A method to detect single-nucleotide polymorphisms accounting for a linkage signal using covariate-based affected relative pair linkage analysis

Abstract

We evaluate an approach to detect single-nucleotide polymorphisms (SNPs) that account for a linkage signal with covariate-based affected relative pair linkage analysis in a conditional-logistic model framework using all 200 replicates of the Genetic Analysis Workshop 17 family data set. We begin by combining the multiple known covariate values into a single variable, a propensity score. We also use each SNP as a covariate, using an additive coding based on the number of minor alleles. We evaluate the distribution of the difference between LOD scores with the propensity score covariate only and LOD scores with the propensity score covariate and a SNP covariate. The inclusion of causal SNPs in causal genes increases LOD scores more than the inclusion of noncausal SNPs either within causal genes or outside causal genes. We compare the results from this method to results from a family-based association analysis and conclude that it is possible to identify SNPs that account for the linkage signals from genes using a SNP-covariate-based affected relative pair linkage approach.

Incorporating linkage information into a common disease/rare variant framework

“…In the Discussion section, we present results that were observed by several teams. Song et al [2011] looked for an increase in LOD score (the LodDiff) when a causal SNP was included in the trait model. They found that the LodDiff was highly skewed toward true positives.…”

“…Song et al [2011] developed a novel method to analyze affection status. Each affected individual was assigned a propensity score [Doan et al, 2006]; this score is the predicted probability of being affected based on a logistic regression using the significant covariates, calculated in each replicate separately.…”

“…The methods of linkage analysis included affected relative pairs implemented in LODPAL (from the SAGE package [SAGE Project, 2009]), variance components implemented in SOLAR [Almasy and Blangero, 1998], the Elston-Stewart algorithm implemented in FASTLINK [Cottingham et al, 1993;Schäffer et al, 1994], the modified Haseman-Elston algorithm [Elston et al, 2000], the Lander-Green algorithm and variance components implemented in Merlin [Abecasis et al, 2002], and a Markov chain Monte Carlo algorithm implemented in MCLINK [Thomas et al, 2000]. Song et al [2011] developed a novel method to analyze affection status. Each affected individual was assigned a propensity score [Doan et al, 2006]; this score is the predicted probability of being affected based on a logistic regression using the significant covariates, calculated in each replicate separately.…”

The null distribution of likelihood-ratio statistics in the conditional-logistic linkage model

“…A modification of this original two-parameter CL model into a one-parameter model was proposed by Goddard et al ( 2001 ). Linkage analysis using the CL model has been proven to be an effective tool for evaluating genetic linkage (Goddard et al, 2001 ; Arcos-Burgos et al, 2004 ; Reck et al, 2005 ; Doan et al, 2006 ; Rybicki et al, 2007 ; Stein et al, 2007 ; Zandi et al, 2007 ; Song et al, 2011 ).…”