publications | Alexander Strudwick Young

2024

Family-GWAS reveals effects of environment and mating on genetic associations

Tammy Tan, Hariharan Jayashankar, Junming Guan, and 73 more authors

medRxiv, 2024

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Genome-wide association studies (GWAS) have discovered thousands of replicable genetic associations, guiding drug target discovery and powering genetic prediction of human phenotypes and diseases. However, genetic associations can be affected by gene-environment correlations and non-random mating, which can lead to biased inferences in downstream analyses. Family-based GWAS (FGWAS) uses the natural experiment of random assignment of genotype within families to separate out the contribution of direct genetic effects (DGEs) — causal effects of alleles in an individual on an individual — from other factors contributing to genetic associations. Here, we report results from an FGWAS meta-analysis of 34 phenotypes from 17 cohorts. We found evidence that factors uncorrelated with DGEs make substantial contributions to genetic associations for 27 phenotypes, with population stratification confounding — a form of gene-environment correlation — likely the major cause. By estimating SNP heritability and genetic correlations using DGEs, we found evidence that assortative mating has led to overestimation of SNP heritability for 5 phenotypes and overestimation of the degree of shared genetic effects (pleiotropy) between 22 pairs of phenotypes. Polygenic predictors constructed from DGEs are particularly useful for studying natural selection, assortative mating, and indirect genetic effects (effects of relatives’ genes mediated through the family environment). We validate our meta-analysis results by predicting phenotypes in hold-out samples using polygenic predictors constructed from DGEs, achieving statistically significant out-of-sample prediction for 24 phenotypes with little attenuation of predictive power within-families. We provide FGWAS summary statistics for 34 phenotypes that can be used for downstream analyses. Our study provides both a template for performing FGWAS and an argument for its value for debiasing inferences and understanding the impact of environment and mating patterns.

2023

Estimation of indirect genetic effects and heritability under assortative mating

Alexander Strudwick Young

bioRxiv, 2023

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Both direct genetic effects (effects of alleles in an individual on that individual) and indirect genetic effects — effects of alleles in an individual (e.g. parents) on another individual (e.g. offspring) — can contribute to phenotypic variation and genotype-phenotype associations. Here, we consider a phenotype affected by direct and parental indirect genetic effects under assortative mating at equilibrium. We generalize classical theory to derive a decomposition of the equilibrium phenotypic variance in terms of direct and indirect genetic effect components. We extend this theory to show that popular methods for estimating indirect genetic effects or ‘genetic nurture’ through analysis of parental and offspring polygenic predictors (called polygenic indices or scores — PGIs or PGSs) are substantially biased by assortative mating. We propose an improved method for estimating indirect genetic effects while accounting for assortative mating that can also correct heritability estimates for bias due to assortative mating. We validate our method in simulations and apply it to PGIs for height and educational attainment (EA), estimating that the equilibrium heritability of height is 0.699 (S.E. = 0.075) and finding no evidence for indirect genetic effects on height. We estimate a very high correlation between parents’ underlying genetic components for EA, 0.755 (S.E. = 0.035), which is inconsistent with twin based estimates of the heritability of EA, possibly due to confounding in the EA PGI and/or in twin studies. We implement our method in the software package snipar, enabling researchers to apply the method to data including observed and/or imputed parental genotypes. We provide a theoretical framework for understanding the results of PGI analyses and a practical methodology for estimating heritability and indirect genetic effects while accounting for assortative mating.
Discovering genes that affect cognitive ability

Alexander Strudwick Young, and Hilary C Martin

Trends in Genetics, 2023

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2022

Mendelian imputation of parental genotypes improves estimates of direct genetic effects

Alexander Strudwick Young, Seyed Moeen Nehzati, Stefania Benonisdottir, and 7 more authors

Nature genetics, 2022

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Effects estimated by genome-wide association studies (GWASs) include effects of alleles in an individual on that individual (direct genetic effects), indirect genetic effects (for example, effects of alleles in parents on offspring through the environment) and bias from confounding. Within-family genetic variation is random, enabling unbiased estimation of direct genetic effects when parents are genotyped. However, parental genotypes are often missing. We introduce a method that imputes missing parental genotypes and estimates direct genetic effects. Our method, implemented in the software package snipar (single-nucleotide imputation of parents), gives more precise estimates of direct genetic effects than existing approaches. Using 39,614 individuals from the UK Biobank with at least one genotyped sibling/parent, we estimate the correlation between direct genetic effects and effects from standard GWASs for nine phenotypes, including educational attainment (r = 0.739, standard error (s.e.) = 0.086) and cognitive ability (r = 0.490, s.e. = 0.086). Our results demonstrate substantial confounding bias in standard GWASs for some phenotypes..
Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals

Aysu Okbay, Yeda Wu, Nancy Wang, and 8 more authors

Nature genetics, 2022

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We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of 3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12–16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI’s magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant SNPs, and a separate X-chromosome additive GWAS identifies 57.
Discovering missing heritability in whole-genome sequencing data

Alexander I Young

Nature Genetics, 2022

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Novel estimators for family-based genome-wide association studies increase power and robustness

Junming Guan, Seyed Moeen Nehzati, Daniel J Benjamin, and 1 more author

bioRxiv, 2022

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Cross-trait assortative mating is widespread and inflates genetic correlation estimates

Richard Border, Georgios Athanasiadis, Alfonso Buil, and 8 more authors

Science, 2022

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2021

Resource profile and user guide of the Polygenic Index Repository

Joel Becker, Casper AP Burik, Grant Goldman, and 8 more authors

Nature human behaviour, 2021

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2020

How important are parents in the development of child anxiety and depression? A genomic analysis of parent-offspring trios in the Norwegian Mother Father and Child Cohort Study (MoBa)

Rosa Cheesman, Espen Moen Eilertsen, Yasmin I Ahmadzadeh, and 8 more authors

BMC medicine, 2020

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Family analysis with Mendelian imputations

Augustine Kong, Stefania Benonisdottir, and Alexander I Young

BioRxiv, 2020

2019

Deconstructing the sources of genotype-phenotype associations in humans

Alexander Strudwick Young, Stefania Benonisdottir, Molly Przeworski, and 1 more author

Science, 2019

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Efforts to link variation in the human genome to phenotypes have progressed at a tremendous pace in recent decades. Most human traits have been shown to be affected by a large number of genetic variants across the genome. To interpret these associations and to use them reliably—in particular for phenotypic prediction—a better understanding of the many sources of genotype-phenotype associations is necessary. We summarize the progress that has been made in this direction in humans, notably in decomposing direct and indirect genetic effects as well as population structure confounding. We discuss the natural next steps in data collection and methodology development, with a focus on what can be gained by analyzing genotype and phenotype data from close relatives.
Solving the missing heritability problem

Alexander Strudwick Young

PLoS genetics, 2019

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2018

Relatedness disequilibrium regression estimates heritability without environmental bias

Alexander Strudwick Young, Michael L Frigge, Daniel F Gudbjartsson, and 7 more authors

Nature genetics, 2018

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Heritability measures the proportion of trait variation that is due to genetic inheritance. Measurement of heritability is important in the nature-versus-nurture debate. However, existing estimates of heritability may be biased by environmental effects. Here, we introduce relatedness disequilibrium regression (RDR), a novel method for estimating heritability. RDR avoids most sources of environmental bias by exploiting variation in relatedness due to random Mendelian segregation. We used a sample of 54,888 Icelanders who had both parents genotyped to estimate the heritability of 14 traits, including height (55.4%, s.e. 4.4%) and educational attainment (17.0%, s.e. 9.4%). Our results suggest that some other estimates of heritability may be inflated by environmental effects.
Identifying loci affecting trait variability and detecting interactions in genome-wide association studies

Alexander Strudwick Young, Fabian L Wauthier, and Peter Donnelly

Nature genetics, 2018

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Identification of genetic variants with effects on trait variability can provide insights into the biological mechanisms that control variation and can identify potential interactions. We propose a two-degree-of-freedom test for jointly testing mean and variance effects to identify such variants. We implement the test in a linear mixed model, for which we provide an efficient algorithm and software. To focus on biologically interesting settings, we develop a test for dispersion effects, that is, variance effects not driven solely by mean effects when the trait distribution is non-normal. We apply our approach to body mass index in the subsample of the UK Biobank population with British ancestry (n 408,000) and show that our approach can increase the power to detect associated loci. We identify and replicate novel associations with significant variance effects that cannot be explained by the non-normality of body mass index, and we provide suggestive evidence for a connection between leptin levels and body mass index variability.
The nature of nurture: effects of parental genotypes

Augustine Kong, Gudmar Thorleifsson, Michael L Frigge, and 8 more authors

Science, 2018

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2017

Selection against variants in the genome associated with educational attainment

Augustine Kong, Michael L Frigge, Gudmar Thorleifsson, and 8 more authors

Proceedings of the National Academy of Sciences, 2017

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2016

Powerful decomposition of complex traits in a diploid model

Johan Hallin, Kaspar Märtens, Alexander I Young, and 5 more authors

Nature Communications, 2016

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Multiple novel gene-by-environment interactions modify the effect of FTO variants on body mass index

Alexander I Young, Fabian Wauthier, and Peter Donnelly

Nature communications, 2016

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Interactions in complex traits

Alexander Young

University of Oxford, 2016

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2014

Estimation of epistatic variance components and heritability in founder populations and crosses

Alexander I Young, and Richard Durbin

Genetics, 2014

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2011

Quantitative fitness analysis shows that NMD proteins and many other protein complexes suppress or enhance distinct telomere cap defects

Stephen Gregory Addinall, Eva-Maria Holstein, Conor Lawless, and 8 more authors

PLoS genetics, 2011

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2010

Colonyzer: automated quantification of micro-organism growth characteristics on solid agar

Conor Lawless, Darren J Wilkinson, Alexander Young, and 2 more authors

BMC bioinformatics, 2010

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