Family-based gene-environment interaction using sequence kernel association test (FGE-SKAT) for complex quantitative traits

AbstractAfter the genome-wide association studies (GWAS) era, whole-genome sequencing is highly engaged in identifying the association of complex traits with rare variations. A score-based variance-component test has been proposed to identify common and rare genetic variants associated with complex traits while quickly adjusting for covariates. Such kernel score statistic allows for familial dependencies and adjusts for random confounding effects. However, the etiology of complex traits may involve the effects of genetic and environmental factors and the complex interactions between genes and the environment. Therefore, in this research, a novel method is proposed to detect gene and gene-environment interactions in a complex family-based association study with various correlated structures. We also developed an R function for the Fast Gene-Environment Sequence Kernel Association Test (FGE-SKAT), which is freely available as supplementary material for easy GWAS implementation to unveil such family-based joint effects. Simulation studies confirmed the validity of the new strategy and the superior statistical power. The FGE-SKAT was applied to the whole genome sequence data provided by Genetic Analysis Workshop 18 (GAW18) and discovered concordant and discordant regions compared to the methods without considering gene by environment interactions.

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Genetics

10.1093/med/9780198789284.003.0004 ◽

2018 ◽

Author(s):

Carol Kan ◽

Ma-Li Wong

Keyword(s):

Complex Traits ◽

Association Studies ◽

Genetic Correlations ◽

Twin Studies ◽

Epidemiological Studies ◽

Environment Interaction ◽

Genome Wide Association Studies ◽

Small Magnitude ◽

Gene Environment ◽

Genetic Overlap

An association between type 2 diabetes mellitus (T2DM) and depression has been reported in epidemiological studies. Finding a genetic overlap between T2DM and depression will provide evidence to support a common biological pathway to both disorders. Genetic correlations observed from twin studies indicate that a small magnitude of the variance in liability can be attributed to genetic factors. However, no genetic overlap has been observed between T2DM and depression in genome-wide association studies using both the polygenic score and the linkage disequilibrium score regression approaches. Clarifying the shared heritability between these two complex traits is an important next step towards better therapy and treatment. Another area that needs to be explored is gene–environment interaction, since genotypes can affect an individual’s responses to the environment and environment can differentially affect genotypes expression.

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Investigating the Effect of Imputed Structural Variants from Whole-Genome Sequence on Genome-Wide Association and Genomic Prediction in Dairy Cattle

Animals ◽

10.3390/ani11020541 ◽

2021 ◽

Vol 11 (2) ◽

pp. 541

Author(s):

Long Chen ◽

Jennie E. Pryce ◽

Ben J. Hayes ◽

Hans D. Daetwyler

Keyword(s):

Dairy Cattle ◽

Genomic Prediction ◽

Complex Traits ◽

Prediction Accuracy ◽

Association Studies ◽

Genome Wide Association ◽

Whole Genome Sequence ◽

Genome Wide Association Studies ◽

Whole Genome ◽

Genome Wide

Structural variations (SVs) are large DNA segments of deletions, duplications, copy number variations, inversions and translocations in a re-sequenced genome compared to a reference genome. They have been found to be associated with several complex traits in dairy cattle and could potentially help to improve genomic prediction accuracy of dairy traits. Imputation of SVs was performed in individuals genotyped with single-nucleotide polymorphism (SNP) panels without the expense of sequencing them. In this study, we generated 24,908 high-quality SVs in a total of 478 whole-genome sequenced Holstein and Jersey cattle. We imputed 4489 SVs with R2 > 0.5 into 35,568 Holstein and Jersey dairy cattle with 578,999 SNPs with two pipelines, FImpute and Eagle2.3-Minimac3. Genome-wide association studies for production, fertility and overall type with these 4489 SVs revealed four significant SVs, of which two were highly linked to significant SNP. We also estimated the variance components for SNP and SV models for these traits using genomic best linear unbiased prediction (GBLUP). Furthermore, we assessed the effect on genomic prediction accuracy of adding SVs to GBLUP models. The estimated percentage of genetic variance captured by SVs for production traits was up to 4.57% for milk yield in bulls and 3.53% for protein yield in cows. Finally, no consistent increase in genomic prediction accuracy was observed when including SVs in GBLUP.

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Investigation of gene–environment interactions in relation to tic severity

Journal of Neural Transmission ◽

10.1007/s00702-021-02396-y ◽

2021 ◽

Author(s):

Mohamed Abdulkadir ◽

Dongmei Yu ◽

Lisa Osiecki ◽

Robert A. King ◽

Thomas V. Fernandez ◽

...

Keyword(s):

Tourette Syndrome ◽

Association Studies ◽

Autism Spectrum ◽

Environment Interaction ◽

Genome Wide Association Studies ◽

Nucleotide Polymorphisms ◽

Linear Regression Models ◽

Compulsive Disorder ◽

Gene Environment ◽

Tic Severity

AbstractTourette syndrome (TS) is a neuropsychiatric disorder with involvement of genetic and environmental factors. We investigated genetic loci previously implicated in Tourette syndrome and associated disorders in interaction with pre- and perinatal adversity in relation to tic severity using a case-only (N = 518) design. We assessed 98 single-nucleotide polymorphisms (SNPs) selected from (I) top SNPs from genome-wide association studies (GWASs) of TS; (II) top SNPs from GWASs of obsessive–compulsive disorder (OCD), attention-deficit/hyperactivity disorder (ADHD), and autism spectrum disorder (ASD); (III) SNPs previously implicated in candidate-gene studies of TS; (IV) SNPs previously implicated in OCD or ASD; and (V) tagging SNPs in neurotransmitter-related candidate genes. Linear regression models were used to examine the main effects of the SNPs on tic severity, and the interaction effect of these SNPs with a cumulative pre- and perinatal adversity score. Replication was sought for SNPs that met the threshold of significance (after correcting for multiple testing) in a replication sample (N = 678). One SNP (rs7123010), previously implicated in a TS meta-analysis, was significantly related to higher tic severity. We found a gene–environment interaction for rs6539267, another top TS GWAS SNP. These findings were not independently replicated. Our study highlights the future potential of TS GWAS top hits in gene–environment studies.

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Current Challenges in the Genetics of Psoriatic Arthritis: A Report from the GRAPPA 2009 Annual Meeting

The Journal of Rheumatology ◽

10.3899/jrheum.101123 ◽

2011 ◽

Vol 38 (3) ◽

pp. 564-566 ◽

Cited By ~ 2

Author(s):

PROTON RAHMAN

Keyword(s):

Psoriatic Arthritis ◽

Annual Meeting ◽

Association Studies ◽

Environment Interaction ◽

Genome Wide Association Studies ◽

Novel Genes ◽

Gene Environment Interaction ◽

Gene Environment ◽

Genome Wide

Psoriasis and psoriatic arthritis (PsA) are heterogeneous diseases. While both have a strong genetic basis, it is strongest for PsA, where fewer investigators are studying its genetics. Over the last year the number of independent genetic loci associated with psoriasis has substantially increased, mostly due to completion of multiple genome-wide association studies (GWAS) in psoriasis. At least 2 GWAS efforts are now under way in PsA to identify novel genes in this disease; a metaanalysis of genome-wide scans and further studies must follow to examine the genetics of disease expression, epistatic interaction, and gene-environment interaction. In the long term, it is anticipated that genome-wide sequencing is likely to generate another wave of novel genes in PsA. At the annual meeting of the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) in Stockholm, Sweden, in 2009, members discussed issues and challenges regarding the advancement of the genetics of PsA; results of those discussions are summarized here.

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Geographic Confounding in Genome-Wide Association Studies

10.21203/rs.3.rs-362358/v1 ◽

2021 ◽

Author(s):

Abdel Abdellaoui ◽

Karin Verweij ◽

Michel G Nivard

Keyword(s):

Educational Attainment ◽

Social Stratification ◽

Complex Traits ◽

Association Studies ◽

Genetic Correlations ◽

Geographic Region ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Gene Environment ◽

Genome Wide

Abstract Gene-environment correlations can bias associations between genetic variants and complex traits in genome-wide association studies (GWASs). Here, we control for geographic sources of gene-environment correlation in GWASs on 56 complex traits (N = 69,772–271,457). Controlling for geographic region significantly decreases heritability signals for SES-related traits, most strongly for educational attainment and income, indicating that socio-economic differences between regions induce gene-environment correlations that become part of the polygenic signal. For most other complex traits investigated, genetic correlations with educational attainment and income are significantly reduced, most significantly for traits related to BMI, sedentary behavior, and substance use. Controlling for current address has greater impact on the polygenic signal than birth place, suggesting both active and passive sources of gene-environment correlations. Our results show that societal sources of social stratification that extend beyond families introduce regional-level gene-environment correlations that affect GWAS results.

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Geographic Confounding in Genome-Wide Association Studies

10.1101/2021.03.18.435971 ◽

2021 ◽

Author(s):

Abdel Abdellaoui ◽

Karin J.H. Verweij ◽

Michel G. Nivard

Keyword(s):

Educational Attainment ◽

Social Stratification ◽

Complex Traits ◽

Association Studies ◽

Genetic Correlations ◽

Geographic Region ◽

Genome Wide Association ◽

Genome Wide Association Studies ◽

Gene Environment ◽

Genome Wide

Gene-environment correlations can bias associations between genetic variants and complex traits in genome-wide association studies (GWASs). Here, we control for geographic sources of gene-environment correlation in GWASs on 56 complex traits (N=69,772-271,457). Controlling for geographic region significantly decreases heritability signals for SES-related traits, most strongly for educational attainment and income, indicating that socio-economic differences between regions induce gene-environment correlations that become part of the polygenic signal. For most other complex traits investigated, genetic correlations with educational attainment and income are significantly reduced, most significantly for traits related to BMI, sedentary behavior, and substance use. Controlling for current address has greater impact on the polygenic signal than birth place, suggesting both active and passive sources of gene-environment correlations. Our results show that societal sources of social stratification that extend beyond families introduce regional-level gene-environment correlations that affect GWAS results.

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Linkage disequilibrium maps for European and African populations constructed from whole genome sequence data

Scientific Data ◽

10.1038/s41597-019-0227-y ◽

2019 ◽

Vol 6 (1) ◽

Cited By ~ 1

Author(s):

Alejandra Vergara-Lope ◽

M. Reza Jabalameli ◽

Clare Horscroft ◽

Sarah Ennis ◽

Andrew Collins ◽

...

Keyword(s):

Linkage Disequilibrium ◽

Genome Sequence ◽

Sequence Data ◽

Association Studies ◽

Large Population ◽

Whole Genome Sequence ◽

European Ancestry ◽

Genome Wide Association Studies ◽

Whole Genome ◽

Genome Wide

Abstract Quantification of linkage disequilibrium (LD) patterns in the human genome is essential for genome-wide association studies, selection signature mapping and studies of recombination. Whole genome sequence (WGS) data provides optimal source data for this quantification as it is free from biases introduced by the design of array genotyping platforms. The Malécot-Morton model of LD allows the creation of a cumulative map for each choromosome, analogous to an LD form of a linkage map. Here we report LD maps generated from WGS data for a large population of European ancestry, as well as populations of Baganda, Ethiopian and Zulu ancestry. We achieve high average genetic marker densities of 2.3–4.6/kb. These maps show good agreement with prior, low resolution maps and are consistent between populations. Files are provided in BED format to allow researchers to readily utilise this resource.

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HisCoM-G×E: Hierarchical Structural Component Analysis of Gene-Based Gene–Environment Interactions

International Journal of Molecular Sciences ◽

10.3390/ijms21186724 ◽

2020 ◽

Vol 21 (18) ◽

pp. 6724

Author(s):

Sungkyoung Choi ◽

Sungyoung Lee ◽

Iksoo Huh ◽

Heungsun Hwang ◽

Taesung Park

Keyword(s):

Structural Component ◽

Latent Variable ◽

Association Studies ◽

Component Analysis ◽

Environment Interaction ◽

Genome Wide Association Studies ◽

Gene Environment Interaction ◽

Gene Environment ◽

Genome Wide ◽

Missing Heritability Problem

Gene–environment interaction (G×E) studies are one of the most important solutions for understanding the “missing heritability” problem in genome-wide association studies (GWAS). Although many statistical methods have been proposed for detecting and identifying G×E, most employ single nucleotide polymorphism (SNP)-level analysis. In this study, we propose a new statistical method, Hierarchical structural CoMponent analysis of gene-based Gene–Environment interactions (HisCoM-G×E). HisCoM-G×E is based on the hierarchical structural relationship among all SNPs within a gene, and can accommodate all possible SNP-level effects into a single latent variable, by imposing a ridge penalty, and thus more efficiently takes into account the latent interaction term of G×E. The performance of the proposed method was evaluated in simulation studies, and we applied the proposed method to investigate gene–alcohol intake interactions affecting systolic blood pressure (SBP), using samples from the Korea Associated REsource (KARE) consortium data.

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Genotypes and Phenotypes: A Search for Influential Genes in Diabetic Retinopathy

International Journal of Molecular Sciences ◽

10.3390/ijms21082712 ◽

2020 ◽

Vol 21 (8) ◽

pp. 2712

Author(s):

Andrea P. Cabrera ◽

Rushi N. Mankad ◽

Lauren Marek ◽

Ryan Das ◽

Sampath Rangasamy ◽

...

Keyword(s):

Diabetic Retinopathy ◽

Complex Traits ◽

Association Studies ◽

Genome Wide Association Studies ◽

Metabolic Dysfunction ◽

Duration Of Diabetes ◽

Gene Environment ◽

Genome Wide ◽

Novel Biomarkers

Although gene–environment interactions are known to play an important role in the inheritance of complex traits, it is still unknown how a genotype and the environmental factors result in an observable phenotype. Understanding this complex interaction in the pathogenesis of diabetic retinopathy (DR) remains a big challenge as DR appears to be a disease with heterogenous phenotypes with multifactorial influence. In this review, we examine the natural history and risk factors related to DR, emphasizing distinct clinical phenotypes and their natural course in retinopathy. Although there is strong evidence that duration of diabetes and metabolic factors play a key role in the pathogenesis of DR, accumulating new clinical studies reveal that this disease can develop independently of duration of diabetes and metabolic dysfunction. More recently, studies have emphasized the role of genetic factors in DR. However, linkage analyses, candidate gene studies, and genome-wide association studies (GWAS) have not produced any statistically significant results. Our recently initiated genomics study, the Diabetic Retinopathy Genomics (DRGen) Study, aims to examine the contribution of rare and common variants in the development DR, and how they can contribute to clinical phenotype, rate of progression, and response to available therapies. Our preliminary findings reveal a novel set of genetic variants associated with proangiogenic and inflammatory pathways that may contribute to DR pathogenesis. Further investigation of these variants is necessary and may lead to development of novel biomarkers and new therapeutic targets in DR.

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Brain imaging genomics: influences of genomic variability on the structure and function of the human brain

Medizinische Genetik ◽

10.1515/medgen-2020-2007 ◽

2020 ◽

Vol 32 (1) ◽

pp. 47-56

Author(s):

Thomas W. Mühleisen ◽

Andreas J. Forstner ◽

Per Hoffmann ◽

Sven Cichon

Keyword(s):

Brain Imaging ◽

Complex Traits ◽

Molecular Mechanisms ◽

Association Studies ◽

Phenotypic Variability ◽

Effect Sizes ◽

Future Research ◽

Environment Interaction ◽

Genome Wide Association Studies ◽

Imaging Genomics

Abstract Brain imaging genomics is an emerging discipline in which genomic and brain imaging data are integrated in order to elucidate the molecular mechanisms that underly brain phenotypes and diseases, including neuropsychiatric disorders. As with all genetic analyses of complex traits and diseases, brain imaging genomics has evolved from small, individual candidate gene investigations towards large, collaborative genome-wide association studies. Recent investigations, mostly population-based, have studied well-powered cohorts comprising tens of thousands of individuals and identified multiple robust associations of single-nucleotide polymorphisms and copy number variants with structural and functional brain phenotypes. Such systematic genomic screens of millions of genetic variants have generated initial insights into the genetic architecture of brain phenotypes and demonstrated that their etiology is polygenic in nature, involving multiple common variants with small effect sizes and rare variants with larger effect sizes. Ongoing international collaborative initiatives are now working to obtain a more complete picture of the underlying biology. As in other complex phenotypes, novel approaches – such as gene–gene interaction, gene–environment interaction, and epigenetic analyses – are being implemented in order to investigate their contribution to the observed phenotypic variability. An important consideration for future research will be the translation of brain imaging genomics findings into clinical practice.

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