Abstract The problem of identifying complex epistatic quantitative trait loci (QTL) across the entire genome continues to be a formidable challenge for geneticists. The complexity of genome-wide epistatic analysis results mainly from the number of QTL being unknown and the number of possible epistatic effects being huge. In this article, we use a composite model space approach to develop a Bayesian model selection framework for identifying epistatic QTL for complex traits in experimental crosses from two inbred lines. By placing a liberal constraint on the upper bound of the number of detectable QTL we restrict attention to models of fixed dimension, greatly simplifying calculations. Indicators specify which main and epistatic effects of putative QTL are included. We detail how to use prior knowledge to bound the number of detectable QTL and to specify prior distributions for indicators of genetic effects. We develop a computationally efficient Markov chain Monte Carlo (MCMC) algorithm using the Gibbs sampler and Metropolis-Hastings algorithm to explore the posterior distribution. We illustrate the proposed method by detecting new epistatic QTL for obesity in a backcross of CAST/Ei mice onto M16i.
β-Cell mass expansion is one mechanism by which obese animals compensate for insulin resistance and prevent diabetes. FoxM1 is a transcription factor that can regulate the expression of multiple cell cycle genes and is necessary for the maintenance of adult β-cell mass, β-cell proliferation, and glucose homeostasis. We hypothesized that FoxM1 is up-regulated by nondiabetic obesity and initiates a transcriptional program leading to β-cell proliferation. We performed gene expression analysis on islets from the nondiabetic C57BL/6 Leptinob/ob mouse, the diabetic BTBR Leptinob/ob mouse, and an F2 Leptinob/ob population derived from these strains. We identified obesity-driven coordinated up-regulation of islet Foxm1 and its target genes in the nondiabetic strain, correlating with β-cell mass expansion and proliferation. This up-regulation was absent in the diabetic strain. In the F2 Leptinob/ob population, increased expression of Foxm1 and its target genes segregated with higher insulin and lower glucose levels. We next studied the effects of FOXM1b overexpression on isolated mouse and human islets. We found that FoxM1 stimulated mouse and human β-cell proliferation by activating many cell cycle phases. We asked whether FOXM1 expression is also responsive to obesity in human islets by collecting RNA from human islet donors (body mass index range: 24–51). We found that the expression of FOXM1 and its target genes are positively correlated with body mass index. Our data suggest that β-cell proliferation occurs in adult obese humans in an attempt to expand β-cell mass to compensate for insulin resistance, and that the FoxM1 transcriptional program plays a key role in this process.
Interval mapping of quantitative trait loci from breeding experiments plays an important role in understanding the mechanisms of disease, both in humans and other organisms. Standard approaches to estimation involve parametric assumptions for the component distributions and may be sensitive to model misspecification. Some nonparametric tests have been studied. However, nonparametric estimation of the phenotypic distributions has not been considered in the genetics literature, even though such methods might provide essential nonparametric summaries for comparing different loci. We develop a sufficient condition for identifiability of the phenotypic distributions. Simple nonparametric estimators for the distributions are proposed for uncensored and right censored data. They have a closed form and their small and large sample properties are readily established. Their practical utility as numerical summaries which complement nonparametric tests is demonstrated on two recent genetics examples.
Abstract Most quantitative trait loci (QTL) mapping experiments typically collect phenotypic data on multiple correlated complex traits. However, there is a lack of a comprehensive genomewide mapping strategy for correlated traits in the literature. We develop Bayesian multiple-QTL mapping methods for correlated continuous traits using two multivariate models: one that assumes the same genetic model for all traits, the traditional multivariate model, and the other known as the seemingly unrelated regression (SUR) model that allows different genetic models for different traits. We develop computationally efficient Markov chain Monte Carlo (MCMC) algorithms for performing joint analysis. We conduct extensive simulation studies to assess the performance of the proposed methods and to compare with the conventional single-trait model. Our methods have been implemented in the freely available package R/qtlbim (http://www.qtlbim.org), which greatly facilitates the general usage of the Bayesian methodology for unraveling the genetic architecture of complex traits.
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