Nengjun Yi

Bayesian LASSO for Quantitative Trait Loci Mapping

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-χ2 distributions, which result in a Bayesian version of the popular least absolute shrinkage and selection operator (LASSO) model and the well-known Students 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.

Detection of Gene × Gene Interactions in Genome-Wide Association Studies of Human Population Data

Empirical evidence supporting the commonality of gene × gene interactions, coupled with frequent failure to replicate results from previous association studies, has prompted statisticians to develop methods to handle this important subject. Nonparametric methods have generated intense interest because of their capacity to handle high-dimensional data. Genome-wide association analysis of large-scale SNP data is challenging mathematically and computationally. In this paper, we describe major issues and questions arising from this challenge, along with methodological implications. Data reduction and pattern recognition methods seem to be the new frontiers in efforts to detect gene × gene interactions comprehensively. Currently, there is no single method that is recognized as the ‘best’ for detecting, characterizing, and interpreting gene × gene interactions. Instead, a combination of approaches with the aim of balancing their specific strengths may be the optimal approach to investigate gene × gene interactions in human data.

R/qtlbim: QTL with Bayesian Interval Mapping in experimental crosses

UNLABELLED R/qtlbim is an extensible, interactive environment for the Bayesian Interval Mapping of QTL, built on top of R/qtl (Broman et al., 2003), providing Bayesian analysis of multiple interacting quantitative trait loci (QTL) models for continuous, binary and ordinal traits in experimental crosses. It includes several efficient Markov chain Monte Carlo (MCMC) algorithms for evaluating the posterior of genetic architectures, i.e. the number and locations of QTL, their main and epistatic effects and gene-environment interactions. R/qtlbim provides extensive informative graphical and numerical summaries, and model selection and convergence diagnostics of the MCMC output, illustrated through the vignette, example and demo capabilities of R (R Development Core Team 2006). AVAILABILITY The package is freely available from cran.r-project.org.

Cancer cell proliferation is inhibited by specific modulation frequencies.

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.

Mapping quantitative trait loci with epistatic effects.

Epistatic variance can be an important source of variation for complex traits. However, detecting epistatic effects is difficult primarily due to insufficient sample sizes and lack of robust statistical methods. In this paper, we develop a Bayesian method to map multiple quantitative trait loci (QTLs) with epistatic effects. The method can map QTLs in complicated mating designs derived from the cross of two inbred lines. In addition to mapping QTLs for quantitative traits, the proposed method can even map genes underlying binary traits such as disease susceptibility using the threshold model. The parameters of interest are various QTL effects, including additive, dominance and epistatic effects of QTLs, the locations of identified QTLs and even the number of QTLs. When the number of QTLs is treated as an unknown parameter, the dimension of the model becomes a variable. This requires the reversible jump Markov chain Monte Carlo algorithm. The utility of the proposed method is demonstrated through analysis of simulation data.

Hierarchical Generalized Linear Models for Multiple Quantitative Trait Locus Mapping

We develop hierarchical generalized linear models and computationally efficient algorithms for genomewide analysis of quantitative trait loci (QTL) for various types of phenotypes in experimental crosses. The proposed models can fit a large number of effects, including covariates, main effects of numerous loci, and gene–gene (epistasis) and gene–environment (G × E) interactions. The key to the approach is the use of continuous prior distribution on coefficients that favors sparseness in the fitted model and facilitates computation. We develop a fast expectation-maximization (EM) algorithm to fit models by estimating posterior modes of coefficients. We incorporate our algorithm into the iteratively weighted least squares for classical generalized linear models as implemented in the package R. We propose a model search strategy to build a parsimonious model. Our method takes advantage of the special correlation structure in QTL data. Simulation studies demonstrate reasonable power to detect true effects, while controlling the rate of false positives. We illustrate with three real data sets and compare our method to existing methods for multiple-QTL mapping. Our method has been implemented in our freely available package R/qtlbim (www.qtlbim.org), providing a valuable addition to our previous Markov chain Monte Carlo (MCMC) approach.

The role of the fat mass and obesity associated gene (FTO) in breast cancer risk.

BackgroundObesity has been shown to increase breast cancer risk. FTO is a novel gene which has been identified through genome wide association studies (GWAS) to be related to obesity. Our objective was to evaluate tissue expression of FTO in breast and the role of FTO SNPs in predicting breast cancer risk.MethodsWe performed a case-control study of 354 breast cancer cases and 364 controls. This study was conducted at Northwestern University. We examined the role of single nucleotide polymorphisms (SNPs) of intron 1 of FTO in breast cancer risk. We genotyped cases and controls for four SNPs: rs7206790, rs8047395, rs9939609 and rs1477196. We also evaluated tissue expression of FTO in normal and malignant breast tissue.ResultsWe found that all SNPs were significantly associated with breast cancer risk with rs1477196 showing the strongest association. We showed that FTO is expressed both in normal and malignant breast tissue. We found that FTO genotypes provided powerful classifiers to predict breast cancer risk and a model with epistatic interactions further improved the prediction accuracy with a receiver operating characteristic (ROC) curves of 0.68.ConclusionIn conclusion we have shown a significant expression of FTO in malignant and normal breast tissue and that FTO SNPs in intron 1 are significantly associated with breast cancer risk. Furthermore, these FTO SNPs are powerful classifiers in predicting breast cancer risk.

An Efficient Bayesian Model Selection Approach for Interacting Quantitative Trait Loci Models With Many Effects

We extend our Bayesian model selection framework for mapping epistatic QTL in experimental crosses to include environmental effects and gene–environment interactions. We propose a new, fast Markov chain Monte Carlo algorithm to explore the posterior distribution of unknowns. In addition, we take advantage of any prior knowledge about genetic architecture to increase posterior probability on more probable models. These enhancements have significant computational advantages in models with many effects. We illustrate the proposed method by detecting new epistatic and gene–sex interactions for obesity-related traits in two real data sets of mice. Our method has been implemented in the freely available package R/qtlbim (http://www.qtlbim.org) to facilitate the general usage of the Bayesian methodology for genomewide interacting QTL analysis.

Bayesian analysis of rare variants in genetic association studies

Recent advances in next‐generation sequencing technologies facilitate the detection of rare variants, making it possible to uncover the roles of rare variants in complex diseases. As any single rare variants contain little variation, association analysis of rare variants requires statistical methods that can effectively combine the information across variants and estimate their overall effect. In this study, we propose a novel Bayesian generalized linear model for analyzing multiple rare variants within a gene or genomic region in genetic association studies. Our model can deal with complicated situations that have not been fully addressed by existing methods, including issues of disparate effects and nonfunctional variants. Our method jointly models the overall effect and the weights of multiple rare variants and estimates them from the data. This approach produces different weights to different variants based on their contributions to the phenotype, yielding an effective summary of the information across variants. We evaluate the proposed method and compare its performance to existing methods on extensive simulated data. The results show that the proposed method performs well under all situations and is more powerful than existing approaches. Genet. Epidemiol. 35:57–69, 2011.

Genetic segregation of spontaneous erosive arthritis and generalized autoimmune disease in the BXD2 recombinant inbred strain of mice.

The BXD2 strain of mice is one of approximately 80 BXD recombinant inbred (RI) mouse strains derived from an intercross between C57BL/6J (B6) and DBA/2J (D2) strains. We have discovered that adult BXD2 mice spontaneously develop generalized autoimmune disease, including glomerulonephritis (GN), increased serum titres of rheumatoid factor (RF) and anti‐DNA antibody, and a spontaneous erosive arthritis characterized by mononuclear cell infiltration, synovial hyperplasia, and bone and cartilage erosion. The features of lupus and arthritis developed by the BXD2 mice segregate in F2 mice generated by crossing BXD2 mice with the parental B6 and D2 strains. Genetic linkage analysis of the serum levels of anti‐DNA and RF by using the BXD RI strains shows that the serum titers of anti‐DNA and RF were influenced by a genetic locus on mouse chromosome (Chr) 2 near the marker D2Mit412 (78 cm, 163 Mb) and on Chr 4 near D4Mit146 (53.6 cm, 109 Mb), respectively. Both loci are close to the B‐cell hyperactivity, lupus or GN susceptibility loci that have been identified previously. The results of our study suggest that the BXD2 strain of mice is a novel model for complex autoimmune disease that will be useful in identifying the mechanisms critical for the immunopathogenesis and genetic segregation of lupus and erosive arthritis.