Paper Info
Title
Detecting purely epistatic multi-locus interactions by an omnibus permutation test on ensembles of two-locus analyses.
Abstract
Purely epistatic multi-locus interactions cannot generally be detected via single-locus analysis in case-control studies of complex diseases. Recently, many two-locus and multi-locus analysis techniques have been shown to be promising for the epistasis detection. However, exhaustive multi-locus analysis requires prohibitively large computational efforts when problems involve large-scale or genome-wide data. Furthermore, there is no explicit proof that a combination of multiple two-locus analyses can lead to the correct identification of multi-locus interactions.The proposed 2LOmb algorithm performs an omnibus permutation test on ensembles of two-locus analyses. The algorithm consists of four main steps: two-locus analysis, a permutation test, global p-value determination and a progressive search for the best ensemble. 2LOmb is benchmarked against an exhaustive two-locus analysis technique, a set association approach, a correlation-based feature selection (CFS) technique and a tuned ReliefF (TuRF) technique. The simulation results indicate that 2LOmb produces a low false-positive error. Moreover, 2LOmb has the best performance in terms of an ability to identify all causative single nucleotide polymorphisms (SNPs) and a low number of output SNPs in purely epistatic two-, three- and four-locus interaction problems. The interaction models constructed from the 2LOmb outputs via a multifactor dimensionality reduction (MDR) method are also included for the confirmation of epistasis detection. 2LOmb is subsequently applied to a type 2 diabetes mellitus (T2D) data set, which is obtained as a part of the UK genome-wide genetic epidemiology study by the Wellcome Trust Case Control Consortium (WTCCC). After primarily screening for SNPs that locate within or near 372 candidate genes and exhibit no marginal single-locus effects, the T2D data set is reduced to 7,065 SNPs from 370 genes. The 2LOmb search in the reduced T2D data reveals that four intronic SNPs in PGM1 (phosphoglucomutase 1), two intronic SNPs in LMX1A (LIM homeobox transcription factor 1, alpha), two intronic SNPs in PARK2 (Parkinson disease (autosomal recessive, juvenile) 2, parkin) and three intronic SNPs in GYS2 (glycogen synthase 2 (liver)) are associated with the disease. The 2LOmb result suggests that there is no interaction between each pair of the identified genes that can be described by purely epistatic two-locus interaction models. Moreover, there are no interactions between these four genes that can be described by purely epistatic multi-locus interaction models with marginal two-locus effects. The findings provide an alternative explanation for the aetiology of T2D in a UK population.An omnibus permutation test on ensembles of two-locus analyses can detect purely epistatic multi-locus interactions with marginal two-locus effects. The study also reveals that SNPs from large-scale or genome-wide case-control data which are discarded after single-locus analysis detects no association can still be useful for genetic epidemiology studies.
Year
DOI
Venue
2009
10.1186/1471-2105-10-294
BMC Bioinformatics
Keywords
Field
DocType
feature selection,case control study,genetic epidemiology,transcription factor,bioinformatics,false positive,microarrays,autosomal recessive,single nucleotide polymorphism,case control studies,algorithms,glycogen synthase,permutation test,genotype,candidate gene,computational biology,genome wide association study,multifactor dimensionality reduction
Biology,Epistasis,Multifactor dimensionality reduction,Genome-wide association study,Bioinformatics,Locus (genetics),Genetics,Resampling,DNA microarray
Journal
Volume
Issue
ISSN
10
1
1471-2105
Citations 
PageRank 
References 
22
0.79
23
Authors
6
Name
Order
Citations
PageRank
Waranyu Wongseree1554.66
Anunchai Assawamakin2956.16
Theera Piroonratana3523.70
Saravudh Sinsomros4221.13
Chanin Limwongse5292.26
Nachol Chaiyaratana617214.53