A Novel Statistical Approach for Jointly Analyzing RNA-Seq Data from F1 Reciprocal Crosses and Inbred Lines. Issue 1 (1st May 2014)

• Record Type: Journal Article
• Authors: Zou, Fei; Sun, Wei; Crowley, James J; Zhabotynsky, Vasyl; Sullivan, Patrick F; Pardo-Manuel de Villena, Fernando
• Is Part Of: Genetics. Volume 197:Issue 1(2014)
• Page Start: 389
• Page End: 399
• Publication Date: 2014-05-01
• Subjects: allelic imbalance -- imprinting -- overdispersion -- parent-of-origin effect -- Xce; Genetics -- Periodicals; 576.5
• DOI: 10.1534/genetics.113.160119
• Languages: English
• ISSNs: 0016-6731

Abstract

Abstract: RNA sequencing (RNA-seq) not only measures total gene expression but may also measure allele-specific gene expression in diploid individuals. RNA-seq data collected from F1 reciprocal crosses in mice can powerfully dissect strain and parent-of-origin effects on allelic imbalance of gene expression. In this article, we develop a novel statistical approach to analyze RNA-seq data from F1 and inbred strains. Method development was motivated by a study of F1 reciprocal crosses derived from highly divergent mouse strains, to which we apply the proposed method. Our method jointly models the total number of reads and the number of allele-specific reads of each gene, which significantly boosts power for detecting strain and particularly parent-of-origin effects. The method deals with the overdispersion problem commonly observed in read counts and can flexibly adjust for the effects of covariates such as sex and read depth. The X chromosome in mouse presents particular challenges. As in other mammals, X chromosome inactivation silences one of the two X chromosomes in each female cell, although the choice of which chromosome to be silenced can be highly skewed by alleles at the X-linked X-controlling element ( Xce ) and stochastic effects. Our model accounts for these chromosome-wide effects on an individual level, allowing proper analysis of chromosome X expression. Furthermore, we propose a genomic control procedure to properly control type I error for RNA-seq studies. A number of these methodological improvements can also be applied to RNA-seq data from other species as well as other types of next-generation sequencing data sets. Finally, we show through simulations that increasing the number of samples is more beneficial than increasing the library size for mapping both the strain and parent-of-origin effects. Unless sample recruiting is too expensive to conduct, we recommend sequencing more samples with lower coverage.