Genotype-Based Test in Mapping Cis-Regulatory Variants from Allele-Specific Expression Data

Abstract

Identifying and understanding the impact of gene regulatory variation is of considerable importance in evolutionary and medical genetics; such variants are thought to be responsible for human-specific adaptation [1] and to have an important role in genetic disease. Regulatory variation in cis is readily detected in individuals showing uneven expression of a transcript from its two allelic copies, an observation referred to as allelic imbalance (AI). Identifying individuals exhibiting AI allows mapping of regulatory DNA regions and the potential to identify the underlying causal genetic variant(s). However, existing mapping methods require knowledge of the haplotypes, which make them sensitive to phasing errors. In this study, we introduce a genotype-based mapping test that does not require haplotype-phase inference to locate regulatory regions. The test relies on partitioning genotypes of individuals exhibiting AI and those not expressing AI in a 2×3 contingency table. The performance of this test to detect linkage disequilibrium (LD) between a potential regulatory site and a SNP located in this region was examined by analyzing the simulated and the empirical AI datasets. In simulation experiments, the genotype-based test outperforms the haplotype-based tests with the increasing distance separating the regulatory region from its regulated transcript. The genotype-based test performed equally well with the experimental AI datasets, either from genome–wide cDNA hybridization arrays or from RNA sequencing. By avoiding the need of haplotype inference, the genotype-based test will suit AI analyses in population samples of unknown haplotype structure and will additionally facilitate the identification of cis-regulatory variants that are located far away from the regulated transcript.

Integrative eQTL-Based Analyses Reveal the Biology of Breast Cancer Risk Loci

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Genomics and the Evolution of Phenotypic Traits

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“…Pursuing a very different strategy, Kruglyak and colleagues exploited the natural variation between alleles of 643 budding-yeast genes and quantitatively measured protein abundance using large-scale liquid chromatography-mass spectrometry, enabling dissection of the relative contributions of cis elements and trans regulators to expression levels (Khan et al 2012). Such quantitative measurements of allele-specific expression (ASE) have the ability to reveal novel and general features of translational control as shown recently (Artieri and Fraser 2014;McManus et al 2014), but thus far they have largely been applied to transcriptional studies (Ge et al 2009;Pastinen 2010;Pickrell et al 2010;Montgomery et al 2010;Lefebvre et al 2012). …”