A comprehensive approach to haplotype-specific analysis by penalized likelihood

Abstract

Haplotypes can hold key information to understand the role of candidate genes in disease etiology. However, standard haplotype analysis has yet been able to fully reveal the information retained by haplotypes. In most analysis, haplotype inference focuses on relative effects compared to an arbitrarily-chosen baseline haplotype. It does not depict the effect structure unless an additional inference procedure is used in a secondary post-hoc analysis, and such analysis tends to be lack of power. In this work, we propose a penalized regression approach to systematically evaluate the pattern and structure of the haplotype effects. By specifying an L1 penalty on the pairwise difference of the haplotype effects, we present a model-based haplotype analysis to detect and to characterize the haplotypic association signals. The proposed method avoids the need to choose a baseline haplotype; it simultaneously carries out the effect estimation and effect comparison of all haplotypes, and outputs the haplotype group structure based on their effect size. Finally, our penalty weights are theoretically designed to balance the likelihood and the penalty term in an appropriate manner. The proposed method can be used as a tool to comprehend candidate regions identified from a genome or chromosomal scan. Simulation studies reveal the better abilities of the proposed method to identify the haplotype effect structure compared to the traditional haplotype association methods, demonstrating the informativeness and powerfulness of the proposed method.

Kullback–Leibler divergence for detection of rare haplotype common disease association

“…However, there is a continuing realization that rare haplotype variants resulting from common SNVs may also have an important role in understanding complex disease etiology. [2][3][4][5][6][7][8][9][10] The interest in detecting rare haplotype association with common diseases is further fueled by the recognition that rare haplotype may tag rare causal SNVs. [7][8][9][10] There are advantages pursuing rare haplotypes instead of rare SNVs.…”

“…11 For example, generalized linear model (GLM)-based methods [12][13][14] may encounter non-convergence in its expectation-maximization (EM) estimates when challenged with rare haplotypes. Among such new approaches, the majority use likelihood-based regularization methods (eg, Lasso 15 ) to weed out unassociated haplotypes [3][4][5]7,8 so that those that are associated with the disease, especially the rare ones, can be more precisely estimated for their effects on the trait. However, owing to the difficulty in evaluating the effect of the uncertainty of regularization parameters on assessing association, the Bayesian counterpart of Lasso has been proposed for studying rare haplotype association, 6,9,10 as well as the Bayesian hierarchical GLM approach.…”

A Penalized Likelihood Approach for Investigating Gene–Drug Interactions in Pharmacogenetic Studies

“…Chen, Chatterjee, and Carroll (2009) develop an adaptive penalized likelihood framework to address the precision-efficiency tradeoff encountered in haplotype-based retrospective methods. Tzeng and Bondell (2010) modify the adaptive LASSO (Tibshirani, 1996;Zou, 2006) to allow for effect comparisons between all pairs of distinct haplotypes, rather than with respect to an arbitrary baseline haplotype, during the estimation process.…”

Multilocus association testing with penalized regression

“…Lasso (Tibshirani 1996), possesses the ability for variable selection, especially for high-dimensional data, facilitating the interpretation of the final selected and often largely simplified model. While the majority of research on penalized methods focus on prediction and variable selection (Kooperberg et al 2010; Ayer and Cordell 2010), it is somewhat surprising that little attention has been paid to inference with only a few exceptions in methodology (Meinshausen et al 2009; Wasserman and Roeder 2010; Zou and Qiu 2010) and applications (Malo et al 2008; Guo and Lin 2009; Tzeng and Bondell 2010), in which there is still a lack of comparisons with other approaches. In many applications, e.g.…”