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      Genetic Variation in the Social Environment Contributes to Health and Disease

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          Abstract

          Assessing the impact of the social environment on health and disease is challenging. As social effects are in part determined by the genetic makeup of social partners, they can be studied from associations between genotypes of one individual and phenotype of another (social genetic effects, SGE, also called indirect genetic effects). For the first time we quantified the contribution of SGE to more than 100 organismal phenotypes and genome-wide gene expression measured in laboratory mice. We find that genetic variation in cage mates (i.e. SGE) contributes to variation in organismal and molecular measures related to anxiety, wound healing, immune function, and body weight. Social genetic effects explained up to 29% of phenotypic variance, and for several traits their contribution exceeded that of direct genetic effects (effects of an individual’s genotypes on its own phenotype). Importantly, we show that ignoring SGE can severely bias estimates of direct genetic effects (heritability). Thus SGE may be an important source of “missing heritability” in studies of complex traits in human populations. In summary, our study uncovers an important contribution of the social environment to phenotypic variation, sets the basis for using SGE to dissect social effects, and identifies an opportunity to improve studies of direct genetic effects.

          Author Summary

          Daily interactions between individuals can influence their health both in positive and negative ways. Often the mechanisms mediating social effects are unknown, so current approaches to study social effects are limited to a few phenotypes for which the mediating mechanisms are known a priori or suspected. Here we propose to leverage the fact that most traits are genetically controlled to investigate the influence of the social environment. To do so, we study associations between genotypes of one individual and phenotype of another individual (social genetic effects, SGE, also called indirect genetic effects). Importantly, SGE can be studied even when the traits that mediate the influence of the social environment are not known. For the first time we quantified the contribution of SGE to more than 100 organismal phenotypes and genome-wide gene expression measured in laboratory mice. We find that genetic variation in cage mates (i.e. SGE) explains up to 29% of the variation in anxiety, wound healing, immune function, and body weight. Hence our study uncovers an unexpectedly large influence of the social environment. Additionally, we show that ignoring SGE can severely bias estimates of direct genetic effects (effects of an individual’s genotypes on its own phenotype), which has important implications for the study of the genetic basis of complex traits.

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          Most cited references35

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          Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score.

          The first national single-step, full-information (phenotype, pedigree, and marker genotype) genetic evaluation was developed for final score of US Holsteins. Data included final scores recorded from 1955 to 2009 for 6,232,548 Holsteins cows. BovineSNP50 (Illumina, San Diego, CA) genotypes from the Cooperative Dairy DNA Repository (Beltsville, MD) were available for 6,508 bulls. Three analyses used a repeatability animal model as currently used for the national US evaluation. The first 2 analyses used final scores recorded up to 2004. The first analysis used only a pedigree-based relationship matrix. The second analysis used a relationship matrix based on both pedigree and genomic information (single-step approach). The third analysis used the complete data set and only the pedigree-based relationship matrix. The fourth analysis used predictions from the first analysis (final scores up to 2004 and only a pedigree-based relationship matrix) and prediction using a genomic based matrix to obtain genetic evaluation (multiple-step approach). Different allele frequencies were tested in construction of the genomic relationship matrix. Coefficients of determination between predictions of young bulls from parent average, single-step, and multiple-step approaches and their 2009 daughter deviations were 0.24, 0.37 to 0.41, and 0.40, respectively. The highest coefficient of determination for a single-step approach was observed when using a genomic relationship matrix with assumed allele frequencies of 0.5. Coefficients for regression of 2009 daughter deviations on parent-average, single-step, and multiple-step predictions were 0.76, 0.68 to 0.79, and 0.86, respectively, which indicated some inflation of predictions. The single-step regression coefficient could be increased up to 0.92 by scaling differences between the genomic and pedigree-based relationship matrices with little loss in accuracy of prediction. One complete evaluation took about 2h of computing time and 2.7 gigabytes of memory. Computing times for single-step analyses were slightly longer (2%) than for pedigree-based analysis. A national single-step genetic evaluation with the pedigree relationship matrix augmented with genomic information provided genomic predictions with accuracy and bias comparable to multiple-step procedures and could account for any population or data structure. Advantages of single-step evaluations should increase in the future when animals are pre-selected on genotypes. Copyright 2010 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.
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            Peer influence on risk taking, risk preference, and risky decision making in adolescence and adulthood: an experimental study.

            In this study, 306 individuals in 3 age groups--adolescents (13-16), youths (18-22), and adults (24 and older)--completed 2 questionnaire measures assessing risk preference and risky decision making, and 1 behavioral task measuring risk taking. Participants in each age group were randomly assigned to complete the measures either alone or with 2 same-aged peers. Analyses indicated that (a) risk taking and risky decision making decreased with age; (b) participants took more risks, focused more on the benefits than the costs of risky behavior, and made riskier decisions when in peer groups than alone; and (c) peer effects on risk taking and risky decision making were stronger among adolescents and youths than adults. These findings support the idea that adolescents are more inclined toward risky behavior and risky decision making than are adults and that peer influence plays an important role in explaining risky behavior during adolescence.
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              Genome-wide genetic association of complex traits in heterogeneous stock mice.

              Difficulties in fine-mapping quantitative trait loci (QTLs) are a major impediment to progress in the molecular dissection of complex traits in mice. Here we show that genome-wide high-resolution mapping of multiple phenotypes can be achieved using a stock of genetically heterogeneous mice. We developed a conservative and robust bootstrap analysis to map 843 QTLs with an average 95% confidence interval of 2.8 Mb. The QTLs contribute to variation in 97 traits, including models of human disease (asthma, type 2 diabetes mellitus, obesity and anxiety) as well as immunological, biochemical and hematological phenotypes. The genetic architecture of almost all phenotypes was complex, with many loci each contributing a small proportion to the total variance. Our data set, freely available at http://gscan.well.ox.ac.uk, provides an entry point to the functional characterization of genes involved in many complex traits.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Genet
                PLoS Genet
                plos
                plosgen
                PLoS Genetics
                Public Library of Science (San Francisco, CA USA )
                1553-7390
                1553-7404
                25 January 2017
                January 2017
                : 13
                : 1
                : e1006498
                Affiliations
                [1 ]European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, United Kingdom
                [2 ]Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, Tennessee, United States of America
                [3 ]AP-HP, Hôpital Lariboisière, Department of Biochemistry, INSERM U942, Paris, France
                [4 ]Wellcome Trust Centre for Human Genetics, Oxford, United Kingdom
                [5 ]INRA, UMR 1388 GenPhySE, Castanet Tolosan, France
                Stanford University, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                • Conceptualization: AB OS.

                • Formal analysis: AB FPC JK AL.

                • Investigation: AB MKM CJB JFI JC.

                • Methodology: AB MKM FPC AL RWW OS.

                • Resources: AB JML RWW OS.

                • Writing – original draft: AB OS.

                • Writing – review & editing: AB MKM FPC JFI JC JK AL RWW OS.

                Author information
                http://orcid.org/0000-0003-2448-0283
                Article
                PGENETICS-D-16-00490
                10.1371/journal.pgen.1006498
                5266220
                28121987
                3db9bf08-35cc-4b8a-8a65-961b6adcb7e0
                © 2017 Baud et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 2 March 2016
                : 21 November 2016
                Page count
                Figures: 2, Tables: 3, Pages: 25
                Funding
                Funded by: Wellcome Trust (GB)
                Award ID: 090532/Z/09/Z
                Funded by: funder-id http://dx.doi.org/10.13039/100004440, Wellcome Trust;
                Award ID: 105941/Z/14/Z
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100000265, Medical Research Council;
                Award ID: G0900747 91070
                Funded by: European Molecular Biology Organization (DE)
                Award ID: EIPOD
                Award Recipient :
                Funded by: National Institute on Alcohol Abuse and Alcoholism (US)
                Award ID: U01 AA016662
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000027, National Institute on Alcohol Abuse and Alcoholism;
                Award ID: U01 AA013499
                Award Recipient :
                Funded by: National Institute on Alcohol Abuse and Alcoholism (US)
                Award ID: U01 AA014425
                Award Recipient :
                Funded by: UTHSC Center for Integrative and Translational Genomics
                Award Recipient :
                The High-Throughput Genomics Group at the Wellcome Trust Centre for Human Genetics is funded by Wellcome Trust grant reference 090532/Z/09/Z and MRC Hub grant G0900747 91070. AB was supported by fellowships from the EMBL Interdisciplinary Postdoc Programme under Marie Curie COFUND Actions and the Wellcome Trust (105941/Z/14/Z). MKM, JFI, CJB, and RWW are supported in part by NIAAA grants (U01 AA016662, U01 AA013499, U01 AA014425) and the UTHSC Center for Integrative and Translational Genomics. The funders played no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Genetics
                Phenotypes
                Biology and Life Sciences
                Genetics
                Gene Expression
                Research and Analysis Methods
                Experimental Organism Systems
                Inbred Strains
                Biology and Life Sciences
                Evolutionary Biology
                Population Genetics
                Genetic Polymorphism
                Biology and Life Sciences
                Genetics
                Population Genetics
                Genetic Polymorphism
                Biology and Life Sciences
                Population Biology
                Population Genetics
                Genetic Polymorphism
                Physical Sciences
                Mathematics
                Probability Theory
                Random Variables
                Covariance
                Biology and Life Sciences
                Genetics
                Genomics
                Animal Genomics
                Mammalian Genomics
                Biology and Life Sciences
                Physiology
                Physiological Processes
                Tissue Repair
                Wound Healing
                Medicine and Health Sciences
                Physiology
                Physiological Processes
                Tissue Repair
                Wound Healing
                Biology and Life Sciences
                Physiology
                Physiological Parameters
                Body Weight
                Medicine and Health Sciences
                Physiology
                Physiological Parameters
                Body Weight
                Custom metadata
                Gene expression data from the experiment with inbred strains are available from ArrayExpress E-MTAB-5276. Phenotype data for the same experiment are provided as S7 Table.

                Genetics
                Genetics

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