Elimination of Reference Mapping Bias Reveals Robust Immune Related Allele-Specific Expression in Crossbred Sheep

Abstract

Pervasive allelic variation at both gene and single nucleotide level (SNV) between individuals is commonly associated with complex traits in humans and animals. Allele-specific expression (ASE) analysis, using RNA-Seq, can provide a detailed annotation of allelic imbalance and infer the existence of cis-acting transcriptional regulation. However, variant detection in RNA-Seq data is compromised by biased mapping of reads to the reference DNA sequence. In this manuscript, we describe an unbiased standardized computational pipeline for allele-specific expression analysis using RNA-Seq data, which we have adapted and developed using tools available under open license. The analysis pipeline we present is designed to minimize reference bias while providing accurate profiling of allele-specific expression across tissues and cell types. Using this methodology, we were able to profile pervasive allelic imbalance across tissues and cell types, at both the gene and SNV level, in Texel×Scottish Blackface sheep, using the sheep gene expression atlas data set. ASE profiles were pervasive in each sheep and across all tissue types investigated. However, ASE profiles shared across tissues were limited, and instead, they tended to be highly tissue-specific. These tissue-specific ASE profiles may underlie the expression of economically important traits and could be utilized as weighted SNVs, for example, to improve the accuracy of genomic selection in breeding programs for sheep. An additional benefit of the pipeline is that it does not require parental genotypes and can therefore be applied to other RNA-Seq data sets for livestock, including those available on the Functional Annotation of Animal Genomes (FAANG) data portal. This study is the first global characterization of moderate to extreme ASE in tissues and cell types from sheep. We have applied a robust methodology for ASE profiling to provide both a novel analysis of the multi-dimensional sheep gene expression atlas data set and a foundation for identifying the regulatory and expressed elements of the genome that are driving complex traits in livestock.

Allele-specific expression reveals the phenotypic differences between thin- and fat-tailed sheep

“…The great variations in different phenotypic traits of two selected sheep breeds provide an opportunity for studying molecular mechanisms underlying gene expression related to phenotypic difference between thin-and fat-tailed sheep breeds. In this study, the candidate causative genes were detected by ASE analysis, which avoid the effect of trans-acting and has become research focus in recent years [24][25][26][27][28]. We divided all sheep individuals into 15 family trios.…”

“…A total of 128 ASE genes were identified from the collected samples of adipose and skeleton muscle tissues, the expression profile of majority ASE genes in F1 hybrid tissues were tended to be tissue-specific. In a recent study on analysis of ASE in immune-related tissues, the authors reported individual-specific and tissue-specific gene expression for several genes in a crossbred sheep breed (Texel × Scottish Blackface) [28]. In mouse, Pinter et al (2015) showed that 95% and 82% of the identified ASE genes in F1 hybrids and in a reciprocal cross were also with tissue specificity, respectively [54].…”

“…Allele-specific reads at the heterozygous sites were obtained from these BAM files using the WASP software [37]. In order to eliminate the error caused by hetero-allele or repeat-sequence of the reference genome, loci of both alleles with < 10 reads were filtered out, and genes with min 2 and max 100 heterozygous loci were selected [28]. Loci with both alleles < 100 reads and each allele in heterozygous sites with < 3 reads were filtered, which can remove loci exhibiting MAE and a relative lower expression [38][39][40].…”

“…Recently, high-throughput sequencing technologies allowed us to identify ASE genes at a transcriptome-wide level. Several studies in different species such as mouse [24], pig [25], cow [26], goat [27], sheep [28], mule [29] and dzo [29] have shown that the different expression levels of alleles due to ASE may lead to variation in phenotypes.…”

Reducing reference bias using multiple population reference genomes

“…But linearity leads to reference bias: a tendency to miss alignments or report incorrect alignments for reads containing non-reference alleles. This can ultimately lead to confounding of scientific results, especially for analyses concerned with hypervariable regions 2 , allele-specific effects [3][4][5][6] , ancient DNA analysis 7,8 or epigenenomic signals 9 . These problems can be more or less adverse depending on the individual under study; e.g.…”