Dataset Information

Estimation of gene-environment interaction by pooling biospecimens.

ABSTRACT: Case-control studies are prone to low power for testing gene-environment interactions (GXE) given the need for a sufficient number of individuals on each strata of disease, gene, and environment. We propose a new study design to increase power by strategically pooling biospecimens. Pooling biospecimens allows us to increase the number of subjects significantly, thereby providing substantial increase in power. We focus on a special, although realistic case, where disease and environmental statuses are binary, and gene status is ordinal with each individual having 0, 1, or 2 minor alleles. Through pooling, we obtain an allele frequency for each level of disease and environmental status. Using the allele frequencies, we develop a new methodology for estimating and testing GXE that is comparable to the situation when we have complete data on gene status for each individual. We also explore the measurement process and its effect on the GXE estimator. Using an illustration, we show the effectiveness of pooling with an epidemiologic study, which tests an interaction for fiber and paraoxonase on anovulation. Through simulation, we show that taking 12 pooled measurements from 1000 individuals achieves more power than individually genotyping 500 individuals. Our findings suggest that strategic pooling should be considered when an investigator designs a pilot study to test for a GXE.

SUBMITTER: Danaher MR

PROVIDER: S-EPMC4037867 | biostudies-literature | 2012 Nov

REPOSITORIES: biostudies-literature

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Estimation of gene-environment interaction by pooling biospecimens.

Danaher M R MR Schisterman E F EF Roy A A Albert P S PS

Statistics in medicine 20120801 26

Case-control studies are prone to low power for testing gene-environment interactions (GXE) given the need for a sufficient number of individuals on each strata of disease, gene, and environment. We propose a new study design to increase power by strategically pooling biospecimens. Pooling biospecimens allows us to increase the number of subjects significantly, thereby providing substantial increase in power. We focus on a special, although realistic case, where disease and environmental statuse ...[more]

PMID: 22859290

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Project description:The effects of genetic variants on phenotypic traits often depend on environmental and physiological conditions, but such gene-environment interactions are poorly understood. Recently developed approaches that treat transcript abundances of thousands of genes as quantitative traits offer the opportunity to broadly characterize the architecture of gene-environment interactions. We examined the genetic and molecular basis of variation in gene expression between two yeast strains (BY and RM) grown in two different conditions (glucose and ethanol as carbon sources). We observed that most transcripts vary by strain and condition, with 2,996, 3,448, and 2,037 transcripts showing significant strain, condition, and strain-condition interaction effects, respectively. We expression profiled over 100 segregants derived from a cross between BY and RM in both growth conditions, and identified 1,555 linkages for 1,382 transcripts that show significant gene-environment interaction. At the locus level, local linkages, which usually correspond to polymorphisms in cis-regulatory elements, tend to be more stable across conditions, such that they are more likely to show the same effect or the same direction of effect across conditions. Distant linkages, which usually correspond to polymorphisms influencing trans-acting factors, are more condition-dependent, and often show effects in different directions in the two conditions. We characterized a locus that influences expression of many growth-related transcripts, and showed that the majority of the variation is explained by polymorphism in the gene IRA2. The RM allele of IRA2 appears to inhibit Ras/PKA signaling more strongly than the BY allele, and has undergone a change in selective pressure. Our results provide a broad overview of the genetic architecture of gene-environment interactions, as well as a detailed molecular example, and lead to key insights into how the effects of different classes of regulatory variants are modulated by the environment. These observations will guide the design of studies aimed at understanding the genetic basis of complex traits.

Project description:This study is a quantitative trait linkage mapping study targeted at gene-environment interaction. A family of yeast derived from BY4716 and RM11-1a was grown in two carbon sources (glucose and ethanol) and expression profiled. The analysis revealed how the influence of genetic factors changes in different conditions. Abstract: The effects of genetic variants on phenotypic traits often depend on environmental and physiological conditions, but such gene–environment interactions are poorly understood. Recently developed approaches that treat transcript abundances of thousands of genes as quantitative traits offer the opportunity to broadly characterize the architecture of gene–environment interactions. We examined the genetic and molecular basis of variation in gene expression between two yeast strains (BY and RM) grown in two different conditions (glucose and ethanol carbon sources). We observed that most transcripts vary by strain and condition, with 2,996, 3,448, and 2,037 transcripts showing significant strain, condition, and strain–condition interaction effects, respectively. We expression profiled over 100 segregants derived from a cross between RM and BY in both growth conditions, and identified 1,555 linkages for 1,382 transcripts that show significant gene–environment interaction. At the locus level, local linkages, which usually correspond to polymorphisms in cis-regulatory elements, tend to be more stable across conditions, such that they are more likely to show the same effect or the same direction of effect across conditions. Distant linkages, which usually correspond to polymorphisms influencing trans-acting factors, are more condition-dependent, and often show effects in different directions in the two conditions. We characterized a locus that influences expression of many growth- related transcripts, and showed that the majority of the variation is explained by polymorphism in the gene IRA2. The RM allele of IRA2 appears to inhibit Ras/PKA signaling more strongly than the BY allele, and has undergone a change in selective pressure. Our results provide a broad overview of the genetic architecture of gene–environment interactions, as well as a detailed molecular example, and lead to key insights into how the effects of different classes of regulatory variants are modulated by the environment. These observations will guide the design of studies aimed at understanding the genetic basis of complex traits. Keywords: QTL This data set includes 24 parental arrays (6 of each parent in each condition, 218 segregant arrays (109 in both conditions), and 4 allele replacement arrays (IRA2 replacements in both backgrounds in both conditions). All arrays are from an unique biological sample, and many are randomized for dye (no dye-swaps on the same RNA sample). For the 24 parental arrays, there are three of each strain*condition*dye combination. The segregant arrays were randomized for dye. The replacement arrays are all of the Cy5 = experimental, Cy3 = reference type. All arrays are hybridized to a common reference consisting of an equal portions of RNA samples. from BY and RM grown in glucose and ethanol. Analysis was performed using default settings in Agilent Feature Extractor software versions 8.0-9.5. Spots were excluded if their intensity was below 350 or if they were flagged as non-uniformity outliers. Transcripts were considered good if they were present in all parental arrays (for the parental analysis) or 80% of all arrays in each condition for the segregant analysis.

Dataset Information

Estimation of gene-environment interaction by pooling biospecimens.

Publications

Estimation of gene-environment interaction by pooling biospecimens.

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