Development of a genotyping microarray for studying the role of gene-environment interactions in risk for lung cancer.
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ABSTRACT:
SUBMITTER: Baldwin DA
PROVIDER: S-EPMC3792704 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Missing data arise in genetic association studies when genotypes are unknown or when haplotypes are of direct interest. We provide a general likelihood-based framework for making inference on genetic effects and gene-environment interactions with such missing data. We allow genetic and environmental variables to be correlated while leaving the distribution of environmental variables completely unspecified. We consider 3 major study designs-cross-sectional, case-control, and cohort designs-and construct appropriate likelihood functions for all common phenotypes (e.g. case-control status, quantitative traits, and potentially censored ages at onset of disease). The likelihood functions involve both finite- and infinite-dimensional parameters. The maximum likelihood estimators are shown to be consistent, asymptotically normal, and asymptotically efficient. Expectation-Maximization (EM) algorithms are developed to implement the corresponding inference procedures. Extensive simulation studies demonstrate that the proposed inferential and numerical methods perform well in practical settings. Illustration with a genome-wide association study of lung cancer is provided.
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Project description:KPNA1 is a mediator of nucleocytoplasmic transport that is abundantly expressed in the mammalian brain and regulates neuronal differentiation and synaptic function. De novo mutations in Kpna1 have been identified using genome-wide association studies in humans with schizophrenia; however, it remains unclear how KPNA1 contributes to schizophrenia pathogenesis. Recent studies have suggested a complex combination of genetic and environmental factors that are closely related to psychiatric disorders. Here, we found that subchronic administration of phencyclidine, a psychotropic drug, induced vulnerability and behavioral abnormalities consistent with the symptoms of schizophrenia in Kpna1-deficient mice. Microarray assessment revealed that the expression levels of dopamine d1/d2 receptors, an RNA editing enzyme, and a cytoplasmic dynein component were significantly altered in the nucleus accumbens brain region in a gene-environment (G × E) interaction-dependent manner. Our findings demonstrate that Kpna1-deficient mice may be useful as a G × E interaction mouse model for psychiatric disorders and for further investigation into the pathogenesis of such diseases and disorders.
| S-EPMC10858057 | biostudies-literature
Project description:Several epidemiological and experimental studies have demonstrated that many human diseases are not only caused by specific genetic and environmental factors but also by gene-environment interactions. Although it has been widely reported that genetic polymorphisms play a critical role in human susceptibility to cancer and other chronic disease conditions, many single nucleotide polymorphisms (SNPs) are caused by somatic mutations resulting from human exposure to environmental stressors. Scientific evidence suggests that the etiology of many chronic illnesses is caused by the joint effect between genetics and the environment. Research has also pointed out that the interactions of environmental factors with specific allelic variants highly modulate the susceptibility to diseases. Hence, many scientific discoveries on gene-environment interactions have elucidated the impact of their combined effect on the incidence and/or prevalence rate of human diseases. In this review, we provide an overview of the nature of gene-environment interactions, and discuss their role in human cancers, with special emphases on lung, colorectal, bladder, breast, ovarian, and prostate cancers.
| S-EPMC7662361 | biostudies-literature
Project description:BackgroundUsher syndrome, a combination of retinitis pigmentosa (RP) and sensorineural hearing loss with or without vestibular dysfunction, displays a high degree of clinical and genetic heterogeneity. Three clinical subtypes can be distinguished, based on the age of onset and severity of the hearing impairment, and the presence or absence of vestibular abnormalities. Thus far, eight genes have been implicated in the syndrome, together comprising 347 protein-coding exons.MethodsTo improve DNA diagnostics for patients with Usher syndrome, we developed a genotyping microarray based on the arrayed primer extension (APEX) method. Allele-specific oligonucleotides corresponding to all 298 Usher syndrome-associated sequence variants known to date, 76 of which are novel, were arrayed.ResultsApproximately half of these variants were validated using original patient DNAs, which yielded an accuracy of >98%. The efficiency of the Usher genotyping microarray was tested using DNAs from 370 unrelated European and American patients with Usher syndrome. Sequence variants were identified in 64/140 (46%) patients with Usher syndrome type I, 45/189 (24%) patients with Usher syndrome type II, 6/21 (29%) patients with Usher syndrome type III and 6/20 (30%) patients with atypical Usher syndrome. The chip also identified two novel sequence variants, c.400C>T (p.R134X) in PCDH15 and c.1606T>C (p.C536S) in USH2A.ConclusionThe Usher genotyping microarray is a versatile and affordable screening tool for Usher syndrome. Its efficiency will improve with the addition of novel sequence variants with minimal extra costs, making it a very useful first-pass screening tool.
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Project description:Atopic asthma is a complex disease associated with IgE-mediated immune reactions. Numerous genome-wide studies identified more than 100 genes in 22 chromosomes associated with atopic asthma, and different genetic backgrounds in different environments could modulate susceptibility to atopic asthma. Current knowledge emphasizes the effect of tobacco smoke on the development of childhood asthma. This suggests that asthma, although heritable, is significantly affected by gene-gene and gene-environment interactions. Evidence has recently shown that molecular mechanism of a complex disease may be limited to not only DNA sequence differences, but also gene-environmental interactions for epigenetic difference. This paper reviews and summarizes how gene-gene and gene-environment interactions affect IgE production and the development of atopic asthma in prenatal and childhood stages. Based on the mechanisms responsible for perinatal gene-environment interactions on IgE production and development of asthma, we formulate several potential strategies to prevent the development of asthma in the perinatal stage.
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Project description:GWAS and eQTL studies identified thousands of genetic variants associated with complex traits and gene expression. Despite the important role of environmental exposures in complex traits, only a limited number of environmental factors were measured in these studies. Measuring molecular phenotypes in tightly controlled cellular environments provides a more tractable setting to study gene-environment interactions in the absence of other confounding variables. We performed RNA-seq and ATAC-seq in endothelial cells exposed to retinoic acid, dexamethasone, caffeine, and selenium to model genetic and environmental effects on gene regulation in the vascular endothelium-a common site of pathology in cardiovascular disease. We found that genes near regions of differentially accessible chromatin were more likely to be differentially expressed [OR = (3.41, 6.52), [Formula: see text]]. Furthermore, we confirmed that environment-specific changes in transcription factor binding are a key mechanism for cellular response to environmental stimuli. Single nucleotide polymorphisms (SNPs) in these transcription response factor footprints for dexamethasone, caffeine, and retinoic acid were enriched in GTEx eQTLs from artery tissues, indicating that these environmental conditions are latently present in GTEx samples. Additionally, SNPs in footprints for response factors in caffeine are enriched in colocalized eQTLs for coronary artery disease (CAD), suggesting a role for caffeine in CAD risk. By combining GWAS, eQTLs, and response genes, we annotated environmental components that can increase or decrease disease risk through changes in gene expression in 43 genes. Interestingly, each treatment may amplify or buffer genetic risk for CAD, depending on the particular SNP or gene considered.
| S-EPMC6781890 | biostudies-literature
Project description:Uncovering the interaction between genomes and the environment is a principal challenge of modern genomics and preventive medicine. While theoretical models are well defined, little is known of the G?×?E interactions in humans. We used an integrative approach to comprehensively assess the interactions between 1.6 million data points, encompassing a range of environmental exposures, health, and gene expression levels, coupled with whole-genome genetic variation. From ?1000 individuals of a founder population in Quebec, we reveal a substantial impact of the environment on the transcriptome and clinical endophenotypes, overpowering that of genetic ancestry. Air pollution impacts gene expression and pathways affecting cardio-metabolic and respiratory traits, when controlling for genetic ancestry. Finally, we capture four expression quantitative trait loci that interact with the environment (air pollution). Our findings demonstrate how the local environment directly affects disease risk phenotypes and that genetic variation, including less common variants, can modulate individual's response to environmental challenges.
| S-EPMC5840419 | biostudies-literature
Project description:BackgroundHoloprosencephaly is the most common malformation of the forebrain (1 in 250 embryos) with severe consequences for fetal and child development. This study evaluates nongenetic factors associated with holoprosencephaly risk, severity, and gene-environment interactions.MethodsFor this retrospective case control study, we developed an online questionnaire focusing on exposures to common and rare toxins/toxicants before and during pregnancy, nutritional factors, maternal health history, and demographic factors. Patients with holoprosencephaly were primarily ascertained from our ongoing genetic and clinical studies of holoprosencephaly. Controls included children with Williams-Beuren syndrome (WBS) ascertained through online advertisements in a WBD support group and fliers.ResultsDifference in odds of exposures between cases and controls as well as within cases with varying holoprosencephaly severity were studied. Cases included children born with holoprosencephaly (n = 92) and the control group consisted of children with WBS (n = 56). Pregnancy associated risk associated with holoprosencephaly included maternal pregestational diabetes (9.2% of cases and 0 controls, p = .02), higher alcohol consumption (adjusted odds ratio [aOR], 1.73; 95% CI, 0.88-15.71), and exposure to consumer products such as aerosols or sprays including hair sprays (aOR, 2.46; 95% CI, 0.89-7.19). Significant gene-environment interactions were identified including for consumption of cheese (p < .05) and espresso drinks (p = .03).ConclusionThe study identifies modifiable risk factors and gene-environment interactions that should be considered in future prevention of holoprosencephaly. Studies with larger HPE cohorts will be needed to confirm these findings.
| S-EPMC9288838 | biostudies-literature
Project description:An important follow-up step after genetic markers are found to be associated with a disease outcome is a more detailed analysis investigating how the implicated gene or chromosomal region and an established environment risk factor interact to influence the disease risk. The standard approach to this study of gene-environment interaction considers one genetic marker at a time and therefore could misrepresent and underestimate the genetic contribution to the joint effect when one or more functional loci, some of which might not be genotyped, exist in the region and interact with the environment risk factor in a complex way. We develop a more global approach based on a Bayesian model that uses a latent genetic profile variable to capture all of the genetic variation in the entire targeted region and allows the environment effect to vary across different genetic profile categories. We also propose a resampling-based test derived from the developed Bayesian model for the detection of gene-environment interaction. Using data collected in the Environment and Genetics in Lung Cancer Etiology (EAGLE) study, we apply the Bayesian model to evaluate the joint effect of smoking intensity and genetic variants in the 15q25.1 region, which contains a cluster of nicotinic acetylcholine receptor genes and has been shown to be associated with both lung cancer and smoking behavior. We find evidence for gene-environment interaction (P-value?=?0.016), with the smoking effect appearing to be stronger in subjects with a genetic profile associated with a higher lung cancer risk; the conventional test of gene-environment interaction based on the single-marker approach is far from significant.
| S-EPMC3266891 | biostudies-literature
Project description:To estimate whether African ancestry, specific gene polymorphisms, and gene-environment interactions could account for some of the unexplained preterm birth variance within African American women.We genotyped 1,509 African ancestry-informative markers, cytochrome P450 1A1 (CYP1A1), and glutathione S-transferases Theta 1 (GSTT1) variants in 1,030 self-reported African American mothers. We estimated the African ancestral proportion using the ancestry-informative markers for all 1,030 self-reported African American mothers. We examined the effect of African ancestry and CYP1A1- and GSTT1-smoking interactions on preterm birth cases as a whole and within its subgroups: very preterm birth (gestational age less than 34 weeks); and late preterm birth (gestational age greater than 34 and less than 37 weeks). We applied logistic regression and receiver operating characteristic curve analysis, separately, to evaluate whether African ancestry and CYP1A1- and GSTT1-smoking interactions could make additional contributions to preterm birth beyond epidemiologic factors.We found significant associations of African ancestry with preterm birth (22% compared with 31%, odds ratio [OR] 1.11, 95% confidence interval [CI] 1.02-1.20) and very preterm birth (23% compared with 33%, OR 1.17, 95% CI 1.03-1.33), but not with late preterm birth (22% compared with 29%, OR 1.06, 95% CI 0.97-1.16). In addition, the receiver operating characteristic curve analysis suggested that African ancestry and CYP1A1- and GSTT1-smoking interactions made substantial contributions to very preterm birth beyond epidemiologic factors.Our data underscore the importance of simultaneously considering epidemiologic factors, African ancestry, specific gene polymorphisms, and gene-environment interactions to better understand preterm birth racial disparity and to improve our ability to predict preterm birth, especially very preterm birth.
| S-EPMC3218119 | biostudies-literature