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Smolen C, Jensen M, Dyer L, Pizzo L, Tyryshkina A, Banerjee D, Rohan L, Huber E, El Khattabi L, Prontera P, Caberg JH, Van Dijck A, Schwartz C, Faivre L, Callier P, Mosca-Boidron AL, Lefebvre M, Pope K, Snell P, Lockhart PJ, Castiglia L, Galesi O, Avola E, Mattina T, Fichera M, Luana Mandarà GM, Bruccheri MG, Pichon O, Le Caignec C, Stoeva R, Cuinat S, Mercier S, Bénéteau C, Blesson S, Nordsletten A, Martin-Coignard D, Sistermans E, Kooy RF, Amor DJ, Romano C, Isidor B, Juusola J, Girirajan S. Assortative mating and parental genetic relatedness contribute to the pathogenicity of variably expressive variants. Am J Hum Genet 2023; 110:2015-2028. [PMID: 37979581 PMCID: PMC10716518 DOI: 10.1016/j.ajhg.2023.10.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 10/25/2023] [Accepted: 10/27/2023] [Indexed: 11/20/2023] Open
Abstract
We examined more than 97,000 families from four neurodevelopmental disease cohorts and the UK Biobank to identify phenotypic and genetic patterns in parents contributing to neurodevelopmental disease risk in children. We identified within- and cross-disorder correlations between six phenotypes in parents and children, such as obsessive-compulsive disorder (R = 0.32-0.38, p < 10-126). We also found that measures of sub-clinical autism features in parents are associated with several autism severity measures in children, including biparental mean Social Responsiveness Scale scores and proband Repetitive Behaviors Scale scores (regression coefficient = 0.14, p = 3.38 × 10-4). We further describe patterns of phenotypic similarity between spouses, where spouses show correlations for six neurological and psychiatric phenotypes, including a within-disorder correlation for depression (R = 0.24-0.68, p < 0.001) and a cross-disorder correlation between anxiety and bipolar disorder (R = 0.09-0.22, p < 10-92). Using a simulated population, we also found that assortative mating can lead to increases in disease liability over generations and the appearance of "genetic anticipation" in families carrying rare variants. We identified several families in a neurodevelopmental disease cohort where the proband inherited multiple rare variants in disease-associated genes from each of their affected parents. We further identified parental relatedness as a risk factor for neurodevelopmental disorders through its inverse relationship with variant pathogenicity and propose that parental relatedness modulates disease risk by increasing genome-wide homozygosity in children (R = 0.05-0.26, p < 0.05). Our results highlight the utility of assessing parent phenotypes and genotypes toward predicting features in children who carry rare variably expressive variants and implicate assortative mating as a risk factor for increased disease severity in these families.
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Affiliation(s)
- Corrine Smolen
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA; Bioinformatics and Genomics Graduate program, Pennsylvania State University, University Park, PA 16802, USA
| | - Matthew Jensen
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA; Bioinformatics and Genomics Graduate program, Pennsylvania State University, University Park, PA 16802, USA
| | | | - Lucilla Pizzo
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA
| | - Anastasia Tyryshkina
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA; Neuroscience Graduate Program, Pennsylvania State University, University Park, PA 16802, USA
| | - Deepro Banerjee
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA; Bioinformatics and Genomics Graduate program, Pennsylvania State University, University Park, PA 16802, USA
| | - Laura Rohan
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA
| | - Emily Huber
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA
| | - Laila El Khattabi
- Assistance Publique-Hôpitaux de Paris, Department of Medical Genetics, Armand Trousseau and Pitié-Salpêtrière Hospitals, Paris, France
| | - Paolo Prontera
- Medical Genetics Unit, Hospital "Santa Maria della Misericordia", Perugia, Italy
| | - Jean-Hubert Caberg
- Centre Hospitalier Universitaire de Liège. Domaine Universitaire du Sart Tilman, Liège, Belgium
| | - Anke Van Dijck
- Department of Medical Genetics, University and University Hospital Antwerp, Antwerp, Belgium
| | | | - Laurence Faivre
- Centre de Genetique et Cenre de Référence Anomalies du développement et syndromes malformatifs, Hôpital d'Enfants, CHU Dijon, Dijon, France; GAD INSERM UMR1231, FHU TRANSLAD, Université de Bourgogne Franche Comté, Dijon, France
| | - Patrick Callier
- Centre de Genetique et Cenre de Référence Anomalies du développement et syndromes malformatifs, Hôpital d'Enfants, CHU Dijon, Dijon, France; GAD INSERM UMR1231, FHU TRANSLAD, Université de Bourgogne Franche Comté, Dijon, France
| | | | - Mathilde Lefebvre
- GAD INSERM UMR1231, FHU TRANSLAD, Université de Bourgogne Franche Comté, Dijon, France
| | - Kate Pope
- Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia
| | - Penny Snell
- Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia
| | - Paul J Lockhart
- Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia; Bruce Lefroy Center, Murdoch Children's Research Institute, Melbourne, VIC, Australia
| | - Lucia Castiglia
- Research Unit of Rare Diseases and Neurodevelopmental Disorders, Oasi Research Institute-IRCCS, 94018 Troina, Italy
| | - Ornella Galesi
- Research Unit of Rare Diseases and Neurodevelopmental Disorders, Oasi Research Institute-IRCCS, 94018 Troina, Italy
| | - Emanuela Avola
- Research Unit of Rare Diseases and Neurodevelopmental Disorders, Oasi Research Institute-IRCCS, 94018 Troina, Italy
| | - Teresa Mattina
- Medical Genetics, Department of Biomedical and Biotechnological Sciences, University of Catania, 95123 Catania, Italy
| | - Marco Fichera
- Research Unit of Rare Diseases and Neurodevelopmental Disorders, Oasi Research Institute-IRCCS, 94018 Troina, Italy; Medical Genetics, Department of Biomedical and Biotechnological Sciences, University of Catania, 95123 Catania, Italy
| | | | - Maria Grazia Bruccheri
- Research Unit of Rare Diseases and Neurodevelopmental Disorders, Oasi Research Institute-IRCCS, 94018 Troina, Italy
| | - Olivier Pichon
- CHU Nantes, Department of Medical Genetics, Nantes, France
| | - Cedric Le Caignec
- CHU Toulouse, Department of Medical Genetics, Toulouse, France; ToNIC, Toulouse Neuro Imaging, Center, Inserm, UPS, Université de Toulouse, Toulouse, France
| | - Radka Stoeva
- Service de Cytogenetique, CHU de Le Mans, Le Mans, France
| | | | - Sandra Mercier
- CHU Nantes, Department of Medical Genetics, Nantes, France
| | | | - Sophie Blesson
- Department of Genetics, Bretonneau University Hospital, Tours, France
| | | | | | - Erik Sistermans
- Department of Clinical Genetics, Amsterdam UMC, Amsterdam, the Netherlands
| | - R Frank Kooy
- Department of Medical Genetics, University and University Hospital Antwerp, Antwerp, Belgium
| | - David J Amor
- Bruce Lefroy Center, Murdoch Children's Research Institute, Melbourne, VIC, Australia
| | - Corrado Romano
- Medical Genetics, Department of Biomedical and Biotechnological Sciences, University of Catania, 95123 Catania, Italy; Medical Genetics, ASP Ragusa, Ragusa, Italy
| | | | | | - Santhosh Girirajan
- Department of Biochemistry and Molecular Biology, Pennsylvania State University, University Park, PA 16802, USA; Bioinformatics and Genomics Graduate program, Pennsylvania State University, University Park, PA 16802, USA; Neuroscience Graduate Program, Pennsylvania State University, University Park, PA 16802, USA; Department of Anthropology, Pennsylvania State University, University Park, PA 16802, USA.
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2
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Gouveia MH, Bentley AR, Leal TP, Tarazona-Santos E, Bustamante CD, Adeyemo AA, Rotimi CN, Shriner D. Unappreciated subcontinental admixture in Europeans and European Americans and implications for genetic epidemiology studies. Nat Commun 2023; 14:6802. [PMID: 37935687 PMCID: PMC10630423 DOI: 10.1038/s41467-023-42491-0] [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: 03/16/2023] [Accepted: 10/12/2023] [Indexed: 11/09/2023] Open
Abstract
European-ancestry populations are recognized as stratified but not as admixed, implying that residual confounding by locus-specific ancestry can affect studies of association, polygenic adaptation, and polygenic risk scores. We integrate individual-level genome-wide data from ~19,000 European-ancestry individuals across 79 European populations and five European American cohorts. We generate a new reference panel that captures ancestral diversity missed by both the 1000 Genomes and Human Genome Diversity Projects. Both Europeans and European Americans are admixed at the subcontinental level, with admixture dates differing among subgroups of European Americans. After adjustment for both genome-wide and locus-specific ancestry, associations between a highly differentiated variant in LCT (rs4988235) and height or LDL-cholesterol were confirmed to be false positives whereas the association between LCT and body mass index was genuine. We provide formal evidence of subcontinental admixture in individuals with European ancestry, which, if not properly accounted for, can produce spurious results in genetic epidemiology studies.
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Affiliation(s)
- Mateus H Gouveia
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Amy R Bentley
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Thiago P Leal
- Department of Genomic Medicine, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44197, USA
| | - Eduardo Tarazona-Santos
- Departamento de Genética, Ecologia e Evolução, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, 31270-910, Brazil
| | - Carlos D Bustamante
- Center for Computational, Evolutionary and Human Genomics (CEHG), Stanford University, Stanford, CA, 94305, USA
| | - Adebowale A Adeyemo
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Charles N Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
| | - Daniel Shriner
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA.
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Otten K, Mandemakers JJ. Partners in Health: Investigating Social Genetic Effects Among Married and Cohabiting Couples. Behav Genet 2023; 53:348-358. [PMID: 37284978 PMCID: PMC10276063 DOI: 10.1007/s10519-023-10147-w] [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: 11/10/2022] [Accepted: 05/30/2023] [Indexed: 06/08/2023]
Abstract
Partners resemble each other in health behaviors and outcomes such as alcohol use, smoking, physical activity, and obesity. While this is consistent with social contagion theory suggesting partner influence, it is notoriously difficult to establish causality because of assortative mating and contextual confounding. We offer a novel approach to studying social contagion in health in long-term partnerships by combining genetic data of both partners in married/cohabiting couples with longitudinal data on their health behaviors and outcomes. We examine the influence of the partner's genetic predisposition for three health outcomes and behaviors (BMI, smoking, and drinking) among married/cohabiting couples. We use longitudinal data from the Health and Retirement Study and the English Longitudinal Study of Ageing with data on health outcomes and genotypes for both partners. Results show that changes over time in BMI, smoking, and drinking depend on the partner's genetic predispositions to these traits. These findings underline the importance of people's social surroundings for their health and highlight the potential of targeting health interventions at couples.
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Affiliation(s)
- Kasper Otten
- Department of Sociology, Utrecht University, Padualaan 14, 3584 CH Utrecht, the Netherlands
| | - Jornt J Mandemakers
- Department of Sociology, Utrecht University, Padualaan 14, 3584 CH Utrecht, the Netherlands
- Atlas Research, Amsterdam, the Netherlands
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4
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Caliebe A, Tekola‐Ayele F, Darst BF, Wang X, Song YE, Gui J, Sebro RA, Balding DJ, Saad M, Dubé M. Including diverse and admixed populations in genetic epidemiology research. Genet Epidemiol 2022; 46:347-371. [PMID: 35842778 PMCID: PMC9452464 DOI: 10.1002/gepi.22492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 05/31/2022] [Accepted: 06/06/2022] [Indexed: 11/25/2022]
Abstract
The inclusion of ancestrally diverse participants in genetic studies can lead to new discoveries and is important to ensure equitable health care benefit from research advances. Here, members of the Ethical, Legal, Social, Implications (ELSI) committee of the International Genetic Epidemiology Society (IGES) offer perspectives on methods and analysis tools for the conduct of inclusive genetic epidemiology research, with a focus on admixed and ancestrally diverse populations in support of reproducible research practices. We emphasize the importance of distinguishing socially defined population categorizations from genetic ancestry in the design, analysis, reporting, and interpretation of genetic epidemiology research findings. Finally, we discuss the current state of genomic resources used in genetic association studies, functional interpretation, and clinical and public health translation of genomic findings with respect to diverse populations.
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Affiliation(s)
- Amke Caliebe
- Institute of Medical Informatics and StatisticsKiel University and University Hospital Schleswig‐HolsteinKielGermany
| | - Fasil Tekola‐Ayele
- Epidemiology Branch, Division of Population Health Research, Division of Intramural Research, Eunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institutes of HealthBethesdaMarylandUSA
| | - Burcu F. Darst
- Center for Genetic EpidemiologyUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
- Public Health Sciences DivisionFred Hutchinson Cancer Research CenterSeattleWashingtonUSA
| | - Xuexia Wang
- Department of MathematicsUniversity of North TexasDentonTexasUSA
| | - Yeunjoo E. Song
- Department of Population and Quantitative Health SciencesCase Western Reserve UniversityClevelandOhioUSA
| | - Jiang Gui
- Department of Biomedical Data Science, Geisel School of Medicine, Dartmouth CollegeOne Medical Center Dr.LebanonNew HampshireUSA
| | | | - David J. Balding
- Melbourne Integrative Genomics, Schools of BioSciences and of Mathematics & StatisticsUniversity of MelbourneMelbourneAustralia
| | - Mohamad Saad
- Qatar Computing Research InstituteHamad Bin Khalifa UniversityDohaQatar
- Neuroscience Research Center, Faculty of Medical SciencesLebanese UniversityBeirutLebanon
| | - Marie‐Pierre Dubé
- Department of Medicine, and Social and Preventive MedicineUniversité de MontréalMontréalQuébecCanada
- Beaulieu‐Saucier Pharmacogenomcis CentreMontreal Heart InstituteMontrealCanada
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5
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Gopalan S, Smith SP, Korunes K, Hamid I, Ramachandran S, Goldberg A. Human genetic admixture through the lens of population genomics. Philos Trans R Soc Lond B Biol Sci 2022; 377:20200410. [PMID: 35430881 PMCID: PMC9014191 DOI: 10.1098/rstb.2020.0410] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Over the past 50 years, geneticists have made great strides in understanding how our species' evolutionary history gave rise to current patterns of human genetic diversity classically summarized by Lewontin in his 1972 paper, ‘The Apportionment of Human Diversity’. One evolutionary process that requires special attention in both population genetics and statistical genetics is admixture: gene flow between two or more previously separated source populations to form a new admixed population. The admixture process introduces ancestry-based structure into patterns of genetic variation within and between populations, which in turn influences the inference of demographic histories, identification of genetic targets of selection and prediction of complex traits. In this review, we outline some challenges for admixture population genetics, including limitations of applying methods designed for populations without recent admixture to the study of admixed populations. We highlight recent studies and methodological advances that aim to overcome such challenges, leveraging genomic signatures of admixture that occurred in the past tens of generations to gain insights into human history, natural selection and complex trait architecture. This article is part of the theme issue ‘Celebrating 50 years since Lewontin's apportionment of human diversity’.
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Affiliation(s)
- Shyamalika Gopalan
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27708, USA
| | - Samuel Pattillo Smith
- Center for Computational Molecular Biology, Brown University, Providence, RI 02912, USA
- Department of Ecology, Evolution and Organismal Biology, Brown University, Providence, RI 02912, USA
| | - Katharine Korunes
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27708, USA
| | - Iman Hamid
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27708, USA
| | - Sohini Ramachandran
- Center for Computational Molecular Biology, Brown University, Providence, RI 02912, USA
- Department of Ecology, Evolution and Organismal Biology, Brown University, Providence, RI 02912, USA
- Data Science Initiative, Brown University, Providence, RI 02912, USA
| | - Amy Goldberg
- Department of Evolutionary Anthropology, Duke University, Durham, NC 27708, USA
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Versluys TMM, Flintham EO, Mas-Sandoval A, Savolainen V. Why do we pick similar mates, or do we? Biol Lett 2021; 17:20210463. [PMID: 34813721 DOI: 10.1098/rsbl.2021.0463] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Humans often mate with those resembling themselves, a phenomenon described as positive assortative mating (PAM). The causes of this attract broad interest, but there is little agreement on the topic. This may be because empirical studies and reviews sometimes focus on just a few explanations, often based on disciplinary conventions. This review presents an interdisciplinary conceptual framework on the causes of PAM in humans, drawing on human and non-human biology, the social sciences, and the humanities. Viewing causality holistically, we first discuss the proximate causes (i.e. the 'how') of PAM, considering three mechanisms: stratification, convergence and mate choice. We also outline methods to control for confounders when studying mate choice. We then discuss ultimate explanations (i.e. 'the why') for PAM, including adaptive and non-adaptive processes. We conclude by suggesting a focus on interdisciplinarity in future research.
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Affiliation(s)
- Thomas M M Versluys
- Georgina Mace Centre for the Living Planet, Imperial College London, Silwood Park Campus, Buckhurst Road, Ascot, Berkshire SL5 7PY, United Kingdom
| | - Ewan O Flintham
- Georgina Mace Centre for the Living Planet, Imperial College London, Silwood Park Campus, Buckhurst Road, Ascot, Berkshire SL5 7PY, United Kingdom
| | - Alex Mas-Sandoval
- Georgina Mace Centre for the Living Planet, Imperial College London, Silwood Park Campus, Buckhurst Road, Ascot, Berkshire SL5 7PY, United Kingdom
| | - Vincent Savolainen
- Georgina Mace Centre for the Living Planet, Imperial College London, Silwood Park Campus, Buckhurst Road, Ascot, Berkshire SL5 7PY, United Kingdom
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7
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Howe LJ, Battram T, Morris TT, Hartwig FP, Hemani G, Davies NM, Smith GD. Assortative mating and within-spouse pair comparisons. PLoS Genet 2021; 17:e1009883. [PMID: 34735433 PMCID: PMC8594845 DOI: 10.1371/journal.pgen.1009883] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 11/16/2021] [Accepted: 10/15/2021] [Indexed: 12/20/2022] Open
Abstract
Spousal comparisons have been proposed as a design that can both reduce confounding and estimate effects of the shared adulthood environment. However, assortative mating, the process by which individuals select phenotypically (dis)similar mates, could distort associations when comparing spouses. We evaluated the use of spousal comparisons, as in the within-spouse pair (WSP) model, for aetiological research such as genetic association studies. We demonstrated that the WSP model can reduce confounding but may be susceptible to collider bias arising from conditioning on assorted spouse pairs. Analyses using UK Biobank spouse pairs found that WSP genetic association estimates were smaller than estimates from random pairs for height, educational attainment, and BMI variants. Within-sibling pair estimates, robust to demographic and parental effects, were also smaller than random pair estimates for height and educational attainment, but not for BMI. WSP models, like other within-family models, may reduce confounding from demographic factors in genetic association estimates, and so could be useful for triangulating evidence across study designs to assess the robustness of findings. However, WSP estimates should be interpreted with caution due to potential collider bias.
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Affiliation(s)
- Laurence J. Howe
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Thomas Battram
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Tim T. Morris
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Fernando P. Hartwig
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas, Brazil
| | - Gibran Hemani
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Neil M. Davies
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
- K.G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway
| | - George Davey Smith
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, University of Bristol, Bristol, United Kingdom
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
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Abstract
Throughout human history, large-scale migrations have facilitated the formation of populations with ancestry from multiple previously separated populations. This process leads to subsequent shuffling of genetic ancestry through recombination, producing variation in ancestry between populations, among individuals in a population, and along the genome within an individual. Recent methodological and empirical developments have elucidated the genomic signatures of this admixture process, bringing previously understudied admixed populations to the forefront of population and medical genetics. Under this theme, we present a collection of recent PLOS Genetics publications that exemplify recent progress in human genetic admixture studies, and we discuss potential areas for future work.
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Affiliation(s)
- Katharine L. Korunes
- Department of Evolutionary Anthropology, Duke University, Durham, North Carolina, United States of America
| | - Amy Goldberg
- Department of Evolutionary Anthropology, Duke University, Durham, North Carolina, United States of America
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9
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Goldberg A, Rastogi A, Rosenberg NA. Assortative mating by population of origin in a mechanistic model of admixture. Theor Popul Biol 2020; 134:129-146. [PMID: 32275920 DOI: 10.1016/j.tpb.2020.02.004] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Revised: 02/11/2020] [Accepted: 02/27/2020] [Indexed: 02/01/2023]
Abstract
Populations whose mating pairs have levels of similarity in phenotypes or genotypes that differ systematically from the level expected under random mating are described as experiencing assortative mating. Excess similarity in mating pairs is termed positive assortative mating, and excess dissimilarity is negative assortative mating. In humans, empirical studies suggest that mating pairs from various admixed populations - whose ancestry derives from two or more source populations - possess correlated ancestry components that indicate the occurrence of positive assortative mating on the basis of ancestry. Generalizing a two-sex mechanistic admixture model, we devise a model of one form of ancestry-assortative mating that occurs through preferential mating based on source population. Under the model, we study the moments of the admixture fraction distribution for different assumptions about mating preferences, including both positive and negative assortative mating by population. We demonstrate that whereas the mean admixture under assortative mating is equivalent to that of a corresponding randomly mating population, the variance of admixture depends on the level and direction of assortative mating. We consider two special cases of assortative mating by population: first, a single admixture event, and second, constant contributions to the admixed population over time. In contrast to standard settings in which positive assortment increases variation within a population, certain assortative mating scenarios allow the variance of admixture to decrease relative to a corresponding randomly mating population: with the three populations we consider, the variance-increasing effect of positive assortative mating within a population might be overwhelmed by a variance-decreasing effect emerging from mating preferences involving other pairs of populations. The effect of assortative mating is smaller on the X chromosome than on the autosomes because inheritance of the X in males depends only on the mother's ancestry, not on the mating pair. Because the variance of admixture is informative about the timing of admixture and possibly about sex-biased admixture contributions, the effects of assortative mating are important to consider in inferring features of population history from distributions of admixture values. Our model provides a framework to quantitatively study assortative mating under flexible scenarios of admixture over time.
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Affiliation(s)
- Amy Goldberg
- Department of Evolutionary Anthropology, Duke University, Durham, NC, USA; Department of Biology, Stanford University, Stanford, CA, USA.
| | - Ananya Rastogi
- Department of Systems Immunology & Braunschweig Integrated Centre of Systems Biology, Helmholtz Centre for Infection Research, Braunschweig, Germany
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10
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Nishi A, Alexander M, Fowler JH, Christakis NA. Assortative mating at loci under recent natural selection in humans. Biosystems 2019; 187:104040. [PMID: 31585150 DOI: 10.1016/j.biosystems.2019.104040] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2019] [Revised: 09/23/2019] [Accepted: 09/24/2019] [Indexed: 11/25/2022]
Abstract
Genetic correlation between mates at specific loci can greatly alter the evolutionary trajectory of a species. Genetic assortative mating has been documented in humans, but its existence beyond population stratification (shared ancestry) has been a matter of controversy. Here, we develop a method to measure assortative mating across the genome at 1,044,854 single-nucleotide polymorphisms (SNPs), controlling for population stratification and cohort-specific cryptic relatedness. Using data on 1683 human couples from two data sources, we find evidence for both assortative and disassortative mating at specific, discernible loci throughout the entire genome. Then, using the composite of multiple signals (CMS) score, we also show that the group of SNPs exhibiting the most assortativity has been under stronger recent positive selection. Simulations using realistic inputs confirm that assortative mating might indeed affect changes in allele frequency over time. These results suggest that genetic assortative mating may be speeding up evolution in humans.
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Affiliation(s)
- Akihiro Nishi
- Department of Epidemiology, UCLA Fielding School of Public Health, Los Angeles, CA 90095, USA.
| | - Marcus Alexander
- Yale Institute for Network Science, Yale University, CT 06520, USA.
| | - James H Fowler
- Division of Medical Genetics and Department of Political Science, University of California, San Diego, La Jolla, CA, 92103, USA.
| | - Nicholas A Christakis
- Yale Institute for Network Science, Yale University, CT 06520, USA; Department of Sociology, Ecology and Evolutionary Biology, Medicine, Biomedical Engineering, and Statistics & Data Science, Yale University, New Haven, CT, USA.
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11
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Connolly S, Anney R, Gallagher L, Heron EA. Evidence of Assortative Mating in Autism Spectrum Disorder. Biol Psychiatry 2019; 86:286-293. [PMID: 31200929 DOI: 10.1016/j.biopsych.2019.04.014] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2017] [Revised: 04/02/2019] [Accepted: 04/03/2019] [Indexed: 10/27/2022]
Abstract
BACKGROUND Assortative mating is a nonrandom mating system in which individuals with similar genotypes and/or phenotypes mate with one another more frequently than would be expected in a random mating system. Assortative mating has been hypothesized to play a role in autism spectrum disorder (ASD) in an attempt to explain some of the increase in the prevalence of ASD that has recently been observed. ASD is considered to be a heritable neurodevelopmental disorder, but there is limited understanding of its causes. Assortative mating can be explored through both phenotypic and genotypic data, but up until now it has never been investigated through genotypic measures in ASD. METHODS We investigated genotypically similar mating pairs using genome-wide single nucleotide polymorphism data on trio families (Autism Genome Project data [1590 parents] and Simons Simplex Collection data [1962 parents]). To determine whether or not an excess in genetic similarity was present, we employed kinship coefficients and examined spousal correlation between the principal components in both the Autism Genome Project and Simons Simplex Collection datasets. We also examined assortative mating using phenotype data on the parents to detect any correlation between ASD traits. RESULTS We found significant evidence of genetic similarity between the parents of ASD offspring using both methods in the Autism Genome Project dataset. In the Simons Simplex Collection, there was also significant evidence of genetic similarity between the parents when explored through spousal correlation. CONCLUSIONS This study gives further support to the hypothesis that positive assortative mating plays a role in ASD.
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Affiliation(s)
- Siobhan Connolly
- Neuropsychiatric Genetics Research Group, Department of Psychiatry, Trinity College Dublin, Trinity Centre for Health Sciences, Dublin, Ireland; Computer Science and Mathematics Department, Dundalk Institute of Technology, Dundalk, Ireland.
| | - Richard Anney
- Neuropsychiatric Genetics Research Group, Department of Psychiatry, Trinity College Dublin, Trinity Centre for Health Sciences, Dublin, Ireland; Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cathays, Cardiff, United Kingdom
| | - Louise Gallagher
- Neuropsychiatric Genetics Research Group, Department of Psychiatry, Trinity College Dublin, Trinity Centre for Health Sciences, Dublin, Ireland
| | - Elizabeth A Heron
- Neuropsychiatric Genetics Research Group, Department of Psychiatry, Trinity College Dublin, Trinity Centre for Health Sciences, Dublin, Ireland
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Norris ET, Rishishwar L, Wang L, Conley AB, Chande AT, Dabrowski AM, Valderrama-Aguirre A, Jordan IK. Assortative Mating on Ancestry-Variant Traits in Admixed Latin American Populations. Front Genet 2019; 10:359. [PMID: 31105740 PMCID: PMC6491930 DOI: 10.3389/fgene.2019.00359] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2018] [Accepted: 04/04/2019] [Indexed: 11/13/2022] Open
Abstract
Assortative mating is a universal feature of human societies, and individuals from ethnically diverse populations are known to mate assortatively based on similarities in genetic ancestry. However, little is currently known regarding the exact phenotypic cues, or their underlying genetic architecture, which inform ancestry-based assortative mating. We developed a novel approach, using genome-wide analysis of ancestry-specific haplotypes, to evaluate ancestry-based assortative mating on traits whose expression varies among the three continental population groups – African, European, and Native American – that admixed to form modern Latin American populations. Application of this method to genome sequences sampled from Colombia, Mexico, Peru, and Puerto Rico revealed widespread ancestry-based assortative mating. We discovered a number of anthropometric traits (body mass, height, and facial development) and neurological attributes (educational attainment and schizophrenia) that serve as phenotypic cues for ancestry-based assortative mating. Major histocompatibility complex (MHC) loci show population-specific patterns of both assortative and disassortative mating in Latin America. Ancestry-based assortative mating in the populations analyzed here appears to be driven primarily by African ancestry. This study serves as an example of how population genomic analyses can yield novel insights into human behavior.
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Affiliation(s)
- Emily T Norris
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, United States.,IHRC-Georgia Tech Applied Bioinformatics Laboratory, Atlanta, GA, United States.,PanAmerican Bioinformatics Institute, Cali, Colombia
| | - Lavanya Rishishwar
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, United States.,IHRC-Georgia Tech Applied Bioinformatics Laboratory, Atlanta, GA, United States.,PanAmerican Bioinformatics Institute, Cali, Colombia
| | - Lu Wang
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, United States.,PanAmerican Bioinformatics Institute, Cali, Colombia
| | - Andrew B Conley
- IHRC-Georgia Tech Applied Bioinformatics Laboratory, Atlanta, GA, United States
| | - Aroon T Chande
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, United States.,IHRC-Georgia Tech Applied Bioinformatics Laboratory, Atlanta, GA, United States.,PanAmerican Bioinformatics Institute, Cali, Colombia
| | - Adam M Dabrowski
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, United States
| | | | - I King Jordan
- School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, United States.,IHRC-Georgia Tech Applied Bioinformatics Laboratory, Atlanta, GA, United States.,PanAmerican Bioinformatics Institute, Cali, Colombia
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13
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Yengo L, Robinson MR, Keller MC, Kemper KE, Yang Y, Trzaskowski M, Gratten J, Turley P, Cesarini D, Benjamin DJ, Wray NR, Goddard ME, Yang J, Visscher PM. Imprint of assortative mating on the human genome. Nat Hum Behav 2018; 2:948-954. [PMID: 30988446 DOI: 10.1038/s41562-018-0476-3] [Citation(s) in RCA: 60] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2018] [Accepted: 10/22/2018] [Indexed: 11/09/2022]
Abstract
Preference for mates with similar phenotypes; that is, assortative mating, is widely observed in humans1-5 and has evolutionary consequences6-8. Under Fisher's classical theory6, assortative mating is predicted to induce a signature in the genome at trait-associated loci that can be detected and quantified. Here, we develop and apply a method to quantify assortative mating on a specific trait by estimating the correlation (θ) between genetic predictors of the trait from single nucleotide polymorphisms on odd- versus even-numbered chromosomes. We show by theory and simulation that the effect of assortative mating can be quantified in the presence of population stratification. We applied this approach to 32 complex traits and diseases using single nucleotide polymorphism data from ~400,000 unrelated individuals of European ancestry. We found significant evidence of assortative mating for height (θ = 3.2%) and educational attainment (θ = 2.7%), both of which were consistent with theoretical predictions. Overall, our results imply that assortative mating involves multiple traits and affects the genomic architecture of loci that are associated with these traits, and that the consequence of mate choice can be detected from a random sample of genomes.
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Affiliation(s)
- Loic Yengo
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.
| | - Matthew R Robinson
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.,Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
| | - Matthew C Keller
- Department of Psychology and Neuroscience, Institute for Behavioral Genetics, University of Colorado at Boulder, Boulder, CO, USA
| | - Kathryn E Kemper
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Yuanhao Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Maciej Trzaskowski
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
| | - Jacob Gratten
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.,Mater Research, Translational Research Institute, Brisbane, Queensland, Australia
| | - Patrick Turley
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.,Stanley Centre for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - David Cesarini
- National Bureau of Economic Research, Cambridge, MA, USA.,Department of Economics, New York University, New York, NY, USA.,Center for Experimental Social Science, New York University, New York, NY, USA
| | - Daniel J Benjamin
- National Bureau of Economic Research, Cambridge, MA, USA.,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
| | - Naomi R Wray
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.,Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - Michael E Goddard
- Faculty of Veterinary and Agricultural Science, University of Melbourne, Melbourne, Victoria, Australia.,Biosciences Research Division, Department of Economic Development, Jobs, Transport and Resources Government of Victoria, Bundoora, Victoria, Australia
| | - Jian Yang
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia.,Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia
| | - Peter M Visscher
- Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia. .,Queensland Brain Institute, The University of Queensland, Brisbane, Queensland, Australia.
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14
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Li X, Redline S, Zhang X, Williams S, Zhu X. Height associated variants demonstrate assortative mating in human populations. Sci Rep 2017; 7:15689. [PMID: 29146993 PMCID: PMC5691191 DOI: 10.1038/s41598-017-15864-x] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2017] [Accepted: 11/03/2017] [Indexed: 12/23/2022] Open
Abstract
Understanding human mating patterns, which can affect population genetic structure, is important for correctly modeling populations and performing genetic association studies. Prior studies of assortative mating in humans focused on trait similarity among spouses and relatives via phenotypic correlations. Limited research has quantified the genetic consequences of assortative mating. The degree to which the non-random mating influences genetic architecture remains unclear. Here, we studied genetic variants associated with human height to assess the degree of height-related assortative mating in European-American and African-American populations. We compared the inbreeding coefficient estimated using known height associated variants with that calculated from frequency matched sets of random variants. We observed significantly higher inbreeding coefficients for the height associated variants than from frequency matched random variants (P < 0.05), demonstrating height-related assortative mating in both populations.
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Affiliation(s)
- Xiaoyin Li
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Susan Redline
- Departments of Medicine, Brigham and Women's Hospital and Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Xiang Zhang
- College of Information Sciences and Technology, The Pennsylvania State University, University Park, State College, PA, USA
| | - Scott Williams
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA
| | - Xiaofeng Zhu
- Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, 44106, USA.
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15
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Estimating Allele Frequencies. Methods Mol Biol 2017. [PMID: 28980242 DOI: 10.1007/978-1-4939-7274-6_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
Methods of estimating allele frequencies from data on unrelated and related individuals are described in this chapter. For samples of unrelated individuals with simple codominant markers, the natural estimators of allele frequencies can be used. For genetic data on related individuals, maximum likelihood estimation (MLE) can be applied to compute allele frequencies. Factors that influence allele frequencies in populations are also explained.
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16
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Algee-Hewitt BFB. Temporal trends in craniometric estimates of admixture for a modern American sample. AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY 2017; 163:729-740. [DOI: 10.1002/ajpa.23242] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2016] [Revised: 02/23/2017] [Accepted: 04/23/2017] [Indexed: 11/09/2022]
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17
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Abstract
Genetic similarity of spouses can reflect factors influencing mate choice, such as physical/behavioral characteristics, and patterns of social endogamy. Spouse correlations for both genetic ancestry and measured traits may impact genotype distributions (Hardy Weinberg and linkage equilibrium), and therefore genetic association studies. Here we evaluate white spouse-pairs from the Framingham Heart Study (FHS) original and offspring cohorts (N = 124 and 755, respectively) to explore spousal genetic similarity and its consequences. Two principal components (PCs) of the genome-wide association (GWA) data were identified, with the first (PC1) delineating clines of Northern/Western to Southern European ancestry and the second (PC2) delineating clines of Ashkenazi Jewish ancestry. In the original (older) cohort, there was a striking positive correlation between the spouses in PC1 (r = 0.73, P = 3x10-22) and also for PC2 (r = 0.80, P = 7x10-29). In the offspring cohort, the spouse correlations were lower but still highly significant for PC1 (r = 0.38, P = 7x10-28) and for PC2 (r = 0.45, P = 2x10-39). We observed significant Hardy-Weinberg disequilibrium for single nucleotide polymorphisms (SNPs) loading heavily on PC1 and PC2 across 3 generations, and also significant linkage disequilibrium between unlinked SNPs; both decreased with time, consistent with reduced ancestral endogamy over generations and congruent with theoretical calculations. Ignoring ancestry, estimates of spouse kinship have a mean significantly greater than 0, and more so in the earlier generations. Adjusting kinship estimates for genetic ancestry through the use of PCs led to a mean spouse kinship not different from 0, demonstrating that spouse genetic similarity could be fully attributed to ancestral assortative mating. These findings also have significance for studies of heritability that are based on distantly related individuals (kinship less than 0.05), as we also demonstrate the poor correlation of kinship estimates in that range when ancestry is or is not taken into account. We analyzed three generations of whites from the Framingham Heart Study (FHS) using genome-wide genotype data to characterize their genetic ancestry. By examination of spouse-pairs, we observed that individuals of Northern/Western European, Southern European and Ashkenazi ancestry preferentially chose spouses of the same ancestry, however, the degree of endogamy decreased in each successive generation, especially between Northern/Western and Southern Europeans. We then showed that the mating pattern results in Hardy-Weinberg disequilibrium (HWD) at ancestrally-informative SNPs, and also results in linkage disequilibrium (LD) between unlinked loci. The HWD and LD decrease as theoretically expected with the decrease in endogamy noted in each generation. In the FHS sample, spouse genetic similarity can be explained by ancestry-related assortative mating.
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18
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19
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Hugh-Jones D, Verweij KJ, St. Pourcain B, Abdellaoui A. Assortative mating on educational attainment leads to genetic spousal resemblance for polygenic scores. INTELLIGENCE 2016. [DOI: 10.1016/j.intell.2016.08.005] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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20
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Detecting Heterogeneity in Population Structure Across the Genome in Admixed Populations. Genetics 2016; 204:43-56. [PMID: 27440868 DOI: 10.1534/genetics.115.184184] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2015] [Accepted: 06/11/2016] [Indexed: 11/18/2022] Open
Abstract
The genetic structure of human populations is often characterized by aggregating measures of ancestry across the autosomal chromosomes. While it may be reasonable to assume that population structure patterns are similar genome-wide in relatively homogeneous populations, this assumption may not be appropriate for admixed populations, such as Hispanics and African-Americans, with recent ancestry from two or more continents. Recent studies have suggested that systematic ancestry differences can arise at genomic locations in admixed populations as a result of selection and nonrandom mating. Here, we propose a method, which we refer to as the chromosomal ancestry differences (CAnD) test, for detecting heterogeneity in population structure across the genome. CAnD can incorporate either local or chromosome-wide ancestry inferred from SNP genotype data to identify chromosomes harboring genomic regions with ancestry contributions that are significantly different than expected. In simulation studies with real genotype data from phase III of the HapMap Project, we demonstrate the validity and power of CAnD. We apply CAnD to the HapMap Mexican-American (MXL) and African-American (ASW) population samples; in this analysis the software RFMix is used to infer local ancestry at genomic regions, assuming admixing from Europeans, West Africans, and Native Americans. The CAnD test provides strong evidence of heterogeneity in population structure across the genome in the MXL sample ([Formula: see text]), which is largely driven by elevated Native American ancestry and deficit of European ancestry on the X chromosomes. Among the ASW, all chromosomes are largely African derived and no heterogeneity in population structure is detected in this sample.
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21
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Genetic and socioeconomic study of mate choice in Latinos reveals novel assortment patterns. Proc Natl Acad Sci U S A 2015; 112:13621-6. [PMID: 26483472 DOI: 10.1073/pnas.1501741112] [Citation(s) in RCA: 36] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Nonrandom mating in human populations has important implications for genetics and medicine as well as for economics and sociology. In this study, we performed an integrative analysis of a large cohort of Mexican and Puerto Rican couples using detailed socioeconomic attributes and genotypes. We found that in ethnically homogeneous Latino communities, partners are significantly more similar in their genomic ancestries than expected by chance. Consistent with this, we also found that partners are more closely related--equivalent to between third and fourth cousins in Mexicans and Puerto Ricans--than matched random male-female pairs. Our analysis showed that this genomic ancestry similarity cannot be explained by the standard socioeconomic measurables alone. Strikingly, the assortment of genomic ancestry in couples was consistently stronger than even the assortment of education. We found enriched correlation of partners' genotypes at genes known to be involved in facial development. We replicated our results across multiple geographic locations. We discuss the implications of assortment and assortment-specific loci on disease dynamics and disease mapping methods in Latinos.
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22
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Read TD, Massey RC. Characterizing the genetic basis of bacterial phenotypes using genome-wide association studies: a new direction for bacteriology. Genome Med 2014; 6:109. [PMID: 25593593 PMCID: PMC4295408 DOI: 10.1186/s13073-014-0109-z] [Citation(s) in RCA: 68] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Genome-wide association studies (GWASs) have become an increasingly important approach for eukaryotic geneticists, facilitating the identification of hundreds of genetic polymorphisms that are responsible for inherited diseases. Despite the relative simplicity of bacterial genomes, the application of GWASs to identify polymorphisms responsible for important bacterial phenotypes has only recently been made possible through advances in genome sequencing technologies. Bacterial GWASs are now about to come of age thanks to the availability of massive datasets, and because of the potential to bridge genomics and traditional genetic approaches that is provided by improving validation strategies. A small number of pioneering GWASs in bacteria have been published in the past 2 years, examining from 75 to more than 3,000 strains. The experimental designs have been diverse, taking advantage of different processes in bacteria for generating variation. Analysis of data from bacterial GWASs can, to some extent, be performed using software developed for eukaryotic systems, but there are important differences in genome evolution that must be considered. The greatest experimental advantage of bacterial GWASs is the potential to perform downstream validation of causality and dissection of mechanism. We review the recent advances and remaining challenges in this field and propose strategies to improve the validation of bacterial GWASs.
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Affiliation(s)
- Timothy D Read
- Department of Medicine, Division of Infectious Diseases, Emory University School of Medicine, Atlanta, GA 30322 USA ; Department of Human Genetics, Emory University School of Medicine, Atlanta, GA 30322 USA
| | - Ruth C Massey
- Department of Biology and Biochemistry, University of Bath, Bath, BA2 7AY UK
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23
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24
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Abstract
Understanding the social and biological mechanisms that lead to homogamy (similar individuals marrying one another) has been a long-standing issue across many fields of scientific inquiry. Using a nationally representative sample of non-Hispanic white US adults from the Health and Retirement Study and information from 1.7 million single-nucleotide polymorphisms, we compare genetic similarity among married couples to noncoupled pairs in the population. We provide evidence for genetic assortative mating in this population but the strength of this association is substantially smaller than the strength of educational assortative mating in the same sample. Furthermore, genetic similarity explains at most 10% of the assortative mating by education levels. Results are replicated using comparable data from the Framingham Heart Study.
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25
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Dani SU, März W, Neves PMS, Walter GF. Pairomics, the omics way to mate choice. J Hum Genet 2013; 58:643-56. [PMID: 23945982 DOI: 10.1038/jhg.2013.86] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2013] [Revised: 06/17/2013] [Accepted: 07/03/2013] [Indexed: 11/09/2022]
Abstract
The core aspects of the biology and evolution of sexual reproduction are reviewed with a focus on the diploid, sexually reproducing, outbreeding, polymorphic, unspecialized, altricial and cultural human species. Human mate choice and pair bonding are viewed as central to individuals' lives and to the evolution of the species, and genetic assistance in reproduction is viewed as a universal human right. Pairomics is defined as an emerging branch of the omics science devoted to the study of mate choice at the genomic level and its consequences for present and future generations. In pairomics, comprehensive genetic information of individual genomes is stored in a database. Computational tools are employed to analyze the mating schemes and rules that govern mating among the members of the database. Mating models and algorithms simulate the outcomes of mating any given genome with each of a number of genomes represented in the database. The analyses and simulations may help to understand mating schemes and their outcomes, and also contribute a new cue to the multicued schemes of mate choice. The scientific, medical, evolutionary, ethical, legal and social implications of pairomics are far reaching. The use of genetic information as a search tool in mate choice may influence our health, lifestyle, behavior and culture. As knowledge on genomics, population genetics and gene-environment interactions, as well as the size of genomic databases expand, so does the ability of pairomics to investigate and predict the consequences of mate choice for the present and future generations.
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Affiliation(s)
- Sergio Ulhoa Dani
- Medawar Institute for Medical and Environmental Research, Acangau Foundation, Paracatu, Brazil
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26
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Abstract
G. H. Hardy (1877-1947) and Wilhelm Weinberg (1862-1937) had very different lives, but in the minds of geneticists, the two are inextricably linked through the ownership of an apparently simple law called the Hardy-Weinberg law. We demonstrate that the simplicity is more apparent than real. Hardy derived the well-known trio of frequencies {q 2, 2pq, p 2} with a concise demonstration, whereas for Weinberg it was the prelude to an ingenious examination of the inheritance of twinning in man. Hardy's recourse to an identity relating to the distribution of types among offspring following random mating, rather than an identity relating to the mating matrix, may be the reason why he did not realize that Hardy-Weinberg equilibrium can be reached and sustained with non-random mating. The phrase 'random mating' always comes up in reference to the law. The elusive nature of this phrase is part of the reason for the misunderstandings that occur but also because, as we explain, mathematicians are able to use it in a different way from the man-in-the-street. We question the unthinking appeal to random mating as a model and explanation of the distribution of genotypes even when they are close to Hardy-Weinberg proportions. Such sustained proportions are possible under non-random mating.
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Han M, Hu YQ, Lin S. Joint detection of association, imprinting and maternal effects using all children and their parents. Eur J Hum Genet 2013; 21:1449-56. [PMID: 23531864 DOI: 10.1038/ejhg.2013.49] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2012] [Revised: 01/24/2013] [Accepted: 02/19/2013] [Indexed: 01/13/2023] Open
Abstract
Genomic imprinting and maternal effects have been increasingly explored for their contributions to complex diseases. Statistical methods have been proposed to detect both imprinting and maternal effects simultaneously based on nuclear families. However, these methods only make use of case-parents triads and possibly control-parents triads, thus wasting valuable information contained in the siblings. More seriously, most existing methods are full-likelihood based and have to make strong assumptions concerning mating-type probabilities (nuisance parameters) to avoid over-parametrization. In this paper, we develop a partial Likelihood approach for detecting Imprinting and Maternal Effects (LIME), using nuclear families with an arbitrary number of affected and unaffected children. By matching affected children with unaffected ones (within or across families) having the same triad/pair familial genotype combination, we derive a partial likelihood that is free of nuisance parameters. This alleviates the need to make strong, yet unrealistic assumptions about the population, leading to a procedure that is robust to departure from Hardy-Weinberg equilibrium. Power gain by including siblings and robustness of LIME under a variety of settings are demonstrated. Our simulation study also indicates that it is more profitable to recruit additional siblings than additional families when the total number of individuals is kept the same. We applied LIME to the Framingham Heart Study data to demonstrate its utility in analyzing real data. Many of our findings are consistent with results in the literature; potentially novel genes for hypertension have also emerged.
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Affiliation(s)
- Miao Han
- State Key Laboratory of Genetic Engineering, Institute of Biostatistics, School of Life Sciences, Fudan University, Shanghai, China
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Kim Y, Ripke S, Kirov G, Sklar P, Purcell SM, Owen MJ, O'Donovan MC, Sullivan PF. Non-random mating, parent-of-origin, and maternal-fetal incompatibility effects in schizophrenia. Schizophr Res 2013; 143:11-7. [PMID: 23177929 PMCID: PMC4197457 DOI: 10.1016/j.schres.2012.11.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2012] [Revised: 10/30/2012] [Accepted: 11/01/2012] [Indexed: 01/10/2023]
Abstract
Although the association of common genetic variation in the extended MHC region with schizophrenia is the most significant yet discovered, the MHC region is one of the more complex regions of the human genome, with unusually high gene density and long-range linkage disequilibrium. The statistical test on which the MHC association is based is a relatively simple, additive model which uses logistic regression of SNP genotypes to predict case-control status. However, it is plausible that more complex models underlie this association. Using a well-characterized sample of trios, we evaluated more complex models by looking for evidence for: (a) non-random mating for HLA alleles, schizophrenia risk profiles, and ancestry; (b) parent-of-origin effects for HLA alleles; and (c) maternal-fetal genotype incompatibility in the HLA. We found no evidence for non-random mating in the parents of individuals with schizophrenia in terms of MHC genotypes or schizophrenia risk profile scores. However, there was evidence of non-random mating that appeared mostly to be driven by ancestry. We did not detect over-transmission of HLA alleles to affected offspring via the general TDT test (without regard to parent of origin) or preferential transmission via paternal or maternal inheritance. We evaluated the hypothesis that maternal-fetal HLA incompatibility may increase risk for schizophrenia using eight classical HLA loci. The most significant alleles were in HLA-B, HLA-C, HLA-DQB1, and HLA-DRB1 but none was significant after accounting for multiple comparisons. We did not find evidence to support more complex models of gene action, but statistical power may have been limiting.
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Affiliation(s)
- Yunjung Kim
- Department of Genetics, University of North Carolina at Chapel Hill, NC, USA
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Yu Z, Gillen D, Li CF, Demetriou M. Incorporating parental information into family-based association tests. Biostatistics 2012; 14:556-72. [PMID: 23266418 DOI: 10.1093/biostatistics/kxs048] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Assumptions regarding the true underlying genetic model, or mode of inheritance, are necessary when quantifying genetic associations with disease phenotypes. Here we propose new methods to ascertain the underlying genetic model from parental data in family-based association studies. Specifically, for parental mating-type data, we propose a novel statistic to test whether the underlying genetic model is additive, dominant, or recessive; for parental genotype-phenotype data, we propose three strategies to determine the true mode of inheritance. We illustrate how to incorporate the information gleaned from these strategies into family-based association tests. Because family-based association tests are conducted conditional on parental genotypes, the type I error rate of these procedures is not inflated by the information learned from parental data. This result holds even if such information is weak or when the assumption of Hardy-Weinberg equilibrium is violated. Our simulations demonstrate that incorporating parental data into family-based association tests can improve power under common inheritance models. The application of our proposed methods to a candidate-gene study of type 1 diabetes successfully detects a recessive effect in MGAT5 that would otherwise be missed by conventional family-based association tests.
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Affiliation(s)
- Zhaoxia Yu
- Department of Statistics, University of California at Irvine, Irvine, CA 92697, USA.
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Thornton T, Tang H, Hoffmann T, Ochs-Balcom H, Caan B, Risch N. Estimating kinship in admixed populations. Am J Hum Genet 2012; 91:122-38. [PMID: 22748210 PMCID: PMC3397261 DOI: 10.1016/j.ajhg.2012.05.024] [Citation(s) in RCA: 162] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2012] [Revised: 04/29/2012] [Accepted: 05/31/2012] [Indexed: 12/21/2022] Open
Abstract
Genome-wide association studies (GWASs) are commonly used for the mapping of genetic loci that influence complex traits. A problem that is often encountered in both population-based and family-based GWASs is that of identifying cryptic relatedness and population stratification because it is well known that failure to appropriately account for both pedigree and population structure can lead to spurious association. A number of methods have been proposed for identifying relatives in samples from homogeneous populations. A strong assumption of population homogeneity, however, is often untenable, and many GWASs include samples from structured populations. Here, we consider the problem of estimating relatedness in structured populations with admixed ancestry. We propose a method, REAP (relatedness estimation in admixed populations), for robust estimation of identity by descent (IBD)-sharing probabilities and kinship coefficients in admixed populations. REAP appropriately accounts for population structure and ancestry-related assortative mating by using individual-specific allele frequencies at SNPs that are calculated on the basis of ancestry derived from whole-genome analysis. In simulation studies with related individuals and admixture from highly divergent populations, we demonstrate that REAP gives accurate IBD-sharing probabilities and kinship coefficients. We apply REAP to the Mexican Americans in Los Angeles, California (MXL) population sample of release 3 of phase III of the International Haplotype Map Project; in this sample, we identify third- and fourth-degree relatives who have not previously been reported. We also apply REAP to the African American and Hispanic samples from the Women's Health Initiative SNP Health Association Resource (WHI-SHARe) study, in which hundreds of pairs of cryptically related individuals have been identified.
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Affiliation(s)
- Timothy Thornton
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Hua Tang
- Department of Genetics, Stanford University, Stanford, CA 94305, USA
| | - Thomas J. Hoffmann
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94143, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94107, USA
| | - Heather M. Ochs-Balcom
- Department of Social and Preventive Medicine, University at Buffalo, Buffalo, NY 14214, USA
| | - Bette J. Caan
- Division of Research, Kaiser Permanente, Oakland, CA 94612, USA
| | - Neil Risch
- Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94143, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA 94107, USA
- Division of Research, Kaiser Permanente, Oakland, CA 94612, USA
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Sebro R, Risch NJ. A brief note on the resemblance between relatives in the presence of population stratification. Heredity (Edinb) 2012; 108:563-8. [PMID: 22234249 DOI: 10.1038/hdy.2011.124] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023] Open
Abstract
Population stratification occurs when a study population is comprised of several sub-populations, and can result in increased false positive findings in genomewide-association studies. Recently published work shows that sub-population-specific positive assortative mating at the genotypic level results in population stratification. We show that if the allele frequency of a single nucleotide polymorphism responsible for a trait varies between sub-populations and there is no dominance variance, then the heritability of the trait increases, primarily due to an increase in the additive genetic variance of the trait.
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Affiliation(s)
- R Sebro
- Institute for Human Genetics, University of California San Francisco, San Francisco, CA 94143-0794, USA.
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Abstract
Methods of estimating allele frequencies from data on unrelated and related individuals are described in this chapter. For samples of unrelated individuals with simple codominant markers, the natural estimator of allele frequencies can be used. For genetic data on related individuals, maximum likelihood estimation (MLE) can be applied to compute allele frequencies. Factors that influence allele frequencies in populations are also explained.
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Affiliation(s)
- Indra Adrianto
- Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA
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Sebro R, Lange C, Laird NM, Rogus JJ, Risch NJ. Differentiating Population Stratification from Genotyping Error Using Family Data. Ann Hum Genet 2011; 76:42-52. [DOI: 10.1111/j.1469-1809.2011.00689.x] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Bender N, Allemann N, Marek D, Vollenweider P, Waeber G, Mooser V, Egger M, Bochud M. Association between variants of the leptin receptor gene (LEPR) and overweight: a systematic review and an analysis of the CoLaus study. PLoS One 2011; 6:e26157. [PMID: 22028824 PMCID: PMC3196514 DOI: 10.1371/journal.pone.0026157] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2011] [Accepted: 09/21/2011] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND Three non-synonymous single nucleotide polymorphisms (Q223R, K109R and K656N) of the leptin receptor gene (LEPR) have been tested for association with obesity-related outcomes in multiple studies, showing inconclusive results. We performed a systematic review and meta-analysis on the association of the three LEPR variants with BMI. In addition, we analysed 15 SNPs within the LEPR gene in the CoLaus study, assessing the interaction of the variants with sex. METHODOLOGY/PRINCIPAL FINDINGS We searched electronic databases, including population-based studies that investigated the association between LEPR variants Q223R, K109R and K656N and obesity- related phenotypes in healthy, unrelated subjects. We furthermore performed meta-analyses of the genotype and allele frequencies in case-control studies. Results were stratified by SNP and by potential effect modifiers. CoLaus data were analysed by logistic and linear regressions and tested for interaction with sex. The meta-analysis of published data did not show an overall association between any of the tested LEPR variants and overweight. However, the choice of a BMI cut-off value to distinguish cases from controls was crucial to explain heterogeneity in Q223R. Differences in allele frequencies across ethnic groups are compatible with natural selection of derived alleles in Q223R and K109R and of the ancient allele in K656N in Asians. In CoLaus, the rs10128072, rs3790438 and rs3790437 variants showed interaction with sex for their association with overweight, waist circumference and fat mass in linear regressions. CONCLUSIONS Our systematic review and analysis of primary data from the CoLaus study did not show an overall association between LEPR SNPs and overweight. Most studies were underpowered to detect small effect sizes. A potential effect modification by sex, population stratification, as well as the role of natural selection should be addressed in future genetic association studies.
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Affiliation(s)
- Nicole Bender
- Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
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Bourgey M, Healy J, Saint-Onge P, Massé H, Sinnett D, Roy-Gagnon MH. Genome-wide detection and characterization of mating asymmetry in human populations. Genet Epidemiol 2011; 35:526-35. [DOI: 10.1002/gepi.20602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2011] [Revised: 04/22/2011] [Accepted: 05/20/2011] [Indexed: 11/06/2022]
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Khankhanian P, Gourraud PA, Caillier SJ, Santaniello A, Hauser SL, Baranzini SE, Oksenberg JR. Genetic variation in the odorant receptors family 13 and the mhc loci influence mate selection in a multiple sclerosis dataset. BMC Genomics 2010; 11:626. [PMID: 21067613 PMCID: PMC3091764 DOI: 10.1186/1471-2164-11-626] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2010] [Accepted: 11/10/2010] [Indexed: 12/17/2022] Open
Abstract
Background When selecting mates, many vertebrate species seek partners with major histocompatibility complex (MHC) genes different from their own, presumably in response to selective pressure against inbreeding and towards MHC diversity. Attempts at replication of these genetic results in human studies, however, have reached conflicting conclusions. Results Using a multi-analytical strategy, we report validated genome-wide relationships between genetic identity and human mate choice in 930 couples of European ancestry. We found significant similarity between spouses in the MHC at class I region in chromosome 6p21, and at the odorant receptor family 13 locus in chromosome 9. Conversely, there was significant dissimilarity in the MHC class II region, near the HLA-DQA1 and -DQB1 genes. We also found that genomic regions with significant similarity between spouses show excessive homozygosity in the general population (assessed in the HapMap CEU dataset). Conversely, loci that were significantly dissimilar among spouses were more likely to show excessive heterozygosity in the general population. Conclusions This study highlights complex patterns of genomic identity among partners in unrelated couples, consistent with a multi-faceted role for genetic factors in mate choice behavior in human populations.
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Affiliation(s)
- Pouya Khankhanian
- Department of Neurology, University of California, San Francisco, CA 94143-0435, USA
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