Year: 2011
Type: article
Abstract: A rapid increase in the number of human genetic variants available for study has led genetic epidemiologists to an agnostic approach to disease-gene mapping.In recent years the field has been dominated by the genome-wide association study in which the entire genome is interrogated for single nucleotide polymorphisms (SNPs) responsible for disease.This method has resulted in the identification of only a small portion of the expected disease susceptibility sites, and it is our belief that many of the remaining SNPs contribute such a small amount to overall heritability that studies of this type cannot overcome the inherent difficulties that arise from the vast amount of multiple testing associated with the agnostic approach.Here we develop a statistical method designed for a genetic association signal that is more representative of the remaining disease susceptibility SNPs.The genetic association signal that we seek is no longer a peak, because the increase in SNP density, coupled with low effect sizes give rise to a plateau-like signal with gaps.We address this by formulating our method as a penalized least squares regression estimator based on the linkage disequilibrium present between SNPs in the same haplotype block.The method which we call the LD LASSO is an adaptation of the fused LASSO used for subset selection in a regression framework when the signal is sparse and block-like.We implement this method in the R package ldlasso, and make it available to all.We demonstrate the use of the method by examining six regions on chromosome 8 suspected to contain variants associated with Late Onset Alzheimer's Disease.x
Author: Samuel Younkin
Institution: Case Western Reserve University
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2245011818