A linear mixed model framework for gene-based gene-environment interaction tests in twin studies

Brandon J. Coombes; Saonli Basu; Matt McGue

doi:10.1002/gepi.22150

Efficient gene-environment interaction tests for large biobank-scale sequencing studies

10.1101/2020.04.28.067173 ◽

2020 ◽

Author(s):

Xinyu Wang ◽

Elise Lim ◽

Ching-Ti Liu ◽

Yun Ju Sung ◽

Dabeeru C. Rao ◽

...

Keyword(s):

Large Scale ◽

Mixed Model ◽

Linear Mixed Model ◽

Environment Interaction ◽

Whole Genome ◽

Gene Environment Interaction ◽

Gene Environment ◽

Sequencing Studies ◽

Related Individuals ◽

Main Effects

ABSTRACTComplex human diseases are affected by genetic and environmental risk factors and their interactions. Gene-environment interaction (GEI) tests for aggregate genetic variant sets have been developed in recent years. However, existing statistical methods become rate limiting for large biobank-scale sequencing studies with correlated samples. We propose efficient Mixed-model Association tests for GEne-Environment interactions (MAGEE), for testing GEI between an aggregate variant set and environmental exposures on quantitative and binary traits in large-scale sequencing studies with related individuals. Joint tests for the aggregate genetic main effects and GEI effects are also developed. A null generalized linear mixed model adjusting for covariates but without any genetic effects is fit only once in a whole genome GEI analysis, thereby vastly reducing the overall computational burden. Score tests for variant sets are performed as a combination of genetic burden and variance component tests by accounting for the genetic main effects using matrix projections. The computational complexity is dramatically reduced in a whole genome GEI analysis, which makes MAGEE scalable to hundreds of thousands of individuals. We applied MAGEE to the exome sequencing data of 41,144 related individuals from the UK Biobank, and the analysis of 18,970 protein coding genes finished within 10.4 CPU hours.

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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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Linear Mixed Models

Multivariate Statistical Machine Learning Methods for Genomic Prediction ◽

10.1007/978-3-030-89010-0_5 ◽

2022 ◽

pp. 141-170

Author(s):

Osval Antonio Montesinos López ◽

Abelardo Montesinos López ◽

Jose Crossa

Keyword(s):

Parameter Estimation ◽

Maximum Likelihood ◽

Mixed Models ◽

Mixed Model ◽

Linear Mixed Model ◽

Linear Mixed Models ◽

Prediction Performance ◽

Environment Interaction ◽

Model Framework ◽

Genotype Environment Interaction

AbstractThe linear mixed model framework is explained in detail in this chapter. We explore three methods of parameter estimation (maximum likelihood, EM algorithm, and REML) and illustrate how genomic-enabled predictions are performed under this framework. We illustrate the use of linear mixed models by using the predictor several components such as environments, genotypes, and genotype × environment interaction. Also, the linear mixed model is illustrated under a multi-trait framework that is important in the prediction performance when the degree of correlation between traits is moderate or large. We illustrate the use of single-trait and multi-trait linear mixed models and provide the R codes for performing the analyses.

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GxEMM: Extending linear mixed models to general gene-environment interactions

10.1101/397638 ◽

2018 ◽

Cited By ~ 5

Author(s):

Andy Dahl ◽

Na Cai ◽

Jonathan Flint ◽

Noah Zaitlen

Keyword(s):

Major Depression ◽

Mixed Models ◽

Environmental Variables ◽

Mixed Model ◽

Environment Interaction ◽

Gene Environment Interaction ◽

Gene Environment ◽

Aggregate Effect ◽

Additive Inheritance ◽

Categorical Groups

AbstractGene-environment interaction (GxE) is a well-known source of non-additive inheritance. GxE can be important in applications ranging from basic functional genomics to precision medical treatment. Further, GxE effects elude inherently-linear LMMs and may explain missing heritability. We propose a simple, unifying mixed model for polygenic interactions (GxEMM) to capture the aggregate effect of small GxE effects spread across the genome. GxEMM extends existing LMMs for GxE in two important ways. First, it extends to arbitrary environmental variables, not just categorical groups. Second, GxEMM can estimate and test for environment-specific heritability. In simulations where the assumptions of existing methods do not hold, we show that GxEMM improves estimates of ordinary and GxE heritability and increases power to test for polygenic GxE. We then use GxEMM to prove that the heritability of major depression (MD) is reduced by stress, which we previously conjectured but could not prove with prior methods, and that a tail of polygenic GxE effects remains unexplained by MD GWAS.

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An Exploration of Gene–Environment Interaction and Asthma in a Large Sample of 5-Year-Old Dutch Twins

Twin Research and Human Genetics ◽

10.1375/twin.11.2.143 ◽

2008 ◽

Vol 11 (2) ◽

pp. 143-149 ◽

Cited By ~ 9

Author(s):

Toos C. E. M. van Beijsterveldt ◽

Dorret I. Boomsma

Keyword(s):

Risk Factors ◽

Child Care ◽

Environmental Risk ◽

Premature Birth ◽

Family Members ◽

Twin Studies ◽

Environmental Risk Factors ◽

Environment Interaction ◽

Gene Environment Interaction ◽

Gene Environment

AbstractA consistent finding from twin studies is that the environment shared by family members does not contribute to the variation in susceptibility to asthma. At the same time, it is known that environmental risk factors that are shared by family members are associated with the liability for asthma. We hypothesize that the absence of a main effect of shared environmental factors in twin studies can be explained by gene–environment interaction, that is, that the effect of an environmental factor shared by family members depends on the genotype of the individual. We explore this hypothesis by modeling the resemblance in asthma liability in twin pairs as a function of various environmental risk factors and test for gene–environment interaction. Asthma data were obtained by parental report for nearly 12,000 5-year-old twin pairs. A series of environmental risk factors was examined: birth cohort, gestational age, time spent in incubator, breastfeeding, maternal educational level, maternal smoking during pregnancy, current smoking of parents, having older siblings, and amount of child care outside home. Results revealed that being a boy, born in the 1990s, premature birth, longer incubator time, and child care outside home increased the risk for asthma. With the exception of premature birth, however, none of these factors modified the genetic effects on asthma. In very premature children shared environmental influences were important. In children born after a gestation of 32 weeks or more only genetic factors were important to explain familial resemblance for asthma.

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Sources of Individual Differences in Pain

Annual Review of Neuroscience ◽

10.1146/annurev-neuro-092820-105941 ◽

2021 ◽

Vol 44 (1) ◽

pp. 1-25

Author(s):

Jeffrey S. Mogil

Keyword(s):

Genetic Variants ◽

Pain Sensitivity ◽

Genetic Factors ◽

Twin Studies ◽

Environment Interaction ◽

Interindividual Differences ◽

Personality Variables ◽

Gene Environment Interaction ◽

Gene Environment ◽

Societal Challenge

Pain is an immense clinical and societal challenge, and the key to understanding and treating it is variability. Robust interindividual differences are consistently observed in pain sensitivity, susceptibility to developing painful disorders, and response to analgesic manipulations. This review examines the causes of this variability, including both organismic and environmental sources. Chronic pain development is a textbook example of a gene-environment interaction, requiring both chance initiating events (e.g., trauma, infection) and more immutable risk factors. The focus is on genetic factors, since twin studies have determined that a plurality of the variance likely derives from inherited genetic variants, but sex, age, ethnicity, personality variables, and environmental factors are also considered.

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