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.

Understanding how hybrids integrate lineage-specific regulatory variants at the haplotype level is crucial for elucidating the genetic basis of heterosis in livestock. In this study, we established three crossbred pig families derived from distant genetic lineages and systematically identified variants from different lineages, including single nucleotide polymorphisms (SNPs) and structural variations (SVs). At the phase level, we quantitatively analyzed gene expression, four histone modifications (H3K4me3, H3K27ac, H3K4me1, and H3K27me3), and the binding strength of transcription factor (CTCF) in backfat (BF) and longissimus dorsi (LD) muscle. By colocalization analysis of phased genetic variants with phased gene expression levels and with phased epigenetic modifications, we identified 18,670 expression quantitative trait loci (eQTL) (FDR < 0.05) and 8,652 epigenetic modification quantitative trait loci (epiQTL) (FDR < 0.05). The integration of eQTL and epiQTL allowed us to explore the potential regulatory mechanisms by which lineage-specific genetic variants simultaneously influence gene expression and epigenetic modifications. For example, we identified a Large White lineage-specific duplication (DUP) encompassing the KIT gene that was significantly associated with its promoter activity (FDR = 7.83 × 10⁻⁴) and expression levels (FDR = 9.03 × 10⁻⁴). Additionally, we found that a Duroc lineage-specific SNP located upstream of AMIGO2 was significantly associated with a Duroc-specific H3K27ac peak (FDR = 0.035) and also showed a significant association with AMIGO2 expression levels (FDR = 5.12 × 10⁻⁴). These findings underscore the importance of phased regulatory variants in shaping lineage-specific transcriptional programs and highlight how the haplotype-resolved integration of eQTL and epigenetic signals can reveal the mechanistic underpinnings of hybrid regulatory architecture. Our results offer insights for molecular marker development in precision pig breeding.

The longissimus dorsi muscle and backfat are important components of pork and complement each other in physiological function, significantly influencing ke