Genome-wide association and fine-mapping analyses identify novel candidate genes affecting serum cortisol levels...

In swine breeding programs, it has now become critically important to emphasize selection for resilience to external environmental stress factors that have negatively impacted the productivity of pigs, such as those due to climate change induced temperature increases, or the intensification of housing environments. Secretion of cortisol, a neurophysiological change mediated by the hypothalamic-pituitary-adrenal axis, is a central mechanism in the biological stress response. This hormone is closely related to pig robustness and health and can serve as an informative indicator of stress resistance and robustness in pigs. To identify positional candidate genes and their genetic variants influencing blood cortisol levels, we conducted genome-wide association study (GWAS), joint linkage and linkage disequilibrium (LALD) mapping and Bayesian fine-mapping analysis in an F₂ resource population generated by crossing Duroc pigs with Korean native pigs. The data used in the study included 243 F₂ animals. We utilized imputed whole-genome sequencing data for our analyses. GWAS results revealed a genome-wide significant quantitative trait locus (q-value < 0.05) located within a ~2.46 Mb region between single nucleotide polymorphisms 7:114031215 and 7:116497417 on pig chromosome 7, which accounted for 12.65% of the phenotypic variation. LALD mapping analysis was performed to narrow down the confidence interval (CI) of the quantitative trait locus which resulted in a CI of 2.39 Mb (7:114409266~116803751). Further, to identify candidate causal genes within the 2.39 Mb region, fine-mapping analysis was performed within the region. The fine-mapping analysis identified SERPINA1, ITPK1, CLMN, SERPINA12, and PRIMA1, in addition to SERPINA6, which was previously shown to be associated with blood cortisol levels. Our results identified positional candidate genes and genetic variants associated with serum cortisol concentrations that can be included in marker panels for genomic prediction to improve selection for robustness in pigs.