Comparison of adopted and non-adopted individuals reveals gene-environment interplay for education in the UK Biobank

AbstractIndividual-level polygenic scores can now explain ∼10% of the variation in number of years of completed education. However, associations between polygenic scores and education capture not only genetic propensity but information about the environment that individuals are exposed to. This is because individuals passively inherit effects of parental genotypes, since their parents typically also provide the rearing environment. In other words, the strong correlation between offspring and parent genotypes results in an association between the offspring genotypes and the rearing environment. This is termed passive gene-environment correlation. We present an approach to test for the extent of passive gene-environment correlation for education without requiring intergenerational data. Specifically, we use information from 6311 individuals in the UK Biobank who were adopted in childhood to compare genetic influence on education between adoptees and non-adopted individuals. Adoptees’ rearing environments are less correlated with their genotypes, because they do not share genes with their adoptive parents. We find that polygenic scores are twice as predictive of years of education in non-adopted individuals compared to adoptees (R2= 0.074 vs 0.037, difference test p= 8.23 × 10−24). We provide another kind of evidence for the influence of parental behaviour on offspring education: individuals in the lowest decile of education polygenic score attain significantly more education if they are adopted, possibly due to educationally supportive adoptive environments. Overall, these results suggest that genetic influences on education are mediated via the home environment. As such, polygenic prediction of educational attainment represents gene-environment correlations just as much as it represents direct genetic effects.

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Comparison of Adopted and Nonadopted Individuals Reveals Gene–Environment Interplay for Education in the UK Biobank

Psychological Science ◽

10.1177/0956797620904450 ◽

2020 ◽

Vol 31 (5) ◽

pp. 582-591 ◽

Cited By ~ 7

Author(s):

Rosa Cheesman ◽

Avina Hunjan ◽

Jonathan R. I. Coleman ◽

Yasmin Ahmadzadeh ◽

Robert Plomin ◽

...

Keyword(s):

Educational Attainment ◽

Home Environment ◽

Family Environment ◽

Adoptive Parents ◽

Genetic Influences ◽

Uk Biobank ◽

Gene Environment ◽

Polygenic Scores ◽

The Family ◽

The Uk

Polygenic scores now explain approximately 10% of the variation in educational attainment. However, they capture not only genetic propensity but also information about the family environment. This is because of passive gene–environment correlation, whereby the correlation between offspring and parent genotypes results in an association between offspring genotypes and the rearing environment. We measured passive gene–environment correlation using information on 6,311 adoptees in the UK Biobank. Adoptees’ genotypes were less correlated with their rearing environments because they did not share genes with their adoptive parents. We found that polygenic scores were twice as predictive of years of education in nonadopted individuals compared with adoptees ( R2s = .074 vs. .037, p = 8.23 × 10−24). Individuals in the lowest decile of polygenic scores for education attained significantly more education if they were adopted, possibly because of educationally supportive adoptive environments. Overall, these results suggest that genetic influences on education are mediated via the home environment.

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Contrasting Broad- and Clinically- defined Polygenic Indicators of Depression and Depression-related Phenotypes in Adults and Children

10.31234/osf.io/pn9vb ◽

2020 ◽

Author(s):

John E. McGeary ◽

Chelsie Benca-Bachman ◽

Victoria Risner ◽

Christopher G Beevers ◽

Brandon Gibb ◽

...

Keyword(s):

Suicidal Ideation ◽

Cognitive Reappraisal ◽

Twin Studies ◽

European Ancestry ◽

Summary Statistics ◽

Depression Severity ◽

Uk Biobank ◽

Polygenic Scores ◽

Adults And Children ◽

The Uk

Twin studies indicate that 30-40% of the disease liability for depression can be attributed to genetic differences. Here, we assess the explanatory ability of polygenic scores (PGS) based on broad- (PGSBD) and clinical- (PGSMDD) depression summary statistics from the UK Biobank using independent cohorts of adults (N=210; 100% European Ancestry) and children (N=728; 70% European Ancestry) who have been extensively phenotyped for depression and related neurocognitive phenotypes. PGS associations with depression severity and diagnosis were generally modest, and larger in adults than children. Polygenic prediction of depression-related phenotypes was mixed and varied by PGS. Higher PGSBD, in adults, was associated with a higher likelihood of having suicidal ideation, increased brooding and anhedonia, and lower levels of cognitive reappraisal; PGSMDD was positively associated with brooding and negatively related to cognitive reappraisal. Overall, PGS based on both broad and clinical depression phenotypes have modest utility in adult and child samples of depression.

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Estimating the effective sample size in association studies of quantitative traits

G3 Genes|Genome|Genetics ◽

10.1093/g3journal/jkab057 ◽

2021 ◽

Author(s):

Andrey Ziyatdinov ◽

Jihye Kim ◽

Dmitry Prokopenko ◽

Florian Privé ◽

Fabien Laporte ◽

...

Keyword(s):

Statistical Power ◽

Quantitative Traits ◽

Mixed Model ◽

Association Studies ◽

Effective Sample Size ◽

Environment Interaction ◽

Uk Biobank ◽

Gene Environment Interaction ◽

Gene Environment ◽

The Uk

Abstract The effective sample size (ESS) is a metric used to summarize in a single term the amount of correlation in a sample. It is of particular interest when predicting the statistical power of genome-wide association studies (GWAS) based on linear mixed models. Here, we introduce an analytical form of the ESS for mixed-model GWAS of quantitative traits and relate it to empirical estimators recently proposed. Using our framework, we derived approximations of the ESS for analyses of related and unrelated samples and for both marginal genetic and gene-environment interaction tests. We conducted simulations to validate our approximations and to provide a quantitative perspective on the statistical power of various scenarios, including power loss due to family relatedness and power gains due to conditioning on the polygenic signal. Our analyses also demonstrate that the power of gene-environment interaction GWAS in related individuals strongly depends on the family structure and exposure distribution. Finally, we performed a series of mixed-model GWAS on data from the UK Biobank and confirmed the simulation results. We notably found that the expected power drop due to family relatedness in the UK Biobank is negligible.

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Polygenic risk for schizophrenia and season of birth within the UK Biobank cohort

Psychological Medicine ◽

10.1017/s0033291718000454 ◽

2018 ◽

Vol 49 (15) ◽

pp. 2499-2504 ◽

Cited By ~ 7

Author(s):

Valentina Escott-Price ◽

Daniel J. Smith ◽

Kimberley Kendall ◽

Joey Ward ◽

George Kirov ◽

...

Keyword(s):

Environmental Exposure ◽

Copy Number Variants ◽

Season Of Birth ◽

Uk Biobank ◽

Month Of Birth ◽

Risk Alleles ◽

Gene Environment ◽

Increased Risk ◽

The Uk ◽

Pathogenic Process

AbstractBackgroundThere is strong evidence that people born in winter and in spring have a small increased risk of schizophrenia. As this ‘season of birth’ effect underpins some of the most influential hypotheses concerning potentially modifiable risk exposures, it is important to exclude other possible explanations for the phenomenon.MethodsHere we sought to determine whether the season of birth effect reflects gene-environment confounding rather than a pathogenic process indexing environmental exposure. We directly measured, in 136 538 participants from the UK Biobank (UKBB), the burdens of common schizophrenia risk alleles and of copy number variants known to increase the risk for the disorder, and tested whether these were correlated with a season of birth.ResultsNeither genetic measure was associated with season or month of birth within the UKBB sample.ConclusionsAs our study was highly powered to detect small effects, we conclude that the season of birth effect in schizophrenia reflects a true pathogenic effect of environmental exposure.

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Socioeconomic deprivation index is associated with psychiatric disorders: an observational and genome-wide gene-environment interaction analysis in the UK Biobank cohort

Biological Psychiatry ◽

10.1016/j.biopsych.2020.11.019 ◽

2020 ◽

Author(s):

Jing Ye ◽

Yan Wen ◽

Xifang Sun ◽

Xiaomeng Chu ◽

Ping Li ◽

...

Keyword(s):

Psychiatric Disorders ◽

Interaction Analysis ◽

Socioeconomic Deprivation ◽

Deprivation Index ◽

Environment Interaction ◽

Uk Biobank ◽

Gene Environment Interaction ◽

Gene Environment ◽

Genome Wide ◽

The Uk

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Polygenic scores for UK Biobank scale data

10.1101/252270 ◽

2018 ◽

Cited By ~ 5

Author(s):

Timothy Shin Heng Mak ◽

Robert Milan Porsch ◽

Shing Wan Choi ◽

Pak Chung Sham

Keyword(s):

External Source ◽

Summary Statistics ◽

Uk Biobank ◽

Validation Data ◽

Raw Data ◽

Cross Prediction ◽

Polygenic Scores ◽

The Difference ◽

The Uk ◽

Meta Analyses

AbstractPolygenic scores (PGS) are estimated scores representing the genetic tendency of an individual for a disease or trait and have become an indispensible tool in a variety of analyses. Typically they are linear combination of the genotypes of a large number of SNPs, with the weights calculated from an external source, such as summary statistics from large meta-analyses. Recently cohorts with genetic data have become very large, such that it would be a waste if the raw data were not made use of in constructing PGS. Making use of raw data in calculating PGS, however, presents us with problems of overfitting. Here we discuss the essence of overfitting as applied in PGS calculations and highlight the difference between overfitting due to the overlap between the target and the discovery data (OTD), and overfitting due to the overlap between the target the the validation data (OTV). We propose two methods — cross prediction and split validation — to overcome OTD and OTV respectively. Using these two methods, PGS can be calculated using raw data without overfitting. We show that PGSs thus calculated have better predictive power than those using summary statistics alone for six phenotypes in the UK Biobank data.

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Maximizing GWAS discovery and genomic prediction accuracy in Biobank data

10.1101/2021.08.12.456099 ◽

2021 ◽

Author(s):

Etienne J Orliac ◽

Daniel Trejo Banos ◽

Sven Erik Ojavee ◽

Kristi Läll ◽

Reedik Mägi ◽

...

Keyword(s):

Genomic Prediction ◽

Prediction Accuracy ◽

Large Scale ◽

Mixed Linear Model ◽

Snp Markers ◽

Genetic Associations ◽

Baseline Model ◽

Individual Level ◽

Polygenic Scores ◽

The Uk

Across 21 heritable traits in the UK and Estonian Biobank data, a Bayesian grouped mixture of regressions model (GMRM) obtains the highest genomic prediction accuracy reported to date, 15% (SD 10%) greater than a baseline model without MAF-LD-annotation groups, and 106% (SD 50%) greater than mixed-linear model association (MLMA) estimate polygenic scores. Prediction accuracy was up to 13% (mean 4%, SD 3%) higher than theoretical expectations, at 76% of the h2SNP for height (R2 of 47%) and over 50% of the h2SNP for 12 traits. Using these predictors in MLMA, increased the independent GWAS loci detected from 16,899 using standard approaches to 18,837 using GMRM, an 11.5% increase. Modelling genetic associations accounting for MAF and LD differences among SNP markers, and incorporating prior knowledge of genomic function, is important for large-scale individual-level biobank-scale analyses and is facilitated by our scalable highly parallel open source GMRM software.

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Polynomial Mendelian Randomization reveals widespread non-linear causal effects in the UK Biobank

10.1101/2021.12.08.471751 ◽

2021 ◽

Author(s):

Jonathan Sulc ◽

Jenny Sjaarda ◽

Zoltan Kutalik

Keyword(s):

Causal Inference ◽

Large Scale ◽

Causal Effect ◽

Causal Effects ◽

Mendelian Randomisation ◽

Uk Biobank ◽

Individual Level ◽

Glucose Levels ◽

Causal Function ◽

The Uk

Causal inference is a critical step in improving our understanding of biological processes and Mendelian randomisation (MR) has emerged as one of the foremost methods to efficiently interrogate diverse hypotheses using large-scale, observational data from biobanks. Although many extensions have been developed to address the three core assumptions of MR-based causal inference (relevance, exclusion restriction, and exchangeability), most approaches implicitly assume that any putative causal effect is linear. Here we propose PolyMR, an MR-based method which provides a polynomial approximation of an (arbitrary) causal function between an exposure and an outcome. We show that this method provides accurate inference of the shape and magnitude of causal functions with greater accuracy than existing methods. We applied this method to data from the UK Biobank, testing for effects between anthropometric traits and continuous health-related phenotypes and found most of these (84%) to have causal effects which deviate significantly from linear. These deviations ranged from slight attenuation at the extremes of the exposure distribution, to large changes in the magnitude of the effect across the range of the exposure (e.g. a 1 kg/m2 change in BMI having stronger effects on glucose levels if the initial BMI was higher), to non-monotonic causal relationships (e.g. the effects of BMI on cholesterol forming an inverted U shape). Finally, we show that the linearity assumption of the causal effect may lead to the misinterpretation of health risks at the individual level or heterogeneous effect estimates when using cohorts with differing average exposure levels.

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