Finding the basic neighborhood in variable range Markov random fields: application in SNP association studies

TL;DR

This paper introduces a novel method using variable range Markov random fields to identify dependence neighborhoods among SNPs, enhancing gene mapping and disease association analysis without fixed dependence range assumptions.

Contribution

The paper proposes a consistent estimator based on penalized likelihood for variable range dependence in SNPs, allowing simultaneous inference of dependence structure and disease association.

Findings

Effective identification of dependence neighborhoods in SNP data.

Application to rheumatoid arthritis data demonstrates practical utility.

Method outperforms fixed-range models in flexibility and accuracy.

Abstract

The SNPs (Single Nucleotide Polymorphisms) genotyping platforms are of great value for gene mapping of complex diseases. Nowadays, the high-density of these molecular markers enables studies of dependence patterns between loci over the genome, allowing a simultaneous inference of dependence structure and disease association. In this paper we propose a method based on the theory of variable range Markov random fields to estimate the extent of dependence among SNPs allowing variable windows along the genome. The advantage of this method is that it allows the simultaneous prediction of dependence and independence regions among SNPs, without restricting a priori the range of dependence. We introduce an estimator based on the idea of penalized maximum likelihood to find the conditional dependence neighborhood of each SNP in the sample and we prove its consistency. We apply our method to autosomal SNPs genotypic data with unknown phase in the context of case-control association studies. By examining rheumatoid arthritis data from the Genetic Analysis Workshop 16 (GAW16), we show the utility of the Markov model under variable range dependence.