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Gong T, Karlsson R, Yao S, Magnusson PKE, Ajnakina O, Steptoe A, Bhatta L, Brumpton B, Kumar A, Mélen E, Lin KH, Tian C, Fall T, Almqvist C. The genetic architecture of dog ownership: large-scale genome-wide association study in 97,552 European-ancestry individuals. G3 (BETHESDA, MD.) 2024; 14:jkae116. [PMID: 38820132 PMCID: PMC11304603 DOI: 10.1093/g3journal/jkae116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 12/10/2023] [Accepted: 04/16/2024] [Indexed: 06/02/2024]
Abstract
Dog ownership has been associated with several complex traits, and there is evidence of genetic influence. We performed a genome-wide association study of dog ownership through a meta-analysis of 31,566 Swedish twins in 5 discovery cohorts and an additional 65,986 European-ancestry individuals in 3 replication cohorts from Sweden, Norway, and the United Kingdom. Association tests with >7.4 million single-nucleotide polymorphisms were meta-analyzed using a fixed effect model after controlling for population structure and relatedness. We identified 2 suggestive loci using discovery cohorts, which did not reach genome-wide significance after meta-analysis with replication cohorts. Single-nucleotide polymorphism-based heritability of dog ownership using linkage disequilibrium score regression was estimated at 0.123 (CI 0.038-0.207) using the discovery cohorts and 0.018 (CI -0.002 to 0.039) when adding in replication cohorts. Negative genetic correlation with complex traits including type 2 diabetes, depression, neuroticism, and asthma was only found using discovery summary data. Furthermore, we did not identify any genes/gene-sets reaching even a suggestive level of significance. This genome-wide association study does not, by itself, provide clear evidence on common genetic variants that influence dog ownership among European-ancestry individuals.
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Affiliation(s)
- Tong Gong
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm SE-171 77, Sweden
| | - Robert Karlsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm SE-171 77, Sweden
| | - Shuyang Yao
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm SE-171 77, Sweden
| | - Patrik K E Magnusson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm SE-171 77, Sweden
| | - Olesya Ajnakina
- Department of Behavioural Science and Health, Institute of Epidemiology and Health Care, University College London, London WC1E 7HBUK
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London SE5 8AF, UK
| | - Andrew Steptoe
- Department of Behavioural Science and Health, Institute of Epidemiology and Health Care, University College London, London WC1E 7HBUK
| | - Laxmi Bhatta
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim NO-7491, Norway
| | - Ben Brumpton
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Trondheim NO-7491, Norway
- HUNT Research Centre, Department of Public Health and Nursing, NTNU, Norwegian University of Science and Technology, Levanger 7600, Norway
- Clinic of Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim NO-7030, Norway
| | - Ashish Kumar
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm SE-171 77, Sweden
- Department of Clinical Sciences and Education, Södersjukhuset, Karolinska Institutet, Stockholm SE-118 83, Sweden
| | - Erik Mélen
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm SE-171 77, Sweden
- Department of Clinical Sciences and Education, Södersjukhuset, Karolinska Institutet, Stockholm SE-118 83, Sweden
- Sachs' Children's Hospital, South General Hospital, Stockholm SE-118 61, Sweden
| | | | - Chao Tian
- 23andMe, Inc., Sunnyvale, CA 94086, USA
| | - Tove Fall
- Molecular Epidemiology, Department of Medical Sciences, and Science for Life Laboratory, Uppsala University, Uppsala SE-751 85, Sweden
| | - Catarina Almqvist
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm SE-171 77, Sweden
- Pediatric Allergy and Pulmonology Unit at Astrid Lindgren Children's Hospital, Karolinska University Hospital, Stockholm SE-141 86, Sweden
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Feng B, Song J, Wang S, Chao L. The impact of PM 2.5 on lung function and chronic respiratory diseases: insights from genetic evidence. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2024:10.1007/s00484-024-02728-z. [PMID: 38904841 DOI: 10.1007/s00484-024-02728-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 05/20/2024] [Accepted: 06/19/2024] [Indexed: 06/22/2024]
Abstract
BACKGROUND PM2.5 has been associated with various adverse health effects, particularly affecting lung function and chronic respiratory diseases. However, the genetic causality relationship between PM2.5 exposure and lung function as well as chronic respiratory diseases remains poorly understood. METHOD We conducted a two-sample Mendelian randomization analysis to investigate the causal impact of PM2.5 on lung function and chronic respiratory diseases. Instrumental variables were carefully selected, with significance thresholds (P < 5 × 10- 8), and linkage disequilibrium with an r2 value below 0.001. Additionally, SNPs with an F-statistic exceeding 10 were included to mitigate potential bias stemming from weak instrumental variables. The primary analytical approach employed the Inverse Variance Weighted method, supplemented by the Weighted Median, MR-Egger, Simple Model, and Weighted Model. Furthermore, pleiotropy and heterogeneity were evaluated through the MR-Egger intercept test and Cochrane's Q test, with a sensitivity analysis conducted using the leave-one-out method. RESULTS Eight SNPs significantly associated with PM2.5 exposure were identified as Instrumental variables. Mendelian randomization analysis revealed a significant causal association between PM2.5 exposure and lung function (FEV), with an OR of 0.7284 (95% CI: 0.5799-0.9150). Similarly, PM2.5 exposure demonstrated a substantial causal effect on asthma, with an OR of 1.5280 (95% CI: 1.0470-2.2299). However, no causal association was observed between PM2.5 exposure and chronic obstructive pulmonary disease, with an OR of 1.5176 (95% CI: 0.8294-2.7768). CONCLUSION These findings emphasize the necessity for continued research efforts in environmental health to develop effective strategies for the prevention and management of chronic respiratory diseases.
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Affiliation(s)
- Bin Feng
- School of health Management, Environmental Health Section, Xinxiang Medical University, Xinxiang Health Technology Supervision Center, Xinxiang, 453003, Henan Province, China
| | - Jie Song
- School of Public Health, Xinxiang Medical University, Xinxiang, 453003, Henan Province, China
| | - Shouying Wang
- School of health Management, Environmental Health Section, Xinxiang Medical University, Xinxiang Health Technology Supervision Center, Xinxiang, 453003, Henan Province, China.
| | - Ling Chao
- School of Public Health, Xinxiang Medical University, Xinxiang, 453003, Henan Province, China.
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Zhang J, Weissenkampen JD, Kember RL, Grove J, Børglum AD, Robinson EB, Brodkin ES, Almasy L, Bucan M, Sebro R. Phenotypic and ancestry-related assortative mating in autism. Mol Autism 2024; 15:27. [PMID: 38877467 PMCID: PMC11177537 DOI: 10.1186/s13229-024-00605-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 05/30/2024] [Indexed: 06/16/2024] Open
Abstract
BACKGROUND Positive assortative mating (AM) in several neuropsychiatric traits, including autism, has been noted. However, it is unknown whether the pattern of AM is different in phenotypically defined autism subgroups [e.g., autism with and without intellectually disability (ID)]. It is also unclear what proportion of the phenotypic AM can be explained by the genetic similarity between parents of children with an autism diagnosis, and the consequences of AM on the genetic structure of the population. METHODS To address these questions, we analyzed two family-based autism collections: the Simons Foundation Powering Autism Research for Knowledge (SPARK) (1575 families) and the Simons Simplex Collection (SSC) (2283 families). RESULTS We found a similar degree of phenotypic and ancestry-related AM in parents of children with an autism diagnosis regardless of the presence of ID. We did not find evidence of AM for autism based on autism polygenic scores (PGS) (at a threshold of |r|> 0.1). The adjustment of ancestry-related AM or autism PGS accounted for only 0.3-4% of the fractional change in the estimate of the phenotypic AM. The ancestry-related AM introduced higher long-range linkage disequilibrium (LD) between single nucleotide polymorphisms (SNPs) on different chromosomes that are highly ancestry-informative compared to SNPs that are less ancestry-informative (D2 on the order of 1 × 10-5). LIMITATIONS We only analyzed participants of European ancestry, limiting the generalizability of our results to individuals of non-European ancestry. SPARK and SSC were both multicenter studies. Therefore, there could be ancestry-related AM in SPARK and SSC due to geographic stratification. The study participants from each site were unknown, so we were unable to evaluate for geographic stratification. CONCLUSIONS This study showed similar patterns of AM in autism with and without ID, and demonstrated that the common genetic influences of autism are likely relevant to both autism groups. The adjustment of ancestry-related AM and autism PGS accounted for < 5% of the fractional change in the estimate of the phenotypic AM. Future studies are needed to evaluate if the small increase of long-range LD induced by ancestry-related AM has impact on the downstream analysis.
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Affiliation(s)
- Jing Zhang
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | | | - Rachel L Kember
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Jakob Grove
- Center for Genomics and Personalized Medicine, Aarhus University, Aarhus, Denmark
- Department of Biomedicine (Human Genetics) and iSEQ Center, Aarhus University, Aarhus, Denmark
- Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
| | - Anders D Børglum
- Center for Genomics and Personalized Medicine, Aarhus University, Aarhus, Denmark
- Department of Biomedicine (Human Genetics) and iSEQ Center, Aarhus University, Aarhus, Denmark
- The Lundbeck Foundation Initiative for Integrative Psychiatric Research, iPSYCH, Aarhus, Denmark
| | - Elise B Robinson
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - Edward S Brodkin
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Laura Almasy
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Maja Bucan
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Ronnie Sebro
- Department of Radiology, Mayo Clinic, Jacksonville, FL, USA.
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Sunde HF, Eftedal NH, Cheesman R, Corfield EC, Kleppesto TH, Seierstad AC, Ystrom E, Eilertsen EM, Torvik FA. Genetic similarity between relatives provides evidence on the presence and history of assortative mating. Nat Commun 2024; 15:2641. [PMID: 38531929 PMCID: PMC10966108 DOI: 10.1038/s41467-024-46939-9] [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: 08/10/2023] [Accepted: 03/13/2024] [Indexed: 03/28/2024] Open
Abstract
Assortative mating - the non-random mating of individuals with similar traits - is known to increase trait-specific genetic variance and genetic similarity between relatives. However, empirical evidence is limited for many traits, and the implications hinge on whether assortative mating has started recently or many generations ago. Here we show theoretically and empirically that genetic similarity between relatives can provide evidence on the presence and history of assortative mating. First, we employed path analysis to understand how assortative mating affects genetic similarity between family members across generations, finding that similarity between distant relatives is more affected than close relatives. Next, we correlated polygenic indices of 47,135 co-parents from the Norwegian Mother, Father, and Child Cohort Study (MoBa) and found genetic evidence of assortative mating in nine out of sixteen examined traits. The same traits showed elevated similarity between relatives, especially distant relatives. Six of the nine traits, including educational attainment, showed greater genetic variance among offspring, which is inconsistent with stable assortative mating over many generations. These results suggest an ongoing increase in familial similarity for these traits. The implications of this research extend to genetic methodology and the understanding of social and economic disparities.
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Affiliation(s)
- Hans Fredrik Sunde
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway.
- Department of Psychology, University of Oslo, Oslo, Norway.
| | | | - Rosa Cheesman
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Elizabeth C Corfield
- Nic Waals Institute, Lovisenberg Diakonale Hospital, Oslo, Norway
- PsychGen Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Thomas H Kleppesto
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- Department of Psychology, Norwegian University of Science and Technology, Trondheim, Norway
| | | | - Eivind Ystrom
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- PsychGen Centre for Genetic Epidemiology and Mental Health, Norwegian Institute of Public Health, Oslo, Norway
| | - Espen Moen Eilertsen
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Fartein Ask Torvik
- Centre for Fertility and Health, Norwegian Institute of Public Health, Oslo, Norway
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
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Xiao XP, Dai YJ, Zhang Y, Yang M, Xie J, Chen G, Yang ZJ. Investigating the causal associations between five anthropometric indicators and nonalcoholic fatty liver disease: Mendelian randomization study. World J Clin Cases 2024; 12:1215-1226. [PMID: 38524522 PMCID: PMC10955530 DOI: 10.12998/wjcc.v12.i7.1215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 01/14/2024] [Accepted: 02/06/2024] [Indexed: 02/29/2024] Open
Abstract
BACKGROUND Although the etiology of nonalcoholic fatty liver disease (NAFLD) has not been thoroughly understood, the emerging roles of anthropometric indicators in assessing and predicting the risk of NAFLD have been highlighted by accumulating evidence. AIM To evaluate the causal relationships between five anthropometric indicators and NAFLD employing Mendelian randomization (MR) design. METHODS The Anthropometric Consortium provided genetic exposure data for five anthropometric indicators, including hip circumference (HC), waist circumference (WC), waist-to-hip ratio (WHR), body mass index (BMI), and body fat percentage (BF). Genetic outcome data for NAFLD were obtained from the United Kingdom Biobank and FinnGen Consortium. Genome-wide significant single nucleotide polymorphisms were chosen as instrumental variables. Univariable MR (UVMR) and multivariable MR (MVMR) designs with analytical approaches, including inverse variance weighted (IVW), MR-Egger, weighted median (WM), and weighted mode methods, were used to assess the causal relationships between anthropometric indicators and NAFLD. RESULTS Causal relationships were revealed by UVMR, indicating that a higher risk of NAFLD was associated with a per-unit increase in WC [IVW: odds ratio (OR) = 2.67, 95%CI: 1.42-5.02, P = 2.25 × 10-3], and BF was causally associated with an increased risk of NAFLD (WM: OR = 2.23, 95%CI: 1.07-4.66, P = 0.033). The presence of causal effects of WC on the decreased risk of NAFLD was supported by MVMR after adjusting for BMI and smoking. However, no causal association between BF and NAFLD was observed. In addition, other causal relationships of HC, WHR (BMI adjusted), and BMI with the risk of NAFLD were not retained after FDR correction. CONCLUSION This study establishes a causal relationship, indicating that an increase in WC is associated with a higher risk of NAFLD. This demonstrates that a suitable decrease in WC is advantageous for preventing NAFLD.
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Affiliation(s)
- Xian-Pei Xiao
- Department of Oncology, Luojiang District People's Hospital of Deyang City, Deyang 618000, Sichuan Province, China
| | - Yong-Jun Dai
- Department of Orthopaedics, Luojiang District People's Hospital of Deyang City, Deyang 618000, Sichuan Province, China
| | - Yu Zhang
- Department of Oncology, Luojiang District People's Hospital of Deyang City, Deyang 618000, Sichuan Province, China
| | - Meng Yang
- Department of Oncology, Luojiang District People's Hospital of Deyang City, Deyang 618000, Sichuan Province, China
| | - Jian Xie
- Department of Oncology, Luojiang District People's Hospital of Deyang City, Deyang 618000, Sichuan Province, China
| | - Guo Chen
- Department of Infectious Diseases, Hospital of Chengdu University of Traditional Chinese Medical, Chengdu 610500, Sichuan Province, China
| | - Zheng-Jun Yang
- Department of Oncology, Luojiang District People's Hospital of Deyang City, Deyang 618000, Sichuan Province, China
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Nivard MG, Belsky DW, Harden KP, Baier T, Andreassen OA, Ystrøm E, van Bergen E, Lyngstad TH. More than nature and nurture, indirect genetic effects on children's academic achievement are consequences of dynastic social processes. Nat Hum Behav 2024:10.1038/s41562-023-01796-2. [PMID: 38225408 DOI: 10.1038/s41562-023-01796-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Accepted: 11/29/2023] [Indexed: 01/17/2024]
Abstract
Families transmit genes and environments across generations. When parents' genetics affect their children's environments, these two modes of inheritance can produce an 'indirect genetic effect'. Such indirect genetic effects may account for up to half of the estimated genetic variance in educational attainment. Here we tested if indirect genetic effects reflect within-nuclear-family transmission ('genetic nurture') or instead a multi-generational process of social stratification ('dynastic effects'). We analysed indirect genetic effects on children's academic achievement in their fifth to ninth years of schooling in N = 37,117 parent-offspring trios in the Norwegian Mother, Father, and Child Cohort Study (MoBa). We used pairs of genetically related families (parents were siblings, children were cousins; N = 10,913) to distinguish within-nuclear-family genetic-nurture effects from dynastic effects shared by cousins in different nuclear families. We found that indirect genetic effects on children's academic achievement cannot be explained by processes that operate exclusively within the nuclear family.
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Affiliation(s)
- Michel G Nivard
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
| | - Daniel W Belsky
- Department of Epidemiology, Columbia University Mailman School of Public Health, New York, NY, USA
- Robert N. Butler Columbia Aging Center, Columbia University, New York, NY, USA
| | - K Paige Harden
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
- Population Research Center, University of Texas at Austin, Austin, TX, USA
| | - Tina Baier
- Department of Sociology and Human Geography, University of Oslo, Oslo, Norway
| | - Ole A Andreassen
- NORMENT Centre, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
| | - Eivind Ystrøm
- PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
- Department of Mental Disorders, Norwegian Institute of Public Health, Oslo, Norway
| | - Elsje van Bergen
- Department of Biological Psychology, Vrije Universiteit, Amsterdam, the Netherlands
- Amsterdam Public Health Research Institute, Amsterdam, the Netherlands
- Research Institute LEARN!, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Torkild H Lyngstad
- Department of Sociology and Human Geography, University of Oslo, Oslo, Norway.
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McClellan JM, Zoghbi AW, Buxbaum JD, Cappi C, Crowley JJ, Flint J, Grice DE, Gulsuner S, Iyegbe C, Jain S, Kuo PH, Lattig MC, Passos-Bueno MR, Purushottam M, Stein DJ, Sunshine AB, Susser ES, Walsh CA, Wootton O, King MC. An evolutionary perspective on complex neuropsychiatric disease. Neuron 2024; 112:7-24. [PMID: 38016473 PMCID: PMC10842497 DOI: 10.1016/j.neuron.2023.10.037] [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: 08/02/2022] [Revised: 08/09/2023] [Accepted: 10/26/2023] [Indexed: 11/30/2023]
Abstract
The forces of evolution-mutation, selection, migration, and genetic drift-shape the genetic architecture of human traits, including the genetic architecture of complex neuropsychiatric illnesses. Studying these illnesses in populations that are diverse in genetic ancestry, historical demography, and cultural history can reveal how evolutionary forces have guided adaptation over time and place. A fundamental truth of shared human biology is that an allele responsible for a disease in anyone, anywhere, reveals a gene critical to the normal biology underlying that condition in everyone, everywhere. Understanding the genetic causes of neuropsychiatric disease in the widest possible range of human populations thus yields the greatest possible range of insight into genes critical to human brain development. In this perspective, we explore some of the relationships between genes, adaptation, and history that can be illuminated by an evolutionary perspective on studies of complex neuropsychiatric disease in diverse populations.
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Affiliation(s)
- Jon M McClellan
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA 98195, USA
| | - Anthony W Zoghbi
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX 77030, USA
| | - Joseph D Buxbaum
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Carolina Cappi
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - James J Crowley
- Department of Genetics, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Jonathan Flint
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Dorothy E Grice
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Suleyman Gulsuner
- Department of Medicine and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Conrad Iyegbe
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Sanjeev Jain
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru 560029, India
| | - Po-Hsiu Kuo
- Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei 100, Taiwan
| | | | | | - Meera Purushottam
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru 560029, India
| | - Dan J Stein
- SAMRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, University of Cape Town, Cape Town, South Africa
| | - Anna B Sunshine
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA 98195, USA; Department of Medicine and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Ezra S Susser
- Department of Epidemiology, Mailman School of Public Health, and New York State Psychiatric Institute, Columbia University, New York, NY 10032, USA
| | - Christopher A Walsh
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Division of Genetics and Genomics and Howard Hughes Medical Institute, Boston Children's Hospital, Boston, MA 02115, USA; Departments of Pediatrics and Neurology, Harvard Medical School, Boston, MA 02115, USA
| | - Olivia Wootton
- SAMRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, University of Cape Town, Cape Town, South Africa
| | - Mary-Claire King
- Department of Medicine and Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.
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Hu X, Zhao Y, He T, Gao ZX, Zhang P, Fang Y, Ge M, Xu YQ, Pan HF, Wang P. Causal Relationships between Air Pollutant Exposure and Bone Mineral Density and the Risk of Bone Fractures: Evidence from a Two-Stage Mendelian Randomization Analysis. TOXICS 2023; 12:27. [PMID: 38250984 PMCID: PMC10820864 DOI: 10.3390/toxics12010027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 11/25/2023] [Accepted: 12/26/2023] [Indexed: 01/23/2024]
Abstract
A number of studies from the literature have suggested that exposure to air pollutants is associated with a declined bone mineral density (BMD), and increased risks of osteoporosis (OP) and bone fractures. This study was performed to systemically assess the genetically causal associations of air pollutants with site-/age-specific BMD and risk of bone fractures with the implementation of two-sample Mendelian randomization (TSMR) and multivariate Mendelian randomization (MVMR). The TSMR analysis was implemented to infer the causal associations between air pollutants and BMD and the risk of bone fractures, additional MVMR analysis was used to further estimate the direct causal effects between air pollutants and BMD, the occurrence of OP, and bone fractures. The results showed that NOx exposure contributed to lower femoral neck BMD (FN-BMD) (β = -0.71, 95%CI: -1.22, -0.20, p = 0.006) and total body BMD (TB-BMD) (β = -0.55, 95%CI: -0.90, -0.21, p = 0.002). Additionally, exposure to PM10 was found to be associated with a decreased TB-BMD (B β = -0.42, 95%CI: -0.66, -0.18, p = 0.001), further age-specific subgroup analysis demonstrated the causal effect of PM10 exposure on the decreased TB-BMD in a subgroup aged 45 to 60 years (β = -0.70, 95%CI: -1.12, -0.29, p = 0.001). Moreover, the findings of the MVMR analysis implied that there was a direct causal effect between PM10 exposure and the decreased TB-BMD (45 < age < 60), after adjusting for PM2.5 and PM2.5 -10 exposure. Our study provides additional evidence to support the causal associations of higher concentrations of air pollutant exposure with decreased BMD, especially in those populations aged between 45 to 60 years, suggesting that early intervention measures and public policy should be considered to improve public health awareness and promote bone health.
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Affiliation(s)
- Xiao Hu
- Teaching Center for Preventive Medicine, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China;
- Institute of Kidney Disease, Inflammation & Immunity Mediated Diseases, The Second Hospital of Anhui Medical University, Hefei 230032, China; (Y.Z.); (T.H.); (Z.-X.G.); (P.Z.); (Y.F.); (M.G.); (Y.-Q.X.)
| | - Yan Zhao
- Institute of Kidney Disease, Inflammation & Immunity Mediated Diseases, The Second Hospital of Anhui Medical University, Hefei 230032, China; (Y.Z.); (T.H.); (Z.-X.G.); (P.Z.); (Y.F.); (M.G.); (Y.-Q.X.)
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China
| | - Tian He
- Institute of Kidney Disease, Inflammation & Immunity Mediated Diseases, The Second Hospital of Anhui Medical University, Hefei 230032, China; (Y.Z.); (T.H.); (Z.-X.G.); (P.Z.); (Y.F.); (M.G.); (Y.-Q.X.)
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China
| | - Zhao-Xing Gao
- Institute of Kidney Disease, Inflammation & Immunity Mediated Diseases, The Second Hospital of Anhui Medical University, Hefei 230032, China; (Y.Z.); (T.H.); (Z.-X.G.); (P.Z.); (Y.F.); (M.G.); (Y.-Q.X.)
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China
| | - Peng Zhang
- Institute of Kidney Disease, Inflammation & Immunity Mediated Diseases, The Second Hospital of Anhui Medical University, Hefei 230032, China; (Y.Z.); (T.H.); (Z.-X.G.); (P.Z.); (Y.F.); (M.G.); (Y.-Q.X.)
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China
| | - Yang Fang
- Institute of Kidney Disease, Inflammation & Immunity Mediated Diseases, The Second Hospital of Anhui Medical University, Hefei 230032, China; (Y.Z.); (T.H.); (Z.-X.G.); (P.Z.); (Y.F.); (M.G.); (Y.-Q.X.)
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China
| | - Man Ge
- Institute of Kidney Disease, Inflammation & Immunity Mediated Diseases, The Second Hospital of Anhui Medical University, Hefei 230032, China; (Y.Z.); (T.H.); (Z.-X.G.); (P.Z.); (Y.F.); (M.G.); (Y.-Q.X.)
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China
| | - Yi-Qing Xu
- Institute of Kidney Disease, Inflammation & Immunity Mediated Diseases, The Second Hospital of Anhui Medical University, Hefei 230032, China; (Y.Z.); (T.H.); (Z.-X.G.); (P.Z.); (Y.F.); (M.G.); (Y.-Q.X.)
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China
| | - Hai-Feng Pan
- Institute of Kidney Disease, Inflammation & Immunity Mediated Diseases, The Second Hospital of Anhui Medical University, Hefei 230032, China; (Y.Z.); (T.H.); (Z.-X.G.); (P.Z.); (Y.F.); (M.G.); (Y.-Q.X.)
- Department of Epidemiology and Biostatistics, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China
| | - Peng Wang
- Teaching Center for Preventive Medicine, School of Public Health, Anhui Medical University, 81 Meishan Road, Hefei 230032, China;
- Institute of Kidney Disease, Inflammation & Immunity Mediated Diseases, The Second Hospital of Anhui Medical University, Hefei 230032, China; (Y.Z.); (T.H.); (Z.-X.G.); (P.Z.); (Y.F.); (M.G.); (Y.-Q.X.)
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9
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Privé F, Albiñana C, Arbel J, Pasaniuc B, Vilhjálmsson BJ. Inferring disease architecture and predictive ability with LDpred2-auto. Am J Hum Genet 2023; 110:2042-2055. [PMID: 37944514 PMCID: PMC10716363 DOI: 10.1016/j.ajhg.2023.10.010] [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: 06/14/2023] [Revised: 10/15/2023] [Accepted: 10/17/2023] [Indexed: 11/12/2023] Open
Abstract
LDpred2 is a widely used Bayesian method for building polygenic scores (PGSs). LDpred2-auto can infer the two parameters from the LDpred model, the SNP heritability h2 and polygenicity p, so that it does not require an additional validation dataset to choose best-performing parameters. The main aim of this paper is to properly validate the use of LDpred2-auto for inferring multiple genetic parameters. Here, we present a new version of LDpred2-auto that adds an optional third parameter α to its model, for modeling negative selection. We then validate the inference of these three parameters (or two, when using the previous model). We also show that LDpred2-auto provides per-variant probabilities of being causal that are well calibrated and can therefore be used for fine-mapping purposes. We also introduce a formula to infer the out-of-sample predictive performance r2 of the resulting PGS directly from the Gibbs sampler of LDpred2-auto. Finally, we extend the set of HapMap3 variants recommended to use with LDpred2 with 37% more variants to improve the coverage of this set, and we show that this new set of variants captures 12% more heritability and provides 6% more predictive performance, on average, in UK Biobank analyses.
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Affiliation(s)
- Florian Privé
- National Centre for Register-based Research, Aarhus University, Aarhus, Denmark.
| | - Clara Albiñana
- National Centre for Register-based Research, Aarhus University, Aarhus, Denmark
| | - Julyan Arbel
- University Grenoble Alpes, Inria, CNRS, Grenoble INP, LJK, Grenoble, France
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA; Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Bjarni J Vilhjálmsson
- National Centre for Register-based Research, Aarhus University, Aarhus, Denmark; Bioinformatics Research Centre, Aarhus University, Aarhus, Denmark; Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute, Cambridge, MA, USA
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10
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Veller C, Przeworski M, Coop G. Causal interpretations of family GWAS in the presence of heterogeneous effects. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.11.13.566950. [PMID: 38014124 PMCID: PMC10680648 DOI: 10.1101/2023.11.13.566950] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Family-based genome-wide association studies (GWAS) have emerged as a gold standard for assessing causal effects of alleles and polygenic scores. Notably, family studies are often claimed to provide an unbiased estimate of the average causal effect (or average treatment effect; ATE) of an allele, on the basis of an analogy between the random transmission of alleles from parents to children and a randomized controlled trial. Here, we show that this interpretation does not hold in general. Because Mendelian segregation only randomizes alleles among children of heterozygotes, the effects of alleles in the children of homozygotes are not observable. Consequently, if an allele has different average effects in the children of homozygotes and heterozygotes, as can arise in the presence of gene-by-environment interactions, gene-by-gene interactions, or differences in LD patterns, family studies provide a biased estimate of the average effect in the sample. At a single locus, family-based association studies can be thought of as providing an unbiased estimate of the average effect in the children of heterozygotes (i.e., a local average treatment effect; LATE). This interpretation does not extend to polygenic scores, however, because different sets of SNPs are heterozygous in each family. Therefore, other than under specific conditions, the within-family regression slope of a PGS cannot be assumed to provide an unbiased estimate for any subset or weighted average of families. Instead, family-based studies can be reinterpreted as enabling an unbiased estimate of the extent to which Mendelian segregation at loci in the PGS contributes to the population-level variance in the trait. Because this estimate does not include the between-family variance, however, this interpretation applies to only (roughly) half of the sample PGS variance. In practice, the potential biases of a family-based GWAS are likely smaller than those arising from confounding in a standard, population-based GWAS, and so family studies remain important for the dissection of genetic contributions to phenotypic variation. Nonetheless, the causal interpretation of family-based GWAS estimates is less straightforward than has been widely appreciated.
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Affiliation(s)
- Carl Veller
- Department of Ecology and Evolution, University of Chicago
| | - Molly Przeworski
- Department of Biological Sciences, Columbia University
- Department of Systems Biology, Columbia University
| | - Graham Coop
- Center for Population Biology and Department of Evolution and Ecology, University of California, Davis
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11
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Dapas M, Lee YL, Wentworth-Sheilds W, Im HK, Ober C, Schoettler N. Revealing polygenic pleiotropy using genetic risk scores for asthma. HGG ADVANCES 2023; 4:100233. [PMID: 37663543 PMCID: PMC10474095 DOI: 10.1016/j.xhgg.2023.100233] [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: 06/02/2023] [Accepted: 08/11/2023] [Indexed: 09/05/2023] Open
Abstract
In this study we examined how genetic risk for asthma associates with different features of the disease and with other medical conditions and traits. Using summary statistics from two multi-ancestry genome-wide association studies of asthma, we modeled polygenic risk scores (PRSs) and validated their predictive performance in the UK Biobank. We then performed phenome-wide association studies of the asthma PRSs with 371 heritable traits in the UK Biobank. We identified 228 total significant associations across a variety of organ systems, including associations that varied by PRS model, sex, age of asthma onset, ancestry, and human leukocyte antigen region alleles. Our results highlight pervasive pleiotropy between asthma and numerous other traits and conditions and elucidate pathways that contribute to asthma and its comorbidities.
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Affiliation(s)
- Matthew Dapas
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Yu Lin Lee
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
- Biological Sciences Collegiate Division, University of Chicago, Chicago, IL, USA
| | | | - Hae Kyung Im
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Carole Ober
- Department of Human Genetics, University of Chicago, Chicago, IL, USA
| | - Nathan Schoettler
- Section of Pulmonary and Critical Care Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
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12
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Bilghese M, Manansala R, Jaishankar D, Jala J, Benjamin DJ, Kimball M, Auer PL, Livermore MA, Turley P. A General Approach to Adjusting Genetic Studies for Assortative Mating. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.01.555983. [PMID: 37732240 PMCID: PMC10508715 DOI: 10.1101/2023.09.01.555983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
The effects of assortative mating (AM) on estimates from genetic studies has been receiving increasing attention in recent years. We extend existing AM theory to more general models of sorting and conclude that correct theory-based AM adjustments require knowledge of complicated, unknown historical sorting patterns. We propose a simple, general-purpose approach using polygenic indexes (PGIs). Our approach can estimate the fraction of genetic variance and genetic correlation that is driven by AM. Our approach is less effective when applied to Mendelian randomization (MR) studies for two reasons: AM can induce a form of selection bias in MR studies that remains after our adjustment; and, in the MR context, the adjustment is particularly sensitive to PGI estimation error. Using data from the UK Biobank, we find that AM inflates genetic correlation estimates between health traits and education by 14% on average. Our results suggest caution in interpreting genetic correlations or MR estimates for traits subject to AM.
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Affiliation(s)
- Marta Bilghese
- Department of Finance, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Regina Manansala
- Center for Health Economics Research & Modelling Infectious Diseases, Vaccine & Infectious Disease Institute, University of Antwerp, Campus Drie Eiken, Antwerp, Belgium
| | | | - Jonathan Jala
- UCLA Anderson School of Management, Los Angeles, CA, USA
| | - Daniel J Benjamin
- UCLA Anderson School of Management, Los Angeles, CA, USA
- National Bureau of Economic Research, Cambridge, MA, USA
- Human Genetics Department, UCLA David Geffen School of Medicine, Los Angeles, CA, USA
| | - Miles Kimball
- Department of Economics, University of Colorado Boulder, Boulder, CO, USA
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA
| | - Paul L Auer
- Division of Biostatistics, Institute for Health & Equity, and Cancer Center, Medical College of Wisconsin, Milwaukee WI, USA
| | | | - Patrick Turley
- Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA
- Department of Economics, University of Southern California, Los Angeles, CA, USA
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13
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Horwitz TB, Balbona JV, Paulich KN, Keller MC. Evidence of correlations between human partners based on systematic reviews and meta-analyses of 22 traits and UK Biobank analysis of 133 traits. Nat Hum Behav 2023; 7:1568-1583. [PMID: 37653148 DOI: 10.1038/s41562-023-01672-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Accepted: 07/03/2023] [Indexed: 09/02/2023]
Abstract
Positive correlations between mates can increase trait variation and prevalence, as well as bias estimates from genetically informed study designs. While past studies of similarity between human mating partners have largely found evidence of positive correlations, to our knowledge, no formal meta-analysis has examined human partner correlations across multiple categories of traits. Thus, we conducted systematic reviews and random-effects meta-analyses of human male-female partner correlations across 22 traits commonly studied by psychologists, economists, sociologists, anthropologists, epidemiologists and geneticists. Using ScienceDirect, PubMed and Google Scholar, we incorporated 480 partner correlations from 199 peer-reviewed studies of co-parents, engaged pairs, married pairs and/or cohabitating pairs that were published on or before 16 August 2022. We also calculated 133 trait correlations using up to 79,074 male-female couples in the UK Biobank (UKB). Estimates of the 22 mean meta-analysed correlations ranged from rmeta = 0.08 (adjusted 95% CI = 0.03, 0.13) for extraversion to rmeta = 0.58 (adjusted 95% CI = 0.50, 0.64) for political values, with funnel plots showing little evidence of publication bias across traits. The 133 UKB correlations ranged from rUKB = -0.18 (adjusted 95% CI = -0.20, -0.16) for chronotype (being a 'morning' or 'evening' person) to rUKB = 0.87 (adjusted 95% CI = 0.86, 0.87) for birth year. Across analyses, political and religious attitudes, educational attainment and some substance use traits showed the highest correlations, while psychological (that is, psychiatric/personality) and anthropometric traits generally yielded lower but positive correlations. We observed high levels of between-sample heterogeneity for most meta-analysed traits, probably because of both systematic differences between samples and true differences in partner correlations across populations.
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Affiliation(s)
- Tanya B Horwitz
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA.
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA.
| | - Jared V Balbona
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
| | - Katie N Paulich
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA
| | - Matthew C Keller
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA.
- Department of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO, USA.
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14
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Salehi Nowbandegani P, Wohns AW, Ballard JL, Lander ES, Bloemendal A, Neale BM, O'Connor LJ. Extremely sparse models of linkage disequilibrium in ancestrally diverse association studies. Nat Genet 2023; 55:1494-1502. [PMID: 37640881 DOI: 10.1038/s41588-023-01487-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 07/24/2023] [Indexed: 08/31/2023]
Abstract
Linkage disequilibrium (LD) is the correlation among nearby genetic variants. In genetic association studies, LD is often modeled using large correlation matrices, but this approach is inefficient, especially in ancestrally diverse studies. In the present study, we introduce LD graphical models (LDGMs), which are an extremely sparse and efficient representation of LD. LDGMs are derived from genome-wide genealogies; statistical relationships among alleles in the LDGM correspond to genealogical relationships among haplotypes. We published LDGMs and ancestry-specific LDGM precision matrices for 18 million common variants (minor allele frequency >1%) in five ancestry groups, validated their accuracy and demonstrated order-of-magnitude improvements in runtime for commonly used LD matrix computations. We implemented an extremely fast multiancestry polygenic prediction method, BLUPx-ldgm, which performs better than a similar method based on the reference LD correlation matrix. LDGMs will enable sophisticated methods that scale to ancestrally diverse genetic association data across millions of variants and individuals.
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Affiliation(s)
- Pouria Salehi Nowbandegani
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
| | - Anthony Wilder Wohns
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
- Stanford University School of Medicine, Stanford, CA, USA.
| | - Jenna L Ballard
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Graduate Group in Genomics and Computational Biology, University of Pennsylvania, Philadelphia, PA, USA
| | - Eric S Lander
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biology, MIT, Cambridge, MA, USA
- Department of Systems Biology, Harvard Medical School, Boston, MA, USA
| | - Alex Bloemendal
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Benjamin M Neale
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Luke J O'Connor
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
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15
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Border R, Malik OA. rBahadur: efficient simulation of structured high-dimensional genotype data with applications to assortative mating. BMC Bioinformatics 2023; 24:314. [PMID: 37596553 PMCID: PMC10439545 DOI: 10.1186/s12859-023-05442-6] [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: 08/09/2023] [Indexed: 08/20/2023] Open
Abstract
Existing methods for generating synthetic genotype data are ill-suited for replicating the effects of assortative mating (AM). We propose rb_dplr, a novel and computationally efficient algorithm for generating high-dimensional binary random variates that effectively recapitulates AM-induced genetic architectures using the Bahadur order-2 approximation of the multivariate Bernoulli distribution. The rBahadur R library is available through the Comprehensive R Archive Network at https://CRAN.R-project.org/package=rBahadur .
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Affiliation(s)
- Richard Border
- Neurology and Computer Science, University of California, Los Angeles, 675 Charles E Young Dr S, Los Angeles, CA, 90095, USA.
| | - Osman Asif Malik
- Applied Mathematics and Computational Research Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA, 94720, USA
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16
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van Kippersluis H, Biroli P, Dias Pereira R, Galama TJ, von Hinke S, Meddens SFW, Muslimova D, Slob EAW, de Vlaming R, Rietveld CA. Overcoming attenuation bias in regressions using polygenic indices. Nat Commun 2023; 14:4473. [PMID: 37491308 PMCID: PMC10368647 DOI: 10.1038/s41467-023-40069-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2021] [Accepted: 07/11/2023] [Indexed: 07/27/2023] Open
Abstract
Measurement error in polygenic indices (PGIs) attenuates the estimation of their effects in regression models. We analyze and compare two approaches addressing this attenuation bias: Obviously Related Instrumental Variables (ORIV) and the PGI Repository Correction (PGI-RC). Through simulations, we show that the PGI-RC performs slightly better than ORIV, unless the prediction sample is very small (N < 1000) or when there is considerable assortative mating. Within families, ORIV is the best choice since the PGI-RC correction factor is generally not available. We verify the empirical validity of the simulations by predicting educational attainment and height in a sample of siblings from the UK Biobank. We show that applying ORIV between families increases the standardized effect of the PGI by 12% (height) and by 22% (educational attainment) compared to a meta-analysis-based PGI, yet estimates remain slightly below the PGI-RC estimates. Furthermore, within-family ORIV regression provides the tightest lower bound for the direct genetic effect, increasing the lower bound for the standardized direct genetic effect on educational attainment from 0.14 to 0.18 (+29%), and for height from 0.54 to 0.61 (+13%) compared to a meta-analysis-based PGI.
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Affiliation(s)
- Hans van Kippersluis
- Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands.
- Tinbergen Institute, Amsterdam, The Netherlands.
| | - Pietro Biroli
- Department of Economics, University of Bologna, Bologna, Italy
| | - Rita Dias Pereira
- Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Tinbergen Institute, Amsterdam, The Netherlands
| | - Titus J Galama
- Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Tinbergen Institute, Amsterdam, The Netherlands
- School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Center for Social and Economic Research, University of Southern California, Los Angeles, CA, USA
| | - Stephanie von Hinke
- Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Tinbergen Institute, Amsterdam, The Netherlands
- School of Economics, University of Bristol, Bristol, UK
| | - S Fleur W Meddens
- Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Statistics Netherlands, The Hague, The Netherlands
| | - Dilnoza Muslimova
- Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Tinbergen Institute, Amsterdam, The Netherlands
| | - Eric A W Slob
- Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Medical Research Council Biostatistics Unit, Cambridge University, Cambridge, UK
- Erasmus University Rotterdam Institute for Behavior and Biology, Rotterdam, The Netherlands
| | - Ronald de Vlaming
- Tinbergen Institute, Amsterdam, The Netherlands
- School of Business and Economics, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Cornelius A Rietveld
- Erasmus School of Economics, Erasmus University Rotterdam, Rotterdam, The Netherlands
- Tinbergen Institute, Amsterdam, The Netherlands
- Erasmus University Rotterdam Institute for Behavior and Biology, Rotterdam, The Netherlands
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17
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Schoeler T, Speed D, Porcu E, Pirastu N, Pingault JB, Kutalik Z. Participation bias in the UK Biobank distorts genetic associations and downstream analyses. Nat Hum Behav 2023; 7:1216-1227. [PMID: 37106081 PMCID: PMC10365993 DOI: 10.1038/s41562-023-01579-9] [Citation(s) in RCA: 52] [Impact Index Per Article: 52.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 03/07/2023] [Indexed: 04/29/2023]
Abstract
While volunteer-based studies such as the UK Biobank have become the cornerstone of genetic epidemiology, the participating individuals are rarely representative of their target population. To evaluate the impact of selective participation, here we derived UK Biobank participation probabilities on the basis of 14 variables harmonized across the UK Biobank and a representative sample. We then conducted weighted genome-wide association analyses on 19 traits. Comparing the output from weighted genome-wide association analyses (neffective = 94,643 to 102,215) with that from standard genome-wide association analyses (n = 263,464 to 283,749), we found that increasing representativeness led to changes in SNP effect sizes and identified novel SNP associations for 12 traits. While heritability estimates were less impacted by weighting (maximum change in h2, 5%), we found substantial discrepancies for genetic correlations (maximum change in rg, 0.31) and Mendelian randomization estimates (maximum change in βSTD, 0.15) for socio-behavioural traits. We urge the field to increase representativeness in biobank samples, especially when studying genetic correlates of behaviour, lifestyles and social outcomes.
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Affiliation(s)
- Tabea Schoeler
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
- Department of Clinical, Educational and Health Psychology, University College London, London, UK.
| | - Doug Speed
- Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
| | - Eleonora Porcu
- Precision Medicine Unit, Biomedical Data Science Center, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Nicola Pirastu
- Genomics Research Centre, Human Technopole, Milan, Italy
| | - Jean-Baptiste Pingault
- Department of Clinical, Educational and Health Psychology, University College London, London, UK
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Zoltán Kutalik
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
- University Center for Primary Care and Public Health, Lausanne, Switzerland.
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18
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Flint J. The genetic basis of major depressive disorder. Mol Psychiatry 2023; 28:2254-2265. [PMID: 36702864 PMCID: PMC10611584 DOI: 10.1038/s41380-023-01957-9] [Citation(s) in RCA: 28] [Impact Index Per Article: 28.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 12/30/2022] [Accepted: 01/11/2023] [Indexed: 01/27/2023]
Abstract
The genetic dissection of major depressive disorder (MDD) ranks as one of the success stories of psychiatric genetics, with genome-wide association studies (GWAS) identifying 178 genetic risk loci and proposing more than 200 candidate genes. However, the GWAS results derive from the analysis of cohorts in which most cases are diagnosed by minimal phenotyping, a method that has low specificity. I review data indicating that there is a large genetic component unique to MDD that remains inaccessible to minimal phenotyping strategies and that the majority of genetic risk loci identified with minimal phenotyping approaches are unlikely to be MDD risk loci. I show that inventive uses of biobank data, novel imputation methods, combined with more interviewer diagnosed cases, can identify loci that contribute to the episodic severe shifts of mood, and neurovegetative and cognitive changes that are central to MDD. Furthermore, new theories about the nature and causes of MDD, drawing upon advances in neuroscience and psychology, can provide handles on how best to interpret and exploit genetic mapping results.
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Affiliation(s)
- Jonathan Flint
- Department of Psychiatry and Biobehavioral Sciences, Billy and Audrey Wilder Endowed Chair in Psychiatry and Neuroscience, Center for Neurobehavioral Genetics, 695 Charles E. Young Drive South, 3357B Gonda, Box 951761, Los Angeles, CA, 90095-1761, USA.
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19
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Evans LM, Arehart CH, Grotzinger AD, Mize TJ, Brasher MS, Stitzel JA, Ehringer MA, Hoeffer CA. Transcriptome-wide gene-gene interaction associations elucidate pathways and functional enrichment of complex traits. PLoS Genet 2023; 19:e1010693. [PMID: 37216417 PMCID: PMC10237671 DOI: 10.1371/journal.pgen.1010693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 06/02/2023] [Accepted: 03/06/2023] [Indexed: 05/24/2023] Open
Abstract
It remains unknown to what extent gene-gene interactions contribute to complex traits. Here, we introduce a new approach using predicted gene expression to perform exhaustive transcriptome-wide interaction studies (TWISs) for multiple traits across all pairs of genes expressed in several tissue types. Using imputed transcriptomes, we simultaneously reduce the computational challenge and improve interpretability and statistical power. We discover (in the UK Biobank) and replicate (in independent cohorts) several interaction associations, and find several hub genes with numerous interactions. We also demonstrate that TWIS can identify novel associated genes because genes with many or strong interactions have smaller single-locus model effect sizes. Finally, we develop a method to test gene set enrichment of TWIS associations (E-TWIS), finding numerous pathways and networks enriched in interaction associations. Epistasis is may be widespread, and our procedure represents a tractable framework for beginning to explore gene interactions and identify novel genomic targets.
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Affiliation(s)
- Luke M. Evans
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado, United States of America
- Department of Ecology & Evolutionary Biology, University of Colorado Boulder, Boulder, Colorado, United States of America
| | - Christopher H. Arehart
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado, United States of America
- Department of Ecology & Evolutionary Biology, University of Colorado Boulder, Boulder, Colorado, United States of America
| | - Andrew D. Grotzinger
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado, United States of America
- Department of Psychology & Neuroscience, University of Colorado Boulder, Boulder, Colorado, United States of America
| | - Travis J. Mize
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado, United States of America
- Department of Ecology & Evolutionary Biology, University of Colorado Boulder, Boulder, Colorado, United States of America
| | - Maizy S. Brasher
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado, United States of America
- Department of Ecology & Evolutionary Biology, University of Colorado Boulder, Boulder, Colorado, United States of America
| | - Jerry A. Stitzel
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado, United States of America
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, Colorado, United States of America
| | - Marissa A. Ehringer
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado, United States of America
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, Colorado, United States of America
| | - Charles A. Hoeffer
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, Colorado, United States of America
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, Colorado, United States of America
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20
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Sjaarda J, Kutalik Z. Partner choice, confounding and trait convergence all contribute to phenotypic partner similarity. Nat Hum Behav 2023; 7:776-789. [PMID: 36928782 PMCID: PMC10202815 DOI: 10.1038/s41562-022-01500-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 11/17/2022] [Indexed: 03/18/2023]
Abstract
Partners are often similar in terms of their physical and behavioural traits, such as their education, political affiliation and height. However, it is currently unclear what exactly causes this similarity-partner choice, partner influence increasing similarity over time or confounding factors such as shared environment or indirect assortment. Here, we applied Mendelian randomization to the data of 51,664 couples in the UK Biobank and investigated partner similarity in 118 traits. We found evidence of partner choice for 64 traits, 40 of which had larger phenotypic correlation than causal effect. This suggests that confounders contribute to trait similarity, among which household income, overall health rating and education accounted for 29.8, 14.1 and 11.6% of correlations between partners, respectively. Finally, mediation analysis revealed that most causal associations between different traits in the two partners are indirect. In summary, our results show the mechanisms through which indirect assortment increases the observed partner similarity.
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Affiliation(s)
- Jennifer Sjaarda
- University Center for Primary Care and Public Health, Lausanne, Switzerland
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Zoltán Kutalik
- University Center for Primary Care and Public Health, Lausanne, Switzerland.
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics, Lausanne, Switzerland.
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21
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Plomin R. Celebrating a Century of Research in Behavioral Genetics. Behav Genet 2023; 53:75-84. [PMID: 36662387 PMCID: PMC9922236 DOI: 10.1007/s10519-023-10132-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 12/28/2022] [Accepted: 01/04/2023] [Indexed: 01/21/2023]
Abstract
A century after the first twin and adoption studies of behavior in the 1920s, this review looks back on the journey and celebrates milestones in behavioral genetic research. After a whistle-stop tour of early quantitative genetic research and the parallel journey of molecular genetics, the travelogue focuses on the last fifty years. Just as quantitative genetic discoveries were beginning to slow down in the 1990s, molecular genetics made it possible to assess DNA variation directly. From a rocky start with candidate gene association research, by 2005 the technological advance of DNA microarrays enabled genome-wide association studies, which have successfully identified some of the DNA variants that contribute to the ubiquitous heritability of behavioral traits. The ability to aggregate the effects of thousands of DNA variants in polygenic scores has created a DNA revolution in the behavioral sciences by making it possible to use DNA to predict individual differences in behavior from early in life.
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Affiliation(s)
- Robert Plomin
- Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
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22
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How rare mutations contribute to complex traits. Nature 2023; 614:418-419. [PMID: 36755145 DOI: 10.1038/d41586-023-00272-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
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23
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Conery M, Grant SFA. Human height: a model common complex trait. Ann Hum Biol 2023; 50:258-266. [PMID: 37343163 PMCID: PMC10368389 DOI: 10.1080/03014460.2023.2215546] [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: 12/05/2022] [Revised: 04/10/2023] [Accepted: 05/09/2023] [Indexed: 06/23/2023]
Abstract
CONTEXT Like other complex phenotypes, human height reflects a combination of environmental and genetic factors, but is notable for being exceptionally easy to measure. Height has therefore been commonly used to make observations later generalised to other phenotypes though the appropriateness of such generalisations is not always considered. OBJECTIVES We aimed to assess height's suitability as a model for other complex phenotypes and review recent advances in height genetics with regard to their implications for complex phenotypes more broadly. METHODS We conducted a comprehensive literature search in PubMed and Google Scholar for articles relevant to the genetics of height and its comparatibility to other phenotypes. RESULTS Height is broadly similar to other phenotypes apart from its high heritability and ease of measurment. Recent genome-wide association studies (GWAS) have identified over 12,000 independent signals associated with height and saturated height's common single nucleotide polymorphism based heritability of height within a subset of the genome in individuals similar to European reference populations. CONCLUSIONS Given the similarity of height to other complex traits, the saturation of GWAS's ability to discover additional height-associated variants signals potential limitations to the omnigenic model of complex-phenotype inheritance, indicating the likely future power of polygenic scores and risk scores, and highlights the increasing need for large-scale variant-to-gene mapping efforts.
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Affiliation(s)
- Mitchell Conery
- Division of Human Genetics, Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pediatrics, Perelman School of Medicine at the University of PA, Philadelphia, PA, USA
- Department of Pharmacology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Struan F A Grant
- Division of Human Genetics, Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pediatrics, Perelman School of Medicine at the University of PA, Philadelphia, PA, USA
- Division of Diabetes and Endocrinology, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
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24
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Kun E, Javan EM, Smith O, Gulamali F, de la Fuente J, Flynn BI, Vajrala K, Trutner Z, Jayakumar P, Tucker-Drob EM, Sohail M, Singh T, Narasimhan VM. The genetic architecture of the human skeletal form. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.03.521284. [PMID: 36712136 PMCID: PMC9881884 DOI: 10.1101/2023.01.03.521284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
The human skeletal form underlies our ability to walk on two legs, but unlike standing height, the genetic basis of limb lengths and skeletal proportions is less well understood. Here we applied a deep learning model to 31,221 whole body dual-energy X-ray absorptiometry (DXA) images from the UK Biobank (UKB) to extract 23 different image-derived phenotypes (IDPs) that include all long bone lengths as well as hip and shoulder width, which we analyzed while controlling for height. All skeletal proportions are highly heritable (∼40-50%), and genome-wide association studies (GWAS) of these traits identified 179 independent loci, of which 102 loci were not associated with height. These loci are enriched in genes regulating skeletal development as well as associated with rare human skeletal diseases and abnormal mouse skeletal phenotypes. Genetic correlation and genomic structural equation modeling indicated that limb proportions exhibited strong genetic sharing but were genetically independent of width and torso proportions. Phenotypic and polygenic risk score analyses identified specific associations between osteoarthritis (OA) of the hip and knee, the leading causes of adult disability in the United States, and skeletal proportions of the corresponding regions. We also found genomic evidence of evolutionary change in arm-to-leg and hip-width proportions in humans consistent with striking anatomical changes in these skeletal proportions in the hominin fossil record. In contrast to cardiovascular, auto-immune, metabolic, and other categories of traits, loci associated with these skeletal proportions are significantly enriched in human accelerated regions (HARs), and regulatory elements of genes differentially expressed through development between humans and the great apes. Taken together, our work validates the use of deep learning models on DXA images to identify novel and specific genetic variants affecting the human skeletal form and ties a major evolutionary facet of human anatomical change to pathogenesis.
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Affiliation(s)
- Eucharist Kun
- Department of Integrative Biology, The University of Texas at Austin
| | - Emily M Javan
- Department of Integrative Biology, The University of Texas at Austin
| | - Olivia Smith
- Department of Integrative Biology, The University of Texas at Austin
| | | | | | - Brianna I Flynn
- Department of Integrative Biology, The University of Texas at Austin
| | - Kushal Vajrala
- Department of Integrative Biology, The University of Texas at Austin
| | - Zoe Trutner
- Department of Surgery and Perioperative Care, The University of Texas at Austin
| | - Prakash Jayakumar
- Department of Surgery and Perioperative Care, The University of Texas at Austin
| | | | - Mashaal Sohail
- Centro de Ciencias Genómicas (CCG), Universidad Nacional Autónoma de México (UNAM)
| | - Tarjinder Singh
- The Department of Psychiatry at Columbia University Irving Medical Center
- The New York Genome Center
- Mortimer B. Zuckerman Mind Brain Behavior Institute at Columbia University
| | - Vagheesh M Narasimhan
- Department of Integrative Biology, The University of Texas at Austin
- Department of Statistics and Data Science, The University of Texas at Austin
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25
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Song J, Zou Y, Wu Y, Miao J, Yu Z, Fletcher JM, Lu Q. Decomposing heritability and genetic covariance by direct and indirect effect paths. PLoS Genet 2023; 19:e1010620. [PMID: 36689559 PMCID: PMC9894552 DOI: 10.1371/journal.pgen.1010620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 02/02/2023] [Accepted: 01/16/2023] [Indexed: 01/24/2023] Open
Abstract
Estimation of heritability and genetic covariance is crucial for quantifying and understanding complex trait genetic architecture and is employed in almost all recent genome-wide association studies (GWAS). However, many existing approaches for heritability estimation and almost all methods for estimating genetic correlation ignore the presence of indirect genetic effects, i.e., genotype-phenotype associations confounded by the parental genome and family environment, and may thus lead to incorrect interpretation especially for human sociobehavioral phenotypes. In this work, we introduce a statistical framework to decompose heritability and genetic covariance into multiple components representing direct and indirect effect paths. Applied to five traits in UK Biobank, we found substantial involvement of indirect genetic components in shared genetic architecture across traits. These results demonstrate the effectiveness of our approach and highlight the importance of accounting for indirect effects in variance component analysis of complex traits.
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Affiliation(s)
- Jie Song
- Department of Statistics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Yiqing Zou
- Department of Statistics, Stanford University, Stanford, CA, United States of America
| | - Yuchang Wu
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Wisconsin, United States of America
- Center for Demography of Health and Aging, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Jiacheng Miao
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Wisconsin, United States of America
| | - Ze Yu
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Wisconsin, United States of America
| | - Jason M. Fletcher
- Center for Demography of Health and Aging, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Department of Sociology, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- La Follette School of Public Affairs, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Qiongshi Lu
- Department of Statistics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Department of Biostatistics and Medical Informatics, University of Wisconsin–Madison, Wisconsin, United States of America
- Center for Demography of Health and Aging, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- * E-mail:
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26
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Border R, Athanasiadis G, Buil A, Schork AJ, Cai N, Young AI, Werge T, Flint J, Kendler KS, Sankararaman S, Dahl AW, Zaitlen NA. Cross-trait assortative mating is widespread and inflates genetic correlation estimates. Science 2022; 378:754-761. [PMID: 36395242 PMCID: PMC9901291 DOI: 10.1126/science.abo2059] [Citation(s) in RCA: 54] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The observation of genetic correlations between disparate human traits has been interpreted as evidence of widespread pleiotropy. Here, we introduce cross-trait assortative mating (xAM) as an alternative explanation. We observe that xAM affects many phenotypes and that phenotypic cross-mate correlation estimates are strongly associated with genetic correlation estimates (R2=74%). We demonstrate that existing xAM plausibly accounts for substantial fractions of genetic correlation estimates and that previously reported genetic correlation estimates between some pairs of psychiatric disorders are congruent with xAM alone. Finally, we provide evidence for a history of xAM at the genetic level using cross-trait even/odd chromosome polygenic score correlations. Together, our results demonstrate that previous reports have likely overestimated the true genetic similarity between many phenotypes.
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Affiliation(s)
- Richard Border
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.,Department of Computer Science, University of California, Los Angeles, Los Angeles, CA 90095, USA.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Georgios Athanasiadis
- Institute of Biological Psychiatry, Mental Health Center Sct Hans, Copenhagen University Hospital-Mental Health Services CPH, 2100 Copenhagen, Denmark.,Department of Evolutionary Biology, Ecology, and Environmental Sciences, University of Barcelona, 08028 Barcelona, Spain
| | - Alfonso Buil
- Institute of Biological Psychiatry, Mental Health Center Sct Hans, Copenhagen University Hospital-Mental Health Services CPH, 2100 Copenhagen, Denmark.,Globe Institute, University of Copenhagen, 1350 Copenhagen, Denmark
| | - Andrew J Schork
- Institute of Biological Psychiatry, Mental Health Center Sct Hans, Copenhagen University Hospital-Mental Health Services CPH, 2100 Copenhagen, Denmark.,Globe Institute, University of Copenhagen, 1350 Copenhagen, Denmark.,Neurogenomics Division, The Translational Genomics Research Institute, Phoenix, AZ 85004, USA
| | - Na Cai
- Helmholtz Pioneer Campus, Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Alexander I Young
- Anderson School of Management, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Thomas Werge
- Institute of Biological Psychiatry, Mental Health Center Sct Hans, Copenhagen University Hospital-Mental Health Services CPH, 2100 Copenhagen, Denmark.,Globe Institute, University of Copenhagen, 1350 Copenhagen, Denmark.,Department of Clinical Medicine, University of Copenhagen, 1165 Copenhagen, Denmark
| | - Jonathan Flint
- Center for Neurobehavioral Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Kenneth S Kendler
- Department of Psychiatry, Virginia Institute for Psychiatric and Behavioral Genetics, Virginia Commonwealth University, Richmond, VA 23298, USA
| | - Sriram Sankararaman
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA 90095, USA.,Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA.,Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Andy W Dahl
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Noah A Zaitlen
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA.,Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA 90095, USA.,Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
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27
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Jefsen OH, Nudel R, Wang Y, Bybjerg-Grauholm J, Hemager N, Christiani CAJ, Burton BK, Spang KS, Ellersgaard D, Gantriis DL, Plessen KJ, Jepsen JRM, Thorup AAE, Werge T, Nordentoft M, Mors O, Greve AN. Genetic assortative mating for schizophrenia and bipolar disorder. Eur Psychiatry 2022; 65:e53. [PMID: 35996886 PMCID: PMC9491077 DOI: 10.1192/j.eurpsy.2022.2304] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 06/17/2022] [Accepted: 06/25/2022] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND Psychiatric disorders are highly polygenic and show patterns of partner resemblance. Partner resemblance has direct population-level genetic implications if it is caused by assortative mating, but not if it is caused by convergence or social homogamy. Using genetics may help distinguish these different mechanisms. Here, we investigated whether partner resemblance for schizophrenia and bipolar disorder is influenced by assortative mating using polygenic risk scores (PRSs). METHODS PRSs from The Danish High-Risk and Resilience Study-VIA 7 were compared between parents in three subsamples: population-based control parent pairs (N=198), parent pairs where at least one parent had schizophrenia (N=193), and parent pairs where at least one parent had bipolar disorder (N=115). RESULTS The PRS for schizophrenia was predictive of schizophrenia in the full sample and showed a significant correlation between parent pairs (r=0.121, p=0.0440), indicative of assortative mating. The PRS for bipolar disorder was also correlated between parent pairs (r=0.162, p=0.0067), but it was not predictive of bipolar disorder in the full sample, limiting the interpretation. CONCLUSIONS Our study provides genetic evidence for assortative mating for schizophrenia, with important implications for our understanding of the genetics of schizophrenia.
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Affiliation(s)
- Oskar Hougaard Jefsen
- Psychosis Research Unit, Aarhus University Hospital, Central Denmark Region, Aarhus, Denmark
| | - Ron Nudel
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- CORE – Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
| | - Yunpeng Wang
- Centre for Lifespan Changes in Brain and Cognition, Department of Psychology, University of Oslo, Oslo, Norway
| | - Jonas Bybjerg-Grauholm
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Center for Neonatal Screening, Department for Congenital Disorders, Statens Serum Institute, Copenhagen, Denmark
| | - Nicoline Hemager
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- CORE – Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
- Child and Adolescent Mental Health Centre – Research Unit, Mental Health Services in the Capital Region of Denmark, Copenhagen, Denmark
| | - Camilla A. J. Christiani
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- CORE – Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
| | - Birgitte K. Burton
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Child and Adolescent Mental Health Centre – Research Unit, Mental Health Services in the Capital Region of Denmark, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Katrine S. Spang
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Child and Adolescent Mental Health Centre – Research Unit, Mental Health Services in the Capital Region of Denmark, Copenhagen, Denmark
| | - Ditte Ellersgaard
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- CORE – Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
| | - Ditte L. Gantriis
- Psychosis Research Unit, Aarhus University Hospital, Central Denmark Region, Aarhus, Denmark
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
| | - Kerstin Jessica Plessen
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Child and Adolescent Mental Health Centre – Research Unit, Mental Health Services in the Capital Region of Denmark, Copenhagen, Denmark
- Division of Child and Adolescent Psychiatry, Department of Psychiatry, University Hospital Lausanne and University of Lausanne, Lausanne, Switzerland
| | - Jens Richardt M. Jepsen
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- CORE – Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
- Child and Adolescent Mental Health Centre – Research Unit, Mental Health Services in the Capital Region of Denmark, Copenhagen, Denmark
- Mental Health Services in the Capital Region of Denmark, Center for Neuropsychiatric Schizophrenia Research and Center for Clinical Intervention and Neuropsychiatric Schizophrenia Research, Hellerup, Denmark
| | - Anne A. E. Thorup
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Child and Adolescent Mental Health Centre – Research Unit, Mental Health Services in the Capital Region of Denmark, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Thomas Werge
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Institute of Biological Psychiatry, Mental Health Centre Sct. Hans, Mental Health Services Copenhagen, Roskilde, Denmark
| | - Merete Nordentoft
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- CORE – Copenhagen Research Centre for Mental Health, Mental Health Centre Copenhagen, Copenhagen University Hospital, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Ole Mors
- Psychosis Research Unit, Aarhus University Hospital, Central Denmark Region, Aarhus, Denmark
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
| | - Aja Neergaard Greve
- Psychosis Research Unit, Aarhus University Hospital, Central Denmark Region, Aarhus, Denmark
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
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28
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Modeling assortative mating and genetic similarities between partners, siblings, and in-laws. Nat Commun 2022; 13:1108. [PMID: 35233010 PMCID: PMC8888605 DOI: 10.1038/s41467-022-28774-y] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 02/10/2022] [Indexed: 12/17/2022] Open
Abstract
Assortative mating on heritable traits can have implications for the genetic resemblance between siblings and in-laws in succeeding generations. We studied polygenic scores and phenotypic data from pairs of partners (n = 26,681), siblings (n = 2,170), siblings-in-law (n = 3,905), and co-siblings-in-law (n = 1,763) in the Norwegian Mother, Father and Child Cohort Study. Using structural equation models, we estimated associations between measurement error-free latent genetic and phenotypic variables. We found evidence of genetic similarity between partners for educational attainment (rg = 0.37), height (rg = 0.13), and depression (rg = 0.08). Common genetic variants associated with educational attainment correlated between siblings above 0.50 (rg = 0.68) and between siblings-in-law (rg = 0.25) and co-siblings-in-law (rg = 0.09). Indirect assortment on secondary traits accounted for partner similarity in education and depression, but not in height. Comparisons between the genetic similarities of partners and siblings indicated that genetic variances were in intergenerational equilibrium. This study shows genetic similarities between extended family members and that assortative mating has taken place for several generations.
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