Affordable Access

Publisher Website

Using an Uncertainty-Coding Matrix in Bayesian Regression Models for Haplotype-Specific Risk Detection in Family Association Studies

Authors
Journal
PLoS ONE
1932-6203
Publisher
Public Library of Science
Publication Date
Volume
6
Issue
7
Identifiers
DOI: 10.1371/journal.pone.0021890
Keywords
  • Research Article
  • Biology
  • Computational Biology
  • Population Genetics
  • Haplotypes
  • Evolutionary Biology
  • Genetics
  • Human Genetics
  • Genetic Association Studies
  • Population Biology
  • Epidemiology
  • Genetic Epidemiology
  • Mathematics
  • Statistics
  • Biostatistics
  • Medicine
Disciplines
  • Biology
  • Computer Science
  • Logic

Abstract

Haplotype association studies based on family genotype data can provide more biological information than single marker association studies. Difficulties arise, however, in the inference of haplotype phase determination and in haplotype transmission/non-transmission status. Incorporation of the uncertainty associated with haplotype inference into regression models requires special care. This task can get even more complicated when the genetic region contains a large number of haplotypes. To avoid the curse of dimensionality, we employ a clustering algorithm based on the evolutionary relationship among haplotypes and retain for regression analysis only the ancestral core haplotypes identified by it. To integrate the three sources of variation, phase ambiguity, transmission status and ancestral uncertainty, we propose an uncertainty-coding matrix which combines these three types of variability simultaneously. Next we evaluate haplotype risk with the use of such a matrix in a Bayesian conditional logistic regression model. Simulation studies and one application, a schizophrenia multiplex family study, are presented and the results are compared with those from other family based analysis tools such as FBAT. Our proposed method (Bayesian regression using uncertainty-coding matrix, BRUCM) is shown to perform better and the implementation in R is freely available.

There are no comments yet on this publication. Be the first to share your thoughts.