A STATISTICAL MODEL TO ASSESS (ALLELE-SPECIFIC) ASSOCIATIONS BETWEEN GENE EXPRESSION AND EPIGENETIC FEATURES USING SEQUENCING DATA

Strategies for eQTL mapping in allopolyploid organisms

KEY MESSAGE

This study uses simulations to explore statistical power and false-positive rates for eQTL mapping in allopolyploid organisms and provides guidelines to apply eQTL mapping in these organisms. In recent years, RNA-seq has become the dominant technology for eQTL studies. However, most work has been in diploid organisms. Many species of economic and environmental importance are polyploid, and approaches for eQTL mapping in polyploids are not well developed. High similarity between duplicated genes in polyploids will cause misassignment of sequence reads and may cause false-positive results and/or lack of power to detect eQTL. In this paper, we first explore the similarity of homoeologous transcripts in polyploid organisms. We find that 5-20% of genes (varying with organism) in important agricultural plants such as wheat, soybean, and switchgrass are not sufficiently diverged between duplicated genomes to allow unambiguous assignment of reads. Second, we examine the impact of misassigned reads on eQTL mapping and show that both false-positive and false-negative rates can be greatly inflated. Third, we compare four strategies for dealing with ambiguous reads: (1) dividing ambiguous reads evenly between homoeologous transcripts, (2) assigning them proportionally, (3) using all reads for all genes, and (4) discarding ambiguous reads. We find that the strategy of discarding ambiguous reads gives the best balance of false-positive and false-negative rates for most genes. However, for genes that are very similar between genomes, using all reads is the only choice. This leads to reduced power, but false-positive rates will be maintained. We also discuss QTL mapping in polyploids using allele-specific expression (ASE) and show how the proportion of ASE-informative reads varies according to the divergence between homoeologous genes.

A STATISTICAL MODEL TO ASSESS (ALLELE-SPECIFIC) ASSOCIATIONS BETWEEN GENE EXPRESSION AND EPIGENETIC FEATURES USING SEQUENCING DATA

Sequencing techniques have been widely used to assess gene expression (i.e., RNA-seq) or the presence of epigenetic features (e.g., DNase-seq to identify open chromatin regions). In contrast to traditional microarray platforms, sequencing data are typically summarized in the form of discrete counts, and they are able to delineate allele-specific signals, which are not available from microarrays. The presence of epigenetic features are often associated with gene expression, both of which have been shown to be affected by DNA polymorphisms. However, joint models with the flexibility to assess interactions between gene expression, epigenetic features and DNA polymorphisms are currently lacking. In this paper, we develop a statistical model to assess the associations between gene expression and epigenetic features using sequencing data, while explicitly modeling the effects of DNA polymorphisms in either an allele-specific or nonallele-specific manner. We show that in doing so we provide the flexibility to detect associations between gene expression and epigenetic features, as well as conditional associations given DNA polymorphisms. We evaluate the performance of our method using simulations and apply our method to study the association between gene expression and the presence of DNase I Hypersensitive sites (DHSs) in HapMap individuals. Our model can be generalized to exploring the relationships between DNA polymorphisms and any two types of sequencing experiments, a useful feature as the variety of sequencing experiments continue to expand.