Khansefid et al. BMC Genomics (2018) 19:793 https://doi.org/10.1186/s12864-018-5181-0 RESEARCHARTICLE Open Access Comparing allele specific expression and local expression quantitative trait loci and the influence of gene expression on complex trait variation in cattle Majid Khansefid1,2* , Jennie E. Pryce2,3, Sunduimijid Bolormaa2, Yizhou Chen4, Catriona A. Millen5, Amanda J. Chamberlain2, Christy J. Vander Jagt2 and Michael E. Goddard1,2 Abstract Background: The mutations changing the expression level of a gene, or expression quantitative trait loci (eQTL), can be identified by testing the association between genetic variants and gene expression in multiple individuals (eQTL mapping), or by comparing the expression of the alleles in a heterozygous individual (allele specific expression or ASE analysis). The aims of the study were to find and compare ASE and local eQTL in 4 bovine RNA-sequencing (RNA-Seq) datasets, validate them in an independent ASE study and investigate if they are associated with complex trait variation. Results: We present a novel method for distinguishing between ASE driven by polymorphisms in cis and parent of origin effects. We found that single nucleotide polymorphisms (SNPs) driving ASE are also often local eQTL and therefore presumably cis eQTL. These SNPs often, but not always, affect gene expression in multiple tissues and, when they do, the allele increasing expression is usually the same. However, there were systematic differences between ASE and local eQTL and between tissues and breeds. We also found that SNPs significantly associated with gene expression (p < 0.001) were likely to influence some complex traits (p < 0.001), which means that some mutations influence variation in complex traits by changing the expression level of genes. Conclusion: We conclude that ASE detects phenomenon that overlap with local eQTL, but there are also systematic differences between the SNPs discovered by the two methods. Some mutations influencing complex traits are actually eQTL and can be discovered using RNA-Seq including eQTL in the genes CAST, CAPN1, LCORL and LEPROTL1. Keywords: RNA-sequencing, Allele specific expression, ASE analysis, eQTL mapping, Genome-wide association study, Genetic variation of complex traits Background so understanding gene expression may improve our Gene expression varies between tissues and individuals knowledge of the genetic architecture of complex traits. and can be measured by counting the number of mRNA In traditional gene expression studies with microar- copies of the gene [1, 2]. The variation in gene expres- rays, it is not possible to discriminate between eQTL sion between individuals can influence their phenotype responsible for changing the expression of a gene on the [3]. Some of this variation in gene expression is genetic, same chromosome (cis eQTL) from mutations influen- cing the expression of a gene on both chromosomes (trans eQTL) [4]. However, it has become common to * Correspondence: [email protected] regard polymorphisms that are close to the gene whose 1 Department of Agriculture and Food Systems, The University of Melbourne, expression they affect as cis eQTL, although a better Parkville, VIC, Australia 2Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, name would be local eQTL [5]. Australia Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Khansefid et al. BMC Genomics (2018) 19:793 Page 2 of 18 High-throughput RNA sequencing with next gener- strains. Here we introduce a novel statistical analysis to ation sequencing technology (RNA-Seq), can be used to distinguish between these two causes of allele specific measure gene expression and to detect heterozygous expression. Our novel method can be used to explain sites in the transcript [6]. If there is a polymorphic site some of the discrepancy between the results from ASE in the transcript, then RNA-Seq can also be used to de- analyses and local eQTL mapping. tect allele specific expression (ASE) or allelic bias in Finding the biological background of how mutations which one allele is expressed more highly than the other. cause variation in a trait is very useful to confirm the To detect ASE, we require a SNP to be in the transcript causality of the statistically significant association be- (tSNP), but the polymorphism causing the difference in tween mutations and phenotypic records. However, expression between the alleles (the driver SNP or dSNP) many of the polymorphisms associated with complex is likely to be in non-coding, regulatory DNA (Fig. 1a). traits (quantitative trait loci or QTL), found by genome An individual that is heterozygous for a cis eQTL should wide association (GWA) studies, are non-coding variants show ASE. Therefore, RNA-Seq data can be used for [8]. Therefore, it seems reasonable to assume that at eQTL mapping of global RNA expression and also for least some of the causal variants that affect complex ASE analysis, and hence it is possible to distinguish cis traits, do so by regulating the expression of genes. If this eQTL from trans eQTL even among local eQTL [7]. is true, we expect that some QTL are actually eQTL. In ASE analysis, the difference in expression of the al- However, only a few QTL have been shown to be eQTL leles is a within individual comparison (Fig. 1b and c), (e.g. in humans the SNP which is located in an enhancer while in local eQTL mapping, the association between of Nitric Oxide Synthase 1 Adaptor Protein gene affects gene expression level and genotypes at polymorphic loci cardiac function by increasing the expression of relies on comparisons between individuals (Fig. 1d). NOS1AP)[3]. Here we examine the overlap between Therefore, the two methods use independent data and QTL for dairy and beef traits and eQTL. combining them should increase power. For the detec- The gene expression profile and imbalance can be dif- tion of cis eQTL, ASE has an advantage over local eQTL ferent across tissues and breeds. Therefore, in our study, because it relies on comparisons within an individual, so RNA-Seq of 4 datasets (45 Angus bull muscle samples, sources of error between individuals, such as environ- 37 Angus bull liver samples and 20 Holstein cow white mental or trans eQTL effects, are eliminated [8]. How- blood cell (WBC) and liver samples) and corresponding ever, this advantage in power is offset by the fact that phased genotypes were used to find local eQTL and only RNA reads that include a heterozygous site are use- measure ASE and PO-ASE within 50 kb of the genes ful for ASE, whereas all reads from a gene are useful for expressed in each tissue. We compared the local eQTL, local eQTL mapping. ASE and PO-ASE measurement in different datasets to Higher expression of one allele compared with the find if the SNP associated with allelic imbalance in ASE other can be due to cis eQTL, but it can also be due to studies and gene expression in eQTL mapping are the parent of origin of allelic expression (PO-ASE, Fig. 1b) same and if so, whether the same allele is associated or imprinting, where maternal or paternal inheritance of with higher expression in both cases. We compared the the allele determines the allelic imbalance ratio [9]. cases of ASE and eQTL found in these 4 datasets to the Thus, before using ASE to distinguish between cis and ASE found in 18 tissues from a single Holstein cow [11], trans local eQTL, we need to know whether ASE is to broaden the range of tissues examined. The SNP as- largely due to cis eQTL (Fig. 1c), or largely due to other sociated with gene expression in each RNA-Seq dataset phenomenon such as parent of origin effects. However, (p < 0.001) and SNP associated with 21 complex traits to date the reported concordance rates between local and a multi-trait test (p < 0.001), detected by GWAS, eQTL mapping and ASE studies have not been very high were compared to test if QTL are also likely to be due to biological and technical factors [10]. Hasin- eQTL. Brumshtein et al. (2014) reported only 15–20% of the The aims of the study were to find and compare ASE local eQTL were found as cis eQTL by ASE analysis in and local eQTL, validate them in an independent ASE mouse adipose tissue [5]. Failure of ASE and local eQTL study and investigate if they are associated with complex analyses to agree could be due to insufficient statistical trait variation. power in one or both analyses, to parent of origin effects or to local trans eQTL. In this paper we examine the ex- Results tent of systematic differences between local eQTL and RNA-Seq quality control ASE. About 80% of the RNA-Seq data passed the quality Distinguishing between ASE due to cis eQTL and par- check (QC) filters. The summary of raw reads, reads ent of origin effects is more complex but more inform- passing QC, aligned paired reads and concordant pair ative in outbred individuals than in crosses of inbred alignment rate (%) of the animals in each RNA-Seq Khansefid et al. BMC Genomics (2018) 19:793 Page 3 of 18 Fig.