Academic journal article Genetics

Gene-Centric Genomewide Association Study Via Entropy

Academic journal article Genetics

Gene-Centric Genomewide Association Study Via Entropy

Article excerpt

ABSTRACT

Genes are the functional units in most organisms. Compared to genetic variants located outside genes, genic variants are more likely to affect disease risk. The development of the human HapMap project provides an unprecedented opportunity for genetic association studies at the genomewide level for elucidating disease etiology. Currently, most association studies at the single-nucleotide polymorphism (SNP) or the haplotype level rely on the linkage information between SNP markers and disease variants, with which association findings are difficult to replicate. Moreover, variants in genes might not be sufficiently covered by currently available methods. In this article, we present a gene-centric approach via entropy statistics for a genomewide association study to identify disease genes. The new entropy-based approach considers genic variants within one gene simultaneously and is developed on the basis of a joint genotype distribution among genetic variants for an association test. A grouping algorithm based on a penalized entropy measure is proposed to reduce the dimension of the test statistic. Type I error rates and power of the entropy test are evaluated through extensive simulation studies. The results indicate that the entropy test has stable power under different disease models with a reasonable sample size. Compared to single SNP-based analysis, the gene-centric approach has greater power, especially when there is more than one disease variant in a gene. As the genomewide genic SNPs become available, our entropy-based gene-centric approach would provide a robust and computationally efficient way for gene-based genomewide association study.

(ProQuest: ... denotes formulae omitted.)

THE family-based linkage study has been the traditional means of disease gene discovery followed by a variety of fine-mapping techniques. For finer resolution, larger pedigrees are required, which largely restricts its utility, especially for identifying multiple low-penetrance variants involved in common diseases (Boehnke 1994). In the past decade, population-based association mapping, as an alternative for disease gene discovery, has been rapidly developed either at the single-variant or at the candidate gene level. Risch and Merikangas (1996) first showed that an association study has comparatively greater power than the linkage analysis in detecting disease variants with minor or modest effect size. Therefore genomewide association (GWA) studies are feasible. With the development of recent high-throughput genotyping technologies, it is now possible to conduct a disease gene search with millions of single-nucleotide polymorphism (SNP) markers covering the whole human genome (International Hapmap Consortium 2005). This rapid escalation in disease gene search froma family-based linkage scan to a population-based association study has greatly facilitated the process of disease gene discovery.

The analysis of association has been historically focused on alleles and the association has been primarily referred to as allelic association. With the high-density SNP markers generated by human HapMap and the availability of empirical linkage disequilibrium (LD) information across the genome, the haplotype-based association study is gaining popularity. However, both types of association studies at the SNP or the haplotype level have potential pitfalls in the context of replication (Neale and Sham 2004). Due to population histories and evolutionary forces, there has been inconsistency between different studies, caused by aberrant LD patterns acrossmarker loci, different allele frequencies, and LD patterns across populations (Morton and Collins 1998; Pritchard and Przeworski 2001; Stephens et al. 2001; Freedman et al. 2004). There also has been controversy concerning statistical analysis and interpretation of association findings (Neale and Sham 2004). In a recent comprehensive review, Neale and Sham (2004) pointed out common problems associated with the single SNP and the haplotype-based analysis and proposed the prospects of a gene-based association study. …

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