Automated construction and testing of multi-locus gene-gene associations.

Ryan Abo, Stacey Knight, Alun Thomas, Nicola J Camp
Author Information
  1. Ryan Abo: Department of Biomedical Informatics, University of Utah School of Medicine, UT, USA. ryan.abo@hsc.utah.edu

Abstract

It has been argued that the missing heritability in common diseases may be in part due to rare variants and gene-gene effects. Haplotype analyses provide more power for rare variants and joint analyses across genes can address multi-gene effects. Currently, methods are lacking to perform joint multi-locus association analyses across more than one gene/region. Here, we present a haplotype-mining gene-gene analysis method, which considers multi-locus data for two genes/regions simultaneously. This approach extends our single region haplotype-mining algorithm, hapConstructor, to two genes/regions. It allows construction of multi-locus SNP sets at both genes and tests joint gene-gene effects and interactions between single variants or haplotype combinations. A Monte Carlo framework is used to provide statistical significance assessment of the joint and interaction statistics, thus the method can also be used with related individuals. This tool provides a flexible data-mining approach to identifying gene-gene effects that otherwise is currently unavailable.
AVAILABILITY: http://bioinformatics.med.utah.edu/Genie/hapConstructor.html.

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Grants

  1. CA 098364/NCI NIH HHS
  2. T15 LM0724/NLM NIH HHS

MeSH Term

Algorithms
Genes
Genetic Association Studies
Genetic Loci
Haplotypes
Monte Carlo Method
Polymorphism, Single Nucleotide
Software

Word Cloud

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