Abstract
Lactation persistency and milk production are among the most economically important traits in the dairy industry. In this study, we explored the association of over 6.1 million imputed whole-genome sequence variants with lactation persistency (LP), milk yield (MILK), fat yield (FAT), fat percentage (FAT%), protein yield (PROT), and protein percentage (PROT%) in North American Holstein cattle. We identified 49, 3991, 2607, 4459, 805, and 5519 SNPs significantly associated with LP, MILK, FAT, FAT%, PROT, and PROT%, respectively. Various known associations were confirmed while several novel candidate genes were also revealed, including ARHGAP35, NPAS1, TMEM160, ZC3H4, SAE1, ZMIZ1, PPIF, LDB2, ABI3, SERPINB6, and SERPINB9 for LP; NIM1K, ZNF131, GABRG1, GABRA2, DCHS1, and SPIDR for MILK; NR6A1, OLFML2A, EXT2, POLD1, GOT1, and ETV6 for FAT; DPP6, LRRC26, and the KCN gene family for FAT%; CDC14A, RTCA, HSTN, and ODAM for PROT; and HERC3, HERC5, LALBA, CCL28, and NEURL1 for PROT%. Most of these genes are involved in relevant gene ontology (GO) terms such as fatty acid homeostasis, transporter regulator activity, response to progesterone and estradiol, response to steroid hormones, and lactation. The significant genomic regions found contribute to a better understanding of the molecular mechanisms related to LP and milk production in North American Holstein cattle.
Highlights
Milk production and composition are the most intensively selected traits in dairy cattle breeding programs around the world due to their direct economic impact to the industry and close link with nutritional properties [1,2]
The most significant regions for lactation persistency (LP) were observed in BTA28 (p-value = 5.28 × 10−7) and BTA18 (p-value = 8.56 × 10−7), in which, for the BTA28 peak, the most significant SNPs were associated with ZMIZ1 and PPIF; and for the BTA18 peak, with ARHGAP35, NPAS1, TMEM160, ZC3H4, and SAE1 (Table 2)
Based on imputed WGS (iWGS)-based genomewide association studies (GWAS), we identified novel genomic regions of interest, revealing novel candidate genes (ARHGAP35, NPAS1, TMEM160, ZC3H4, SAE1, ZMIZ1, PPIF, LDB2, ABI3, SERPINB6, and SERPINB9 for LP; NIM1K, zinc finger protein 131 (ZNF131), GABRG1, GABRA2, DCHS1, and SPIDR for milk yield (MILK); NR6A1, EXT2, POLD1, GOT1, and ETV6 for fat yield (FAT); DPP6, LRRC26, and the KCN gene family for FAT%; CDC14A, RTCA, HSTN, and ODAM for protein yield (PROT); and HERC3, HERC5, LALBA, and NEURL1 for PROT%), and confirmed previously reported associations
Summary
Milk production and composition are the most intensively selected traits in dairy cattle breeding programs around the world due to their direct economic impact to the industry and close link with nutritional properties [1,2]. Identifying genomic regions and candidate genes related to milk production traits is crucial to better understand the biological mechanisms underlying their phenotypic expression and to optimize genomic evaluation of milk-related traits [14]. In this context, genomewide association studies (GWAS) have been extensively performed in recent years to find associations between genomic polymorphisms and economically important traits in dairy cattle populations [15,16,17]. More accurate quantitative trait loci (QTL), causative mutations, and candidate genes, are expected to be identified based on WGS [18]. The potential of imputed WGS (iWGS) data to discover genetic variants in GWAS has been shown in previous studies of dairy cattle [20,21] and other species [22,23,24]
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