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Kouakou MR, Cabrera-Mendoza B, Pathak GA, Cannon TD, Polimanti R. Genetically Informed Study Highlights Income-Independent Effect of Schizophrenia Liability on Mental and Physical Health. Schizophr Bull 2024:sbae093. [PMID: 38848523 DOI: 10.1093/schbul/sbae093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/09/2024]
Abstract
BACKGROUND AND HYPOTHESIS Individuals with schizophrenia (SCZ) suffer from comorbidities that substantially reduce their life expectancy. Socioeconomic inequalities could contribute to many of the negative health outcomes associated with SCZ. STUDY DESIGN We investigated genome-wide datasets related to SCZ (52 017 cases and 75 889 controls) from the Psychiatric Genomics Consortium, household income (HI; N = 361 687) from UK Biobank, and 2202 medical endpoints assessed in up to 342 499 FinnGen participants. A phenome-wide genetic correlation analysis of SCZ and HI was performed, also assessing whether SCZ genetic correlations were influenced by the HI effect on SCZ. Additionally, SCZ and HI direct effects on medical endpoints were estimated using multivariable Mendelian randomization (MR). STUDY RESULTS SCZ and HI showed overlapping genetic correlations with 70 traits (P < 2.89 × 10-5), including mental health, substance use, gastrointestinal illnesses, reproductive outcomes, liver diseases, respiratory problems, and musculoskeletal phenotypes. SCZ genetic correlations with these traits were not affected by the HI effect on SCZ. Considering Bonferroni multiple testing correction (P < 7.14 × 10-4), MR analysis indicated that SCZ and HI may affect medical abortion (SCZ OR = 1.07; HI OR = 0.78), panic disorder (SCZ OR = 1.20; HI OR = 0.60), personality disorders (SCZ OR = 1.31; HI OR = 0.67), substance use (SCZ OR = 1.2; HI OR = 0.68), and adjustment disorders (SCZ OR = 1.18; HI OR = 0.78). Multivariable MR analysis confirmed that SCZ effects on these outcomes were independent of HI. CONCLUSIONS The effect of SCZ genetic liability on mental and physical health may not be strongly affected by socioeconomic differences. This suggests that SCZ-specific strategies are needed to reduce negative health outcomes affecting patients and high-risk individuals.
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Affiliation(s)
- Manuela R Kouakou
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Brenda Cabrera-Mendoza
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Cooperative Studies Program Clinical Epidemiology Research Center (CSP-CERC), VA Connecticut Healthcare System, West Haven, CT, USA
| | - Gita A Pathak
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Cooperative Studies Program Clinical Epidemiology Research Center (CSP-CERC), VA Connecticut Healthcare System, West Haven, CT, USA
| | - Tyrone D Cannon
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychology, Yale University, New Haven, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
| | - Renato Polimanti
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Cooperative Studies Program Clinical Epidemiology Research Center (CSP-CERC), VA Connecticut Healthcare System, West Haven, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
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2
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de Oliveira LF, Veroneze R, Sousa KRS, Mulim HA, Araujo AC, Huang Y, Johnson JS, Brito LF. Genomic regions, candidate genes, and pleiotropic variants associated with physiological and anatomical indicators of heat stress response in lactating sows. BMC Genomics 2024; 25:467. [PMID: 38741036 DOI: 10.1186/s12864-024-10365-4] [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: 02/12/2024] [Accepted: 04/29/2024] [Indexed: 05/16/2024] Open
Abstract
BACKGROUND Heat stress (HS) poses significant threats to the sustainability of livestock production. Genetically improving heat tolerance could enhance animal welfare and minimize production losses during HS events. Measuring phenotypic indicators of HS response and understanding their genetic background are crucial steps to optimize breeding schemes for improved climatic resilience. The identification of genomic regions and candidate genes influencing the traits of interest, including variants with pleiotropic effects, enables the refinement of genotyping panels used to perform genomic prediction of breeding values and contributes to unraveling the biological mechanisms influencing heat stress response. Therefore, the main objectives of this study were to identify genomic regions, candidate genes, and potential pleiotropic variants significantly associated with indicators of HS response in lactating sows using imputed whole-genome sequence (WGS) data. Phenotypic records for 18 traits and genomic information from 1,645 lactating sows were available for the study. The genotypes from the PorcineSNP50K panel containing 50,703 single nucleotide polymorphisms (SNPs) were imputed to WGS and after quality control, 1,622 animals and 7,065,922 SNPs were included in the analyses. RESULTS A total of 1,388 unique SNPs located on sixteen chromosomes were found to be associated with 11 traits. Twenty gene ontology terms and 11 biological pathways were shown to be associated with variability in ear skin temperature, shoulder skin temperature, rump skin temperature, tail skin temperature, respiration rate, panting score, vaginal temperature automatically measured every 10 min, vaginal temperature measured at 0800 h, hair density score, body condition score, and ear area. Seven, five, six, two, seven, 15, and 14 genes with potential pleiotropic effects were identified for indicators of skin temperature, vaginal temperature, animal temperature, respiration rate, thermoregulatory traits, anatomical traits, and all traits, respectively. CONCLUSIONS Physiological and anatomical indicators of HS response in lactating sows are heritable but highly polygenic. The candidate genes found are associated with important gene ontology terms and biological pathways related to heat shock protein activities, immune response, and cellular oxidative stress. Many of the candidate genes with pleiotropic effects are involved in catalytic activities to reduce cell damage from oxidative stress and cellular mechanisms related to immune response.
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Affiliation(s)
- Letícia Fernanda de Oliveira
- Department of Animal Science, Federal University of Viçosa, Viçosa, MG, Brazil
- Department of Animal Sciences, Purdue University, West Lafayette, IN, USA
| | - Renata Veroneze
- Department of Animal Science, Federal University of Viçosa, Viçosa, MG, Brazil
| | - Katiene Régia Silva Sousa
- Department of Animal Sciences, Purdue University, West Lafayette, IN, USA
- Department of Oceanography and Limnology, Federal University of Maranhão, São Luís, MA, Brazil
| | - Henrique A Mulim
- Department of Animal Sciences, Purdue University, West Lafayette, IN, USA
| | | | | | - Jay S Johnson
- USDA-ARS Livestock Behavior Research Unit, West Lafayette, IN, USA
| | - Luiz F Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN, USA.
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3
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Fang Z, Jia S, Mou X, Li Z, Hu T, Tu Y, Zhao J, Zhang T, Lin W, Lu Y, Feng C, Xia S. Shared genetic architecture and causal relationship between liver and heart disease. iScience 2024; 27:109431. [PMID: 38523778 PMCID: PMC10959668 DOI: 10.1016/j.isci.2024.109431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 01/08/2024] [Accepted: 03/04/2024] [Indexed: 03/26/2024] Open
Abstract
This study investigates the relationship and genetic mechanisms of liver and heart diseases, focusing on the liver-heart axis (LHA) as a fundamental biological basis. Through genome-wide association study analysis, we explore shared genes and pathways related to LHA. Shared genetic factors are found in 8 out of 20 pairs, indicating genetic correlations. The analysis reveals 53 loci with pleiotropic effects, including 8 loci exhibiting shared causality across multiple traits. Based on SNP-p level tissue-specific multi-marker analysis of genomic annotation (MAGMA) analysis demonstrates significant enrichment of pleiotropy in liver and heart diseases within different cardiovascular tissues and female reproductive appendages. Gene-specific MAGMA analysis identifies 343 pleiotropic genes associated with various traits; these genes show tissue-specific enrichment primarily in the liver, cardiovascular system, and other tissues. Shared risk loci between immune cells and both liver and cardiovascular diseases are also discovered. Mendelian randomization analyses provide support for causal relationships among the investigated trait pairs.
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Affiliation(s)
- Ziyi Fang
- Department of Gastroenterology, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu 322000, China
| | - Sixiang Jia
- Department of Cardiology, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu 322000, China
| | - Xuanting Mou
- Department of Cardiology, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu 322000, China
| | - Zhe Li
- Department of Cardiology, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu 322000, China
| | - Tianli Hu
- Department of Cardiology, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu 322000, China
| | - Yiting Tu
- Department of Orthopedics, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jianqiang Zhao
- Department of Cardiology, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu 322000, China
| | - Tianlong Zhang
- Department of Critical Care Medicine, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu 322000, China
| | - Wenting Lin
- Department of Cardiology, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu 322000, China
| | - Yile Lu
- Department of Cardiology, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu 322000, China
| | - Chao Feng
- Department of Cardiology, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu 322000, China
| | - Shudong Xia
- Department of Cardiology, The Fourth Affiliated Hospital of School of Medicine, and International School of Medicine, International Institutes of Medicine, Zhejiang University, Yiwu 322000, China
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Chen SY, Gloria LS, Pedrosa VB, Doucette J, Boerman JP, Brito LF. Unraveling the genomic background of resilience based on variability in milk yield and milk production levels in North American Holstein cattle through genome-wide association study and Mendelian randomization analyses. J Dairy Sci 2024; 107:1035-1053. [PMID: 37776995 DOI: 10.3168/jds.2023-23650] [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: 04/21/2023] [Accepted: 09/04/2023] [Indexed: 10/02/2023]
Abstract
Breeding more resilient animals will benefit the dairy cattle industry in the long term, especially as global climate changes become more severe. Previous studies have reported genetic parameters for various milk yield-based resilience indicators, but the underlying genomic background of these traits remain unknown. In this study, we conducted GWAS of 62,029 SNPs with 4 milk yield-based resilience indicators, including the weighted occurrence frequency (wfPert) and accumulated milk losses (dPert) of milk yield perturbations, and log-transformed variance (LnVar) and lag-1 autocorrelation (rauto) of daily yield residuals. These variables were previously derived from 5.6 million daily milk yield records from 21,350 lactations (parities 1-3) of 11,787 North American Holstein cows. The average daily milk yield (ADMY) throughout lactation was also included to compare the shared genetic background of resilience indicators with milk yield. The differential genetic background of these indicators was first revealed by the significant genomic regions identified and significantly enriched biological pathways of positional candidate genes, which confirmed the genetic difference among resilience indicators. Interestingly, the functional analyses of candidate genes suggested that the regulation of intestinal homeostasis is most likely affecting resilience derived based on variability in milk yield. Based on Mendelian randomization analyses of multiple instrumental SNPs, we further found an unfavorable causal association of ADMY with LnVar. In conclusion, the resilience indicators evaluated are genetically different traits, and there are causal associations of milk yield with some of the resilience indicators evaluated. In addition to providing biological insights into the molecular regulation mechanisms of resilience derived based on variability in milk yield, this study also indicates the need for developing selection indexes combining multiple indicator traits and taking into account their genetic relationship for breeding more resilient dairy cattle.
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Affiliation(s)
- Shi-Yi Chen
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907; Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu, Sichuan, 611130, China
| | - Leonardo S Gloria
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907
| | - Victor B Pedrosa
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907
| | - Jarrod Doucette
- Agriculture Information Technology (AgIT), Purdue University, West Lafayette, IN 47907
| | | | - Luiz F Brito
- Department of Animal Sciences, Purdue University, West Lafayette, IN 47907.
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5
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Li R, Benz L, Duan R, Denny JC, Hakonarson H, Mosley JD, Smoller JW, Wei WQ, Ritchie MD, Moore JH, Chen Y. mixWAS: An efficient distributed algorithm for mixed-outcomes genome-wide association studies. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.09.24301073. [PMID: 38260403 PMCID: PMC10802662 DOI: 10.1101/2024.01.09.24301073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Genome-wide association studies (GWAS) have been instrumental in identifying genetic associations for various diseases and traits. However, uncovering genetic underpinnings among traits beyond univariate phenotype associations remains a challenge. Multi-phenotype associations (MPA), or genetic pleiotropy, offer important insights into shared genes and pathways among traits, enhancing our understanding of genetic architectures of complex diseases. GWAS of biobank-linked electronic health record (EHR) data are increasingly being utilized to identify MPA among various traits and diseases. However, methodologies that can efficiently take advantage of distributed EHR to detect MPA are still lacking. Here, we introduce mixWAS, a novel algorithm that efficiently and losslessly integrates multiple EHRs via summary statistics, allowing the detection of MPA among mixed phenotypes while accounting for heterogeneities across EHRs. Simulations demonstrate that mixWAS outperforms the widely used MPA detection method, Phenome-wide association study (PheWAS), across diverse scenarios. Applying mixWAS to data from seven EHRs in the US, we identified 4,534 MPA among blood lipids, BMI, and circulatory diseases. Validation in an independent EHR data from UK confirmed 97.7% of the associations. mixWAS fundamentally improves the detection of MPA and is available as a free, open-source software.
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Affiliation(s)
- Ruowang Li
- Department of Computational Biomedicine, Cedars-Sinai Medical Center
| | - Luke Benz
- Department of Biostatistics, Harvard T.H. Chan School of Public Health
| | - Rui Duan
- Department of Biostatistics, Harvard T.H. Chan School of Public Health
| | - Joshua C Denny
- National Human Genome Research Institute, National Institutes of Health
| | - Hakon Hakonarson
- Division of Human Genetics, Children's Hospital of Philadelphia
- Center for Applied Genomics, Children's Hospital of Philadelphia
- Department of Pediatrics, University of Pennsylvania, Perelman School of Medicine
| | - Jonathan D Mosley
- Department of Medicine, Vanderbilt University Medical Center
- Department of Biomedical Informatics, Vanderbilt University Medical Center
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center
| | - Marylyn D Ritchie
- Department of Genetics and Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center
| | - Yong Chen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania
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6
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Roy DG, Singh L, Chaturvedi HK, Chinnaswamy S. Gender-dependent multiple cross-phenotype association of interferon lambda genetic variants with peripheral blood profiles in healthy individuals. Mol Genet Genomic Med 2024; 12:e2292. [PMID: 37795763 PMCID: PMC10767428 DOI: 10.1002/mgg3.2292] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 09/08/2023] [Accepted: 09/19/2023] [Indexed: 10/06/2023] Open
Abstract
BACKGROUND Type III interferons (IFN), also called as lambda IFNs (IFN-λs), are antiviral and immunomodulatory cytokines that are evolutionarily important in humans. Given their central roles in innate immunity, they could be influencing other aspects of human biology. This study aimed to examine the association of genetic variants that control the expression and/or activity of IFN-λ3 and IFN-λ4 with multiple phenotypes in blood profiles of healthy individuals. METHODS In a cohort of about 550 self-declared healthy individuals, after applying several exclusion criteria to determine their health status, we measured 30 blood parameters, including cellular, biochemical, and metabolic profiles. We genotyped them at rs12979860 and rs28416813 using competitive allele-specific PCR assays and tested their association with the blood profiles under dominant and recessive models for the minor allele. IFN-λ4 variants rs368234815 and rs117648444 were also genotyped or inferred. RESULTS We saw no association in the combined cohort under either of the models for any of the phenotypes. When we stratified the cohort based on gender, we saw a significant association only in males with monocyte (p = 1 × 10-3 ) and SGOT (p = 7 × 10-3 ) levels under the dominant model and with uric acid levels (p = 0.01) under the recessive model. When we tested the IFN-λ4 activity modifying variant within groupings based on absence or presence of one or two copies of IFN-λ4 and on different activity levels of IFN-λ4, we found significant (p < 0.05) association with several phenotypes like monocyte, triglyceride, VLDL, ALP, and uric acid levels, only in males. All the above significant associations did not show any confounding when we tested for the same with up to ten different demographic and lifestyle variables. CONCLUSIONS These results show that lambda interferons can have pleiotropic effects. However, gender seems to be an effect modifier, with males being more sensitive than females to the effect.
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Affiliation(s)
- Debarati Guha Roy
- Infectious Disease GeneticsNational Institute of Biomedical GenomicsKalyaniIndia
- Regional Centre for BiotechnologyFaridabadIndia
| | - Lucky Singh
- ICMR‐National Institute of Medical StatisticsNew DelhiIndia
| | | | - Sreedhar Chinnaswamy
- Infectious Disease GeneticsNational Institute of Biomedical GenomicsKalyaniIndia
- Regional Centre for BiotechnologyFaridabadIndia
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7
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Park HRP, Chilver MR, Quidé Y, Montalto A, Schofield PR, Williams LM, Gatt JM. Heritability of cognitive and emotion processing during functional MRI in a twin sample. Hum Brain Mapp 2024; 45:e26557. [PMID: 38224545 PMCID: PMC10785190 DOI: 10.1002/hbm.26557] [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: 05/24/2023] [Revised: 11/22/2023] [Accepted: 11/25/2023] [Indexed: 01/17/2024] Open
Abstract
Despite compelling evidence that brain structure is heritable, the evidence for the heritability of task-evoked brain function is less robust. Findings from previous studies are inconsistent possibly reflecting small samples and methodological variations. In a large national twin sample, we systematically evaluated heritability of task-evoked brain activity derived from functional magnetic resonance imaging. We used established standardised tasks to engage brain regions involved in cognitive and emotional functions. Heritability was evaluated across a conscious and nonconscious Facial Expressions of Emotion Task (FEET), selective attention Oddball Task, N-back task of working memory maintenance, and a Go-NoGo cognitive control task in a sample of Australian adult twins (N ranged from 136 to 226 participants depending on the task and pairs). Two methods for quantifying associations of heritability and brain activity were utilised; a multivariate independent component analysis (ICA) approach and a univariate brain region-of-interest (ROI) approach. Using ICA, we observed that a significant proportion of task-evoked brain activity was heritable, with estimates ranging from 23% to 26% for activity elicited by nonconscious facial emotion stimuli, 27% to 34% for N-back working memory maintenance and sustained attention, and 32% to 33% for selective attention in the Oddball task. Using the ROI approach, we found that activity of regions specifically implicated in emotion processing and selective attention showed significant heritability for three ROIs, including estimates of 33%-34% for the left and right amygdala in the nonconscious processing of sad faces and 29% in the medial superior prefrontal cortex for the Oddball task. Although both approaches show similar levels of heritability for the Nonconscious Faces and Oddball tasks, ICA results displayed a more extensive network of heritable brain function, including additional regions beyond the ROI analysis. Furthermore, multivariate twin modelling of both ICA networks and ROI activation suggested a mix of common genetic and unique environmental factors that contribute to the associations between networks/regions. Together, the results indicate a complex relationship between genetic factors and environmental interactions that ultimately give rise to neural activation underlying cognition and emotion.
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Affiliation(s)
- Haeme R. P. Park
- Neuroscience Research AustraliaSydneyNew South WalesAustralia
- School of PsychologyUniversity of New South WalesSydneyNew South WalesAustralia
| | - Miranda R. Chilver
- Neuroscience Research AustraliaSydneyNew South WalesAustralia
- School of PsychologyUniversity of New South WalesSydneyNew South WalesAustralia
| | - Yann Quidé
- Neuroscience Research AustraliaSydneyNew South WalesAustralia
- School of PsychologyUniversity of New South WalesSydneyNew South WalesAustralia
| | - Arthur Montalto
- Neuroscience Research AustraliaSydneyNew South WalesAustralia
- School of PsychologyUniversity of New South WalesSydneyNew South WalesAustralia
| | - Peter R. Schofield
- Neuroscience Research AustraliaSydneyNew South WalesAustralia
- School of Biomedical SciencesUniversity of New South WalesSydneyNew South WalesAustralia
| | - Leanne M. Williams
- Psychiatry and Behavioral Sciences, Stanford School of MedicineStanford UniversityCaliforniaUSA
| | - Justine M. Gatt
- Neuroscience Research AustraliaSydneyNew South WalesAustralia
- School of PsychologyUniversity of New South WalesSydneyNew South WalesAustralia
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Suzuki Y, Ménager H, Brancotte B, Vernet R, Nerin C, Boetto C, Auvergne A, Linhard C, Torchet R, Lechat P, Troubat L, Cho MH, Bouzigon E, Aschard H, Julienne H. Trait selection strategy in multi-trait GWAS: Boosting SNPs discoverability. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.27.564319. [PMID: 37961722 PMCID: PMC10634875 DOI: 10.1101/2023.10.27.564319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Since the first Genome-Wide Association Studies (GWAS), thousands of variant-trait associations have been discovered. However, the sample size required to detect additional variants using standard univariate association screening is increasingly prohibitive. Multi-trait GWAS offers a relevant alternative: it can improve statistical power and lead to new insights about gene function and the joint genetic architecture of human phenotypes. Although many methodological hurdles of multi-trait testing have been discussed, the strategy to select trait, among overwhelming possibilities, has been overlooked. In this study, we conducted extensive multi-trait tests using JASS (Joint Analysis of Summary Statistics) and assessed which genetic features of the analysed sets were associated with an increased detection of variants as compared to univariate screening. Our analyses identified multiple factors associated with the gain in the association detection in multi-trait tests. Together, these factors of the analysed sets are predictive of the gain of the multi-trait test (Pearson's ρ equal to 0.43 between the observed and predicted gain, P < 1.6 × 10-60). Applying an alternative multi-trait approach (MTAG, multi-trait analysis of GWAS), we found that in most scenarios but particularly those with larger numbers of traits, JASS outperformed MTAG. Finally, we benchmark several strategies to select set of traits including the prevalent strategy of selecting clinically similar traits, which systematically underperformed selecting clinically heterogenous traits or selecting sets that issued from our data-driven models. This work provides a unique picture of the determinant of multi-trait GWAS statistical power and outline practical strategies for multi-trait testing.
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Affiliation(s)
- Yuka Suzuki
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France
| | - Hervé Ménager
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, F-75015 Paris, France
| | - Bryan Brancotte
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, F-75015 Paris, France
| | - Raphaël Vernet
- Université Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-1124, Group of Genomic Epidemiology of Multifactorial Diseases, Paris, France
| | - Cyril Nerin
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France
| | - Christophe Boetto
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France
| | - Antoine Auvergne
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France
| | - Christophe Linhard
- Université Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-1124, Group of Genomic Epidemiology of Multifactorial Diseases, Paris, France
| | - Rachel Torchet
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, F-75015 Paris, France
| | - Pierre Lechat
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, F-75015 Paris, France
| | - Lucie Troubat
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France
| | - Michael H. Cho
- Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, 181 Longwood Ave, Boston, MA, 02115, USA
- Division of Pulmonary and Critical Care Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Emmanuelle Bouzigon
- Université Paris Cité, Institut National de la Santé et de la Recherche Médicale (INSERM), UMR-1124, Group of Genomic Epidemiology of Multifactorial Diseases, Paris, France
| | - Hugues Aschard
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France
| | - Hanna Julienne
- Institut Pasteur, Université Paris Cité, Department of Computational Biology, Paris, 75015 France
- Institut Pasteur, Université Paris Cité, Bioinformatics of Biostatistics Hub, F-75015 Paris, France
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9
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Ting BW, Wright FA, Zhou YH. Simultaneous modeling of multivariate heterogeneous responses and heteroskedasticity via a two-stage composite likelihood. Biom J 2023; 65:e2200029. [PMID: 37212427 PMCID: PMC10524370 DOI: 10.1002/bimj.202200029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 02/08/2023] [Accepted: 03/13/2023] [Indexed: 05/23/2023]
Abstract
Multivariate heterogeneous responses and heteroskedasticity have attracted increasing attention in recent years. In genome-wide association studies, effective simultaneous modeling of multiple phenotypes would improve statistical power and interpretability. However, a flexible common modeling system for heterogeneous data types can pose computational difficulties. Here we build upon a previous method for multivariate probit estimation using a two-stage composite likelihood that exhibits favorable computational time while retaining attractive parameter estimation properties. We extend this approach to incorporate multivariate responses of heterogeneous data types (binary and continuous), and possible heteroskedasticity. Although the approach has wide applications, it would be particularly useful for genomics, precision medicine, or individual biomedical prediction. Using a genomics example, we explore statistical power and confirm that the approach performs well for hypothesis testing and coverage percentages under a wide variety of settings. The approach has the potential to better leverage genomics data and provide interpretable inference for pleiotropy, in which a locus is associated with multiple traits.
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Affiliation(s)
- Bryan W. Ting
- Bioinformatics Research Center, North Carolina State University, NC, USA
| | - Fred A. Wright
- Bioinformatics Research Center, North Carolina State University, NC, USA
- Department of Statistics, North Carolina State University, NC, USA
- Department of Biological Sciences, North Carolina State University, NC, USA
| | - Yi-Hui Zhou
- Bioinformatics Research Center, North Carolina State University, NC, USA
- Department of Statistics, North Carolina State University, NC, USA
- Department of Biological Sciences, North Carolina State University, NC, USA
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10
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Deng Q, Gupta A, Jeon H, Nam JH, Yilmaz AS, Chang W, Pietrzak M, Li L, Kim HJ, Chung D. graph-GPA 2.0: improving multi-disease genetic analysis with integration of functional annotation data. Front Genet 2023; 14:1079198. [PMID: 37501720 PMCID: PMC10370274 DOI: 10.3389/fgene.2023.1079198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 06/21/2023] [Indexed: 07/29/2023] Open
Abstract
Genome-wide association studies (GWAS) have successfully identified a large number of genetic variants associated with traits and diseases. However, it still remains challenging to fully understand the functional mechanisms underlying many associated variants. This is especially the case when we are interested in variants shared across multiple phenotypes. To address this challenge, we propose graph-GPA 2.0 (GGPA 2.0), a statistical framework to integrate GWAS datasets for multiple phenotypes and incorporate functional annotations within a unified framework. Our simulation studies showed that incorporating functional annotation data using GGPA 2.0 not only improves the detection of disease-associated variants, but also provides a more accurate estimation of relationships among diseases. Next, we analyzed five autoimmune diseases and five psychiatric disorders with the functional annotations derived from GenoSkyline and GenoSkyline-Plus, along with the prior disease graph generated by biomedical literature mining. For autoimmune diseases, GGPA 2.0 identified enrichment for blood-related epigenetic marks, especially B cells and regulatory T cells, across multiple diseases. Psychiatric disorders were enriched for brain-related epigenetic marks, especially the prefrontal cortex and the inferior temporal lobe for bipolar disorder and schizophrenia, respectively. In addition, the pleiotropy between bipolar disorder and schizophrenia was also detected. Finally, we found that GGPA 2.0 is robust to the use of irrelevant and/or incorrect functional annotations. These results demonstrate that GGPA 2.0 can be a powerful tool to identify genetic variants associated with each phenotype or those shared across multiple phenotypes, while also promoting an understanding of functional mechanisms underlying the associated variants.
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Affiliation(s)
- Qiaolan Deng
- The Interdisciplinary PhD Program in Biostatistics, The Ohio State University, Columbus, OH, United States
| | - Arkobrato Gupta
- The Interdisciplinary PhD Program in Biostatistics, The Ohio State University, Columbus, OH, United States
| | - Hyeongseon Jeon
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, United States
| | - Jin Hyun Nam
- Division of Big Data Science, Korea University Sejong Campus, Sejong, Republic of Korea
| | - Ayse Selen Yilmaz
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States
| | - Won Chang
- Division of Statistics and Data Science, University of Cincinnati, Cincinnati, OH, United States
| | - Maciej Pietrzak
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States
| | - Lang Li
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States
| | - Hang J. Kim
- Division of Statistics and Data Science, University of Cincinnati, Cincinnati, OH, United States
| | - Dongjun Chung
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, United States
- Pelotonia Institute for Immuno-Oncology, The James Comprehensive Cancer Center, The Ohio State University, Columbus, OH, United States
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11
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Peng Q, Wilhelmsen KC, Ehlers CL. Pleiotropic loci for cannabis use disorder severity in multi-ancestry high-risk populations. Mol Cell Neurosci 2023; 125:103852. [PMID: 37061172 PMCID: PMC10247496 DOI: 10.1016/j.mcn.2023.103852] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 04/04/2023] [Accepted: 04/07/2023] [Indexed: 04/17/2023] Open
Abstract
Cannabis use disorder (CUD) is common and has in part a genetic basis. The risk factors underlying its development likely involve multiple genes that are polygenetic and interact with each other and the environment to ultimately lead to the disorder. Co-morbidity and genetic correlations have been identified between CUD and other disorders and traits in select populations primarily of European descent. If two or more traits, such as CUD and another disorder, are affected by the same genetic locus, they are said to be pleiotropic. The present study aimed to identify specific pleiotropic loci for the severity level of CUD in three high-risk population cohorts: American Indians (AI), Mexican Americans (MA), and European Americans (EA). Using a previously developed computational method based on a machine learning technique, we leveraged the entire GWAS catalog and identified 114, 119, and 165 potentially pleiotropic variants for CUD severity in AI, MA, and EA respectively. Ten pleiotropic loci were shared between the cohorts although the exact variants from each cohort differed. While majority of the pleiotropic genes were distinct in each cohort, they converged on numerous enriched biological pathways. The gene ontology terms associated with the pleiotropic genes were predominately related to synaptic functions and neurodevelopment. Notable pathways included Wnt/β-catenin signaling, lipoprotein assembly, response to UV radiation, and components of the complement system. The pleiotropic genes were the most significantly differentially expressed in frontal cortex and coronary artery, up-regulated in adipose tissue, and down-regulated in testis, prostate, and ovary. They were significantly up-regulated in most brain tissues but were down-regulated in the cerebellum and hypothalamus. Our study is the first to attempt a large-scale pleiotropy detection scan for CUD severity. Our findings suggest that the different population cohorts may have distinct genetic factors for CUD, however they share pleiotropic genes from underlying pathways related to Alzheimer's disease, neuroplasticity, immune response, and reproductive endocrine systems.
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Affiliation(s)
- Qian Peng
- Department of Neuroscience, The Scripps Research Institute, La Jolla, CA 92037, USA.
| | - Kirk C Wilhelmsen
- Department of Neurology, West Virginia University, Morgantown, WV 26506, USA
| | - Cindy L Ehlers
- Department of Neuroscience, The Scripps Research Institute, La Jolla, CA 92037, USA
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12
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Nosková A, Mehrotra A, Kadri NK, Lloret-Villas A, Neuenschwander S, Hofer A, Pausch H. Comparison of two multi-trait association testing methods and sequence-based fine mapping of six additive QTL in Swiss Large White pigs. BMC Genomics 2023; 24:192. [PMID: 37038103 PMCID: PMC10084639 DOI: 10.1186/s12864-023-09295-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 04/04/2023] [Indexed: 04/12/2023] Open
Abstract
BACKGROUND Genetic correlations between complex traits suggest that pleiotropic variants contribute to trait variation. Genome-wide association studies (GWAS) aim to uncover the genetic underpinnings of traits. Multivariate association testing and the meta-analysis of summary statistics from single-trait GWAS enable detecting variants associated with multiple phenotypes. In this study, we used array-derived genotypes and phenotypes for 24 reproduction, production, and conformation traits to explore differences between the two methods and used imputed sequence variant genotypes to fine-map six quantitative trait loci (QTL). RESULTS We considered genotypes at 44,733 SNPs for 5,753 pigs from the Swiss Large White breed that had deregressed breeding values for 24 traits. Single-trait association analyses revealed eleven QTL that affected 15 traits. Multi-trait association testing and the meta-analysis of the single-trait GWAS revealed between 3 and 6 QTL, respectively, in three groups of traits. The multi-trait methods revealed three loci that were not detected in the single-trait GWAS. Four QTL that were identified in the single-trait GWAS, remained undetected in the multi-trait analyses. To pinpoint candidate causal variants for the QTL, we imputed the array-derived genotypes to the sequence level using a sequenced reference panel consisting of 421 pigs. This approach provided genotypes at 16 million imputed sequence variants with a mean accuracy of imputation of 0.94. The fine-mapping of six QTL with imputed sequence variant genotypes revealed four previously proposed causal mutations among the top variants. CONCLUSIONS Our findings in a medium-size cohort of pigs suggest that multivariate association testing and the meta-analysis of summary statistics from single-trait GWAS provide very similar results. Although multi-trait association methods provide a useful overview of pleiotropic loci segregating in mapping populations, the investigation of single-trait association studies is still advised, as multi-trait methods may miss QTL that are uncovered in single-trait GWAS.
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Affiliation(s)
- A Nosková
- ETH Zürich, Universitätstrasse 2, 8092, Zürich, Switzerland.
| | - A Mehrotra
- ETH Zürich, Universitätstrasse 2, 8092, Zürich, Switzerland
| | - N K Kadri
- ETH Zürich, Universitätstrasse 2, 8092, Zürich, Switzerland
| | | | | | - A Hofer
- SUISAG, Allmend 10, 6204, Sempach, Switzerland
| | - H Pausch
- ETH Zürich, Universitätstrasse 2, 8092, Zürich, Switzerland
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13
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Costanzo MC, von Grotthuss M, Massung J, Jang D, Caulkins L, Koesterer R, Gilbert C, Welch RP, Kudtarkar P, Hoang Q, Boughton AP, Singh P, Sun Y, Duby M, Moriondo A, Nguyen T, Smadbeck P, Alexander BR, Brandes M, Carmichael M, Dornbos P, Green T, Huellas-Bruskiewicz KC, Ji Y, Kluge A, McMahon AC, Mercader JM, Ruebenacker O, Sengupta S, Spalding D, Taliun D, Smith P, Thomas MK, Akolkar B, Brosnan MJ, Cherkas A, Chu AY, Fauman EB, Fox CS, Kamphaus TN, Miller MR, Nguyen L, Parsa A, Reilly DF, Ruetten H, Wholley D, Zaghloul NA, Abecasis GR, Altshuler D, Keane TM, McCarthy MI, Gaulton KJ, Florez JC, Boehnke M, Burtt NP, Flannick J. The Type 2 Diabetes Knowledge Portal: An open access genetic resource dedicated to type 2 diabetes and related traits. Cell Metab 2023; 35:695-710.e6. [PMID: 36963395 PMCID: PMC10231654 DOI: 10.1016/j.cmet.2023.03.001] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 10/23/2022] [Accepted: 02/28/2023] [Indexed: 03/26/2023]
Abstract
Associations between human genetic variation and clinical phenotypes have become a foundation of biomedical research. Most repositories of these data seek to be disease-agnostic and therefore lack disease-focused views. The Type 2 Diabetes Knowledge Portal (T2DKP) is a public resource of genetic datasets and genomic annotations dedicated to type 2 diabetes (T2D) and related traits. Here, we seek to make the T2DKP more accessible to prospective users and more useful to existing users. First, we evaluate the T2DKP's comprehensiveness by comparing its datasets with those of other repositories. Second, we describe how researchers unfamiliar with human genetic data can begin using and correctly interpreting them via the T2DKP. Third, we describe how existing users can extend their current workflows to use the full suite of tools offered by the T2DKP. We finally discuss the lessons offered by the T2DKP toward the goal of democratizing access to complex disease genetic results.
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Affiliation(s)
- Maria C Costanzo
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Marcin von Grotthuss
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Jeffrey Massung
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Dongkeun Jang
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Lizz Caulkins
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Ryan Koesterer
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Clint Gilbert
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Ryan P Welch
- Department of Biostatistics and The Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Parul Kudtarkar
- Department of Pediatrics, University of California San Diego, La Jolla, CA 92161, USA
| | - Quy Hoang
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Andrew P Boughton
- Department of Biostatistics and The Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Preeti Singh
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Ying Sun
- Department of Pediatrics, University of California San Diego, La Jolla, CA 92161, USA
| | - Marc Duby
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Annie Moriondo
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Trang Nguyen
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Patrick Smadbeck
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Benjamin R Alexander
- Simulation and Modeling Sciences, Pfizer Worldwide Research, Development and Medical, Cambridge, MA 02139, USA
| | - MacKenzie Brandes
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Mary Carmichael
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Peter Dornbos
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA; Department of Pediatrics, Boston Children's Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
| | - Todd Green
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Kenneth C Huellas-Bruskiewicz
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Yue Ji
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Alexandria Kluge
- Genomics Platform, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Aoife C McMahon
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Josep M Mercader
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA; Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Oliver Ruebenacker
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Sebanti Sengupta
- Department of Biostatistics and The Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Dylan Spalding
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Daniel Taliun
- Department of Biostatistics and The Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Philip Smith
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
| | - Melissa K Thomas
- Tailored Therapeutics-Diabetes, Eli Lilly and Company, Lilly Corporate Center DC 0545, Indianapolis, IN 46285, USA
| | - Beena Akolkar
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
| | - M Julia Brosnan
- Internal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, MA 02139, USA
| | - Andriy Cherkas
- Team Early Projects Type 1 Diabetes, Therapeutic Area Diabetes and Cardiovascular Medicine, Research & Development, Sanofi, Industriepark Höchst-H831, Frankfurt am Main 65926, Germany
| | - Audrey Y Chu
- Merck Research Laboratories, Boston, MA 02115, USA
| | - Eric B Fauman
- Integrative Biology, Internal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, MA 02139, USA
| | | | | | - Melissa R Miller
- Internal Medicine Research Unit, Pfizer Worldwide Research, Development and Medical, Cambridge, MA 02139, USA
| | - Lynette Nguyen
- Foundation for the National Institutes of Health, North Bethesda, MD 20852, USA
| | - Afshin Parsa
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
| | | | - Hartmut Ruetten
- CardioMetabolism & Respiratory Medicine, Boehringer Ingelheim International GmbH, 55216 Ingelheim/Rhein, Germany
| | - David Wholley
- Foundation for the National Institutes of Health, North Bethesda, MD 20852, USA
| | - Norann A Zaghloul
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD 20892, USA
| | - Gonçalo R Abecasis
- Department of Biostatistics and The Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA; Regeneron Pharmaceuticals, Tarrytown, NY 10591, USA
| | - David Altshuler
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA
| | - Thomas M Keane
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SA, UK
| | - Mark I McCarthy
- Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 9DU, UK; Oxford Centre for Diabetes Endocrinology & Metabolism, University of Oxford, Oxford OX3 7BN, UK
| | - Kyle J Gaulton
- Department of Pediatrics, University of California San Diego, La Jolla, CA 92161, USA
| | - Jose C Florez
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA; Diabetes Unit and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Michael Boehnke
- Department of Biostatistics and The Center for Statistical Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Noël P Burtt
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA.
| | - Jason Flannick
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA 02132, USA; Department of Pediatrics, Boston Children's Hospital, Boston, MA 02115, USA; Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA.
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14
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Han P, Wang C, Zhang W, Wu Y, Wang D, Zhao S, Zhu M. Pleiotropic architectures of porcine immune and growth trait pairs revealed by a self-product-based transcriptome method. Anim Genet 2023; 54:123-131. [PMID: 36478569 DOI: 10.1111/age.13282] [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: 07/06/2022] [Revised: 09/18/2022] [Accepted: 11/24/2022] [Indexed: 12/12/2022]
Abstract
Pleiotropy is an important biological phenomenon with complicated genetic architectures for multiple traits. To date, pleiotropy has been mainly identified by multi-trait genome-wide association studies, but this method has its disadvantages, and new developments for pleiotropy detection methods are needed. Here we define a novel metric, self-product, to measure individual-level co-variation of two traits, and develop a novel self-product-based transcriptome method to detect pleiotropic genes (PGs). Our method was tested using four immune-growth trait pairs and four immune-immune trait pairs in pigs. Comparative transcriptome analyses identified hundreds of candidate PGs related to eight trait pairs from two tails of self-product distribution. Gene Ontology enrichment analysis indicated that most of identified PGs were involved in immune- or growth-related biological processes. We established PG interaction networks to exhibit core genes shared by eight trait pairs, of which CCL5 and IL-10 genes were the hub genes. Genetic association analyses showed that SmaI-polymorphisms of CCL5 and IL-10 genes had significant associations with phenotypic co-variations of multiple trait pairs, indicating that the variants in pleiotropic genes were also pleiotropic variants. Taken together, the validity of our proposed method was preliminarily verified, and our findings provide new insights into the genetic basis of pleiotropic architectures of immune and growth trait pairs in pigs.
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Affiliation(s)
- Pingping Han
- Key Lab of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan, China
| | - Chao Wang
- Key Lab of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan, China
| | - Wei Zhang
- Key Lab of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan, China
| | - Yalan Wu
- Key Lab of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan, China
| | - Daoyuan Wang
- Key Lab of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan, China
| | - Shuhong Zhao
- Key Lab of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan, China.,The Cooperative Innovation Center for Sustainable Pig Production, Huazhong Agricultural University, Wuhan, China
| | - Mengjin Zhu
- Key Lab of Agricultural Animal Genetics, Breeding, and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan, China.,The Cooperative Innovation Center for Sustainable Pig Production, Huazhong Agricultural University, Wuhan, China
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15
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Genetic correlation and gene-based pleiotropy analysis for four major neurodegenerative diseases with summary statistics. Neurobiol Aging 2023; 124:117-128. [PMID: 36740554 DOI: 10.1016/j.neurobiolaging.2022.12.012] [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: 11/10/2021] [Revised: 03/25/2022] [Accepted: 12/27/2022] [Indexed: 01/02/2023]
Abstract
Recent genome-wide association studies suggested shared genetic components between neurodegenerative diseases. However, pleiotropic association patterns among them remain poorly understood. We here analyzed 4 major neurodegenerative diseases including Alzheimer's disease (AD), Parkinson's disease (PD), frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS), and found suggestively positive genetic correlation. We next implemented a gene-centric pleiotropy analysis with a powerful method called PLACO and detected 280 pleiotropic associations (226 unique genes) with these diseases. Functional analyses demonstrated that these genes were enriched in the pancreas, liver, heart, blood, brain, and muscle tissues; and that 42 pleiotropic genes exhibited drug-gene interactions with 341 drugs. Using Mendelian randomization, we discovered that AD and PD can increase the risk of developing ALS, and that AD and ALS can also increase the risk of developing FTD, respectively. Overall, this study provides in-depth insights into shared genetic components and causal relationship among the 4 major neurodegenerative diseases, indicating genetic overlap and causality commonly drive their co-occurrence. It also has important implications on the etiology understanding, drug development and therapeutic targets for neurodegenerative diseases.
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16
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Sajal IH, Biswas S. Bivariate quantitative Bayesian LASSO for detecting association of rare haplotypes with two correlated continuous phenotypes. Front Genet 2023; 14:1104727. [PMID: 36968609 PMCID: PMC10033866 DOI: 10.3389/fgene.2023.1104727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 02/21/2023] [Indexed: 03/12/2023] Open
Abstract
In genetic association studies, the multivariate analysis of correlated phenotypes offers statistical and biological advantages compared to analyzing one phenotype at a time. The joint analysis utilizes additional information contained in the correlation and avoids multiple testing. It also provides an opportunity to investigate and understand shared genetic mechanisms of multiple phenotypes. Bivariate logistic Bayesian LASSO (LBL) was proposed earlier to detect rare haplotypes associated with two binary phenotypes or one binary and one continuous phenotype jointly. There is currently no haplotype association test available that can handle multiple continuous phenotypes. In this study, by employing the framework of bivariate LBL, we propose bivariate quantitative Bayesian LASSO (QBL) to detect rare haplotypes associated with two continuous phenotypes. Bivariate QBL removes unassociated haplotypes by regularizing the regression coefficients and utilizing a latent variable to model correlation between two phenotypes. We carry out extensive simulations to investigate the performance of bivariate QBL and compare it with that of a standard (univariate) haplotype association test, Haplo.score (applied twice to two phenotypes individually). Bivariate QBL performs better than Haplo.score in all simulations with varying degrees of power gain. We analyze Genetic Analysis Workshop 19 exome sequencing data on systolic and diastolic blood pressures and detect several rare haplotypes associated with the two phenotypes.
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17
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Barrio-Hernandez I, Schwartzentruber J, Shrivastava A, Del-Toro N, Gonzalez A, Zhang Q, Mountjoy E, Suveges D, Ochoa D, Ghoussaini M, Bradley G, Hermjakob H, Orchard S, Dunham I, Anderson CA, Porras P, Beltrao P. Network expansion of genetic associations defines a pleiotropy map of human cell biology. Nat Genet 2023; 55:389-398. [PMID: 36823319 PMCID: PMC10011132 DOI: 10.1038/s41588-023-01327-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 01/30/2023] [Indexed: 02/25/2023]
Abstract
Interacting proteins tend to have similar functions, influencing the same organismal traits. Interaction networks can be used to expand the list of candidate trait-associated genes from genome-wide association studies. Here, we performed network-based expansion of trait-associated genes for 1,002 human traits showing that this recovers known disease genes or drug targets. The similarity of network expansion scores identifies groups of traits likely to share an underlying genetic and biological process. We identified 73 pleiotropic gene modules linked to multiple traits, enriched in genes involved in processes such as protein ubiquitination and RNA processing. In contrast to gene deletion studies, pleiotropy as defined here captures specifically multicellular-related processes. We show examples of modules linked to human diseases enriched in genes with known pathogenic variants that can be used to map targets of approved drugs for repurposing. Finally, we illustrate the use of network expansion scores to study genes at inflammatory bowel disease genome-wide association study loci, and implicate inflammatory bowel disease-relevant genes with strong functional and genetic support.
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Affiliation(s)
- Inigo Barrio-Hernandez
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
- Open Targets, Cambridge, UK
| | - Jeremy Schwartzentruber
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
- Open Targets, Cambridge, UK
- Wellcome Sanger Institute, Cambridge, UK
| | - Anjali Shrivastava
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
- Open Targets, Cambridge, UK
| | - Noemi Del-Toro
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
- Open Targets, Cambridge, UK
| | - Asier Gonzalez
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
- Open Targets, Cambridge, UK
| | - Qian Zhang
- Wellcome Sanger Institute, Cambridge, UK
| | - Edward Mountjoy
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
- Open Targets, Cambridge, UK
| | - Daniel Suveges
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
- Open Targets, Cambridge, UK
| | - David Ochoa
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
- Open Targets, Cambridge, UK
| | - Maya Ghoussaini
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
- Open Targets, Cambridge, UK
| | - Glyn Bradley
- Computational Biology, Genomic Sciences, GSK, Stevenage, UK
| | - Henning Hermjakob
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
- Open Targets, Cambridge, UK
| | - Sandra Orchard
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
- Open Targets, Cambridge, UK
| | - Ian Dunham
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
- Open Targets, Cambridge, UK
- Wellcome Sanger Institute, Cambridge, UK
| | - Carl A Anderson
- Open Targets, Cambridge, UK
- Wellcome Sanger Institute, Cambridge, UK
| | - Pablo Porras
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK
- Open Targets, Cambridge, UK
| | - Pedro Beltrao
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge, UK.
- Open Targets, Cambridge, UK.
- Institute of Molecular Systems Biology, ETH Zürich, Zürich, Switzerland.
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18
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Integrated Single-Trait and Multi-Trait GWASs Reveal the Genetic Architecture of Internal Organ Weight in Pigs. Animals (Basel) 2023; 13:ani13050808. [PMID: 36899665 PMCID: PMC10000129 DOI: 10.3390/ani13050808] [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: 01/07/2023] [Revised: 02/18/2023] [Accepted: 02/20/2023] [Indexed: 03/12/2023] Open
Abstract
Internal organ weight is an essential indicator of growth status as it reflects the level of growth and development in pigs. However, the associated genetic architecture has not been well explored because phenotypes are difficult to obtain. Herein, we performed single-trait and multi-trait genome-wide association studies (GWASs) to map the genetic markers and genes associated with six internal organ weight traits (including heart weight, liver weight, spleen weight, lung weight, kidney weight, and stomach weight) in 1518 three-way crossbred commercial pigs. In summation, single-trait GWASs identified a total of 24 significant single- nucleotide polymorphisms (SNPs) and 5 promising candidate genes, namely, TPK1, POU6F2, PBX3, UNC5C, and BMPR1B, as being associated with the six internal organ weight traits analyzed. Multi-trait GWAS identified four SNPs with polymorphisms localized on the APK1, ANO6, and UNC5C genes and improved the statistical efficacy of single-trait GWASs. Furthermore, our study was the first to use GWASs to identify SNPs associated with stomach weight in pigs. In conclusion, our exploration of the genetic architecture of internal organ weights helps us better understand growth traits, and the key SNPs identified could play a potential role in animal breeding programs.
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Gong W, Guo P, Li Y, Liu L, Yan R, Liu S, Wang S, Xue F, Zhou X, Yuan Z. Role of the Gut-Brain Axis in the Shared Genetic Etiology Between Gastrointestinal Tract Diseases and Psychiatric Disorders: A Genome-Wide Pleiotropic Analysis. JAMA Psychiatry 2023; 80:360-370. [PMID: 36753304 PMCID: PMC9909581 DOI: 10.1001/jamapsychiatry.2022.4974] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
Abstract
IMPORTANCE Comorbidities and genetic correlations between gastrointestinal tract diseases and psychiatric disorders have been widely reported, with the gut-brain axis (GBA) hypothesized as a potential biological basis. However, the degree to which the shared genetic determinants are involved in these associations underlying the GBA is unclear. OBJECTIVE To investigate the shared genetic etiology between gastrointestinal tract diseases and psychiatric disorders and to identify shared genomic loci, genes, and pathways. DESIGN, SETTING, AND PARTICIPANTS This genome-wide pleiotropic association study using genome-wide association summary statistics from publicly available data sources was performed with various statistical genetic approaches to sequentially investigate the pleiotropic associations from genome-wide single-nucleotide variation (SNV; formerly single-nucleotide polymorphism [SNP]), and gene levels and biological pathways to disentangle the underlying shared genetic etiology between 4 gastrointestinal tract diseases (inflammatory bowel disease, irritable bowel syndrome, peptic ulcer disease, and gastroesophageal reflux disease) and 6 psychiatric disorders (schizophrenia, bipolar disorder, major depressive disorder, attention-deficit/hyperactivity disorder, posttraumatic stress disorder, and anorexia nervosa). Data were collected from March 10, 2021, to August 25, 2021, and analysis was performed from January 8 through May 30, 2022. MAIN OUTCOMES AND MEASURES The primary outcomes consisted of a list of genetic loci, genes, and pathways shared between gastrointestinal tract diseases and psychiatric disorders. RESULTS Extensive genetic correlations and genetic overlaps were found among 22 of 24 trait pairs. Pleiotropic analysis under a composite null hypothesis identified 2910 significant potential pleiotropic SNVs in 19 trait pairs, with 83 pleiotropic loci and 24 colocalized loci detected. Gene-based analysis found 158 unique candidate pleiotropic genes, which were highly enriched in certain GBA-related phenotypes and tissues, whereas pathway enrichment analysis further highlighted biological pathways primarily involving cell adhesion, synaptic structure and function, and immune cell differentiation. Several identified pleiotropic loci also shared causal variants with gut microbiomes. Mendelian randomization analysis further illustrated vertical pleiotropy across 8 pairwise traits. Notably, many pleiotropic loci were identified for multiple pairwise traits, such as 1q32.1 (INAVA), 19q13.33 (FUT2), 11q23.2 (NCAM1), and 1p32.3 (LRP8). CONCLUSIONS AND RELEVANCE These findings suggest that the pleiotropic genetic determinants between gastrointestinal tract diseases and psychiatric disorders are extensively distributed across the genome. These findings not only support the shared genetic basis underlying the GBA but also have important implications for intervention and treatment targets of these diseases simultaneously.
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Affiliation(s)
- Weiming Gong
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
| | - Ping Guo
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
| | - Yuanming Li
- School of Medicine, Cheeloo College of Medicine, Shandong University Jinan, China
| | - Lu Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
| | - Ran Yan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
| | - Shuai Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
| | - Shukang Wang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
| | - Fuzhong Xue
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
| | - Xiang Zhou
- Department of Biostatistics, University of Michigan, Ann Arbor,Center for Statistical Genetics, University of Michigan, Ann Arbor
| | - Zhongshang Yuan
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China,Institute for Medical Dataology, Shandong University, Jinan, China
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20
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Zhang JZ, Heinsberg LW, Krishnan M, Hawley NL, Major TJ, Carlson JC, Hindmarsh JH, Watson H, Qasim M, Stamp LK, Dalbeth N, Murphy R, Sun G, Cheng H, Naseri T, Reupena MS, Kershaw EE, Deka R, McGarvey ST, Minster RL, Merriman TR, Weeks DE. Multivariate analysis of a missense variant in CREBRF reveals associations with measures of adiposity in people of Polynesian ancestries. Genet Epidemiol 2023; 47:105-118. [PMID: 36352773 PMCID: PMC9892232 DOI: 10.1002/gepi.22508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 09/02/2022] [Accepted: 10/05/2022] [Indexed: 11/11/2022]
Abstract
The minor allele of rs373863828, a missense variant in CREB3 Regulatory Factor, is associated with several cardiometabolic phenotypes in Polynesian peoples. To better understand the variant, we tested the association of rs373863828 with a panel of correlated phenotypes (body mass index [BMI], weight, height, HDL cholesterol, triglycerides, and total cholesterol) using multivariate Bayesian association and network analyses in a Samoa cohort (n = 1632), Aotearoa New Zealand cohort (n = 1419), and combined cohort (n = 2976). An expanded set of phenotypes (adding estimated fat and fat-free mass, abdominal circumference, hip circumference, and abdominal-hip ratio) was tested in the Samoa cohort (n = 1496). In the Samoa cohort, we observed significant associations (log10 Bayes Factor [BF] ≥ 5.0) between rs373863828 and the overall phenotype panel (8.81), weight (8.30), and BMI (6.42). In the Aotearoa New Zealand cohort, we observed suggestive associations (1.5 < log10 BF < 5) between rs373863828 and the overall phenotype panel (4.60), weight (3.27), and BMI (1.80). In the combined cohort, we observed concordant signals with larger log10 BFs. In the Samoa-specific expanded phenotype analyses, we also observed significant associations between rs373863828 and fat mass (5.65), abdominal circumference (5.34), and hip circumference (5.09). Bayesian networks provided evidence for a direct association of rs373863828 with weight and indirect associations with height and BMI.
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Affiliation(s)
- Jerry Z. Zhang
- Department of Biostatistics, School of Public Health, University of Pittsburgh, Pittsburgh, PA
| | - Lacey W. Heinsberg
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA
| | - Mohanraj Krishnan
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA
| | - Nicola L. Hawley
- Department of Chronic Disease Epidemiology, Yale University School of Public Health, New Haven, CT
| | - Tanya J. Major
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
| | - Jenna C. Carlson
- Department of Biostatistics, School of Public Health, University of Pittsburgh, Pittsburgh, PA
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA
| | | | - Huti Watson
- Ngāti Porou Hauora Charitable Trust, Te Puia Springs, Tairāwhiti, New Zealand
| | - Muhammad Qasim
- Ngāti Porou Hauora Charitable Trust, Te Puia Springs, Tairāwhiti, New Zealand
| | - Lisa K. Stamp
- Department of Medicine, University of Otago, Christchurch, New Zealand
| | - Nicola Dalbeth
- Department of Medicine, University of Auckland, Auckland, New Zealand
| | - Rinki Murphy
- Department of Medicine, University of Auckland, Auckland, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, Auckland, New Zealand
| | - Guangyun Sun
- Department of Environmental and Public Health Sciences, College of Medicine, University of Cincinnati, Cincinnati, OH
| | - Hong Cheng
- Department of Environmental and Public Health Sciences, College of Medicine, University of Cincinnati, Cincinnati, OH
| | - Take Naseri
- Ministry of Health, Government of Samoa, Apia, Samoa
| | | | - Erin E. Kershaw
- Division of Endocrinology and Metabolism, Department of Medicine, University of Pittsburgh, Pittsburgh, PA
| | - Ranjan Deka
- Department of Environmental and Public Health Sciences, College of Medicine, University of Cincinnati, Cincinnati, OH
| | - Stephen T. McGarvey
- International Health Institute, Department of Epidemiology, School of Public Health, Brown University, Providence, RI
- Department of Anthropology, Brown University, Providence, RI
| | - Ryan L. Minster
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA
| | - Tony R. Merriman
- Department of Biochemistry, University of Otago, Dunedin, New Zealand
- Maurice Wilkins Centre for Molecular Biodiscovery, Auckland, New Zealand
- Division of Clinical Immunology and Rheumatology, University of Alabama Birmingham, Birmingham, AL
| | - Daniel E. Weeks
- Department of Biostatistics, School of Public Health, University of Pittsburgh, Pittsburgh, PA
- Department of Human Genetics, School of Public Health, University of Pittsburgh, Pittsburgh, PA
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21
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Defo J, Awany D, Ramesar R. From SNP to pathway-based GWAS meta-analysis: do current meta-analysis approaches resolve power and replication in genetic association studies? Brief Bioinform 2023; 24:6972298. [PMID: 36611240 DOI: 10.1093/bib/bbac600] [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: 09/08/2022] [Revised: 11/30/2022] [Accepted: 12/06/2022] [Indexed: 01/09/2023] Open
Abstract
Genome-wide association studies (GWAS) have benefited greatly from enhanced high-throughput technology in recent decades. GWAS meta-analysis has become increasingly popular to highlight the genetic architecture of complex traits, informing about the replicability and variability of effect estimations across human ancestries. A wealth of GWAS meta-analysis methodologies have been developed depending on the input data and the outcome information of interest. We present a survey of current approaches from SNP to pathway-based meta-analysis by acknowledging the range of resources and methodologies in the field, and we provide a comprehensive review of different categories of Genome-Wide Meta-analysis methods employed. These methods highlight different levels at which GWAS meta-analysis may be done, including Single Nucleotide Polymorphisms, Genes and Pathways, for which we describe their framework outline. We also discuss the strengths and pitfalls of each approach and make suggestions regarding each of them.
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Affiliation(s)
- Joel Defo
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, 7925, Observatory, South Africa.,South African Medical Research Council Genomic and Personalized Medicine Research Unit
| | - Denis Awany
- South African Tuberculosis Vaccine Initiative (SATVI), University of Cape Town, 7925, South Africa
| | - Raj Ramesar
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, 7925, Observatory, South Africa.,South African Medical Research Council Genomic and Personalized Medicine Research Unit
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22
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Valette T, Leitwein M, Lascaux JM, Desmarais E, Berrebi P, Guinand B. Redundancy analysis, genome-wide association studies and the pigmentation of brown trout (Salmo trutta L.). JOURNAL OF FISH BIOLOGY 2023; 102:96-118. [PMID: 36218076 DOI: 10.1111/jfb.15243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Accepted: 10/04/2022] [Indexed: 06/16/2023]
Abstract
The association of molecular variants with phenotypic variation is a main issue in biology, often tackled with genome-wide association studies (GWAS). GWAS are challenging, with increasing, but still limited, use in evolutionary biology. We used redundancy analysis (RDA) as a complimentary ordination approach to single- and multitrait GWAS to explore the molecular basis of pigmentation variation in brown trout (Salmo trutta) belonging to wild populations impacted by hatchery fish. Based on 75,684 single nucleotide polymorphic (SNP) markers, RDA, single- and multitrait GWAS allowed the extraction of 337 independent colour patterning loci (CPLs) associated with trout pigmentation traits, such as the number of red and black spots on flanks. Collectively, these CPLs (i) mapped onto 35 out of 40 brown trout linkage groups indicating a polygenic genomic architecture of pigmentation, (ii) were found to be associated with 218 candidate genes, including 197 genes formerly mentioned in the literature associated to skin pigmentation, skin patterning, differentiation or structure notably in a close relative, the rainbow trout (Onchorhynchus mykiss), and (iii) related to functions relevant to pigmentation variation (e.g., calcium- and ion-binding, cell adhesion). Annotated CPLs include genes with well-known pigmentation effects (e.g., PMEL, SLC45A2, SOX10), but also markers associated with genes formerly found expressed in rainbow or brown trout skins. RDA was also shown to be useful to investigate management issues, especially the dynamics of trout pigmentation submitted to several generations of hatchery introgression.
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23
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Romero C, Werme J, Jansen PR, Gelernter J, Stein MB, Levey D, Polimanti R, de Leeuw C, Posthuma D, Nagel M, van der Sluis S. Exploring the genetic overlap between twelve psychiatric disorders. Nat Genet 2022; 54:1795-1802. [PMID: 36471075 DOI: 10.1038/s41588-022-01245-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Accepted: 10/25/2022] [Indexed: 12/12/2022]
Abstract
The widespread comorbidity among psychiatric disorders demonstrated in epidemiological studies1-5 is mirrored by non-zero, positive genetic correlations from large-scale genetic studies6-10. To identify shared biological processes underpinning this observed phenotypic and genetic covariance and enhance molecular characterization of general psychiatric disorder liability11-13, we used several strategies aimed at uncovering pleiotropic, that is, cross-trait-associated, single-nucleotide polymorphisms (SNPs), genes and biological pathways. We conducted cross-trait meta-analysis on 12 psychiatric disorders to identify pleiotropic SNPs. The meta-analytic signal was driven by schizophrenia, hampering interpretation and joint biological characterization of the cross-trait meta-analytic signal. Subsequent pairwise comparisons of psychiatric disorders identified substantial pleiotropic overlap, but mainly among pairs of psychiatric disorders, and mainly at less stringent P-value thresholds. Only annotations related to evolutionarily conserved genomic regions were significant for multiple (9 out of 12) psychiatric disorders. Overall, identification of shared biological mechanisms remains challenging due to variation in power and genetic architecture between psychiatric disorders.
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Affiliation(s)
- Cato Romero
- Department of Child and Adolescent Psychology and Psychiatry, section Complex Trait Genetics, Amsterdam Neuroscience, VU University Medical Centre, Amsterdam, The Netherlands
| | - Josefin Werme
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Philip R Jansen
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Human Genetics, section Clinical Genetic, Amsterdam University Medical Centers, Amsterdam, the Netherlands
| | - Joel Gelernter
- VA Connecticut Healthcare System, Psychiatry Service, West Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Murray B Stein
- VA San Diego Healthcare System, Psychiatry Service, San Diego, CA, USA
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
- Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, CA, USA
| | - Daniel Levey
- VA Connecticut Healthcare System, Psychiatry Service, West Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Renato Polimanti
- VA Connecticut Healthcare System, Psychiatry Service, West Haven, CT, USA
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Christiaan de Leeuw
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Danielle Posthuma
- Department of Child and Adolescent Psychology and Psychiatry, section Complex Trait Genetics, Amsterdam Neuroscience, VU University Medical Centre, Amsterdam, The Netherlands
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Mats Nagel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Sophie van der Sluis
- Department of Child and Adolescent Psychology and Psychiatry, section Complex Trait Genetics, Amsterdam Neuroscience, VU University Medical Centre, Amsterdam, The Netherlands.
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24
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Akohoue F, Koch S, Plieske J, Miedaner T. Separation of the effects of two reduced height (Rht) genes and genomic background to select for less Fusarium head blight of short-strawed winter wheat (Triticum aestivum L.) varieties. TAG. THEORETICAL AND APPLIED GENETICS. THEORETISCHE UND ANGEWANDTE GENETIK 2022; 135:4303-4326. [PMID: 36152062 PMCID: PMC9734223 DOI: 10.1007/s00122-022-04219-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Accepted: 09/08/2022] [Indexed: 06/16/2023]
Abstract
FHB resistance shared pleiotropic loci with plant height and anther retention. Genomic prediction allows to select for genomic background reducing FHB susceptibility in the presence of the dwarfing allele Rht-D1b. With the high interest for semi-dwarf cultivars in wheat, finding locally adapted resistance sources against Fusarium head blight (FHB) and FHB-neutral reduced height (Rht) genes is of utmost relevance. In this study, 401 genotypes of European origin without/with dwarfing alleles of Rht-D1 and/or Rht24 were analysed across five environments on FHB severity and the morphological traits such as plant height (PH), anther retention (AR), number of spikelets per ear, ear length and ear density. Data were analysed by combined correlation and path analyses, association mapping and coupling single- and multi-trait genome-wide association studies (ST-GWAS and MT-GWAS, respectively) and genomic prediction (GP). All FHB data were corrected for flowering date or heading stage. High genotypic correlation (rg = 0.74) and direct path effect (0.57) were detected between FHB severity and anther retention (AR). Moderate correlation (rg = - 0.55) was found between FHB severity and plant height (PH) with a high indirect path via AR (- 0.31). Indirect selection for FHB resistance should concentrate on AR and PH. ST-GWAS identified 25 quantitative trait loci (QTL) for FHB severity, PH and AR, while MT-GWAS detected six QTL across chromosomes 2A, 4D, 5A, 6B and 7B conveying pleiotropic effects on the traits. Rht-D1b was associated with high AR and FHB susceptibility. Our study identified a promising positively acting pleiotropic QTL on chromosome 7B which can be utilized to improve FHB resistance while reducing PH and AR. Rht-D1b genotypes having a high resistance genomic background exhibited lower FHB severity and AR. The use of GP for estimating the genomic background was more effective than selection of GWAS-detected markers. We demonstrated that GP has a great potential and should be exploited by selecting for semi-dwarf winter wheat genotypes with higher FHB resistance due to their genomic background.
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Affiliation(s)
- Félicien Akohoue
- State Plant Breeding Institute, University of Hohenheim, Stuttgart, Germany
| | - Silvia Koch
- State Plant Breeding Institute, University of Hohenheim, Stuttgart, Germany
| | - Jörg Plieske
- SGS INSTITUT FRESENIUS GmbH, TraitGenetics Section, Am Schwabeplan 1b, 06466, Seeland OT Gatersleben, Germany
| | - Thomas Miedaner
- State Plant Breeding Institute, University of Hohenheim, Stuttgart, Germany.
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25
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Alamin M, Sultana MH, Lou X, Jin W, Xu H. Dissecting Complex Traits Using Omics Data: A Review on the Linear Mixed Models and Their Application in GWAS. PLANTS (BASEL, SWITZERLAND) 2022; 11:3277. [PMID: 36501317 PMCID: PMC9739826 DOI: 10.3390/plants11233277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 11/23/2022] [Accepted: 11/25/2022] [Indexed: 06/17/2023]
Abstract
Genome-wide association study (GWAS) is the most popular approach to dissecting complex traits in plants, humans, and animals. Numerous methods and tools have been proposed to discover the causal variants for GWAS data analysis. Among them, linear mixed models (LMMs) are widely used statistical methods for regulating confounding factors, including population structure, resulting in increased computational proficiency and statistical power in GWAS studies. Recently more attention has been paid to pleiotropy, multi-trait, gene-gene interaction, gene-environment interaction, and multi-locus methods with the growing availability of large-scale GWAS data and relevant phenotype samples. In this review, we have demonstrated all possible LMMs-based methods available in the literature for GWAS. We briefly discuss the different LMM methods, software packages, and available open-source applications in GWAS. Then, we include the advantages and weaknesses of the LMMs in GWAS. Finally, we discuss the future perspective and conclusion. The present review paper would be helpful to the researchers for selecting appropriate LMM models and methods quickly for GWAS data analysis and would benefit the scientific society.
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Affiliation(s)
- Md. Alamin
- Institute of Bioinformatics, Zhejiang University, Hangzhou 310058, China
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | | | - Xiangyang Lou
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Wenfei Jin
- Department of Biology, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China
| | - Haiming Xu
- Institute of Bioinformatics, Zhejiang University, Hangzhou 310058, China
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26
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Identification of potentially common loci between childhood obesity and coronary artery disease using pleiotropic approaches. Sci Rep 2022; 12:19513. [PMID: 36376549 PMCID: PMC9663585 DOI: 10.1038/s41598-022-24009-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 11/08/2022] [Indexed: 11/15/2022] Open
Abstract
Childhood obesity remains one of the most important issues in global health, which is implicated in many chronic diseases. Converging evidence suggests that a higher body mass index during childhood (CBMI) is significantly associated with increased coronary artery disease (CAD) susceptibility in adulthood, which may partly arise from the shared genetic determination. Despite genome-wide association studies (GWASs) have successfully identified some loci associated with CBMI and CAD individually, the genetic overlap and common biological mechanism between them remains largely unexplored. Here, relying on the results from the two large-scale GWASs (n = 35,668 for CBMI and n = 547,261 for CAD), linkage disequilibrium score regression (LDSC) was used to estimate the genetic correlation of CBMI and CAD in the first step. Then, we applied different pleiotropy-informed methods including conditional false discovery rate ([Formula: see text]) and genetic analysis incorporating pleiotropy and annotation (GPA) to detect potentially common loci for childhood obesity and CAD. By integrating the genetic information from the existing GWASs summary statistics, we found a significant positive genetic correlation ([Formula: see text] = 0.127, p = 2E-4) and strong pleiotropic enrichment between CBMI and CAD (LRT = 79.352, p = 5.2E-19). Importantly, 28 loci were simultaneously discovered to be associated with CBMI, and 13 of them were identified as potentially pleiotropic loci by [Formula: see text] and GPA. Those corresponding pleiotropic genes were enriched in trait-associated gene ontology (GO) terms "amino sugar catabolic process", "regulation of fat cell differentiation" and "synaptic transmission". Overall, the findings of the pleiotropic loci will help to further elucidate the common molecular mechanisms underlying the association of childhood obesity and CAD, and provide a theoretical direction for early disease prevention and potential therapeutic targets.
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27
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Olasege BS, Porto-Neto LR, Tahir MS, Gouveia GC, Cánovas A, Hayes BJ, Fortes MRS. Correlation scan: identifying genomic regions that affect genetic correlations applied to fertility traits. BMC Genomics 2022; 23:684. [PMID: 36195838 PMCID: PMC9533527 DOI: 10.1186/s12864-022-08898-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 09/19/2022] [Indexed: 11/10/2022] Open
Abstract
Although the genetic correlations between complex traits have been estimated for more than a century, only recently we have started to map and understand the precise localization of the genomic region(s) that underpin these correlations. Reproductive traits are often genetically correlated. Yet, we don't fully understand the complexities, synergism, or trade-offs between male and female fertility. In this study, we used reproductive traits in two cattle populations (Brahman; BB, Tropical Composite; TC) to develop a novel framework termed correlation scan (CS). This framework was used to identify local regions associated with the genetic correlations between male and female fertility traits. Animals were genotyped with bovine high-density single nucleotide polymorphisms (SNPs) chip assay. The data used consisted of ~1000 individual records measured through frequent ovarian scanning for age at first corpus luteum (AGECL) and a laboratory assay for serum levels of insulin growth hormone (IGF1 measured in bulls, IGF1b, or cows, IGF1c). The methodology developed herein used correlations of 500-SNP effects in a 100-SNPs sliding window in each chromosome to identify local genomic regions that either drive or antagonize the genetic correlations between traits. We used Fisher's Z-statistics through a permutation method to confirm which regions of the genome harboured significant correlations. About 30% of the total genomic regions were identified as driving and antagonizing genetic correlations between male and female fertility traits in the two populations. These regions confirmed the polygenic nature of the traits being studied and pointed to genes of interest. For BB, the most important chromosome in terms of local regions is often located on bovine chromosome (BTA) 14. However, the important regions are spread across few different BTA's in TC. Quantitative trait loci (QTLs) and functional enrichment analysis revealed many significant windows co-localized with known QTLs related to milk production and fertility traits, especially puberty. In general, the enriched reproductive QTLs driving the genetic correlations between male and female fertility are the same for both cattle populations, while the antagonizing regions were population specific. Moreover, most of the antagonizing regions were mapped to chromosome X. These results suggest regions of chromosome X for further investigation into the trade-offs between male and female fertility. We compared the CS with two other recently proposed methods that map local genomic correlations. Some genomic regions were significant across methods. Yet, many significant regions identified with the CS were overlooked by other methods.
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Affiliation(s)
- Babatunde S Olasege
- The University of Queensland, School of Chemistry and Molecular Biosciences, Saint Lucia Campus, Brisbane, QLD, 4072, Australia.,CSIRO Agriculture and Food, Saint Lucia, QLD, 4067, Australia
| | | | - Muhammad S Tahir
- The University of Queensland, School of Chemistry and Molecular Biosciences, Saint Lucia Campus, Brisbane, QLD, 4072, Australia.,CSIRO Agriculture and Food, Saint Lucia, QLD, 4067, Australia
| | - Gabriela C Gouveia
- Animal Science Department, Veterinary School, Federal University of Minas Gerais, Belo Horizonte, 31270-901, Brazil
| | - Angela Cánovas
- Department of Animal Biosciences, Centre for Genetic Improvement of Livestock, University of Guelph, 50 Stone Rd E, Guelph, ON, N1G 2W1, Canada
| | - Ben J Hayes
- The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Saint Lucia Campus, Brisbane, QLD, 4072, Australia
| | - Marina R S Fortes
- The University of Queensland, School of Chemistry and Molecular Biosciences, Saint Lucia Campus, Brisbane, QLD, 4072, Australia. .,The University of Queensland, Queensland Alliance for Agriculture and Food Innovation (QAAFI), Saint Lucia Campus, Brisbane, QLD, 4072, Australia.
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28
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Genetic pleiotropy underpinning adiposity and inflammation in self-identified Hispanic/Latino populations. BMC Med Genomics 2022; 15:192. [PMID: 36088317 PMCID: PMC9464371 DOI: 10.1186/s12920-022-01352-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 09/02/2022] [Indexed: 01/05/2023] Open
Abstract
BACKGROUND Concurrent variation in adiposity and inflammation suggests potential shared functional pathways and pleiotropic disease underpinning. Yet, exploration of pleiotropy in the context of adiposity-inflammation has been scarce, and none has included self-identified Hispanic/Latino populations. Given the high level of ancestral diversity in Hispanic American population, genetic studies may reveal variants that are infrequent/monomorphic in more homogeneous populations. METHODS Using multi-trait Adaptive Sum of Powered Score (aSPU) method, we examined individual and shared genetic effects underlying inflammatory (CRP) and adiposity-related traits (Body Mass Index [BMI]), and central adiposity (Waist to Hip Ratio [WHR]) in HLA participating in the Population Architecture Using Genomics and Epidemiology (PAGE) cohort (N = 35,871) with replication of effects in the Cameron County Hispanic Cohort (CCHC) which consists of Mexican American individuals. RESULTS Of the > 16 million SNPs tested, variants representing 7 independent loci were found to illustrate significant association with multiple traits. Two out of 7 variants were replicated at statistically significant level in multi-trait analyses in CCHC. The lead variant on APOE (rs439401) and rs11208712 were found to harbor multi-trait associations with adiposity and inflammation. CONCLUSIONS Results from this study demonstrate the importance of considering pleiotropy for improving our understanding of the etiology of the various metabolic pathways that regulate cardiovascular disease development.
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Large-scale genomic analyses reveal insights into pleiotropy across circulatory system diseases and nervous system disorders. Nat Commun 2022; 13:3428. [PMID: 35701404 PMCID: PMC9198016 DOI: 10.1038/s41467-022-30678-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 05/10/2022] [Indexed: 01/18/2023] Open
Abstract
Clinical and epidemiological studies have shown that circulatory system diseases and nervous system disorders often co-occur in patients. However, genetic susceptibility factors shared between these disease categories remain largely unknown. Here, we characterized pleiotropy across 107 circulatory system and 40 nervous system traits using an ensemble of methods in the eMERGE Network and UK Biobank. Using a formal test of pleiotropy, five genomic loci demonstrated statistically significant evidence of pleiotropy. We observed region-specific patterns of direction of genetic effects for the two disease categories, suggesting potential antagonistic and synergistic pleiotropy. Our findings provide insights into the relationship between circulatory system diseases and nervous system disorders which can provide context for future prevention and treatment strategies.
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30
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Wang J, Wang W, Li H. Sparse block signal detection and identification for shared cross-trait association analysis. Ann Appl Stat 2022. [DOI: 10.1214/21-aoas1523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Jianqiao Wang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania
| | - Wanjie Wang
- Department of Statistics and Applied Probability, National University of Singapore
| | - Hongzhe Li
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania
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31
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Xu Q, Yang C, Pei YF. Editorial: Genetic Pleiotropy in Complex Traits and Diseases. Front Genet 2022; 13:897383. [PMID: 35547251 PMCID: PMC9081568 DOI: 10.3389/fgene.2022.897383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 03/25/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Qian Xu
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, China.,Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
| | - Can Yang
- Department of Mathematics, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Yu-Fang Pei
- Department of Epidemiology and Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, China.,Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Medical College of Soochow University, Suzhou, China
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32
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An integrated framework for local genetic correlation analysis. Nat Genet 2022; 54:274-282. [PMID: 35288712 DOI: 10.1038/s41588-022-01017-y] [Citation(s) in RCA: 87] [Impact Index Per Article: 43.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 01/20/2022] [Indexed: 12/16/2022]
Abstract
Genetic correlation (rg) analysis is used to identify phenotypes that may have a shared genetic basis. Traditionally, rg is studied globally, considering only the average of the shared signal across the genome, although this approach may fail when the rg is confined to particular genomic regions or in opposing directions at different loci. Current tools for local rg analysis are restricted to analysis of two phenotypes. Here we introduce LAVA, an integrated framework for local rg analysis that, in addition to testing the standard bivariate local rgs between two phenotypes, can evaluate local heritabilities and analyze conditional genetic relations between several phenotypes using partial correlation and multiple regression. Applied to 25 behavioral and health phenotypes, we show considerable heterogeneity in the bivariate local rgs across the genome, which is often masked by the global rg patterns, and demonstrate how our conditional approaches can elucidate more complex, multivariate genetic relations.
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33
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Cinelli C, LaPierre N, Hill BL, Sankararaman S, Eskin E. Robust Mendelian randomization in the presence of residual population stratification, batch effects and horizontal pleiotropy. Nat Commun 2022; 13:1093. [PMID: 35232963 DOI: 10.1101/2020.10.21.347773] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 01/14/2022] [Indexed: 05/25/2023] Open
Abstract
Mendelian Randomization (MR) studies are threatened by population stratification, batch effects, and horizontal pleiotropy. Although a variety of methods have been proposed to mitigate those problems, residual biases may still remain, leading to highly statistically significant false positives in large databases. Here we describe a suite of sensitivity analysis tools that enables investigators to quantify the robustness of their findings against such validity threats. Specifically, we propose the routine reporting of sensitivity statistics that reveal the minimal strength of violations necessary to explain away the MR results. We further provide intuitive displays of the robustness of the MR estimate to any degree of violation, and formal bounds on the worst-case bias caused by violations multiple times stronger than observed variables. We demonstrate how these tools can aid researchers in distinguishing robust from fragile findings by examining the effect of body mass index on diastolic blood pressure and Townsend deprivation index.
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Affiliation(s)
- Carlos Cinelli
- Department of Statistics, University of Washington, Seattle, WA, USA.
| | - Nathan LaPierre
- Department of Computer Science, University of California, Los Angeles, CA, USA
| | - Brian L Hill
- Department of Computer Science, University of California, Los Angeles, CA, USA
| | - Sriram Sankararaman
- Department of Computer Science, University of California, Los Angeles, CA, USA
- Department of Human Genetics, University of California, Los Angeles, CA, USA
- Department of Computational Medicine, University of California, Los Angeles, CA, USA
| | - Eleazar Eskin
- Department of Computer Science, University of California, Los Angeles, CA, USA
- Department of Human Genetics, University of California, Los Angeles, CA, USA
- Department of Computational Medicine, University of California, Los Angeles, CA, USA
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34
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Robust Mendelian randomization in the presence of residual population stratification, batch effects and horizontal pleiotropy. Nat Commun 2022; 13:1093. [PMID: 35232963 PMCID: PMC8888767 DOI: 10.1038/s41467-022-28553-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 01/14/2022] [Indexed: 01/07/2023] Open
Abstract
Mendelian Randomization (MR) studies are threatened by population stratification, batch effects, and horizontal pleiotropy. Although a variety of methods have been proposed to mitigate those problems, residual biases may still remain, leading to highly statistically significant false positives in large databases. Here we describe a suite of sensitivity analysis tools that enables investigators to quantify the robustness of their findings against such validity threats. Specifically, we propose the routine reporting of sensitivity statistics that reveal the minimal strength of violations necessary to explain away the MR results. We further provide intuitive displays of the robustness of the MR estimate to any degree of violation, and formal bounds on the worst-case bias caused by violations multiple times stronger than observed variables. We demonstrate how these tools can aid researchers in distinguishing robust from fragile findings by examining the effect of body mass index on diastolic blood pressure and Townsend deprivation index.
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35
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Charney E. The "Golden Age" of Behavior Genetics? PERSPECTIVES ON PSYCHOLOGICAL SCIENCE 2022; 17:1188-1210. [PMID: 35180032 DOI: 10.1177/17456916211041602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The search for genetic risk factors underlying the presumed heritability of all human behavior has unfolded in two phases. The first phase, characterized by candidate-gene-association (CGA) studies, has fallen out of favor in the behavior-genetics community, so much so that it has been referred to as a "cautionary tale." The second and current iteration is characterized by genome-wide association studies (GWASs), single-nucleotide polymorphism (SNP) heritability estimates, and polygenic risk scores. This research is guided by the resurrection of, or reemphasis on, Fisher's "infinite infinitesimal allele" model of the heritability of complex phenotypes, first proposed over 100 years ago. Despite seemingly significant differences between the two iterations, they are united in viewing the discovery of risk alleles underlying heritability as a matter of finding differences in allele frequencies. Many of the infirmities that beset CGA studies persist in the era of GWASs, accompanied by a host of new difficulties due to the human genome's underlying complexities and the limitations of Fisher's model in the postgenomics era.
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Affiliation(s)
- Evan Charney
- The Samuel DuBois Cook Center on Social Equity, Duke University
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36
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Grace C, Hopewell JC, Watkins H, Farrall M, Goel A. Robust estimates of heritable coronary disease risk in individuals with type 2 diabetes. Genet Epidemiol 2022; 46:51-62. [PMID: 34672391 PMCID: PMC8983061 DOI: 10.1002/gepi.22434] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 08/12/2021] [Accepted: 10/04/2021] [Indexed: 11/21/2022]
Abstract
Type 2 diabetes (T2D) is an important heritable risk factor for coronary artery disease (CAD), the risk of both diseases being increased by metabolic syndrome (MS). With the availability of large-scale genome-wide association data, we aimed to elucidate the genetic burden of CAD risk in T2D predisposed individuals within the context of MS and their shared genetic architecture. Mendelian randomization (MR) analyses supported a causal relationship between T2D and CAD [odds ratio (OR) = 1.13 per log-odds unit 95% confidence interval (CI): 1.10-1.16; p = 1.59 × 10-17 ]. Simultaneously adjusting MR analyses for the effects of the T2D instrument including blood pressure, dyslipidaemia, and obesity attenuated the association between T2D and CAD (OR = 1.07, 95% CI: 1.04-1.11). Bayesian locus-overlap analysis identified 44 regions with the same causal variant underlying T2D and CAD genetic signals (FDR < 1%) at a posterior probability >0.7; five (MHC, LPL, ABO, RAI1 and MC4R) of these regions contain genome-wide significant (p < 5 × 10-8 ) associations for both traits. Given the small effect sizes observed in genome-wide association studies for complex diseases, even with 44 potential target regions, this has implications for the likely magnitude of CAD risk reduction that might be achievable by pure T2D therapies.
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Affiliation(s)
- Christopher Grace
- Division of Cardiovascular Medicine, Radcliffe Department of MedicineUniversity of OxfordOxfordUK
- Wellcome Centre for Human GeneticsUniversity of OxfordOxfordUK
| | - Jemma C. Hopewell
- Nuffield Department of Population HealthUniversity of OxfordOxfordUK
| | - Hugh Watkins
- Division of Cardiovascular Medicine, Radcliffe Department of MedicineUniversity of OxfordOxfordUK
- Wellcome Centre for Human GeneticsUniversity of OxfordOxfordUK
| | - Martin Farrall
- Division of Cardiovascular Medicine, Radcliffe Department of MedicineUniversity of OxfordOxfordUK
- Wellcome Centre for Human GeneticsUniversity of OxfordOxfordUK
| | - Anuj Goel
- Division of Cardiovascular Medicine, Radcliffe Department of MedicineUniversity of OxfordOxfordUK
- Wellcome Centre for Human GeneticsUniversity of OxfordOxfordUK
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37
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Sutton M, Sugier PE, Truong T, Liquet B. Leveraging pleiotropic association using sparse group variable selection in genomics data. BMC Med Res Methodol 2022; 22:9. [PMID: 34996381 PMCID: PMC8742466 DOI: 10.1186/s12874-021-01491-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 12/03/2021] [Indexed: 12/04/2022] Open
Abstract
Background Genome-wide association studies (GWAS) have identified genetic variants associated with multiple complex diseases. We can leverage this phenomenon, known as pleiotropy, to integrate multiple data sources in a joint analysis. Often integrating additional information such as gene pathway knowledge can improve statistical efficiency and biological interpretation. In this article, we propose statistical methods which incorporate both gene pathway and pleiotropy knowledge to increase statistical power and identify important risk variants affecting multiple traits. Methods We propose novel feature selection methods for the group variable selection in multi-task regression problem. We develop penalised likelihood methods exploiting different penalties to induce structured sparsity at a gene (or pathway) and SNP level across all studies. We implement an alternating direction method of multipliers (ADMM) algorithm for our penalised regression methods. The performance of our approaches are compared to a subset based meta analysis approach on simulated data sets. A bootstrap sampling strategy is provided to explore the stability of the penalised methods. Results Our methods are applied to identify potential pleiotropy in an application considering the joint analysis of thyroid and breast cancers. The methods were able to detect eleven potential pleiotropic SNPs and six pathways. A simulation study found that our method was able to detect more true signals than a popular competing method while retaining a similar false discovery rate. Conclusion We developed feature selection methods for jointly analysing multiple logistic regression tasks where prior grouping knowledge is available. Our method performed well on both simulation studies and when applied to a real data analysis of multiple cancers.
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Affiliation(s)
- Matthew Sutton
- Queensland University of Technology Centre for Data Science, Brisbane, Australia.
| | - Pierre-Emmanuel Sugier
- Laboratoire De Mathématiques et de leurs Applications de PAU E2S UPPA, CNRS, Pau, France.,University Paris-Saclay, UVSQ, Inserm, Gustave Roussy, CESP, Team "Exposome and Heredity", Villejuif, France
| | - Therese Truong
- University Paris-Saclay, UVSQ, Inserm, Gustave Roussy, CESP, Team "Exposome and Heredity", Villejuif, France
| | - Benoit Liquet
- Laboratoire De Mathématiques et de leurs Applications de PAU E2S UPPA, CNRS, Pau, France.,Department of Mathematics and Statistics, Macquarie University, Sydney, Australia
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38
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Maturation and application of phenome-wide association studies. Trends Genet 2022; 38:353-363. [PMID: 34991903 DOI: 10.1016/j.tig.2021.12.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 11/12/2021] [Accepted: 12/02/2021] [Indexed: 12/12/2022]
Abstract
In the past 10 years since its introduction, phenome-wide association studies (PheWAS) have uncovered novel genotype-phenotype relationships. Along the way, PheWAS have evolved in many aspects as a study design with the expanded availability of large data repositories with genome-wide data linked to detailed phenotypic data. Advancement in methods, including algorithms, software, and publicly available integrated resources, makes it feasible to more fully realize the potential of PheWAS, overcoming the previous computational and analytical limitations. We review here the most recent improvements and notable applications of PheWAS since the second half of the decade from its inception. We also note the challenges that remain embedded along the entire PheWAS analytical pipeline that necessitate further development of tools and resources to further advance the understanding of the complex genetic architecture underlying human diseases and traits.
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39
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Kim J, Shen J, Wang A, Mehrotra DV, Ko S, Zhou JJ, Zhou H. VCSEL: Prioritizing SNP-set by penalized variance component selection. Ann Appl Stat 2021; 15:1652-1672. [DOI: 10.1214/21-aoas1491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Juhyun Kim
- Department of Biostatistics, University of California, Los Angeles
| | - Judong Shen
- Biostatistics and Research Decision Sciences, Merck & Co., Inc
| | - Anran Wang
- Biostatistics and Research Decision Sciences, Merck & Co., Inc
| | | | - Seyoon Ko
- Department of Biostatistics, University of California, Los Angeles
| | - Jin J. Zhou
- Department of Medicine, University of California, Los Angeles
| | - Hua Zhou
- Department of Biostatistics, University of California, Los Angeles
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40
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MacArthur JA, Buniello A, Harris LW, Hayhurst J, McMahon A, Sollis E, Cerezo M, Hall P, Lewis E, Whetzel PL, Bahcall OG, Barroso I, Carroll RJ, Inouye M, Manolio TA, Rich SS, Hindorff LA, Wiley K, Parkinson H. Workshop proceedings: GWAS summary statistics standards and sharing. CELL GENOMICS 2021; 1:100004. [PMID: 36082306 PMCID: PMC9451133 DOI: 10.1016/j.xgen.2021.100004] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Genome-wide association studies (GWASs) have enabled robust mapping of complex traits in humans. The open sharing of GWAS summary statistics (SumStats) is essential in facilitating the larger meta-analyses needed for increased power in resolving the genetic basis of disease. However, most GWAS SumStats are not readily accessible because of limited sharing and a lack of defined standards. With the aim of increasing the availability, quality, and utility of GWAS SumStats, the National Human Genome Research Institute-European Bioinformatics Institute (NHGRI-EBI) GWAS Catalog organized a community workshop to address the standards, infrastructure, and incentives required to promote and enable sharing. We evaluated the barriers to SumStats sharing, both technological and sociological, and developed an action plan to address those challenges and ensure that SumStats and study metadata are findable, accessible, interoperable, and reusable (FAIR). We encourage early deposition of datasets in the GWAS Catalog as the recognized central repository. We recommend standard requirements for reporting elements and formats for SumStats and accompanying metadata as guidelines for community standards and a basis for submission to the GWAS Catalog. Finally, we provide recommendations to enable, promote, and incentivize broader data sharing, standards and FAIRness in order to advance genomic medicine.
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Affiliation(s)
- Jacqueline A.L. MacArthur
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
- BHF Data Science Centre, Health Data Research UK, London, UK
| | - Annalisa Buniello
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Laura W. Harris
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - James Hayhurst
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Aoife McMahon
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Elliot Sollis
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Maria Cerezo
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Peggy Hall
- Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Elizabeth Lewis
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Patricia L. Whetzel
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
| | - Orli G. Bahcall
- Cell Genomics, Cell Press, 50 Hampshire St., 5th Floor, Cambridge, MA 02139, USA
| | - Inês Barroso
- Exeter Centre of Excellence in Diabetes (EXCEED), University of Exeter Medical School, Exeter, UK
| | - Robert J. Carroll
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Michael Inouye
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
- Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, 75 Commercial Rd., Melbourne 3004, VIC, Australia
- The Alan Turing Institute, London, UK
| | - Teri A. Manolio
- Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Lucia A. Hindorff
- Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Ken Wiley
- Division of Genomic Medicine, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Helen Parkinson
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, UK
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41
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DeWeese KJ, Osborne MG. Understanding the metabolome and metagenome as extended phenotypes: The next frontier in macroalgae domestication and improvement. JOURNAL OF THE WORLD AQUACULTURE SOCIETY 2021; 52:1009-1030. [PMID: 34732977 PMCID: PMC8562568 DOI: 10.1111/jwas.12782] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 02/25/2021] [Indexed: 06/01/2023]
Abstract
"Omics" techniques (including genomics, transcriptomics, metabolomics, proteomics, and metagenomics) have been employed with huge success in the improvement of agricultural crops. As marine aquaculture of macroalgae expands globally, biologists are working to domesticate species of macroalgae by applying these techniques tested in agriculture to wild macroalgae species. Metabolomics has revealed metabolites and pathways that influence agriculturally relevant traits in crops, allowing for informed crop crossing schemes and genomic improvement strategies that would be pivotal to inform selection on macroalgae for domestication. Advances in metagenomics have improved understanding of host-symbiont interactions and the potential for microbial organisms to improve crop outcomes. There is much room in the field of macroalgal biology for further research toward improvement of macroalgae cultivars in aquaculture using metabolomic and metagenomic analyses. To this end, this review discusses the application and necessary expansion of the omics tool kit for macroalgae domestication as we move to enhance seaweed farming worldwide.
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Affiliation(s)
- Kelly J DeWeese
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, California, Los Angeles
| | - Melisa G Osborne
- Molecular and Computational Biology Section, Department of Biological Sciences, University of Southern California, California, Los Angeles
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42
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Wang T, Lu H, Zeng P. Identifying pleiotropic genes for complex phenotypes with summary statistics from a perspective of composite null hypothesis testing. Brief Bioinform 2021; 23:6375058. [PMID: 34571531 DOI: 10.1093/bib/bbab389] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 08/06/2021] [Accepted: 08/28/2021] [Indexed: 12/13/2022] Open
Abstract
Pleiotropy has important implication on genetic connection among complex phenotypes and facilitates our understanding of disease etiology. Genome-wide association studies provide an unprecedented opportunity to detect pleiotropic associations; however, efficient pleiotropy test methods are still lacking. We here consider pleiotropy identification from a methodological perspective of high-dimensional composite null hypothesis and propose a powerful gene-based method called MAIUP. MAIUP is constructed based on the traditional intersection-union test with two sets of independent P-values as input and follows a novel idea that was originally proposed under the high-dimensional mediation analysis framework. The key improvement of MAIUP is that it takes the composite null nature of pleiotropy test into account by fitting a three-component mixture null distribution, which can ultimately generate well-calibrated P-values for effective control of family-wise error rate and false discover rate. Another attractive advantage of MAIUP is its ability to effectively address the issue of overlapping subjects commonly encountered in association studies. Simulation studies demonstrate that compared with other methods, only MAIUP can maintain correct type I error control and has higher power across a wide range of scenarios. We apply MAIUP to detect shared associated genes among 14 psychiatric disorders with summary statistics and discover many new pleiotropic genes that are otherwise not identified if failing to account for the issue of composite null hypothesis testing. Functional and enrichment analyses offer additional evidence supporting the validity of these identified pleiotropic genes associated with psychiatric disorders. Overall, MAIUP represents an efficient method for pleiotropy identification.
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Affiliation(s)
- Ting Wang
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Haojie Lu
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
| | - Ping Zeng
- Department of Biostatistics, School of Public Health, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China.,Center for Medical Statistics and Data Analysis, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China.,Key Laboratory of Human Genetics and Environmental Medicine, Xuzhou Medical University, Xuzhou, Jiangsu, 221004, China
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43
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Ray D, Venkataraghavan S, Zhang W, Leslie EJ, Hetmanski JB, Weinberg SM, Murray JC, Marazita ML, Ruczinski I, Taub MA, Beaty TH. Pleiotropy method reveals genetic overlap between orofacial clefts at multiple novel loci from GWAS of multi-ethnic trios. PLoS Genet 2021; 17:e1009584. [PMID: 34242216 PMCID: PMC8270211 DOI: 10.1371/journal.pgen.1009584] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 05/06/2021] [Indexed: 12/19/2022] Open
Abstract
Based on epidemiologic and embryologic patterns, nonsyndromic orofacial clefts- the most common craniofacial birth defects in humans- are commonly categorized into cleft lip with or without cleft palate (CL/P) and cleft palate alone (CP), which are traditionally considered to be etiologically distinct. However, some evidence of shared genetic risk in IRF6, GRHL3 and ARHGAP29 regions exists; only FOXE1 has been recognized as significantly associated with both CL/P and CP in genome-wide association studies (GWAS). We used a new statistical approach, PLACO (pleiotropic analysis under composite null), on a combined multi-ethnic GWAS of 2,771 CL/P and 611 CP case-parent trios. At the genome-wide significance threshold of 5 × 10-8, PLACO identified 1 locus in 1q32.2 (IRF6) that appears to increase risk for one OFC subgroup but decrease risk for the other. At a suggestive significance threshold of 10-6, we found 5 more loci with compelling candidate genes having opposite effects on CL/P and CP: 1p36.13 (PAX7), 3q29 (DLG1), 4p13 (LIMCH1), 4q21.1 (SHROOM3) and 17q22 (NOG). Additionally, we replicated the recognized shared locus 9q22.33 (FOXE1), and identified 2 loci in 19p13.12 (RAB8A) and 20q12 (MAFB) that appear to influence risk of both CL/P and CP in the same direction. We found locus-specific effects may vary by racial/ethnic group at these regions of genetic overlap, and failed to find evidence of sex-specific differences. We confirmed shared etiology of the two OFC subtypes comprising CL/P, and additionally found suggestive evidence of differences in their pathogenesis at 2 loci of genetic overlap. Our novel findings include 6 new loci of genetic overlap between CL/P and CP; 3 new loci between pairwise OFC subtypes; and 4 loci not previously implicated in OFCs. Our in-silico validation showed PLACO is robust to subtype-specific effects, and can achieve massive power gains over existing approaches for identifying genetic overlap between disease subtypes. In summary, we found suggestive evidence for new genetic regions and confirmed some recognized OFC genes either exerting shared risk or with opposite effects on risk to OFC subtypes.
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Affiliation(s)
- Debashree Ray
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States of America
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States of America
- * E-mail: (DR); (THB)
| | - Sowmya Venkataraghavan
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Wanying Zhang
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Elizabeth J. Leslie
- Department of Human Genetics, School of Medicine, Emory University, Atlanta, Georgia, United States of America
| | - Jacqueline B. Hetmanski
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Seth M. Weinberg
- Department of Oral Biology, School of Dental Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Jeffrey C. Murray
- Department of Pediatrics, University of Iowa Carver College of Medicine, Iowa City, Iowa, United States of America
| | - Mary L. Marazita
- Department of Oral Biology, School of Dental Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
- Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America
| | - Ingo Ruczinski
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Margaret A. Taub
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Terri H. Beaty
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States of America
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States of America
- * E-mail: (DR); (THB)
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Yoshimura K, Morita Y, Konomi K, Ishida S, Fujiwara D, Kobayashi K, Tanaka M. A web-based survey on various symptoms of computer vision syndrome and the genetic understanding based on a multi-trait genome-wide association study. Sci Rep 2021; 11:9446. [PMID: 33941792 PMCID: PMC8093242 DOI: 10.1038/s41598-021-88827-y] [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: 12/03/2020] [Accepted: 04/16/2021] [Indexed: 11/12/2022] Open
Abstract
A variety of eye-related symptoms due to the overuse of digital devices is collectively referred to as computer vision syndrome (CVS). In this study, a web-based survey about mind and body functions, including eye strain, was conducted on 1998 Japanese volunteers. To investigate the biological mechanisms behind CVS, a multi-trait genome-wide association study (GWAS), a multivariate analysis on individual-level multivariate data, was performed based on the structural equation modeling methodology assuming a causal pathway for a genetic variant to influence each symptom via a single common latent variable. Twelve loci containing lead variants with a suggestive level of significance were identified. Two loci showed relatively strong signals and were associated with TRABD2B relative to the Wnt signaling pathway and SDK1 having neuronal adhesion and immune functions, respectively. By utilizing publicly available eQTL data, colocalization between GWAS and eQTL signals for four loci was detected, and a locus on 2p25.3 showed a strong colocalization (PPH4 > 0.9) on retinal MYT1L, known to play an important role in neuronal differentiation. This study suggested that the use of multivariate questionnaire data and multi-trait GWAS can lead to biologically reasonable findings and enhance our genetic understanding of complex relationships among symptoms related to CVS.
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Affiliation(s)
| | - Yuji Morita
- Kirin Central Research Institute, Kirin Holdings Company, Limited, Yokohama, Japan.
| | - Kenji Konomi
- Clinical and Translational Research Center, Keio University Hospital, Tokyo, Japan
| | | | - Daisuke Fujiwara
- Health Science Department, Kirin Holdings Company, Limited, Tokyo, Japan
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45
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Zhang W, Ghosh D. A general approach to sensitivity analysis for Mendelian randomization. STATISTICS IN BIOSCIENCES 2021; 13:34-55. [PMID: 33737984 DOI: 10.1007/s12561-020-09280-5] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Mendelian Randomization (MR) represents a class of instrumental variable methods using genetic variants. It has become popular in epidemiological studies to account for the unmeasured confounders when estimating the effect of exposure on outcome. The success of Mendelian Randomization depends on three critical assumptions, which are difficult to verify. Therefore, sensitivity analysis methods are needed for evaluating results and making plausible conclusions. We propose a general and easy to apply approach to conduct sensitivity analysis for Mendelian Randomization studies. Bound et al. (1995) derived a formula for the asymptotic bias of the instrumental variable estimator. Based on their work, we derive a new sensitivity analysis formula. The parameters in the formula include sensitivity parameters such as the correlation between instruments and unmeasured confounder, the direct effect of instruments on outcome and the strength of instruments. In our simulation studies, we examined our approach in various scenarios using either individual SNPs or unweighted allele score as instruments. By using a previously published dataset from researchers involving a bone mineral density study, we demonstrate that our proposed method is a useful tool for MR studies, and that investigators can combine their domain knowledge with our method to obtain bias-corrected results and make informed conclusions on the scientific plausibility of their findings.
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Affiliation(s)
- Weiming Zhang
- Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, Colorado, U.S.A
| | - Debashis Ghosh
- Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, Colorado, U.S.A
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46
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Bone WP, Siewert KM, Jha A, Klarin D, Damrauer SM, Chang KM, Tsao PS, Assimes TL, Ritchie MD, Voight BF. Multi-trait association studies discover pleiotropic loci between Alzheimer's disease and cardiometabolic traits. Alzheimers Res Ther 2021; 13:34. [PMID: 33541420 PMCID: PMC7860582 DOI: 10.1186/s13195-021-00773-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2020] [Accepted: 01/11/2021] [Indexed: 12/14/2022]
Abstract
BACKGROUND Identification of genetic risk factors that are shared between Alzheimer's disease (AD) and other traits, i.e., pleiotropy, can help improve our understanding of the etiology of AD and potentially detect new therapeutic targets. Previous epidemiological correlations observed between cardiometabolic traits and AD led us to assess the pleiotropy between these traits. METHODS We performed a set of bivariate genome-wide association studies coupled with colocalization analysis to identify loci that are shared between AD and eleven cardiometabolic traits. For each of these loci, we performed colocalization with Genotype-Tissue Expression (GTEx) project expression quantitative trait loci (eQTL) to identify candidate causal genes. RESULTS We identified three previously unreported pleiotropic trait associations at known AD loci as well as four novel pleiotropic loci. One associated locus was tagged by a low-frequency coding variant in the gene DOCK4 and is potentially implicated in its alternative splicing. Colocalization with GTEx eQTL data identified additional candidate genes for the loci we detected, including ACE, the target of the hypertensive drug class of ACE inhibitors. We found that the allele associated with decreased ACE expression in brain tissue was also associated with increased risk of AD, providing human genetic evidence of a potential increase in AD risk from use of an established anti-hypertensive therapeutic. CONCLUSION Our results support a complex genetic relationship between AD and these cardiometabolic traits, and the candidate causal genes identified suggest that blood pressure and immune response play a role in the pleiotropy between these traits.
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Affiliation(s)
- William P Bone
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Katherine M Siewert
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Anupama Jha
- Department of Computer and Information Science, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Derek Klarin
- Boston VA Healthcare System, Boston, MA, 02130, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, USA
| | - Scott M Damrauer
- Department of Medicine, Perelman School of Medicine, University of Pennsylvania, PA, 19104, Philadelphia, USA
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, 19104, USA
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Kyong-Mi Chang
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, 19104, USA
- Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Philip S Tsao
- VA Palo Alto Health Care System, Palo Alto, CA, 94550, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Themistocles L Assimes
- VA Palo Alto Health Care System, Palo Alto, CA, 94550, USA
- Department of Medicine, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | - Marylyn D Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Center for Precision Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Benjamin F Voight
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, 19104, USA.
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
- Institute for Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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47
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Zhang X, Li R, Ritchie MD. Statistical Impact of Sample Size and Imbalance on Multivariate Analysis in silico and A Case Study in the UK Biobank. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2021; 2020:1383-1391. [PMID: 33936514 PMCID: PMC8075427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Large-scale biobank cohorts coupled with electronic health records offer unprecedented opportunities to study genotype-phenotype relationships. Genome-wide association studies uncovered disease-associated loci through univariate methods, with the focus on one trait at a time. With genetic variants being identifiedfor thousands of traits, researchers found that 90% of human genetic loci are associated with more than one trait, highlighting the ubiquity of pleiotropy. Recently, multivariate methods have been proposed to effectively identify pleiotropy. However, the statistical performance in natural biomedical data, which often have unbalanced case-control sample sizes, is largely known. In this work, we designed 21 scenarios of real-data informed simulations to thoroughly evaluate the statistical characteristics of univariate and multivariate methods. Our results can serve as a reference guide for the application of multivariate methods. We also investigated potential pleiotropy across type II diabetes, Alzheimer's disease, atherosclerosis of arteries, depression, and atherosclerotic heart disease in the UK Biobank.
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Affiliation(s)
- Xinyuan Zhang
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
- Genomics and Computational Biology Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Ruowang Li
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Marylyn D Ritchie
- Institute for Biomedical Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
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48
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Icick R, Bloch V, Prince N, Karsinti E, Lépine JP, Laplanche JL, Mouly S, Marie-Claire C, Brousse G, Bellivier F, Vorspan F. Clustering suicidal phenotypes and genetic associations with brain-derived neurotrophic factor in patients with substance use disorders. Transl Psychiatry 2021; 11:72. [PMID: 33479229 PMCID: PMC7820499 DOI: 10.1038/s41398-021-01200-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Revised: 06/13/2020] [Accepted: 07/03/2020] [Indexed: 11/09/2022] Open
Abstract
Suicide attempts (SA), especially recurrent SA or serious SA, are common in substance use disorders (SUD). However, the genetic component of SA in SUD samples remains unclear. Brain-derived neurotrophic factor (BDNF) alleles and levels have been repeatedly involved in stress-related psychopathology. This investigation uses a within-cases study of BDNF and associated factors in three suicidal phenotypes ('any', 'recurrent', and 'serious') of outpatients seeking treatment for opiate and/or cocaine use disorder. Phenotypic characterization was ascertained using a semi-structured interview. After thorough quality control, 98 SNPs of BDNF and associated factors (the BDNF pathway) were extracted from whole-genome data, leaving 411 patients of Caucasian ancestry, who had reliable data regarding their SA history. Binary and multinomial regression with the three suicidal phenotypes were further performed to adjust for possible confounders, along with hierarchical clustering and compared to controls (N = 2504). Bayesian analyses were conducted to detect pleiotropy across the suicidal phenotypes. Among 154 (37%) ever suicide attempters, 104 (68%) reported at least one serious SA and 96 (57%) two SA or more. The median number of non-tobacco SUDs was three. The BDNF gene remained associated with lifetime SA in SNP-based (rs7934165, rs10835210) and gene-based tests within the clinical sample. rs10835210 clustered with serious SA. Bayesian analysis identified genetic correlation between 'any' and 'serious' SA regarding rs7934165. Despite limitations, 'serious' SA was shown to share both clinical and genetic risk factors of SA-not otherwise specified, suggesting a shared BDNF-related pathophysiology of SA in this population with multiple SUDs.
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Affiliation(s)
- Romain Icick
- Assistance Publique-Hôpitaux de Paris (AP-HP), Groupe Hospitalier Saint-Louis-Lariboisière-Fernand Widal, Paris, France. .,INSERM U1144, "Therapeutic Optimization in Neuropsychopharmacology", Paris, France. .,Université de Paris, Inserm UMR-S1144, Paris, France.
| | - Vanessa Bloch
- grid.50550.350000 0001 2175 4109Assistance Publique-Hôpitaux de Paris (AP-HP), Groupe Hospitalier Saint-Louis–Lariboisière–Fernand Widal, Paris, France ,grid.7429.80000000121866389INSERM U1144, “Therapeutic Optimization in Neuropsychopharmacology”, Paris, France ,Université de Paris, Inserm UMR-S1144, Paris, France
| | - Nathalie Prince
- grid.7429.80000000121866389INSERM U1144, “Therapeutic Optimization in Neuropsychopharmacology”, Paris, France ,Université de Paris, Inserm UMR-S1144, Paris, France
| | - Emily Karsinti
- grid.50550.350000 0001 2175 4109Assistance Publique-Hôpitaux de Paris (AP-HP), Groupe Hospitalier Saint-Louis–Lariboisière–Fernand Widal, Paris, France ,grid.7429.80000000121866389INSERM U1144, “Therapeutic Optimization in Neuropsychopharmacology”, Paris, France ,ED139, Paris Nanterre University, Nanterre, France
| | - Jean-Pierre Lépine
- grid.50550.350000 0001 2175 4109Assistance Publique-Hôpitaux de Paris (AP-HP), Groupe Hospitalier Saint-Louis–Lariboisière–Fernand Widal, Paris, France ,grid.7429.80000000121866389INSERM U1144, “Therapeutic Optimization in Neuropsychopharmacology”, Paris, France ,Université de Paris, Inserm UMR-S1144, Paris, France
| | - Jean-Louis Laplanche
- grid.50550.350000 0001 2175 4109Assistance Publique-Hôpitaux de Paris (AP-HP), Groupe Hospitalier Saint-Louis–Lariboisière–Fernand Widal, Paris, France
| | - Stéphane Mouly
- grid.50550.350000 0001 2175 4109Assistance Publique-Hôpitaux de Paris (AP-HP), Groupe Hospitalier Saint-Louis–Lariboisière–Fernand Widal, Paris, France ,grid.7429.80000000121866389INSERM U1144, “Therapeutic Optimization in Neuropsychopharmacology”, Paris, France ,Université de Paris, Inserm UMR-S1144, Paris, France
| | - Cynthia Marie-Claire
- grid.7429.80000000121866389INSERM U1144, “Therapeutic Optimization in Neuropsychopharmacology”, Paris, France ,Université de Paris, Inserm UMR-S1144, Paris, France
| | - Georges Brousse
- grid.494717.80000000115480420Inserm UMR-1107, Neuro-Dol, Université Clermont-Auvergne, Clermont-Ferrand, France
| | - Frank Bellivier
- grid.50550.350000 0001 2175 4109Assistance Publique-Hôpitaux de Paris (AP-HP), Groupe Hospitalier Saint-Louis–Lariboisière–Fernand Widal, Paris, France ,grid.7429.80000000121866389INSERM U1144, “Therapeutic Optimization in Neuropsychopharmacology”, Paris, France ,Université de Paris, Inserm UMR-S1144, Paris, France
| | - Florence Vorspan
- grid.50550.350000 0001 2175 4109Assistance Publique-Hôpitaux de Paris (AP-HP), Groupe Hospitalier Saint-Louis–Lariboisière–Fernand Widal, Paris, France ,grid.7429.80000000121866389INSERM U1144, “Therapeutic Optimization in Neuropsychopharmacology”, Paris, France ,Université de Paris, Inserm UMR-S1144, Paris, France
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49
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Yuan X, Biswas S. Detecting rare haplotype association with two correlated phenotypes of binary and continuous types. Stat Med 2021; 40:1877-1900. [PMID: 33438281 DOI: 10.1002/sim.8877] [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: 07/07/2020] [Revised: 11/18/2020] [Accepted: 12/25/2020] [Indexed: 11/10/2022]
Abstract
Multiple correlated traits/phenotypes are often collected in genetic association studies and they may share a common genetic mechanism. Joint analysis of correlated phenotypes has well-known advantages over one-at-a-time analysis including gain in power and better understanding of genetic etiology. However, when the phenotypes are of discordant types such as binary and continuous, the joint modeling is more challenging. Another research area of current interest is discovery of rare genetic variants. Currently there is no method available for detecting association of rare (or common) haplotypes with multiple discordant phenotypes jointly. Our goal is to fill this gap specifically for two discordant phenotypes. We consider a rare haplotype association method for a binary phenotype, logistic Bayesian LASSO (univariate LBL) and its extension for two correlated binary phenotypes (bivariate LBL-2B). Under this framework, we propose a haplotype association test with binary and continuous phenotypes jointly (bivariate LBL-BC). Specifically, we use a latent variable to induce correlation between the two phenotypes. We carry out extensive simulations to investigate bivariate LBL-BC and compare it with univariate LBL and bivariate LBL-2B. In most settings, bivariate LBL-BC performs the best. In only two situations, bivariate LBL-BC has similar performance-when the two phenotypes are (1) weakly or not correlated and the target haplotype affects the binary phenotype only and (2) strongly positively correlated and the target haplotype affects both phenotypes in positive direction. Finally, we apply the method to a data set on lung cancer and nicotine dependence and detect several haplotypes including a rare one.
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Affiliation(s)
- Xiaochen Yuan
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, Texas, USA
| | - Swati Biswas
- Department of Mathematical Sciences, University of Texas at Dallas, Richardson, Texas, USA
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50
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Li R, Duan R, Zhang X, Lumley T, Pendergrass S, Bauer C, Hakonarson H, Carrell DS, Smoller JW, Wei WQ, Carroll R, Velez Edwards DR, Wiesner G, Sleiman P, Denny JC, Mosley JD, Ritchie MD, Chen Y, Moore JH. Lossless integration of multiple electronic health records for identifying pleiotropy using summary statistics. Nat Commun 2021; 12:168. [PMID: 33420026 PMCID: PMC7794298 DOI: 10.1038/s41467-020-20211-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2020] [Accepted: 11/13/2020] [Indexed: 11/22/2022] Open
Abstract
Increasingly, clinical phenotypes with matched genetic data from bio-bank linked electronic health records (EHRs) have been used for pleiotropy analyses. Thus far, pleiotropy analysis using individual-level EHR data has been limited to data from one site. However, it is desirable to integrate EHR data from multiple sites to improve the detection power and generalizability of the results. Due to privacy concerns, individual-level patients' data are not easily shared across institutions. As a result, we introduce Sum-Share, a method designed to efficiently integrate EHR and genetic data from multiple sites to perform pleiotropy analysis. Sum-Share requires only summary-level data and one round of communication from each site, yet it produces identical test statistics compared with that of pooled individual-level data. Consequently, Sum-Share can achieve lossless integration of multiple datasets. Using real EHR data from eMERGE, Sum-Share is able to identify 1734 potential pleiotropic SNPs for five cardiovascular diseases.
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Affiliation(s)
- Ruowang Li
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Rui Duan
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Xinyuan Zhang
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Thomas Lumley
- Department of Statistics, University of Auckland, Auckland, New Zealand
| | - Sarah Pendergrass
- Biomedical and Translational Informatics Institute, Geisinger, Danville, PA, USA
| | - Christopher Bauer
- Biomedical and Translational Informatics Institute, Geisinger, Danville, PA, USA
| | - Hakon Hakonarson
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - David S Carrell
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA
| | - Jordan W Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Centre, Nashville, TN, USA
| | - Robert Carroll
- Department of Biomedical Informatics, Vanderbilt University Medical Centre, Nashville, TN, USA
| | - Digna R Velez Edwards
- Clinical and Translational Hereditary Cancer Program, Division of Genetic Medicine, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA
| | - Georgia Wiesner
- Clinical and Translational Hereditary Cancer Program, Division of Genetic Medicine, Department of Medicine, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA
| | - Patrick Sleiman
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Josh C Denny
- Department of Biomedical Informatics, Vanderbilt University Medical Centre, Nashville, TN, USA
| | - Jonathan D Mosley
- Department of Biomedical Informatics, Vanderbilt University Medical Centre, Nashville, TN, USA
| | - Marylyn D Ritchie
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yong Chen
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA, USA.
| | - Jason H Moore
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA, USA.
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