Employing MCMC under the PPL framework to analyze sequence data in large pedigrees

Abstract

The increased feasibility of whole-genome (or whole-exome) sequencing has led to renewed interest in using family data to find disease mutations. For clinical phenotypes that lend themselves to study in large families, this approach can be particularly effective, because it may be possible to obtain strong evidence of a causal mutation segregating in a single pedigree even under conditions of extreme locus and/or allelic heterogeneity at the population level. In this paper, we extend our capacity to carry out positional mapping in large pedigrees, using a combination of linkage analysis and within-pedigree linkage trait-variant disequilibrium analysis to fine map down to the level of individual sequence variants. To do this, we develop a novel hybrid approach to the linkage portion, combining the non-stochastic approach to integration over the trait model implemented in the software package Kelvin, with Markov chain Monte Carlo-based approximation of the marker likelihood using blocked Gibbs sampling as implemented in the McSample program in the JPSGCS package. We illustrate both the positional mapping template, as well as the efficacy of the hybrid algorithm, in application to a single large pedigree with phenotypes simulated under a two-locus trait model.

A genome-wide linkage and association study of musical aptitude identifies loci containing genes related to inner ear development and neurocognitive functions

“…KELVIN is also capable of handling large pedigrees, so we were able to keep the very large families intact. Multipoint linkage analysis was done using a unique “hybrid” approach using Markov chain Monte Carlo (MCMC) for the marker data 19 and exact likelihood calculations for the trait data 20 .…”

A genome-wide linkage study of autism spectrum disorder and the broad autism phenotype in extended pedigrees

“…Linkage analysis was conducted using the software package KELVIN (v2.4.9), which implements the PPL (posterior probability of linkage) class of models for measuring the strength of genetic evidence [ 34 ]. In order to take advantage of the very dense marker coverage in a multipoint setting, and given the size of the pedigrees, MCMC was used to calculate marker likelihoods as described in [ 35 ], while KELVIN’s non-stochastic algorithm was used to calculate trait likelihoods conditional on marker data [ 36 ].…”

The Value of Regenotyping Older Linkage Data Sets with Denser Marker Panels

“…Shown here are results for the posterior probability of linkage (PPL) statistic; for comparison purposes, NPL [7] results as calculated by Merlin are shown in Supplement 2, for the 45 pedigrees Merlin was able to process. (Kelvin performed exact (full) multipoint calculations for these 45 pedigrees and utilized a hybrid MCMC-exact likelihood for the remaining 2 pedigrees [8]. ) Prior to analysis marker allele frequencies were estimated using maximum likelihood.…”