scholarly journals Non-uniform genetic effect sizes of variants associated with refractive error suggests gene-gene or gene-environment interactions are pervasive

2018 ◽  
Author(s):  
Alfred Pozarickij ◽  
Cathy Williams ◽  
Pirro Hysi ◽  
Jeremy A. Guggenheim ◽  
Keyword(s):  
Environmental Factors ◽  
Refractive Error ◽  
Genetic Variants ◽  
Genetic Effect ◽  
Genetic Interactions ◽  
Gene Interactions ◽  
Gxe Interaction ◽  
Gene Environment ◽  
Gxe Interactions ◽  
Or Gene

AbstractRefractive error is a complex ocular trait controlled by genetic and environmental factors. Genome-wide association studies (GWAS) have identified approximately 150 genetic variants associated with refractive error. Among the known environmental factors, education, near-work and time spent outdoors have been demonstrated to have the strongest associations. Currently, the extent of gene-environment or gene-gene interactions in myopia is unknown. Here we show that the majority of genetic variants associated with refractive error show evidence of effect size heterogeneity, which is a hallmark feature of genetic interactions. Using conditional quantile regression, we observed that 88% of genetic variants associated with refractive error have at least nominally-significant non-uniform, non-linear profiles across the refractive error distribution. SNP effects tend to be strongest at the phenotype extremes and have weaker effects in emmetropes. A parsimonious explanation for these findings is that gene-environment or gene-gene interactions in refractive error are pervasive.Author summaryThe prevalence of myopia (nearsightedness) in the United States and East Asia has almost doubled in the past 30 years. Such a rapid rise in prevalence cannot be explained by genetics, which implies that environmental (lifestyle) risk factors play a major role. Nevertheless, diverse approaches have suggested that genetics is also important, and indeed approximately 150 distinct genetic risk loci for myopia have been discovered to date. One attractive explanation for the evidence implicating both genes and environment in myopia is gene-environment (GxE) interaction (a difference in genetic effect in individuals exposed to a high vs. low level of an environmental risk factor). Past studies aiming to discover GxE interactions in myopia have met with limited success, perhaps because information on lifestyle exposures during childhood has rarely been available. Here we used an agnostic approach that does not require information about specific lifestyle exposures in order to detect ‘signatures’ of GxE interaction. We found compelling evidence for widespread genetic interactions in myopia, with 88% of 150 known myopia genetic susceptibility loci showing an interaction signature. These findings suggest that GxE interactions in myopia are pervasive.

scholarly journals A two-step approach to testing overall effect of gene-environment interaction for multiple phenotypes

2020 ◽  
Author(s):  
Arunabha Majumdar ◽  
Kathryn S. Burch ◽  
Sriram Sankararaman ◽  
Bogdan Pasaniuc ◽  
W. James Gauderman ◽  
...  
Keyword(s):  
Genetic Effect ◽  
Single Step ◽  
Environment Interaction ◽  
Step Method ◽  
Gene Environment Interaction ◽  
Gxe Interaction ◽  
Multiple Phenotypes ◽  
Gene Environment ◽  
Genome Wide ◽  
The Uk

AbstractWhile gene-environment (GxE) interactions contribute importantly to many different phenotypes, detecting such interactions requires well-powered studies and has proven difficult. To address this, we combine two approaches to improve GxE power: simultaneously evaluating multiple phenotypes and using a two-step analysis approach. Previous work shows that the power to identify a main genetic effect can be improved by simultaneously analyzing multiple related phenotypes. For a univariate phenotype, two-step methods produce higher power for detecting a GxE interaction compared to single step analysis. Therefore, we propose a two-step approach to test for an overall GxE effect for multiple phenotypes. Using simulations we demonstrate that, when more than one phenotype has GxE effect (i.e., GxE pleiotropy), our approach offers substantial gain in power (18% – 43%) to detect an aggregate-level GxE effect for a multivariate phenotype compared to an analogous two-step method to identify GxE effect for a univariate phenotype. We applied the proposed approach to simultaneously analyze three lipids, LDL, HDL and Triglyceride with the frequency of alcohol consumption as environmental factor in the UK Biobank. The method identified two independent genome-wide significant signals of an overall GxE effect on the vector of lipids.

scholarly journals NAD deficiency due to environmental factors or gene–environment interactions causes congenital malformations and miscarriage in mice

2020 ◽  
Vol 117 (7) ◽  
pp. 3738-3747 ◽  
Author(s):  
Hartmut Cuny ◽  
Melissa Rapadas ◽  
Jessica Gereis ◽  
Ella M. M. A. Martin ◽  
Rosemary B. Kirk ◽  
...  
Keyword(s):  
Environmental Factors ◽  
Congenital Malformation ◽  
Genetic Variants ◽  
Congenital Malformations ◽  
Environment Interaction ◽  
Loss Of Function ◽  
Vitamin B3 ◽  
Gene Environment ◽  
Nad Synthesis ◽  
Embryo Loss

Causes for miscarriages and congenital malformations can be genetic, environmental, or a combination of both. Genetic variants, hypoxia, malnutrition, or other factors individually may not affect embryo development, however, they may do so collectively. Biallelic loss-of-function variants in HAAO or KYNU, two genes of the nicotinamide adenine dinucleotide (NAD) synthesis pathway, are causative of congenital malformation and miscarriage in humans and mice. The variants affect normal embryonic development by disrupting the synthesis of NAD, a key factor in multiple biological processes, from its dietary precursor tryptophan, resulting in NAD deficiency. This study demonstrates that congenital malformations caused by NAD deficiency can occur independent of genetic disruption of NAD biosynthesis. C57BL/6J wild-type mice had offspring exhibiting similar malformations when their supply of the NAD precursors tryptophan and vitamin B3 in the diet was restricted during pregnancy. When the dietary undersupply was combined with a maternal heterozygous variant in Haao, which alone does not cause NAD deficiency or malformations, the incidence of embryo loss and malformations was significantly higher, suggesting a gene–environment interaction. Maternal and embryonic NAD levels were deficient. Mild hypoxia as an additional factor exacerbated the embryo outcome. Our data show that NAD deficiency as a cause of embryo loss and congenital malformation is not restricted to the rare cases of biallelic mutations in NAD synthesis pathway genes. Instead, monoallelic genetic variants and environmental factors can result in similar outcomes. The results expand our understanding of the causes of congenital malformations and the importance of sufficient NAD precursor consumption during pregnancy.

scholarly journals Testing Gene-Environment Interactions in the Presence of Confounders and Mismeasured Environmental Exposures

2021 ◽  
Author(s):  
Chao Cheng ◽  
Donna Spiegelman ◽  
Zuoheng Wang ◽  
Molin Wang
Keyword(s):  
Logistic Regression ◽  
Measurement Errors ◽  
Statistical Power ◽  
Computation Time ◽  
Continuous Exposure ◽  
Susceptibility Loci ◽  
Linear Discriminant ◽  
Gxe Interaction ◽  
Gene Environment ◽  
Gxe Interactions

Abstract Interest in investigating gene-environment (GxE) interactions has rapidly increased over the last decade. Although GxE interactions have been extremely investigated in large studies, few such effects have been identified and replicated, highlighting the need to develop statistical GxE tests with greater statistical power. The reverse test has been proposed for testing the interaction effect between a continuous exposure and genetic variants in relation to a binary disease outcome, which leverages the idea of linear discriminant analysis, significantly increasing statistical power comparing to the standard logistic regression approach. However, this reverse approach did not take into consideration adjustment for confounders. Since GxE interaction studies are inherently non-experimental, adjusting for potential confounding effects is critical for valid evaluation of GxE interactions. In this paper, we extend the reverse test to allow for confounders. The proposed reverse test also allows for exposure measurement errors as typically occurs. Extensive simulation experiments demonstrated that the proposed method not only provides greater statistical power under most simulation scenarios but also provides substantive computational efficiency, which achieves a computation time that is more than sevenfold less than that of the standard logistic regression test. In an illustrative example, we applied the proposed approach to the Veterans Aging Cohort Study (VACS) to search for genetic susceptibility loci modifying the smoking–HIV status association.

Association of Genetic Factors and Gene–Environment Interactions With Risk of Developing Posttraumatic Stress Disorder in a Case–Control Study

2015 ◽  
Vol 17 (4) ◽  
pp. 364-372 ◽  
Author(s):  
Yali Tian ◽  
Hui Liu ◽  
Lorna Guse ◽  
Thomas K. S. Wong ◽  
Jiping Li ◽  
...  
Keyword(s):  
Posttraumatic Stress Disorder ◽  
Environmental Factors ◽  
Posttraumatic Stress ◽  
Stress Disorder ◽  
Interactive Effect ◽  
Genetic Effect ◽  
Positive Effects ◽  
Diagnostic And Statistical Manual ◽  
Gene Environment ◽  
Earthquake Survivors

Background: The dopamine receptor D2 ( DRD2) and serotonin transporter ( 5-HTT) genes are associated with posttraumatic stress disorder (PTSD). However, it remains largely unknown whether these genes interact with environmental factors to affect the development of PTSD. Purpose: The purpose of this study was to examine the associations of gene polymorphisms and gene–environment interactions with the risk of developing PTSD among adolescent earthquake survivors. Method: A total of 183 adolescent survivors from an earthquake-stricken area participated in this study. Measures included a questionnaire about demographic characteristics and earthquake exposure, the PTSD Checklist–Civilian Version and the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, fourth edition disorders. Genotypes were analyzed by using the polymerase chain reaction–based restriction fragment length polymorphism. Results: The 5-HTTLPR and 5-HTTVNTR polymorphisms and earthquake exposure had statistically significant positive effects on PTSD. The interaction effects of 5-HTTLPR × Earthquake Exposure and 5-HTTVNTR × Earthquake Exposure were statistically significant. Conclusion: The development of PTSD is the result not only of a genetic effect and environmental factors but also of the interactive effect between gene and environment.

Genetic Epidemiology and Molecular Genetics

2014 ◽  
Author(s):  
Michael C. Stallings ◽  
Ian R. Gizer ◽  
Kelly C. Young-Wolff
Keyword(s):  
Substance Use ◽  
Environmental Factors ◽  
Genetic Epidemiology ◽  
Twin Studies ◽  
Joint Effect ◽  
Gene Environment ◽  
Genetic Mechanisms ◽  
Or Gene ◽  
Pathway Analyses ◽  
Use Behavior

The tools of genetic epidemiology—family, adoption, and twin studies—show convincingly that substance use behavior and substance use disorders are influenced by both genetic and familial and extrafamilial environmental factors. Environmental factors appear to play a more influential role in the early stages of substance use, whereas genetic factors become more important in the development of problem use and substance use disorder. Moreover, some genetic effects are likely conditional on conducive environments; research employing both behavior genetic approaches and measured genes point to important gene–environment interactions that promote substance use and dependence. Consequently, a full understanding of the addiction process requires investigating substance use behavior within its comorbid context. The identification of specific genetic mechanisms underlying these heritable influences is elusive. These findings have prompted the development of new strategies for testing the joint effect of multiple genetic variants in gene-based or gene pathway analyses.

scholarly journals Leveraging phenotypic variability to identify genetic interactions in human phenotypes

2020 ◽  
Author(s):  
Andrew R. Marderstein ◽  
Emily Davenport ◽  
Scott Kulm ◽  
Cristopher V. Van Hout ◽  
Olivier Elemento ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Statistical Power ◽  
Statistical Tests ◽  
Phenotypic Variability ◽  
Individual Risk ◽  
Statistical Framework ◽  
Gene Environment ◽  
Severe Erosion ◽  
Massive Number ◽  
Gxe Interactions

AbstractWhile thousands of loci have been associated with human phenotypes, the role of gene-environment (GxE) interactions in determining individual risk of human diseases remains unclear. This is partly due to the severe erosion of statistical power resulting from the massive number of statistical tests required to detect such interactions. Here, we focus on improving the power of GxE tests by developing a statistical framework for assessing quantitative trait loci (QTLs) associated with the trait means and/or trait variances. When applying this framework to body mass index (BMI), we find that GxE discovery and replication rates are significantly higher when prioritizing genetic variants associated with the variance of the phenotype (vQTLs) compared to assessing all genetic variants. Moreover, we find that vQTLs are enriched for associations with other non-BMI phenotypes having strong environmental influences, such as diabetes or ulcerative colitis. We show that GxE effects first identified in quantitative traits such as BMI can be used for GxE discovery in disease phenotypes such as diabetes. A clear conclusion is that strong GxE interactions mediate the genetic contribution to body weight and diabetes risk.

scholarly journals Kernel-Based Measure of Variable Importance for Genetic Association Studies

2017 ◽  
Vol 13 (2) ◽  
Author(s):  
Vicente Gallego ◽  
M. Luz Calle ◽  
Ramon Oller
Keyword(s):  
Genetic Association ◽  
Genetic Variants ◽  
Disease Risk ◽  
Association Studies ◽  
Univariate Analysis ◽  
Genetic Association Studies ◽  
Genetic Effect ◽  
Variable Importance ◽  
Genetic Interactions ◽  
Nucleotide Polymorphisms

Abstract The identification of genetic variants that are associated with disease risk is an important goal of genetic association studies. Standard approaches perform univariate analysis where each genetic variant, usually Single Nucleotide Polymorphisms (SNPs), is tested for association with disease status. Though many genetic variants have been identified and validated so far using this univariate approach, for most complex diseases a large part of their genetic component is still unknown, the so called missing heritability. We propose a Kernel-based measure of variable importance (KVI) that provides the contribution of a SNP, or a group of SNPs, to the joint genetic effect of a set of genetic variants. KVI can be used for ranking genetic markers individually, sets of markers that form blocks of linkage disequilibrium or sets of genetic variants that lie in a gene or a genetic pathway. We prove that, unlike the univariate analysis, KVI captures the relationship with other genetic variants in the analysis, even when measured at the individual level for each genetic variable separately. This is specially relevant and powerful for detecting genetic interactions. We illustrate the results with data from an Alzheimer’s disease study and show through simulations that the rankings based on KVI improve those rankings based on two measures of importance provided by the Random Forest. We also prove with a simulation study that KVI is very powerful for detecting genetic interactions.

Genetic Variation: Impact on Folate (and Choline) Bioefficacy

2010 ◽  
Vol 80 (45) ◽  
pp. 319-329 ◽  
Author(s):  
Allyson A. West ◽  
Marie A. Caudill
Keyword(s):  
Carbon Metabolism ◽  
Genetic Variants ◽  
Plasma Homocysteine ◽  
Water Soluble ◽  
Gene Interactions ◽  
Dietary Folate ◽  
Methyl Donors ◽  
Common Genetic Variants ◽  
One Carbon Metabolism ◽  
The Impact

Folate and choline are water-soluble micronutrients that serve as methyl donors in the conversion of homocysteine to methionine. Inadequacy of these nutrients can disturb one-carbon metabolism as evidenced by alterations in circulating folate and/or plasma homocysteine. Among common genetic variants that reside in genes regulating folate absorptive and metabolic processes, homozygosity for the MTHFR 677C > T variant has consistently been shown to have robust effects on status markers. This paper will review the impact of genetic variants in folate-metabolizing genes on folate and choline bioefficacy. Nutrient-gene and gene-gene interactions will be considered along with the need to account for these genetic variants when updating dietary folate and choline recommendations.

Sign in / Sign up

Export Citation Format

Share Document