Abstract
Accurately estimating the genetic parameters and revealing more genetic variants underlying milk production and quality are conducive to the genetic improvement of dairy cows. In this study, we estimate the genetic parameters of five milk-related traits of cows—namely, milk yield (MY), milk fat percentage (MFP), milk fat yield (MFY), milk protein percentage (MPP), and milk protein yield (MPY)—based on a random regression test-day model. A total of 95,375 test-day records of 9,834 cows in the lower reaches of the Yangtze River were used for the estimation. In addition, genome-wide association studies (GWASs) for these traits were conducted, based on adjusted phenotypes. The heritability, as well as the standard errors, of MY, MFP, MFY, MPP, and MPY during lactation ranged from 0.22 ± 0.02 to 0.31 ± 0.04, 0.06 ± 0.02 to 0.15 ± 0.03, 0.09 ± 0.02 to 0.28 ± 0.04, 0.07 ± 0.01 to 0.16 ± 0.03, and 0.14 ± 0.02 to 0.27 ± 0.03, respectively, and the genetic correlations between different days in milk (DIM) within lactations decreased as the time interval increased. Two, six, four, six, and three single nucleotide polymorphisms (SNPs) were detected, which explained 5.44, 12.39, 8.89, 10.65, and 7.09% of the phenotypic variation in MY, MFP, MFY, MPP, and MPY, respectively. Ten Kyoto Encyclopedia of Genes and Genomes pathways and 25 Gene Ontology terms were enriched by analyzing the nearest genes and genes within 200 kb of the detected SNPs. Moreover, 17 genes in the enrichment results that may play roles in milk production and quality were selected as candidates, including CAMK2G, WNT3A, WNT9A, PLCB4, SMAD9, PLA2G4A, ARF1, OPLAH, MGST1, CLIP1, DGAT1, PRMT6, VPS28, HSF1, MAF1, TMEM98, and F7. We hope that this study will provide useful information for in-depth understanding of the genetic architecture of milk production and quality traits, as well as contribute to the genomic selection work of dairy cows in the lower reaches of the Yangtze River.
Highlights
The production and quality of milk are key factors that influence the profitability of dairy enterprises
Five traits evaluating the milk performance of dairy cows were selected for genetic parameter evaluation and association analyses; namely, milk yield (MY), milk fat percentage (MFP), milk fat yield (MFY), milk protein percentage (MPP), and milk protein yield (MPY)
Our study found that heritability of milk fat traits (MFY and MFR) was, most of the time during lactation (89.70% and 91.69%, respectively), lower than that for the corresponding milk protein traits (MPY and MPP; Figure 1), which was in agreement with previous studies (Tijani et al, 1999; Silvestre et al, 2005)
Summary
The production and quality of milk are key factors that influence the profitability of dairy enterprises. Understanding the genetic architecture, estimating the genetic parameters, and revealing more quantitative trait loci (QTL) regions underlying these milkrelated traits are beneficial to the genetic improvement of dairy cows, as the genetic variation information could be utilized more rationally and effectively. Due to the goodness-of-fit and the low correlation among parameters, Legendre polynomials (LP)—especially the high-order polynomials—have generally been used to model the lactation curve of cows (Silvestre et al, 2006; Bignardi et al, 2009a). The appropriate selection of the LP order can improve the calculation efficiency and the goodness-of-fit, resulting in a high accuracy of the genetic parameter evaluation in the testday model (Pereira et al, 2013; Li et al, 2020)
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