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      Research Review: How to interpret associations between polygenic scores, environmental risks, and phenotypes

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          Abstract

          Background

          Genetic influences are ubiquitous as virtually all phenotypes and most exposures typically classified as environmental have been found to be heritable. A polygenic score summarises the associations between millions of genetic variants and an outcome in a single value for each individual. Ever lowering costs have enabled the genotyping of many samples relevant to child psychology and psychiatry research, including cohort studies, leading to the proliferation of polygenic score studies. It is tempting to assume that associations detected between polygenic scores and phenotypes in those studies only reflect genetic effects. However, such associations can reflect many pathways (e.g. via environmental mediation) and biases.

          Methods

          Here, we provide a comprehensive overview of the many reasons why associations between polygenic scores, environmental exposures, and phenotypes exist. We include formal representations of common analyses in polygenic score studies using structural equation modelling. We derive biases, provide illustrative empirical examples and, when possible, mention steps that can be taken to alleviate those biases.

          Results

          Structural equation models and derivations show the many complexities arising from jointly modelling polygenic scores with environmental exposures and phenotypes. Counter‐intuitive examples include that: (a) associations between polygenic scores and phenotypes may exist even in the absence of direct genetic effects; (b) associations between child polygenic scores and environmental exposures can exist in the absence of evocative/active gene–environment correlations; and (c) adjusting an exposure‐outcome association for a polygenic score can increase rather than decrease bias.

          Conclusions

          Strikingly, using polygenic scores may, in some cases, lead to more bias than not using them. Appropriately conducting and interpreting polygenic score studies thus requires researchers in child psychology and psychiatry and beyond to be versed in both epidemiological and genetic methods or build on interdisciplinary collaborations.

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          Most cited references73

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          LD Score regression distinguishes confounding from polygenicity in genome-wide association studies.

          Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size.
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            Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions

            Major depression is a debilitating psychiatric illness that is typically associated with low mood and anhedonia. Depression has a heritable component that has remained difficult to elucidate with current sample sizes due to the polygenic nature of the disorder. To maximize sample size, we meta-analyzed data on 807,553 individuals (246,363 cases and 561,190 controls) from the three largest genome-wide association studies of depression. We identified 102 independent variants, 269 genes, and 15 genesets associated with depression, including both genes and gene pathways associated with synaptic structure and neurotransmission. An enrichment analysis provided further evidence of the importance of prefrontal brain regions. In an independent replication sample of 1,306,354 individuals (414,055 cases and 892,299 controls), 87 of the 102 associated variants were significant after multiple testing correction. These findings advance our understanding of the complex genetic architecture of depression and provide several future avenues for understanding etiology and developing new treatment approaches.
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              Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry

              Recent genome-wide association studies (GWAS) of height and body mass index (BMI) in ∼250000 European participants have led to the discovery of ∼700 and ∼100 nearly independent single nucleotide polymorphisms (SNPs) associated with these traits, respectively. Here we combine summary statistics from those two studies with GWAS of height and BMI performed in ∼450000 UK Biobank participants of European ancestry. Overall, our combined GWAS meta-analysis reaches N ∼700000 individuals and substantially increases the number of GWAS signals associated with these traits. We identified 3290 and 941 near-independent SNPs associated with height and BMI, respectively (at a revised genome-wide significance threshold of P < 1 × 10-8), including 1185 height-associated SNPs and 751 BMI-associated SNPs located within loci not previously identified by these two GWAS. The near-independent genome-wide significant SNPs explain ∼24.6% of the variance of height and ∼6.0% of the variance of BMI in an independent sample from the Health and Retirement Study (HRS). Correlations between polygenic scores based upon these SNPs with actual height and BMI in HRS participants were ∼0.44 and ∼0.22, respectively. From analyses of integrating GWAS and expression quantitative trait loci (eQTL) data by summary-data-based Mendelian randomization, we identified an enrichment of eQTLs among lead height and BMI signals, prioritizing 610 and 138 genes, respectively. Our study demonstrates that, as previously predicted, increasing GWAS sample sizes continues to deliver, by the discovery of new loci, increasing prediction accuracy and providing additional data to achieve deeper insight into complex trait biology. All summary statistics are made available for follow-up studies.
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                Author and article information

                Contributors
                j.pingault@ucl.ac.uk
                Journal
                J Child Psychol Psychiatry
                J Child Psychol Psychiatry
                10.1111/(ISSN)1469-7610
                JCPP
                Journal of Child Psychology and Psychiatry, and Allied Disciplines
                John Wiley and Sons Inc. (Hoboken )
                0021-9630
                1469-7610
                28 March 2022
                October 2022
                : 63
                : 10 , New horizons in gene‐environment interplay in developmental psychopathology ( doiID: 10.1111/jcpp.v63.10 )
                : 1125-1139
                Affiliations
                [ 1 ] ringgold 4919; Division of Psychology and Language Sciences Department of Clinical, Educational and Health Psychology University College London London UK
                [ 2 ] Social, Genetic and Developmental Psychiatry Centre Institute of Psychiatry, Psychology and Neuroscience King’s College London London UK
                [ 3 ] Faculty of Social Sciences Anton de Kom University of Suriname Paramaribo Suriname
                [ 4 ] ringgold 4488; Department of Health Sciences University of Leicester Leicester UK
                Author notes
                [*] [* ] Correspondence

                Jean‐Baptiste Pingault, Division of Psychology and Language Sciences, Department of Clinical, Educational and Health Psychology, University College London, London WC1H 0AP, UK; Email: j.pingault@ 123456ucl.ac.uk

                Author information
                https://orcid.org/0000-0003-2557-4716
                https://orcid.org/0000-0003-4048-4292
                https://orcid.org/0000-0002-2418-3265
                https://orcid.org/0000-0001-6349-8772
                https://orcid.org/0000-0002-5703-5058
                https://orcid.org/0000-0003-4762-2803
                https://orcid.org/0000-0002-8817-8908
                Article
                JCPP13607
                10.1111/jcpp.13607
                9790749
                35347715
                754e0bcf-1ad2-45e0-a9b9-05da65ee758a
                © 2022 The Authors. Journal of Child Psychology and Psychiatry published by John Wiley & Sons Ltd on behalf of Association for Child and Adolescent Mental Health.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 23 February 2022
                Page count
                Figures: 7, Tables: 2, Pages: 1139, Words: 12909
                Funding
                Funded by: H2020 European Research Council , doi 10.13039/100010663;
                Award ID: 863981
                Funded by: Advanced Medical Research Foundation , doi 10.13039/100001927;
                Award ID: MRF‐160‐0002‐ELP‐PINGA
                Funded by: Wellcome Trust , doi 10.13039/100010269;
                Award ID: 215917/Z/19/Z
                Categories
                Research Review
                Research Reviews
                Custom metadata
                2.0
                October 2022
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.2.3 mode:remove_FC converted:25.12.2022

                Clinical Psychology & Psychiatry
                polygenic scores,phenotypes,environment,epidemiology,biases
                Clinical Psychology & Psychiatry
                polygenic scores, phenotypes, environment, epidemiology, biases

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