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

Abstract

Rare haplotypes may tag rare causal variants of common diseases; hence, detection of such rare haplotypes may also contribute to our understanding of complex disease etiology. Because rare haplotypes frequently result from common single-nucleotide polymorphisms (SNPs), focusing on rare haplotypes is much more economical compared with using rare single-nucleotide variants (SNVs) from sequencing, as SNPs are available and 'free' from already amassed genome-wide studies. Further, associated haplotypes may shed light on the underlying disease causal mechanism, a feat unmatched by SNV-based collapsing methods. In recent years, data mining approaches have been adapted to detect rare haplotype association. However, as they rely on an assumed underlying disease model and require the specification of a null haplotype, results can be erroneous if such assumptions are violated. In this paper, we present a haplotype association method based on Kullback-Leibler divergence (hapKL) for case-control samples. The idea is to compare haplotype frequencies for the cases versus the controls by computing symmetrical divergence measures. An important property of such measures is that both the frequencies and logarithms of the frequencies contribute in parallel, thus balancing the contributions from rare and common, and accommodating both deleterious and protective, haplotypes. A simulation study under various scenarios shows that hapKL has well-controlled type I error rates and good power compared with existing data mining methods. Application of hapKL to age-related macular degeneration (AMD) shows a strong association of the complement factor H (CFH) gene with AMD, identifying several individual rare haplotypes with strong signals.

A Survey on Swarm Intelligence Search Methods Dedicated to Detection of High-Order SNP Interactions

“…To better understand detection in SIS, the joint entropy [40], relative entropy [71] and Kullback-Leibler (KL) divergence [72] should be considered in the design of heuristic factors. In addition, research should also focus on gaining insight from 2nd-order and 3rd-order SNP combinations, which should be explored with exhaustive search methods using high-performance computers such as HiSeeker [61].…”

Detecting rare haplotypes associated with complex diseases using both population and family data: Combined logistic Bayesian Lasso

“…Without loss of generality, we assume that h k is the baseline haplotype, which is typically chosen to be the one with the largest frequency though this may not always be the best choice. 26 Consider the following haplotype-based logistic regression model where α is the baseline haplotype effect (the effect for an individual with two copies of haplotype h k ), and X Z is the design vector based on an assumed genetic model and the individual’s haplotype pair Z . For example, suppose an additive model is used, and Z=(hl,hl′) for 1≤l<l′<k, where h l and hl′ are two specific haplotypes, then X Z is a vector of length k – 1 with the l th and l ′th positions being 1 and the rest being 0.…”

Whole-genome single nucleotide variant distribution on genomic regions and its relationship to major depression

“…In our previous work (Yu et al, 2011), we used the Kullback-Leibler divergence (also called relative entropy) to obtain a symmetrised measure and successfully studied the phylogeny of various DNA sequences based on their probability distributions. The Kullback-Leibler divergence, which is a powerful tool to measure the difference between two probability distributions, has been widely used in many fields, such as bioinformatics (Kaitchenko, 2004; Liu et al, 2014; McClendon et al, 2012; Hoang et al, 2015), genomics (Huang et al, 2014; Lin, 2015; Siegel et al, 2015), and machine learning (Lee and Lee, 2006; Rubinstein and Kroese, 2013). By means of this measure, we performed cluster analysis for those subjects including MDD cases and controls.…”