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Bioinformatics. 2017 Aug 1;33(15):2307-2313. doi: 10.1093/bioinformatics/btx142.

Enhanced methods to detect haplotypic effects on gene expression.

Author information

1
Bioinformatics IDP, University of California Los Angeles, Los Angeles, CA, USA.
2
Department of Pathology and Laboratory Medicine.
3
Department of Human Genetics, Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.

Abstract

Motivation:

Expression quantitative trait loci (eQTLs), genetic variants associated with gene expression levels, are identified in eQTL mapping studies. Such studies typically test for an association between single nucleotide polymorphisms (SNPs) and expression under an additive model, which ignores interaction and haplotypic effects. Mismatches between the model tested and the underlying genetic architecture can lead to a loss of association power. Here we introduce a new haplotype-based test for eQTL studies that looks for haplotypic effects on expression levels. Our test is motivated by compound heterozygous architectures, a common disease model for recessive monogenic disorders, where two different alleles can have the same effect on a gene's function.

Results:

When the underlying true causal architecture for a simulated gene is a compound heterozygote, our method is better able to capture the signal than the marginal SNP method. When the underlying model is a single SNP, there is no difference in the power of our method relative to the marginal SNP method. We apply our method to empirical gene expression data measured in 373 European individuals from the GEUVADIS study and find 29 more eGenes (genes with at least one association) than the standard marginal SNP method. Furthermore, in 974 of the 3529 total eGenes, our haplotype-based method results in a stronger association signal than the standard marginal SNP method. This demonstrates our method both increases power over the standard method and provides evidence of haplotypic architectures regulating gene expression.

Availability and Implementation:

http://bogdan.bioinformatics.ucla.edu/software/.

Contact:

rob.brown@ucla.edu or pasaniuc@ucla.edu.

PMID:
28369161
PMCID:
PMC5860109
DOI:
10.1093/bioinformatics/btx142
[Indexed for MEDLINE]
Free PMC Article

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