Role of Rare and Low Frequency Variants in Gene-Alcohol Interactions on Plasma Lipid Levels

AbstractBackgroundAlcohol intake influences plasma lipid levels and such effects may be modulated by genetic variants.ObjectiveWe aimed to characterize the role of aggregated rare and low-frequency variants in gene by alcohol consumption interactions associated with fasting plasma lipid levels.DesignIn the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, fasting plasma triglycerides (TG), and high- and low-density lipoprotein cholesterol (HDL-c and LDL-c) were measured in 34,153 European Americans from five discovery studies and 32,275 individuals from six replication studies. Rare and low-frequency protein coding variants (minor allele frequency ≤ 5%) measured by an exome array were aggregated by genes and evaluated by a gene-environment interaction (GxE) test and a joint test of genetic main and GxE interaction effects. Two dichotomous self-reported alcohol consumption variables, current drinker, defined as any recurrent drinking behavior, and regular drinker, defined as the subset of current drinkers who consume at least two drinks per week, were considered.ResultsWe discovered and replicated 21 gene-lipid associations at 13 known lipid loci through the joint test. Eight loci (PCSK9, LPA, LPL, LIPG, ANGPTL4, APOB, APOC3 and CD300LG) remained significant after conditioning on the common index single nucleotide polymorphism (SNP) identified by previous genome-wide association studies, suggesting an independent role for rare and low-frequency variants at these loci. One significant gene-alcohol interaction on TG was discovered at a Bonferroni corrected significance level (p-value <5×10−5) and replicated (p-value <0.013 for the interaction test) inSMC5.ConclusionsIn conclusion, this study applied new gene-based statistical approaches to uncover the role of rare and low-frequency variants in gene-alcohol consumption interactions on lipid levels.

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Abstract MP62: Role of Rare and Low-Frequency Variants in Gene-Alcohol Interactions on Plasma Lipid Levels

Circulation ◽

10.1161/circ.137.suppl_1.mp62 ◽

2018 ◽

Vol 137 (suppl_1) ◽

Author(s):

Zhe Wang ◽

Han Chen ◽

Daniel I Chasman ◽

Ching-Ti Liu ◽

Raymond Noordam ◽

...

Keyword(s):

Alcohol Consumption ◽

Plasma Lipid ◽

Low Frequency ◽

Joint Analysis ◽

Genome Wide Association Studies ◽

Lipid Levels ◽

Gxe Interaction ◽

Functional Variants ◽

Fasting Plasma

Introduction: Alcohol intake modifies plasma lipid levels and such effects may be modulated by genetic variants. We use emerging statistical methods that extend well-established common variant approaches to characterize the role of aggregated rare and low-frequency variants in gene by alcohol consumption interactions associated with fasting plasma lipid levels. Methods: Up to 247,870 exonic variants on the Illumina HumanExome BeadChip and fasting plasma triglycerides (TG), and high- and low-density lipoprotein cholesterol (HDL-c and LDL-c) were measured in 46,443 European Americans from 4 studies (the Atherosclerosis Risk in Communities (ARIC) study, the Framingham Heart Study, the Netherlands Epidemiology of Obesity Study and the Women’s Genome Health Study) of the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium. Using the gene-based rareGE method, we conducted exome-wide gene-environment (GxE) tests with genetic main effects estimated as fixed and random effects, and a joint analysis of genetic main and GxE interaction effects. Rare and low-frequency (minor allele frequency ≤ 5%) functional variants, (i.e. frameshift, nonsynonymous, stop/gain, stop/loss, and splicing) were aggregated by genes. Two dichotomous self-reported alcohol consumption variables, current drinker (at least 1 drink per week, yes/no) and regular drinker (at least 2 drinks per week, yes/no) were considered. A sample size weighted Z-test (weighted Stouffer’s method) was used to meta-analyze study-specific p -values. Exome-wide significance level was set at p < 3.7*10 -6 (0.05/13368 genes), using a Bonferroni procedure to correct for multiple testing. Results: We identified 24 gene-lipid associations at 13 known lipid loci (within 500kb) harboring rare and low-frequency variants through the joint analysis. In ARIC, numerous genes ( PCSK9, LPL, LIPG, ANGPTL4, APOB, APOC3-A5 ) remained significant after conditioning on common index single nucleotide polymorphisms (SNPs), suggesting an independent role for rare variants at loci highlighted by previous genome-wide association studies. However, no significant gene-alcohol interactions were observed with rare and low-frequency variants on TG, HDL-c or LDL-c. Conclusion: This study applied new statistical approaches to investigate the role of rare and low-frequency variants in gene-alcohol consumption interactions on lipid levels. Results show promise for other larger scale studies analyzing rare variant GxE interactions.

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Role of Rare and Low-Frequency Variants in Gene-Alcohol Interactions on Plasma Lipid Levels

Circulation Genomic and Precision Medicine ◽

10.1161/circgen.119.002772 ◽

2020 ◽

Vol 13 (4) ◽

Cited By ~ 1

Author(s):

Zhe Wang ◽

Han Chen ◽

Traci M. Bartz ◽

Lawrence F. Bielak ◽

Daniel I. Chasman ◽

...

Keyword(s):

Alcohol Consumption ◽

Plasma Lipid ◽

Low Frequency ◽

Environment Interaction ◽

Lipid Levels ◽

Protein Coding ◽

Gene Environment Interaction ◽

Gene Environment ◽

Coding Variants

Background: Alcohol intake influences plasma lipid levels, and such effects may be moderated by genetic variants. We aimed to characterize the role of aggregated rare and low-frequency protein-coding variants in gene by alcohol consumption interactions associated with fasting plasma lipid levels. Methods: In the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium, fasting plasma triglycerides and high- and low-density lipoprotein cholesterol were measured in 34 153 individuals with European ancestry from 5 discovery studies and 32 277 individuals from 6 replication studies. Rare and low-frequency functional protein-coding variants (minor allele frequency, ≤5%) measured by an exome array were aggregated by genes and evaluated by a gene-environment interaction test and a joint test of genetic main and gene-environment interaction effects. Two dichotomous self-reported alcohol consumption variables, current drinker, defined as any recurrent drinking behavior, and regular drinker, defined as the subset of current drinkers who consume at least 2 drinks per week, were considered. Results: We discovered and replicated 21 gene-lipid associations at 13 known lipid loci through the joint test. Eight loci ( PCSK9 , LPA , LPL , LIPG , ANGPTL4 , APOB , APOC3 , and CD300LG ) remained significant after conditioning on the common index single-nucleotide polymorphism identified by previous genome-wide association studies, suggesting an independent role for rare and low-frequency variants at these loci. One significant gene-alcohol interaction on triglycerides in a novel locus was significantly discovered ( P =6.65×10 −6 for the interaction test) and replicated at nominal significance level ( P =0.013) in SMC5 . Conclusions: In conclusion, this study applied new gene-based statistical approaches and suggested that rare and low-frequency genetic variants interacted with alcohol consumption on lipid levels.

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Pairwise effects between lipid GWAS genes modulate lipid plasma levels and cellular uptake

Nature Communications ◽

10.1038/s41467-021-26761-3 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Magdalena Zimoń ◽

Yunfeng Huang ◽

Anthi Trasta ◽

Aliaksandr Halavatyi ◽

Jimmy Z. Liu ◽

...

Keyword(s):

Complex Traits ◽

Human Genetics ◽

Association Studies ◽

Large Population ◽

Plasma Lipid ◽

Model Systems ◽

Lipid Lowering ◽

Genome Wide Association Studies ◽

Lipid Levels ◽

Gene Pairs

AbstractComplex traits are characterized by multiple genes and variants acting simultaneously on a phenotype. However, studying the contribution of individual pairs of genes to complex traits has been challenging since human genetics necessitates very large population sizes, while findings from model systems do not always translate to humans. Here, we combine genetics with combinatorial RNAi (coRNAi) to systematically test for pairwise additive effects (AEs) and genetic interactions (GIs) between 30 lipid genome-wide association studies (GWAS) genes. Gene-based burden tests from 240,970 exomes show that in carriers with truncating mutations in both, APOB and either PCSK9 or LPL (“human double knock-outs”) plasma lipid levels change additively. Genetics and coRNAi identify overlapping AEs for 12 additional gene pairs. Overlapping GIs are observed for TOMM40/APOE with SORT1 and NCAN. Our study identifies distinct gene pairs that modulate plasma and cellular lipid levels primarily via AEs and nominates putative drug target pairs for improved lipid-lowering combination therapies.

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A Simple Approximation To The Bias Of Gene-Envinornment Interactions In Case/Control Studies With Silent Disease

10.1101/444596 ◽

2018 ◽

Author(s):

Iryna Lobach ◽

Joshua Sampson ◽

Siarhei Lobach ◽

Alexander Alekseyenko ◽

Alexandra Pryatinska ◽

...

Keyword(s):

Large Scale ◽

Association Studies ◽

Environmental Variable ◽

Case Control ◽

Environment Interaction ◽

Genome Wide Association Studies ◽

Case Control Studies ◽

Finite Samples ◽

Research Areas ◽

Gxe Interaction

AbstractOne of the most important research areas in case-control Genome-Wide Association Studies is to determine how the effect of a genotype varies across the environment or to measure the gene-environment interaction (GxE). We consider the scenario when some of the “healthy” controls actually have the disease and when the frequency of these latent cases varies by the environmental variable of interest. In this scenario, performing logistic regression of clinically defined case status on the genetic variant, environmental variable, and their interaction will result in biased estimates of GxE interaction. Here, we derive a general theoretical approximation to the bias in the estimates of the GxE interaction and show, through extensive simulation, that this approximation is accurate in finite samples. Moreover, we apply this approximation to evaluate the bias in the effect estimates of the genetic variants related to mitochondrial proteins a large-scale Prostate Cancer study.

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Rare variant analysis in eczema identifies exonic variants in DUSP1, NOTCH4 and SLC9A4

Nature Communications ◽

10.1038/s41467-021-26783-x ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Sarah Grosche ◽

Ingo Marenholz ◽

Jorge Esparza-Gordillo ◽

Aleix Arnau-Soler ◽

Erola Pairo-Castineira ◽

...

Keyword(s):

Cell Function ◽

Immune Cell ◽

Rare Variants ◽

Association Studies ◽

Large Population ◽

Low Frequency ◽

Genome Wide Association Studies ◽

Common Variants ◽

Functional Protein

AbstractPrevious genome-wide association studies revealed multiple common variants involved in eczema but the role of rare variants remains to be elucidated. Here, we investigate the role of rare variants in eczema susceptibility. We meta-analyze 21 study populations including 20,016 eczema cases and 380,433 controls. Rare variants are imputed with high accuracy using large population-based reference panels. We identify rare exonic variants in DUSP1, NOTCH4, and SLC9A4 to be associated with eczema. In DUSP1 and NOTCH4 missense variants are predicted to impact conserved functional domains. In addition, five novel common variants at SATB1-AS1/KCNH8, TRIB1/LINC00861, ZBTB1, TBX21/OSBPL7, and CSF2RB are discovered. While genes prioritized based on rare variants are significantly up-regulated in the skin, common variants point to immune cell function. Over 20% of the single nucleotide variant-based heritability is attributable to rare and low-frequency variants. The identified rare/low-frequency variants located in functional protein domains point to promising targets for novel therapeutic approaches to eczema.

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Environment

10.1093/med/9780199642489.003.0038_update_001 ◽

2016 ◽

Author(s):

Christopher R. Holroyd ◽

Nicholas C. Harvey ◽

Mark H. Edwards ◽

Cyrus Cooper

Keyword(s):

Rheumatoid Arthritis ◽

Life Course ◽

Disease Risk ◽

Association Studies ◽

Population Level ◽

Environment Interaction ◽

Genome Wide Association Studies ◽

Mineral Density ◽

The Individual

Musculoskeletal disease covers a broad spectrum of conditions whose aetiology comprises variable genetic and environmental contributions. More recently it has become clear that, particularly early in life, the interaction of gene and environment is critical to the development of later disease. Additionally, only a small proportion of the variation in adult traits such as bone mineral density has been explained by specific genes in genome-wide association studies, suggesting that gene-environment interaction may explain a much larger part of the inheritance of disease risk than previously thought. It is therefore critically important to evaluate the environmental factors which may predispose to diseases such as osteorthritis, osteoporosis, and rheumatoid arthritis both at the individual and at the population level. In this chapter we describe the environmental contributors, across the whole life course, to osteoarthritis, osteoporosis and rheumatoid arthritis, as exemplar conditions. We consider factors such as age, gender, nutrition (including the role of vitamin D), geography, occupation, and the clues that secular changes of disease pattern may yield. We describe the accumulating evidence that conditions such as osteoporosis may be partly determined by the early interplay of environment and genotype, through aetiological mechanisms such as DNA methylation and other epigenetic phenomena. Such studies, and those examining the role of environmental influences across other stages of the life course, suggest that these issues should be addressed at all ages, starting from before conception, in order to optimally reduce the burden of musculoskeletal disorders in future generations.

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Environment

10.1093/med/9780199642489.003.0038 ◽

2013 ◽

Author(s):

Christopher R. Holroyd ◽

Nicholas C. Harvey ◽

Mark H. Edwards ◽

Cyrus Cooper

Keyword(s):

Rheumatoid Arthritis ◽

Life Course ◽

Disease Risk ◽

Association Studies ◽

Population Level ◽

Environment Interaction ◽

Genome Wide Association Studies ◽

Mineral Density ◽

The Individual

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Bivariate GWAS scan identifies six novel loci associated with lipid levels and coronary artery disease

10.1101/319848 ◽

2018 ◽

Author(s):

Katherine M. Siewert ◽

Benjamin F. Voight

Keyword(s):

Coronary Artery Disease ◽

Coronary Artery ◽

Disease Risk ◽

Association Studies ◽

Meta Analysis ◽

Plasma Lipid ◽

Genome Wide Association Studies ◽

Lipid Levels ◽

Genome Wide ◽

Artery Disease

AbstractBackgroundPlasma lipid levels are heritable and genetically associated with risk of coronary artery disease (CAD). However, genome-wide association studies (GWAS) routinely analyze these traits independently of one another. Joint GWAS for two related phenotypes can lead to a higher-powered analysis to detect variants contributing to both traits.Methods and ResultsWe performed a bivariate GWAS to discover novel loci associated with heart disease, using a CAD Meta-Analysis (122,733 cases and 424,528 controls), and lipid traits, using data from the Global Lipid Genetics Consortium (188,577 subjects). We identified six previously unreported loci at genome-wide significance (P < 5 × 10−8), three which were associated with Triglycerides and CAD, two which were associated with LDL cholesterol and CAD, and one associated with Total Cholesterol and CAD. At several of our loci, the GWAS signals jointly localize with genetic variants associated with expression level changes for one or more neighboring genes, indicating that these loci may be affecting disease risk through regulatory activity.ConclusionsWe discovered six novel variants individually associated with both lipids and coronary artery disease.

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MicroRNAs as Biomarker for Breast Cancer

Endocrine Metabolic & Immune Disorders - Drug Targets ◽

10.2174/1871530320666200428113051 ◽

2020 ◽

Vol 20 (10) ◽

pp. 1597-1610 ◽

Cited By ~ 2

Author(s):

Taru Aggarwal ◽

Ridhima Wadhwa ◽

Riya Gupta ◽

Keshav Raj Paudel ◽

Trudi Collet ◽

...

Keyword(s):

Breast Cancer ◽

Association Studies ◽

Large Family ◽

Breast Cancer Diagnosis ◽

Cell Multiplication ◽

Cell Physiology ◽

Gene Transcript ◽

Genome Wide Association Studies ◽

Non Coding Rnas

Regardless of advances in detection and treatment, breast cancer affects about 1.5 million women all over the world. Since the last decade, genome-wide association studies (GWAS) have been extensively conducted for breast cancer to define the role of miRNA as a tool for diagnosis, prognosis and therapeutics. MicroRNAs are small, non-coding RNAs that are associated with the regulation of key cellular processes such as cell multiplication, differentiation, and death. They cause a disturbance in the cell physiology by interfering directly with the translation and stability of a targeted gene transcript. MicroRNAs (miRNAs) constitute a large family of non-coding RNAs, which regulate target gene expression and protein levels that affect several human diseases and are suggested as the novel markers or therapeutic targets, including breast cancer. MicroRNA (miRNA) alterations are not only associated with metastasis, tumor genesis but also used as biomarkers for breast cancer diagnosis or prognosis. These are explained in detail in the following review. This review will also provide an impetus to study the role of microRNAs in breast cancer.

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

Scientific Reports ◽

10.1038/s41598-021-86871-2 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Chao-Yu Guo ◽

Reng-Hong Wang ◽

Hsin-Chou Yang

Keyword(s):

Complex Traits ◽

Association Studies ◽

Association Test ◽

Whole Genome Sequence ◽

Environment Interaction ◽

Genome Wide Association Studies ◽

Whole Genome ◽

Sequence Kernel Association Test ◽

Gene Environment ◽

Family Based

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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