Dataset Information

Genome-wide genetic heterogeneity discovery with categorical covariates.

ABSTRACT:

Motivation

Genetic heterogeneity is the phenomenon that distinct genetic variants may give rise to the same phenotype. The recently introduced algorithm Fast Automatic Interval Search ( FAIS ) enables the genome-wide search of candidate regions for genetic heterogeneity in the form of any contiguous sequence of variants, and achieves high computational efficiency and statistical power. Although FAIS can test all possible genomic regions for association with a phenotype, a key limitation is its inability to correct for confounders such as gender or population structure, which may lead to numerous false-positive associations.

Results

We propose FastCMH , a method that overcomes this problem by properly accounting for categorical confounders, while still retaining statistical power and computational efficiency. Experiments comparing FastCMH with FAIS and multiple kinds of burden tests on simulated data, as well as on human and Arabidopsis samples, demonstrate that FastCMH can drastically reduce genomic inflation and discover associations that are missed by standard burden tests.

Availability and implementation

An R package fastcmh is available on CRAN and the source code can be found at: https://www.bsse.ethz.ch/mlcb/research/bioinformatics-and-computational-biology/fastcmh.html.

Contact

felipe.llinares@bsse.ethz.ch.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Llinares-Lopez F

PROVIDER: S-EPMC5870548 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Genome-wide genetic heterogeneity discovery with categorical covariates.

Llinares-López Felipe F Papaxanthos Laetitia L Bodenham Dean D Roqueiro Damian D Borgwardt Karsten K

Bioinformatics (Oxford, England) 20170601 12

<h4>Motivation</h4>Genetic heterogeneity is the phenomenon that distinct genetic variants may give rise to the same phenotype. The recently introduced algorithm Fast Automatic Interval Search ( FAIS ) enables the genome-wide search of candidate regions for genetic heterogeneity in the form of any contiguous sequence of variants, and achieves high computational efficiency and statistical power. Although FAIS can test all possible genomic regions for association with a phenotype, a key limitation ...[more]

PMID: 28200033

Similar Datasets

Project description:ObjectiveThe Sjögren's International Collaborative Clinical Alliance (SICCA) is an international data registry and biorepository derived from a multisite observational study of participants in whom genotyping was performed on the Omni2.5M platform and who had undergone deep phenotyping using common protocol-directed methods. The aim of this study was to examine the genetic etiology of Sjögren's syndrome (SS) across ancestry and disease subsets.MethodsWe performed genome-wide association study analyses using SICCA subjects and external controls obtained from dbGaP data sets, one using all participants (1,405 cases, 1,622 SICCA controls, and 3,125 external controls), one using European participants (585, 966, and 580, respectively), and one using Asian participants (460, 224, and 901, respectively) with ancestry adjustments via principal components analyses. We also investigated whether subphenotype distributions differ by ethnicity, and whether this contributes to the heterogeneity of genetic associations.ResultsWe observed significant associations in established regions of the major histocompatibility complex (MHC), IRF5, and STAT4 (P = 3 × 10-42 , P = 3 × 10-14 , and P = 9 × 10-10 , respectively), and several novel suggestive regions (those with 2 or more associations at P < 1 × 10-5 ). Two regions have been previously implicated in autoimmune disease: KLRG1 (P = 6 × 10-7 [Asian cluster]) and SH2D2A (P = 2 × 10-6 [all participants]). We observed striking differences between the associations in Europeans and Asians, with high heterogeneity especially in the MHC; representative single-nucleotide polymorphisms from established and suggestive regions had highly significant differences in the allele frequencies in the study populations. We showed that SSA/SSB autoantibody production and the labial salivary gland focus score criteria were associated with the first worldwide principal component, indicative of higher non-European ancestry (P = 4 × 10-15 and P = 4 × 10-5 , respectively), but that subphenotype differences did not explain most of the ancestry differences in genetic associations.ConclusionGenetic associations with SS differ markedly according to ancestry; however, this is not explained by differences in subphenotypes.

Dataset Information

Genome-wide genetic heterogeneity discovery with categorical covariates.

Motivation

Results

Availability and implementation

Contact

Supplementary information

Publications

Genome-wide genetic heterogeneity discovery with categorical covariates.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets