SNP-Based and Kmer-Based eQTL Analysis Using Transcriptome Data
National Key Laboratory for Swine Genetic Improvement and Germplasm Innovation Technology, Jiangxi Agricultural University, Nanchang 330045, China
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Author to whom correspondence should be addressed.
Animals 2024, 14(20), 2941; https://doi.org/10.3390/ani14202941
Submission received: 18 September 2024
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Revised: 8 October 2024
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Accepted: 9 October 2024
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Published: 11 October 2024
(This article belongs to the Section Animal Genetics and Genomics)
Simple Summary
Expression quantitative trait locus (eQTL) analysis is crucial in revealing the genetic basis of complex traits, advancing the study of human diseases, optimizing the breeding of agricultural plants and animals, and gaining a deeper understanding of specific biological processes. The conventional analysis involves correlating genetic variants from whole-genome sequencing (WGS) data and gene expression, but to improve the power of eQTL detection, it is often necessary to expand the sample size as far as possible, ranging from hundreds to thousands. Large sample sizes for WGS are extremely costly, and this bottleneck is particularly evident in economically important agricultural animals. In contrast, the advantages of eQTL analyses from transcriptome data are obvious: cost-effective, simultaneous acquisition of variants and expression information. We propose to use transcriptome data alone for SNP calling and kmer generation, and then association analysis with gene expression. Here, 87 SNP-based and 35 kmer-based association results were obtained. Subsequently, comparison and validation of these two results revealed that they each have their own strengths and can complement each other, which promotes in-depth exploration of the regulatory relationship between genetic variants and gene expression.
Abstract
Traditional expression quantitative trait locus (eQTL) mapping associates single nucleotide polymorphisms (SNPs) with gene expression, where the SNPs are derived from large-scale whole-genome sequencing (WGS) data or transcriptome data. While WGS provides a high SNP density, it also incurs substantial sequencing costs. In contrast, RNA-seq data, which are more accessible and less expensive, can simultaneously yield gene expressions and SNPs. Thus, eQTL analysis based on RNA-seq offers significant potential applications. Two primary strategies were employed for eQTL in this study. The first involved analyzing expression levels in relation to variant sites detected between populations from RNA-seq data. The second approach utilized kmers, which are sequences of length k derived from RNA-seq reads, to represent variant sites and associated these kmer genotypes with gene expression. We discovered 87 significant association signals involving eGene on the basis of the SNP-based eQTL analysis. These genes include DYNLT1, NMNAT1, and MRLC2, which are closely related to neurological functions such as motor coordination and homeostasis, play a role in cellular energy metabolism, and function in regulating calcium-dependent signaling in muscle contraction, respectively. This study compared the results obtained from eQTL mapping using RNA-seq identified SNPs and gene expression with those derived from kmers. We found that the vast majority (23/30) of the association signals overlapping the two methods could be verified by haplotype block analysis. This comparison elucidates the strengths and limitations of each method, providing insights into their relative efficacy for eQTL identification.
Keywords:
RNA-seq; eQTL; SNP; kmer; haplotype block
