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 MetropolisHastings 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. M ANY complex human diseases and traits of biotive corrections for multiple testing. Non-Bayesian model logical and/or economic importance are deterselection methods combine simultaneous search with a mined by multiple genetic and environmental influsequential procedure such as forward or stepwise selecences (Lynch and Walsh 1998). Mounting evidence tion and apply criteria such as P-values or modified Bayesuggests that interactions among genes (epistasis) play sian information criterion (BIC) to identify well-fitting an important role in the genetic control and evolumultiple-QTL models (Kao et al. 1999; Carlborg et al. tion of complex traits (Cheverud 2000; Carlborg and 2000;Reifsnyder et al. 2000; Bogdan et al. 2004). These Haley 2004). Mapping quantitative trait loci (QTL) is methods, although appealing in their simplicity and popa process of inferring the number of QTL, their genoularity, have several drawbacks, including: (1) the uncermic positions, and genetic effects given observed phenotainty about the model itself is ignored in the final intype and marker genotype data. From a statistical perference, (2) they involve a complex sequential testing spective, two key problems in QTL mapping are model strategy that includes a dynamically changing null hysearch and selection (e.g., Broman and ful and conceptually simple approach to mapping multiExtensions of this approach can allow for main and epiple QTL (Satagopan et al. 1996; Hoeschele 2001; Sen static effects at two or perhaps a few QTL at a time and and Churchill 2001). The Bayesian approach proemploy a multidimensional scan to detect QTL. Howceeds by setting up a likelihood function for the phenoever, such an approach neglects potential confoundtype and assigning prior distributions to all unknowns ing effects from additional QTL and requires prohibiin the prob...

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Conjugated linoleic acid (CLA) is a unique lipid that elicits dramatic reductions in adiposity in several animal models when included at < or = 1% of the diet. Despite a flurry of investigations, the precise mechanisms by which conjugated linoleic acid elicits its dramatic effects in adipose tissue and liver are still largely unknown. In vivo and in vitro analyses of physiological modifications imparted by conjugated linoleic acid on protein and gene expression suggest that conjugated linoleic acid exerts its de-lipidating effects by modulating energy expenditure, apoptosis, fatty acid oxidation, lipolysis, stromal vascular cell differentiation and lipogenesis. The purpose of this review shall be to examine the recent advances and insights into conjugated linoleic acid's effects on obesity and lipid metabolism, specifically focused on changes in gene expression and physiology of liver and adipose tissue.

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