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      Using Polygenic Scores in Social Science Research: Unraveling Childlessness


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          Biological, genetic, and socio-demographic factors are all important in explaining reproductive behavior, yet these factors are typically studied in isolation. In this study, we explore an innovative sociogenomic approach, which entails including key socio-demographic (marriage, education, occupation, religion, cohort) and genetic factors related to both behavioral [age at first birth (AFB), number of children ever born (NEB)] and biological fecundity-related outcomes (endometriosis, age at menopause and menarche, polycystic ovary syndrome, azoospermia, testicular dysgenesis syndrome) to explain childlessness. We examine the association of all sets of factors with childlessness as well as the interplay between them. We derive polygenic scores (PGS) from recent genome-wide association studies (GWAS) and apply these in the Health and Retirement Study ( N = 10,686) and Wisconsin Longitudinal Study ( N = 8,284). Both socio-demographic and genetic factors were associated with childlessness. Whilst socio-demographic factors explain 19–46% in childlessness, the current PGS explains <1% of the variance, and only PGSs from large GWASs are related to childlessness. Our findings also indicate that genetic and socio-demographic factors are not independent, with PGSs for AFB and NEB related to education and age at marriage. The explained variance by polygenic scores on childlessness is limited since it is largely a behavioral trait, with genetic explanations expected to increase somewhat in the future with better-powered GWASs. As genotyping of individuals in social science surveys becomes more prevalent, the method described in this study can be applied to other outcomes.

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          Most cited references56

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          Principal components analysis corrects for stratification in genome-wide association studies.

          Population stratification--allele frequency differences between cases and controls due to systematic ancestry differences-can cause spurious associations in disease studies. We describe a method that enables explicit detection and correction of population stratification on a genome-wide scale. Our method uses principal components analysis to explicitly model ancestry differences between cases and controls. The resulting correction is specific to a candidate marker's variation in frequency across ancestral populations, minimizing spurious associations while maximizing power to detect true associations. Our simple, efficient approach can easily be applied to disease studies with hundreds of thousands of markers.
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            An Atlas of Genetic Correlations across Human Diseases and Traits

            Identifying genetic correlations between complex traits and diseases can provide useful etiological insights and help prioritize likely causal relationships. The major challenges preventing estimation of genetic correlation from genome-wide association study (GWAS) data with current methods are the lack of availability of individual genotype data and widespread sample overlap among meta-analyses. We circumvent these difficulties by introducing a technique – cross-trait LD Score regression – for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use this method to estimate 276 genetic correlations among 24 traits. The results include genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity and associations between educational attainment and several diseases. These results highlight the power of genome-wide analyses, since there currently are no significantly associated SNPs for anorexia nervosa and only three for educational attainment.
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              Finding the missing heritability of complex diseases.

              Genome-wide association studies have identified hundreds of genetic variants associated with complex human diseases and traits, and have provided valuable insights into their genetic architecture. Most variants identified so far confer relatively small increments in risk, and explain only a small proportion of familial clustering, leading many to question how the remaining, 'missing' heritability can be explained. Here we examine potential sources of missing heritability and propose research strategies, including and extending beyond current genome-wide association approaches, to illuminate the genetics of complex diseases and enhance its potential to enable effective disease prevention or treatment.

                Author and article information

                Front Sociol
                Front Sociol
                Front. Sociol.
                Frontiers in Sociology
                Frontiers Media S.A.
                22 November 2019
                : 4
                [1] 1Department of Sociology and ICS, University of Groningen , Groningen, Netherlands
                [2] 2Department of Public Administration and Sociology, Erasmus University Rotterdam , Rotterdam, Netherlands
                [3] 3Department of Sociology and Nuffield College, University of Oxford , Oxford, United Kingdom
                [4] 4Department of Epidemiology, University of Groningen, University Medical Center Groningen , Groningen, Netherlands
                [5] 5Institute of Social and Economic Research, University of Essex , Essex, United Kingdom
                [6] 6École Nationale de la Statistique et de L'administration Économique (ENSAE) , Paris, France
                [7] 7Center for Research in Economics and Statistics (CREST) , Paris, France
                [8] 8Department of Surgery, University of Utah , Salt Lake City, UT, United States
                [9] 9Wellcome Centre for Human Genetics, University of Oxford , Oxford, United Kingdom
                [10] 10Department of Bio and Health Informatics, Technical University of Denmark , Lyngby, Denmark
                [11] 11Department of Growth and Reproduction, Rigshospitalet , Copenhagen, Denmark
                [12] 12Division of Endocrinology, Metabolism, and Molecular Medicine, Department of Medicine, Northwestern University Feinberg School of Medicine , Chicago, IL, United States
                [13] 13Center for Genetic Medicine, Northwestern University Feinberg School of Medicine , Chicago, IL, United States
                [14] 14Department of Anthropology, Northwestern University , Evanston, IL, United States
                [15] 15Department of Endocrinology, Diabetes and Bone Disease, Icahn School of Medicine at Mount Sinai , New York, NY, United States
                [16] 16Department of Sociology, University of North Carolina at Chapel Hill , Chapel Hill, NC, United States
                Author notes

                Edited by: Michelle Luciano, University of Edinburgh, United Kingdom

                Reviewed by: Daniel Briley, University of Illinois at Urbana-Champaign, United States; Brooke M. Huibregtse, University of Colorado Boulder, United States

                *Correspondence: Renske M. Verweij verweij@ 123456essb.eur.nl

                This article was submitted to Evolutionary Sociology and Biosociology, a section of the journal Frontiers in Sociology

                Copyright © 2019 Verweij, Mills, Stulp, Nolte, Barban, Tropf, Carrell, Aston, Zondervan, Rahmioglu, Dalgaard, Skaarup, Hayes, Dunaif, Guo and Snieder.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                Page count
                Figures: 4, Tables: 2, Equations: 0, References: 64, Pages: 14, Words: 10825
                Original Research

                fertility,childlessness,polygenic risk scores,sociogenomics,infertility


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