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Variance-quantitative trait loci enable systematic discovery of gene-environment interactions for cardiometabolic serum biomarkers.

ABSTRACT: Gene-environment interactions represent the modification of genetic effects by environmental exposures and are critical for understanding disease and informing personalized medicine. These often induce differential phenotypic variance across genotypes; these variance-quantitative trait loci can be prioritized in a two-stage interaction detection strategy to greatly reduce the computational and statistical burden and enable testing of a broader range of exposures. We perform genome-wide variance-quantitative trait locus analysis for 20 serum cardiometabolic biomarkers by multi-ancestry meta-analysis of 350,016 unrelated participants in the UK Biobank, identifying 182 independent locus-biomarker pairs (p < 4.5×10^-9). Most are concentrated in a small subset (4%) of loci with genome-wide significant main effects, and 44% replicate (p < 0.05) in the Women's Genome Health Study (N = 23,294). Next, we test each locus-biomarker pair for interaction across 2380 exposures, identifying 847 significant interactions (p < 2.4×10^-7), of which 132 are independent (p < 0.05) after accounting for correlation between exposures. Specific examples demonstrate interaction of triglyceride-associated variants with distinct body mass- versus body fat-related exposures as well as genotype-specific associations between alcohol consumption and liver stress at the ADH1B gene. Our catalog of variance-quantitative trait loci and gene-environment interactions is publicly available in an online portal.

SUBMITTER: Westerman KE

PROVIDER: S-EPMC9271055 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Genetic dissection of the S rat genome has provided strong evidence for the presence of two interacting blood pressure (BP) quantitative trait loci (QTLs), termed QTL1 and QTL2, on rat chromosome 5. However, the identities of the underlying interacting genetic factors remain unknown. Further experiments targeted to identify the interacting genetic factors by the substitution mapping approach alone are difficult because of the interdependency of natural recombinations to occur at the two QTLs. We hypothesized that the interacting genetic factors underlying these two QTLs may interact at the level of gene transcription and thereby represent expression QTLs (eQTLs). To detect these interacting eQTLs, a custom QTL chip containing the annotated genes within QTL1 and QTL2 was developed and used to conduct a transcriptional profiling study of S and two congenic strains that retain either one or both the QTLs. The results uncovered an interaction between two transcription factors, DMRTA2 and NFIA. Further, the âbiological signatureâ elicited by these two transcription factors was differential between the congenic strain that retained LEW alleles at both QTL1 and 2 compared to the congenic strain that retained LEW alleles at QTL1 alone. A network of transcription factors potentially affecting BP could be traced, lending support to our hypothesis. Pairs of Cy5 and Cy3 labeled targets were co-hybridized onto either a custom long oligonucleotide microarray for the interrogation of 231 genes encompassed by QTL1 and QTL2, or a TIGR rat cDNA array consisting of 26,401 probe elements representing 20,465 unique non-QTL genes. A âflip-dyeâ or âbalanced blockâ design was used as the experimental method of choice to account for potential dye-bias labeling effects. Six âbalanced blockâ normalized files are submitted for the long oligonucleotide array interrogating the hearts from S versus S.LEW(5)x6x9 animals, fourteen âflip-dyeâ hybridizations are submitted for the cDNA array interrogating the hearts from S versus S.LEW(5)x6x9 animals, and twelve âflip-dyeâ hybridizations are submitted for the cDNA array interrogating the hearts from S versus S.LEW(5)x6x11 animals. GPR and MEV files cannot be located.

Project description:Complex traits important for humans are often correlated phenotypically and genetically. Joint mapping of quantitative-trait loci (QTLs) for multiple correlated traits plays an important role in unraveling the genetic architecture of complex traits. Compared with single-trait analysis, joint mapping addresses more questions and has advantages for power of QTL detection and precision of parameter estimation. Some statistical methods have been developed to map QTLs underlying multiple traits, most of which are based on maximum-likelihood methods. We develop here a multivariate version of the Bayes methodology for joint mapping of QTLs, using the Markov chain-Monte Carlo (MCMC) algorithm. We adopt a variance-components method to model complex traits in outbred populations (e.g., humans). The method is robust, can deal with an arbitrary number of alleles with arbitrary patterns of gene actions (such as additive and dominant), and allows for multiple phenotype data of various types in the joint analysis (e.g., multiple continuous traits and mixtures of continuous traits and discrete traits). Under a Bayesian framework, parameters--including the number of QTLs--are estimated on the basis of their marginal posterior samples, which are generated through two samplers, the Gibbs sampler and the reversible-jump MCMC. In addition, we calculate the Bayes factor related to each identified QTL, to test coincident linkage versus pleiotropy. The performance of our method is evaluated in simulations with full-sib families. The results show that our proposed Bayesian joint-mapping method performs well for mapping multiple QTLs in situations of either bivariate continuous traits or mixed data types. Compared with the analysis for each trait separately, Bayesian joint mapping improves statistical power, provides stronger evidence of QTL detection, and increases precision in estimation of parameter and QTL position. We also applied the proposed method to a set of real data and detected a coincident linkage responsible for determining bone mineral density and areal bone size of wrist in humans.

Project description:BACKGROUND AND PURPOSE:Addiction vulnerability involves complex gene X environment interactions leading to a pathological response to drugs. Identification of the genes involved in these interactions is an important step in understanding the underlying neurobiology and rarely have such analyses examined sex-specific influences. To dissect this interaction, we examined the impact of prenatal stress (PNS) on cocaine responsiveness in male and female mice of the BXD recombinant inbred panel. EXPERIMENTAL APPROACH:BXD strains were subjected to timed mating and assigned to PNS or control groups. PNS dams were subjected to restraint stress (1-hr restraint, three times daily) starting between embryonic day (E) 11 and 14 and continued until parturition. Adult male and female, control and PNS offspring were tested for locomotor response to initial and repeated cocaine injections (sensitization) as well as cocaine-induced conditioned place preference (CPP). KEY RESULTS:Strain, PNS, and sex interacted to modulate initial and sensitized cocaine-induced locomotion, as well as CPP. Moreover, a quantitative trait locus (QTL) interacting with PNS regulating initial locomotor response to cocaine (chromosome X, 37.91 to 50.95 Mb) was identified. Also PNS-independent, female-specific QTLs regulating CPP (chromosome 11, 65.50 to 81.31 Mb) and sensitized cocaine-induced locomotion (chromosome 16, 95.79 to 98.32 Mb) were identified. Publicly available mRNA expression data were utilized to identify cis-eQTL and transcript covariation with the behavioural phenotype to prioritize candidate genes; including Aifm1. CONCLUSIONS AND IMPLICATIONS:These QTL encompass genes that may moderate genetic susceptibility to PNS and interact with sex to determine adult responsiveness to cocaine and addiction vulnerability. LINKED ARTICLES:This article is part of a themed section on The Importance of Sex Differences in Pharmacology Research. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v176.21/issuetoc.

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Variance-quantitative trait loci enable systematic discovery of gene-environment interactions for cardiometabolic serum biomarkers.

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