child, CHUV, Lausanne 1011, Switzerland
| | - David Baud
- Materno-fetal and Obstetrics Research Unit, Department Woman-Mother-Child, CHUV, Lausanne 1011, Switzerland
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
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- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu 51010, Estonia
| | - Eleonora Porcu
- Center for Integrative Genomics, University of Lausanne, Lausanne 1015, Switzerland; Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland; University Center for Primary Care and Public Health, Lausanne 1010, Switzerland
| | - Alexandre Reymond
- Center for Integrative Genomics, University of Lausanne, Lausanne 1015, Switzerland.
| | - Zoltán Kutalik
- Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland; Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland; University Center for Primary Care and Public Health, Lausanne 1010, Switzerland.
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18
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Tanigawa Y, Qian J, Venkataraman G, Justesen JM, Li R, Tibshirani R, Hastie T, Rivas MA. Significant sparse polygenic risk scores across 813 traits in UK Biobank. PLoS Genet 2022; 18:e1010105. [PMID: 35324888 PMCID: PMC8946745 DOI: 10.1371/journal.pgen.1010105] [Citation(s) in RCA: 37] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 02/15/2022] [Indexed: 01/05/2023] Open
Abstract
We present a systematic assessment of polygenic risk score (PRS) prediction across more than 1,500 traits using genetic and phenotype data in the UK Biobank. We report 813 sparse PRS models with significant (p < 2.5 x 10-5) incremental predictive performance when compared against the covariate-only model that considers age, sex, types of genotyping arrays, and the principal component loadings of genotypes. We report a significant correlation between the number of genetic variants selected in the sparse PRS model and the incremental predictive performance (Spearman's ⍴ = 0.61, p = 2.2 x 10-59 for quantitative traits, ⍴ = 0.21, p = 9.6 x 10-4 for binary traits). The sparse PRS model trained on European individuals showed limited transferability when evaluated on non-European individuals in the UK Biobank. We provide the PRS model weights on the Global Biobank Engine (https://biobankengine.stanford.edu/prs).
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Affiliation(s)
- Yosuke Tanigawa
- Department of Biomedical Data Science, Stanford University, Stanford, California, United States of America
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Junyang Qian
- Department of Statistics, Stanford University, Stanford, California, United States of America
| | - Guhan Venkataraman
- Department of Biomedical Data Science, Stanford University, Stanford, California, United States of America
| | - Johanne Marie Justesen
- Department of Biomedical Data Science, Stanford University, Stanford, California, United States of America
| | - Ruilin Li
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, California, United States of America
| | - Robert Tibshirani
- Department of Biomedical Data Science, Stanford University, Stanford, California, United States of America
- Department of Statistics, Stanford University, Stanford, California, United States of America
| | - Trevor Hastie
- Department of Biomedical Data Science, Stanford University, Stanford, California, United States of America
- Department of Statistics, Stanford University, Stanford, California, United States of America
| | - Manuel A. Rivas
- Department of Biomedical Data Science, Stanford University, Stanford, California, United States of America
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19
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Hertzberg J, Mundlos S, Vingron M, Gallone G. TADA-a machine learning tool for functional annotation-based prioritisation of pathogenic CNVs. Genome Biol 2022; 23:67. [PMID: 35232478 PMCID: PMC8886976 DOI: 10.1186/s13059-022-02631-z] [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] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 02/11/2022] [Indexed: 01/08/2023] Open
Abstract
Few methods have been developed to investigate copy number variants (CNVs) based on their predicted pathogenicity. We introduce TADA, a method to prioritise pathogenic CNVs through assisted manual filtering and automated classification, based on an extensive catalogue of functional annotation supported by rigourous enrichment analysis. We demonstrate that our classifiers are able to accurately predict pathogenic CNVs, outperforming current alternative methods, and produce a well-calibrated pathogenicity score. Our results suggest that functional annotation-based prioritisation of pathogenic CNVs is a promising approach to support clinical diagnostics and to further the understanding of mechanisms controlling the disease impact of larger genomic alterations.
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Affiliation(s)
- Jakob Hertzberg
- Max Planck Institute for Molecular Genetics, Ihnestraße 63, Berlin, 14195, Germany. .,Charité Universitätsmedizin Berlin, Charitéplatz 1, Berlin, 10117, Germany.
| | - Stefan Mundlos
- Max Planck Institute for Molecular Genetics, Ihnestraße 63, Berlin, 14195, Germany.,Charité Universitätsmedizin Berlin, Charitéplatz 1, Berlin, 10117, Germany
| | - Martin Vingron
- Max Planck Institute for Molecular Genetics, Ihnestraße 63, Berlin, 14195, Germany
| | - Giuseppe Gallone
- Max Planck Institute for Molecular Genetics, Ihnestraße 63, Berlin, 14195, Germany
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20
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Stefanski A, Pérez-Palma E, Mrdjen M, McHugh M, Leu C, Lal D. Identification and quantification of oligogenic loss-of-function disorders. Genet Med 2021; 24:729-735. [PMID: 34906500 DOI: 10.1016/j.gim.2021.10.026] [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: 10/29/2021] [Accepted: 10/29/2021] [Indexed: 10/19/2022] Open
Abstract
PURPOSE Monogenic disorders can present clinically heterogeneous symptoms. We hypothesized that in patients with a monogenic disorder caused by a large deletion, frequently additional loss-of-function (LOF)-intolerant genes are affected, potentially contributing to the phenotype. METHODS We investigated the LOF-intolerant gene distribution across the genome and its association with benign population and pathogenic classified deletions from individuals with presumably monogenic disorders. For people with presumably monogenic epilepsy, we compared Human Phenotype Ontology terms in people with large and small deletions. RESULTS We identified LOF-intolerant gene dense regions that were enriched for ClinVar and depleted for population copy number variants. Analysis of data from >143,000 individuals with a suspected monogenic disorder showed that 2.5% of haploinsufficiency disorder-associated deletions can affect at least 1 other LOF-intolerant gene. Focusing on epilepsy, we observed that 13.1% of pathogenic and likely pathogenic ClinVar deletions <3 megabase pair, covering the diagnostically most relevant genes, affected at least 1 additional LOF-intolerant gene. Those patients have potentially more complex phenotypes with increasing deletion size. CONCLUSION We could systematically show that large deletions frequently affected admditional LOF-intolerant genes in addition to the established disease gene. Further research is needed to understand how additional potential disease-relevant genes influence monogenic disorders to improve clinical care and the efficacy of targeted therapies.
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Affiliation(s)
- Arthur Stefanski
- Genomic Medicine Institute, Cleveland Clinic Lerner Research Institute, Cleveland, OH; Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH
| | - Eduardo Pérez-Palma
- Genomic Medicine Institute, Cleveland Clinic Lerner Research Institute, Cleveland, OH; Universidad del Desarrollo, Centro de Genética y Genómica, Facultad de Medicina Clínica Alemana, Santiago, Chile
| | - Marko Mrdjen
- Genomic Medicine Institute, Cleveland Clinic Lerner Research Institute, Cleveland, OH
| | - Megan McHugh
- Genomic Medicine Institute, Cleveland Clinic Lerner Research Institute, Cleveland, OH
| | - Costin Leu
- Genomic Medicine Institute, Cleveland Clinic Lerner Research Institute, Cleveland, OH; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA
| | - Dennis Lal
- Genomic Medicine Institute, Cleveland Clinic Lerner Research Institute, Cleveland, OH; Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA; Cologne Center for Genomics (CCG), University of Cologne, Cologne, Germany.
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21
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Signatures of TSPAN8 variants associated with human metabolic regulation and diseases. iScience 2021; 24:102893. [PMID: 34401672 PMCID: PMC8355918 DOI: 10.1016/j.isci.2021.102893] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 06/18/2021] [Accepted: 07/20/2021] [Indexed: 02/08/2023] Open
Abstract
Here, with the example of common copy number variation (CNV) in the TSPAN8 gene, we present an important piece of work in the field of CNV detection, that is, CNV association with complex human traits such as 1H NMR metabolomic phenotypes and an example of functional characterization of CNVs among human induced pluripotent stem cells (HipSci). We report TSPAN8 exon 11 (ENSE00003720745) as a pleiotropic locus associated with metabolomic regulation and show that its biology is associated with several metabolic diseases such as type 2 diabetes (T2D) and cancer. Our results further demonstrate the power of multivariate association models over univariate methods and define metabolomic signatures for variants in TSPAN8.
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22
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McInnes G, Yee SW, Pershad Y, Altman RB. Genomewide Association Studies in Pharmacogenomics. Clin Pharmacol Ther 2021; 110:637-648. [PMID: 34185318 PMCID: PMC8376796 DOI: 10.1002/cpt.2349] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 06/15/2021] [Indexed: 12/24/2022]
Abstract
The increasing availability of genotype data linked with information about drug-response phenotypes has enabled genomewide association studies (GWAS) that uncover genetic determinants of drug response. GWAS have discovered associations between genetic variants and both drug efficacy and adverse drug reactions. Despite these successes, the design of GWAS in pharmacogenomics (PGx) faces unique challenges. In this review, we analyze the last decade of GWAS in PGx. We review trends in publications over time, including the drugs and drug classes studied and the clinical phenotypes used. Several data sharing consortia have contributed substantially to the PGx GWAS literature. We anticipate increased focus on biobanks and highlight phenotypes that would best enable future PGx discoveries.
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Affiliation(s)
- Gregory McInnes
- Biomedical Informatics Training Program, Stanford University, Stanford, California, USA
| | - Sook Wah Yee
- Department of Bioengineering and Therapeutic Sciences, University of California at San Francisco, San Francisco, California, USA
| | - Yash Pershad
- Department of Bioengineering, Stanford University, Stanford, California, USA
| | - Russ B Altman
- Department of Bioengineering, Stanford University, Stanford, California, USA.,Departments of Genetics, Medicine, Biomedical Data Science, Stanford, California, USA
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23
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Li R, Chang C, Tanigawa Y, Narasimhan B, Hastie T, Tibshirani R, Rivas MA. Fast Numerical Optimization for Genome Sequencing Data in Population Biobanks. Bioinformatics 2021; 37:4148-4155. [PMID: 34146108 PMCID: PMC9206591 DOI: 10.1093/bioinformatics/btab452] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 06/08/2021] [Accepted: 06/15/2021] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Large-scale and high-dimensional genome sequencing data poses computational challenges. General purpose optimization tools are usually not optimal in terms of computational and memory performance for genetic data. RESULTS We develop two efficient solvers for optimization problems arising from large-scale regularized regressions on millions of genetic variants sequenced from hundreds of thousands of individuals. These genetic variants are encoded by the values in the set {0, 1, 2, NA}. We take advantage of this fact and use two bits to represent each entry in a genetic matrix, which reduces memory requirement by a factor of 32 compared to a double precision floating point representation. Using this representation, we implemented an iteratively reweighted least square algorithm to solve Lasso regressions on genetic matrices, which we name snpnet-2.0. When the dataset contains many rare variants, the predictors can be encoded in a sparse matrix. We utilize the sparsity in the predictor matrix to further reduce memory requirement and computational speed. Our sparse genetic matrix implementation uses both the compact 2-bit representation and a simplified version of compressed sparse block format so that matrix-vector multiplications can be effectively parallelized on multiple CPU cores. To demonstrate the effectiveness of this representation, we implement an accelerated proximal gradient method to solve group Lasso on these sparse genetic matrices. This solver is named sparse-snpnet, and will also be included as part of snpnet R package. Our implementation is able to solve Lasso and group Lasso, linear, logistic and Cox regression problems on sparse genetic matrices that contain 1,000,000 variants and almost 100,000 individuals within 10 minutes and using less than 32GB of memory. AVAILABILITY https://github.com/rivas-lab/snpnet/tree/compact.
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Affiliation(s)
- Ruilin Li
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, 94305, United States
| | | | - Yosuke Tanigawa
- Department of Biomedical Data Science, Stanford University, Stanford, 94305, United States
| | - Balasubramanian Narasimhan
- Department of Biomedical Data Science, Stanford University, Stanford, 94305, United States.,Department of Statistics, Stanford University, Stanford, 94305, United States
| | - Trevor Hastie
- Department of Biomedical Data Science, Stanford University, Stanford, 94305, United States.,Department of Statistics, Stanford University, Stanford, 94305, United States
| | - Robert Tibshirani
- Department of Biomedical Data Science, Stanford University, Stanford, 94305, United States.,Department of Statistics, Stanford University, Stanford, 94305, United States
| | - Manuel A Rivas
- Department of Statistics, Stanford University, Stanford, 94305, United States
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24
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Iivonen AP, Kärkinen J, Yellapragada V, Sidoroff V, Almusa H, Vaaralahti K, Raivio T. Kallmann syndrome in a patient with Weiss-Kruszka syndrome and a de novo deletion in 9q31.2. Eur J Endocrinol 2021; 185:57-66. [PMID: 33909591 PMCID: PMC8183635 DOI: 10.1530/eje-20-1387] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 04/28/2021] [Indexed: 12/21/2022]
Abstract
Patients with deletions on chromosome 9q31.2 may exhibit delayed puberty, craniofacial phenotype including cleft lip/palate, and olfactory bulb hypoplasia. We report a patient with congenital HH with anosmia (Kallmann syndrome, KS) and a de novo 2.38 Mb heterozygous deletion in 9q31.2. The deletion breakpoints (determined with whole-genome linked-read sequencing) were in the FKTN gene (9:108,331,353) and in a non-coding area (9:110,707,332) (hg19). The deletion encompassed six protein-coding genes (FKTN, ZNF462, TAL2, TMEM38B, RAD23B, and KLF4). ZNF462 haploinsufficiency was consistent with the patient's Weiss-Kruszka syndrome (craniofacial phenotype, developmental delay, and sensorineural hearing loss), but did not explain his KS. In further analyses, he did not carry rare sequence variants in 32 known KS genes in whole-exome sequencing and displayed no aberrant splicing of 15 KS genes that were expressed in peripheral blood leukocyte transcriptome. The deletion was 1.8 Mb upstream of a KS candidate gene locus (PALM2AKAP2) but did not suppress its expression. In conclusion, this is the first report of a patient with Weiss-Kruszka syndrome and KS. We suggest that patients carrying a microdeletion in 9q31.2 should be evaluated for the presence of KS and KS-related features.
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Affiliation(s)
- Anna-Pauliina Iivonen
- Department of Physiology, Stem Cells and Metabolism Research Program, Faculty of Medicine, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Juho Kärkinen
- Pediatric Research Center, New Children’s Hospital, Helsinki University Hospital, Helsinki, Finland
| | - Venkatram Yellapragada
- Department of Physiology, Stem Cells and Metabolism Research Program, Faculty of Medicine, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | | | - Henrikki Almusa
- Institute for Molecular Medicine Finland, FIMM, University of Helsinki, Helsinki, Finland
| | - Kirsi Vaaralahti
- Department of Physiology, Stem Cells and Metabolism Research Program, Faculty of Medicine, Research Programs Unit, University of Helsinki, Helsinki, Finland
| | - Taneli Raivio
- Department of Physiology, Stem Cells and Metabolism Research Program, Faculty of Medicine, Research Programs Unit, University of Helsinki, Helsinki, Finland
- Pediatric Research Center, New Children’s Hospital, Helsinki University Hospital, Helsinki, Finland
- Correspondence should be addressed to T Raivio;
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25
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Chen L, Abel HJ, Das I, Larson DE, Ganel L, Kanchi KL, Regier AA, Young EP, Kang CJ, Scott AJ, Chiang C, Wang X, Lu S, Christ R, Service SK, Chiang CWK, Havulinna AS, Kuusisto J, Boehnke M, Laakso M, Palotie A, Ripatti S, Freimer NB, Locke AE, Stitziel NO, Hall IM. Association of structural variation with cardiometabolic traits in Finns. Am J Hum Genet 2021; 108:583-596. [PMID: 33798444 PMCID: PMC8059371 DOI: 10.1016/j.ajhg.2021.03.008] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Accepted: 03/03/2021] [Indexed: 02/08/2023] Open
Abstract
The contribution of genome structural variation (SV) to quantitative traits associated with cardiometabolic diseases remains largely unknown. Here, we present the results of a study examining genetic association between SVs and cardiometabolic traits in the Finnish population. We used sensitive methods to identify and genotype 129,166 high-confidence SVs from deep whole-genome sequencing (WGS) data of 4,848 individuals. We tested the 64,572 common and low-frequency SVs for association with 116 quantitative traits and tested candidate associations using exome sequencing and array genotype data from an additional 15,205 individuals. We discovered 31 genome-wide significant associations at 15 loci, including 2 loci at which SVs have strong phenotypic effects: (1) a deletion of the ALB promoter that is greatly enriched in the Finnish population and causes decreased serum albumin level in carriers (p = 1.47 × 10-54) and is also associated with increased levels of total cholesterol (p = 1.22 × 10-28) and 14 additional cholesterol-related traits, and (2) a multi-allelic copy number variant (CNV) at PDPR that is strongly associated with pyruvate (p = 4.81 × 10-21) and alanine (p = 6.14 × 10-12) levels and resides within a structurally complex genomic region that has accumulated many rearrangements over evolutionary time. We also confirmed six previously reported associations, including five led by stronger signals in single nucleotide variants (SNVs) and one linking recurrent HP gene deletion and cholesterol levels (p = 6.24 × 10-10), which was also found to be strongly associated with increased glycoprotein level (p = 3.53 × 10-35). Our study confirms that integrating SVs in trait-mapping studies will expand our knowledge of genetic factors underlying disease risk.
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Affiliation(s)
- Lei Chen
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA; Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Haley J Abel
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA; Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Indraniel Das
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - David E Larson
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA; Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Liron Ganel
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA; Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Krishna L Kanchi
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Allison A Regier
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA; Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Erica P Young
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA; Cardiovascular Division, Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Chul Joo Kang
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Alexandra J Scott
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA; Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Colby Chiang
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA; Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Xinxin Wang
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA; Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Shuangjia Lu
- Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Ryan Christ
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA
| | - Susan K Service
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Charleston W K Chiang
- Center for Genetic Epidemiology, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA; Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, CA 90089, USA
| | - Aki S Havulinna
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki 00014, Finland; Finnish Institute for Health and Welfare (THL), Helsinki 00271, Finland
| | - Johanna Kuusisto
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio 70210, Finland; Department of Medicine, Kuopio University Hospital, Kuopio 70210, Finland
| | - Michael Boehnke
- Department of Biostatistics and Center for Statistical Genetics, University of Michigan School of Public Health, Ann Arbor, MI 48109, USA
| | - Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, Kuopio 70210, Finland; Department of Medicine, Kuopio University Hospital, Kuopio 70210, Finland
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki 00014, Finland; Analytical and Translational Genetics Unit (ATGU), Psychiatric & Neurodevelopmental Genetics Unit, Departments of Psychiatry and Neurology, Massachusetts General Hospital, Boston, MA 02114, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki 00014, Finland; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Department of Public Health, Faculty of Medicine, University of Helsinki, Helsinki 00014, Finland
| | - Nelson B Freimer
- Center for Neurobehavioral Genetics, Jane and Terry Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Adam E Locke
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA; Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Nathan O Stitziel
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA; Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA.
| | - Ira M Hall
- McDonnell Genome Institute, Washington University School of Medicine, St. Louis, MO 63108, USA; Department of Medicine, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Genetics, Yale University School of Medicine, New Haven, CT 06510, USA.
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26
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Li R, Tanigawa Y, Justesen JM, Taylor J, Hastie T, Tibshirani R, Rivas MA. Survival Analysis on Rare Events Using Group-Regularized Multi-Response Cox Regression. Bioinformatics 2021; 37:4437-4443. [PMID: 33560296 PMCID: PMC8652035 DOI: 10.1093/bioinformatics/btab095] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 01/27/2021] [Accepted: 02/05/2021] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION The prediction performance of Cox proportional hazard model suffers when there are only few uncensored events in the training data. RESULTS We propose a Sparse-Group regularized Cox regression method to improve the prediction performance of large-scale and high-dimensional survival data with few observed events. Our approach is applicable when there is one or more other survival responses that 1. has a large number of observed events; 2. share a common set of associated predictors with the rare event response. This scenario is common in the UK Biobank (Sudlow et al., 2015) dataset where records for a large number of common and less prevalent diseases of the same set of individuals are available. By analyzing these responses together, we hope to achieve higher prediction performance than when they are analyzed individually. To make this approach practical for large-scale data, we developed an accelerated proximal gradient optimization algorithm as well as a screening procedure inspired by Qian et al. (2020). AVAILABILITY https://github.com/rivas-lab/multisnpnet-Cox. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Ruilin Li
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, United States
| | - Yosuke Tanigawa
- Department of Biomedical Data Science, Stanford University, Stanford, United States
| | - Johanne M Justesen
- Department of Biomedical Data Science, Stanford University, Stanford, United States
| | - Jonathan Taylor
- Department of Statistics, Stanford University, Stanford, United States
| | - Trevor Hastie
- Department of Biomedical Data Science, Stanford University, Stanford, United States.,Department of Statistics, Stanford University, Stanford, United States
| | - Robert Tibshirani
- Department of Biomedical Data Science, Stanford University, Stanford, United States.,Department of Statistics, Stanford University, Stanford, United States
| | - Manuel A Rivas
- Department of Biomedical Data Science, Stanford University, Stanford, United States
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27
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Aguirre M, Tanigawa Y, Venkataraman GR, Tibshirani R, Hastie T, Rivas MA. Polygenic risk modeling with latent trait-related genetic components. Eur J Hum Genet 2021; 29:1071-1081. [PMID: 33558700 DOI: 10.1038/s41431-021-00813-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Revised: 12/26/2020] [Accepted: 01/14/2021] [Indexed: 02/06/2023] Open
Abstract
Polygenic risk models have led to significant advances in understanding complex diseases and their clinical presentation. While polygenic risk scores (PRS) can effectively predict outcomes, they do not generally account for disease subtypes or pathways which underlie within-trait diversity. Here, we introduce a latent factor model of genetic risk based on components from Decomposition of Genetic Associations (DeGAs), which we call the DeGAs polygenic risk score (dPRS). We compute DeGAs using genetic associations for 977 traits and find that dPRS performs comparably to standard PRS while offering greater interpretability. We show how to decompose an individual's genetic risk for a trait across DeGAs components, with examples for body mass index (BMI) and myocardial infarction (heart attack) in 337,151 white British individuals in the UK Biobank, with replication in a further set of 25,486 non-British white individuals. We find that BMI polygenic risk factorizes into components related to fat-free mass, fat mass, and overall health indicators like physical activity. Most individuals with high dPRS for BMI have strong contributions from both a fat-mass component and a fat-free mass component, whereas a few "outlier" individuals have strong contributions from only one of the two components. Overall, our method enables fine-scale interpretation of the drivers of genetic risk for complex traits.
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Affiliation(s)
- Matthew Aguirre
- Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA.,Department of Pediatrics, School of Medicine, Stanford University, Stanford, CA, USA
| | - Yosuke Tanigawa
- Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA
| | - Guhan Ram Venkataraman
- Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA
| | - Rob Tibshirani
- Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA.,Department of Statistics, Stanford University, Stanford, CA, USA
| | - Trevor Hastie
- Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA.,Department of Statistics, Stanford University, Stanford, CA, USA
| | - Manuel A Rivas
- Department of Biomedical Data Science, School of Medicine, Stanford University, Stanford, CA, USA.
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28
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Abstract
Clinical laboratory tests are a critical component of the continuum of care. We evaluate the genetic basis of 35 blood and urine laboratory measurements in the UK Biobank (n = 363,228 individuals). We identify 1,857 loci associated with at least one trait, containing 3,374 fine-mapped associations and additional sets of large-effect (>0.1 s.d.) protein-altering, human leukocyte antigen (HLA) and copy number variant (CNV) associations. Through Mendelian randomization (MR) analysis, we discover 51 causal relationships, including previously known agonistic effects of urate on gout and cystatin C on stroke. Finally, we develop polygenic risk scores (PRSs) for each biomarker and build 'multi-PRS' models for diseases using 35 PRSs simultaneously, which improved chronic kidney disease, type 2 diabetes, gout and alcoholic cirrhosis genetic risk stratification in an independent dataset (FinnGen; n = 135,500) relative to single-disease PRSs. Together, our results delineate the genetic basis of biomarkers and their causal influences on diseases and improve genetic risk stratification for common diseases.
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29
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Jia X, Burugula BB, Chen V, Lemons RM, Jayakody S, Maksutova M, Kitzman JO. Massively parallel functional testing of MSH2 missense variants conferring Lynch syndrome risk. Am J Hum Genet 2021; 108:163-175. [PMID: 33357406 PMCID: PMC7820803 DOI: 10.1016/j.ajhg.2020.12.003] [Citation(s) in RCA: 54] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Accepted: 12/03/2020] [Indexed: 12/20/2022] Open
Abstract
The lack of functional evidence for the majority of missense variants limits their clinical interpretability and poses a key barrier to the broad utility of carrier screening. In Lynch syndrome (LS), one of the most highly prevalent cancer syndromes, nearly 90% of clinically observed missense variants are deemed “variants of uncertain significance” (VUS). To systematically resolve their functional status, we performed a massively parallel screen in human cells to identify loss-of-function missense variants in the key DNA mismatch repair factor MSH2. The resulting functional effect map is substantially complete, covering 94% of the 17,746 possible variants, and is highly concordant (96%) with existing functional data and expert clinicians’ interpretations. The large majority (89%) of missense variants were functionally neutral, perhaps unexpectedly in light of its evolutionary conservation. These data provide ready-to-use functional evidence to resolve the ∼1,300 extant missense VUSs in MSH2 and may facilitate the prospective classification of newly discovered variants in the clinic.
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30
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Córdova-Palomera A, Tcheandjieu C, Fries JA, Varma P, Chen VS, Fiterau M, Xiao K, Tejeda H, Keavney BD, Cordell HJ, Tanigawa Y, Venkataraman G, Rivas MA, Ré C, Ashley E, Priest JR. Cardiac Imaging of Aortic Valve Area From 34 287 UK Biobank Participants Reveals Novel Genetic Associations and Shared Genetic Comorbidity With Multiple Disease Phenotypes. CIRCULATION-GENOMIC AND PRECISION MEDICINE 2020; 13:e003014. [DOI: 10.1161/circgen.120.003014] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background:
The aortic valve is an important determinant of cardiovascular physiology and anatomic location of common human diseases.
Methods:
From a sample of 34 287 white British ancestry participants, we estimated functional aortic valve area by planimetry from prospectively obtained cardiac magnetic resonance imaging sequences of the aortic valve. Aortic valve area measurements were submitted to genome-wide association testing, followed by polygenic risk scoring and phenome-wide screening, to identify genetic comorbidities.
Results:
A genome-wide association study of aortic valve area in these UK Biobank participants showed 3 significant associations, indexed by rs71190365 (chr13:50764607,
DLEU1
,
P
=1.8×10
−9
), rs35991305 (chr12:94191968,
CRADD
,
P
=3.4×10
−8
), and chr17:45013271:C:T (
GOSR2
,
P
=5.6×10
−8
). Replication on an independent set of 8145 unrelated European ancestry participants showed consistent effect sizes in all 3 loci, although rs35991305 did not meet nominal significance. We constructed a polygenic risk score for aortic valve area, which in a separate cohort of 311 728 individuals without imaging demonstrated that smaller aortic valve area is predictive of increased risk for aortic valve disease (odds ratio, 1.14;
P
=2.3×10
−6
). After excluding subjects with a medical diagnosis of aortic valve stenosis (remaining n=308 683 individuals), phenome-wide association of >10 000 traits showed multiple links between the polygenic score for aortic valve disease and key health-related comorbidities involving the cardiovascular system and autoimmune disease. Genetic correlation analysis supports a shared genetic etiology with between aortic valve area and birth weight along with other cardiovascular conditions.
Conclusions:
These results illustrate the use of automated phenotyping of cardiac imaging data from the general population to investigate the genetic etiology of aortic valve disease, perform clinical prediction, and uncover new clinical and genetic correlates of cardiac anatomy.
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Affiliation(s)
- Aldo Córdova-Palomera
- Department of Pediatrics, Division of Pediatric Cardiology, Stanford University School of Medicine, Stanford, CA (A.C.-P., C.T., K.X., H.T., J.R.P.)
| | - Catherine Tcheandjieu
- Department of Pediatrics, Division of Pediatric Cardiology, Stanford University School of Medicine, Stanford, CA (A.C.-P., C.T., K.X., H.T., J.R.P.)
| | - Jason A. Fries
- Department of Computer Science (J.F., V.S.C., M.F., C.R.), Stanford University, CA
- Center for Biomedical Informatics Research (J.F.), Stanford University, CA
| | - Paroma Varma
- Department of Electrical Engineering (P.V.), Stanford University, CA
| | - Vincent S. Chen
- Department of Computer Science (J.F., V.S.C., M.F., C.R.), Stanford University, CA
| | - Madalina Fiterau
- Department of Computer Science (J.F., V.S.C., M.F., C.R.), Stanford University, CA
| | - Ke Xiao
- Department of Pediatrics, Division of Pediatric Cardiology, Stanford University School of Medicine, Stanford, CA (A.C.-P., C.T., K.X., H.T., J.R.P.)
| | - Heliodoro Tejeda
- Department of Pediatrics, Division of Pediatric Cardiology, Stanford University School of Medicine, Stanford, CA (A.C.-P., C.T., K.X., H.T., J.R.P.)
| | - Bernard D. Keavney
- Division of Cardiovascular Sciences, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, United Kingdom (B.K.)
- Division of Medicine, Manchester University National Health Service Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom (B.K.)
| | - Heather J. Cordell
- Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom (H.J.C.)
| | - Yosuke Tanigawa
- Department of Biomedical Data Science (Y.T., G.V., M.R.), Stanford University, CA
| | - Guhan Venkataraman
- Department of Biomedical Data Science (Y.T., G.V., M.R.), Stanford University, CA
| | - Manuel A. Rivas
- Department of Biomedical Data Science (Y.T., G.V., M.R.), Stanford University, CA
| | - Christopher Ré
- Department of Computer Science (J.F., V.S.C., M.F., C.R.), Stanford University, CA
| | - Euan Ashley
- Department of Medicine (E.A.), Stanford University, CA
- Chan Zuckerberg Biohub, San Francisco, CA (E.A., J.R.P.)
| | - James R. Priest
- Department of Pediatrics, Division of Pediatric Cardiology, Stanford University School of Medicine, Stanford, CA (A.C.-P., C.T., K.X., H.T., J.R.P.)
- Chan Zuckerberg Biohub, San Francisco, CA (E.A., J.R.P.)
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Dibble JJ, McGrath SJ, Ponting CP. Genetic risk factors of ME/CFS: a critical review. Hum Mol Genet 2020; 29:R117-R124. [PMID: 32744306 PMCID: PMC7530519 DOI: 10.1093/hmg/ddaa169] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 07/28/2020] [Accepted: 07/28/2020] [Indexed: 02/06/2023] Open
Abstract
Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex multisystem illness that lacks effective therapy and a biomedical understanding of its causes. Despite a prevalence of ∼0.2-0.4% and its high public health burden, and evidence that it has a heritable component, ME/CFS has not yet benefited from the advances in technology and analytical tools that have improved our understanding of many other complex diseases. Here we critically review existing evidence that genetic factors alter ME/CFS risk before concluding that most ME/CFS candidate gene associations are not replicated by the larger CFS cohort within the UK Biobank. Multiple genome-wide association studies of this cohort also have not yielded consistently significant associations. Ahead of upcoming larger genome-wide association studies, we discuss how these could generate new lines of enquiry into the DNA variants, genes and cell types that are causally involved in ME/CFS disease.
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Affiliation(s)
- Joshua J Dibble
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Crewe Road South, Edinburgh EH4 2XU, UK
| | | | - Chris P Ponting
- MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Crewe Road South, Edinburgh EH4 2XU, UK
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Li R, Chang C, Justesen JM, Tanigawa Y, Qiang J, Hastie T, Rivas MA, Tibshirani R. Fast Lasso method for large-scale and ultrahigh-dimensional Cox model with applications to UK Biobank. Biostatistics 2020; 23:522-540. [PMID: 32989444 DOI: 10.1093/biostatistics/kxaa038] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Revised: 08/15/2020] [Accepted: 08/18/2020] [Indexed: 11/13/2022] Open
Abstract
We develop a scalable and highly efficient algorithm to fit a Cox proportional hazard model by maximizing the $L^1$-regularized (Lasso) partial likelihood function, based on the Batch Screening Iterative Lasso (BASIL) method developed in Qian and others (2019). Our algorithm is particularly suitable for large-scale and high-dimensional data that do not fit in the memory. The output of our algorithm is the full Lasso path, the parameter estimates at all predefined regularization parameters, as well as their validation accuracy measured using the concordance index (C-index) or the validation deviance. To demonstrate the effectiveness of our algorithm, we analyze a large genotype-survival time dataset across 306 disease outcomes from the UK Biobank (Sudlow and others, 2015). We provide a publicly available implementation of the proposed approach for genetics data on top of the PLINK2 package and name it snpnet-Cox.
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Affiliation(s)
- Ruilin Li
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA 94305, USA
| | | | - Johanne M Justesen
- Department of Statistics and Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Yosuke Tanigawa
- Department of Statistics and Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Junyang Qiang
- Department of Statistics and Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Trevor Hastie
- Department of Statistics and Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Manuel A Rivas
- Department of Statistics and Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
| | - Robert Tibshirani
- Department of Statistics and Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA
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Tuke M, Tyrrell J, Ruth KS, Beaumont RN, Wood AR, Murray A, Frayling TM, Weedon MN, Wright CF. Large Copy-Number Variants in UK Biobank Caused by Clonal Hematopoiesis May Confound Penetrance Estimates. Am J Hum Genet 2020; 107:325-329. [PMID: 32574563 DOI: 10.1016/j.ajhg.2020.06.001] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Accepted: 06/02/2020] [Indexed: 12/20/2022] Open
Abstract
Large copy-number variants (CNVs) are strongly associated with both developmental delay and cancer, but the type of disease depends strongly on when and where the mutation occurred, i.e., germline versus somatic. We used microarray data from UK Biobank to investigate the prevalence and penetrance of large autosomal CNVs and chromosomal aneuploidies using a standard CNV detection algorithm not designed for detecting mosaic variants. We found 160 individuals that carry >10 Mb copy number changes, including 56 with whole chromosome aneuploidies. Nineteen (12%) individuals had a diagnosis of Down syndrome or other developmental disorder, while 84 (52.5%) individuals had a diagnosis of hematological malignancies or chronic myeloproliferative disorders. Notably, there was no evidence of mosaicism in the blood for many of these large CNVs, so they could easily be mistaken for germline alleles even when caused by somatic mutations. We therefore suggest that somatic mutations associated with blood cancers may result in false estimates of rare variant penetrance from population biobanks.
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Affiliation(s)
- Marcus Tuke
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter EX2 5DW, UK
| | - Jessica Tyrrell
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter EX2 5DW, UK
| | - Katherine S Ruth
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter EX2 5DW, UK
| | - Robin N Beaumont
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter EX2 5DW, UK
| | - Andrew R Wood
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter EX2 5DW, UK
| | - Anna Murray
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter EX2 5DW, UK
| | - Timothy M Frayling
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter EX2 5DW, UK
| | - Michael N Weedon
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter EX2 5DW, UK
| | - Caroline F Wright
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter EX2 5DW, UK.
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34
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DNA copy number variations – Do these big mutations have a big effect on cardiovascular risk? Int J Cardiol 2020; 298:116-117. [DOI: 10.1016/j.ijcard.2019.08.046] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/16/2019] [Accepted: 08/23/2019] [Indexed: 11/19/2022]
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Iourov IY, Vorsanova SG, Yurov YB. The variome concept: focus on CNVariome. Mol Cytogenet 2019; 12:52. [PMID: 31890032 PMCID: PMC6924070 DOI: 10.1186/s13039-019-0467-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Accepted: 12/13/2019] [Indexed: 02/07/2023] Open
Abstract
Background Variome may be used for designating complex system of interplay between genomic variations specific for an individual or a disease. Despite the recognized complexity of genomic basis for phenotypic traits and diseases, studies of genetic causes of a disease are usually dedicated to the identification of single causative genomic changes (mutations). When such an artificially simplified model is employed, genomic basis of phenotypic outcomes remains elusive in the overwhelming majority of human diseases. Moreover, it is repeatedly demonstrated that multiple genomic changes within an individual genome are likely to underlie the phenome. Probably the best example of cumulative effect of variome on the phenotype is CNV (copy number variation) burden. Accordingly, we have proposed a variome concept based on CNV studies providing the evidence for the existence of a CNVariome (the set of CNV affecting an individual genome), a target for genomic analyses useful for unraveling genetic mechanisms of diseases and phenotypic traits. Conclusion Variome (CNVariome) concept suggests that a genomic milieu is determined by the whole set of genomic variations (CNV) within an individual genome. The genomic milieu is likely to result from interplay between these variations. Furthermore, such kind of variome may be either individual or disease-specific. Additionally, such variome may be pathway-specific. The latter is able to affect molecular/cellular pathways of genome stability maintenance leading to occurrence of genomic/chromosome instability and/or somatic mosaicism resulting in somatic variome. This variome type seems to be important for unraveling disease mechanisms, as well. Finally, it appears that bioinformatic analysis of both individual and somatic variomes in the context of diseases- and pathway-specific variomes is the most promising way to determine genomic basis of the phenome and to unravel disease mechanisms for the management and treatment of currently incurable diseases.
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Affiliation(s)
- Ivan Y Iourov
- Yurov's Laboratory of Molecular Genetics and Cytogenomics of the Brain, Mental Health Research Center, 117152 Moscow, Russia.,2Veltischev Research and Clinical Institute for Pediatrics of the Pirogov Russian National Research Medical University, Ministry of Health of Russian Federation, 125412 Moscow, Russia
| | - Svetlana G Vorsanova
- Yurov's Laboratory of Molecular Genetics and Cytogenomics of the Brain, Mental Health Research Center, 117152 Moscow, Russia.,2Veltischev Research and Clinical Institute for Pediatrics of the Pirogov Russian National Research Medical University, Ministry of Health of Russian Federation, 125412 Moscow, Russia
| | - Yuri B Yurov
- Yurov's Laboratory of Molecular Genetics and Cytogenomics of the Brain, Mental Health Research Center, 117152 Moscow, Russia.,2Veltischev Research and Clinical Institute for Pediatrics of the Pirogov Russian National Research Medical University, Ministry of Health of Russian Federation, 125412 Moscow, Russia
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