No abstract

The mapping of quantitative trait loci (QTL) is to identify molecular markers or genomic loci that influence the variation of complex traits. The problem is complicated by the facts that QTL data usually contain a large number of markers across the entire genome and most of them have little or no effect on the phenotype. In this article, we propose several Bayesian hierarchical models for mapping multiple QTL that simultaneously fit and estimate all possible genetic effects associated with all markers. The proposed models use prior distributions for the genetic effects that are scale mixtures of normal distributions with mean zero and variances distributed to give each effect a high probability of being near zero. We consider two types of priors for the variances, exponential and scaled inverse-x 2 distributions, which result in a Bayesian version of the popular least absolute shrinkage and selection operator (LASSO) model and the well-known Student's t model, respectively. Unlike most applications where fixed values are preset for hyperparameters in the priors, we treat all hyperparameters as unknowns and estimate them along with other parameters. Markov chain Monte Carlo (MCMC) algorithms are developed to simulate the parameters from the posteriors. The methods are illustrated using well-known barley data.

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...

Background:There is clinical evidence that very low and safe levels of amplitude-modulated electromagnetic fields administered via an intrabuccal spoon-shaped probe may elicit therapeutic responses in patients with cancer. However, there is no known mechanism explaining the anti-proliferative effect of very low intensity electromagnetic fields.Methods:To understand the mechanism of this novel approach, hepatocellular carcinoma (HCC) cells were exposed to 27.12 MHz radiofrequency electromagnetic fields using in vitro exposure systems designed to replicate in vivo conditions. Cancer cells were exposed to tumour-specific modulation frequencies, previously identified by biofeedback methods in patients with a diagnosis of cancer. Control modulation frequencies consisted of randomly chosen modulation frequencies within the same 100 Hz–21 kHz range as cancer-specific frequencies.Results:The growth of HCC and breast cancer cells was significantly decreased by HCC-specific and breast cancer-specific modulation frequencies, respectively. However, the same frequencies did not affect proliferation of nonmalignant hepatocytes or breast epithelial cells. Inhibition of HCC cell proliferation was associated with downregulation of XCL2 and PLP2. Furthermore, HCC-specific modulation frequencies disrupted the mitotic spindle.Conclusion:These findings uncover a novel mechanism controlling the growth of cancer cells at specific modulation frequencies without affecting normal tissues, which may have broad implications in oncology.