A Genome-Wide Association Study for Calving Interval in Holstein Dairy Cows Using Weighted Single-Step Genomic BLUP Approach

Abstract

Simple Summary: Reproductive performance is an important factor, which determines productive life and drives culling decisions in dairy herds. There are strong motives for including reproductive performance in genetic selection programs of dairy cows; however, low heritability estimates reported for reproductive performance measures limit the genetic selection efficiency. More effective genetic selection could be achieved using genomic information. The aim of this study was to identify genomic region(s) associated with the length of the calving interval in Holstein cows. The accuracies of genomic estimated breeding values (GEBVs) with single-step genomic BLUP (ssGBLUP) and the pedigree-based BLUP were compared as well. The results showed that the accuracies of GEBVs using the single-step genomic BLUP were much higher than those estimated using the pedigree-based BLUP. We identified three genomic regions (BTA3, BTA6, and BTA7) associated with the length of the calving interval in Holstein dairy cows. Abstract:The aim of the present study was to identify genomic region(s) associated with the length of the calving interval in primiparous (n = 6866) and multiparous (n = 5071) Holstein cows. The single nucleotide polymorphism (SNP) solutions were estimated using a weighted single-step genomic best linear unbiased prediction (WssGBLUP) approach and imputed high-density panel (777 k) genotypes. The effects of markers and the genomic estimated breeding values (GEBV) of the animals were obtained by five iterations of WssGBLUP. The results showed that the accuracies of GEBVs with WssGBLUP improved by +5.4 to +5.7, (primiparous cows) and +9.4 to +9.7 (multiparous cows) percent points over accuracies from the pedigree-based BLUP. The most accurate genomic evaluation was provided at the second iteration of WssGBLUP, which was used to identify associated genomic regions using a windows-based GWAS procedure. The proportion of additive genetic variance explained by windows of 50 consecutive SNPs (with an average of 165 Kb) was calculated and the region(s) that accounted for equal to or more than 0.20% of the total additive genetic variance were used to search for candidate genes. Three windows of 50 consecutive SNPs (BTA3, BTA6, and BTA7) were identified to be associated with the length of the calving interval in primi-and multiparous cows, while the window with the highest percentage of explained genetic variance was located on BTA3 position 49.42 to 49.52 Mb. There were five genes including ARHGAP29, SEC24D, METTL14, SLC36A2, and SLC36A3 inside the windows associated with the length of the calving interval. The biological

Genome-wide association study on milk production and somatic cell score for Thai dairy cattle using weighted single-step approach with random regression test-day model

“…These candidate genes largely describe polygenic inheritance of the traits. Several candidate genes were coincided with other previous reports for milk production in Holstein cattle, especially a large region of genes on BTA14 (Atashi et al, 2020;Cai et al, 2020;Silva et al, 2020;Wang et al, 2020). This could be attributed to the predomination of Holstein blood in Thai dairy cattle population by upgrading the local population with Holstein and later, mating within population to keep the proportion of Holstein blood between 87.5 and 93.75% as being considered the most suitable for tropical condition in Thailand (Department of Livestock Development, 2020).…”

Weighted Single-Step Genomic Best Linear Unbiased Prediction Method Application for Assessing Pigs on Meat Productivity and Reproduction Traits

“…A comparison of the genomic estimate accuracy obtained using wssGBLUP with traditional BLUP estimates was carried out by [40] for Holstein cows based on calving interval. The gain in GEBV accuracy with wssGBLUP was +5.4 to +5.7 (first-calf heifers) and +9.4 to +9.7 (multi-calving cows) percent points compared to pedigree-based BLUP accuracy [40].…”

“…A comparison of the genomic estimate accuracy obtained using wssGBLUP with traditional BLUP estimates was carried out by [40] for Holstein cows based on calving interval. The gain in GEBV accuracy with wssGBLUP was +5.4 to +5.7 (first-calf heifers) and +9.4 to +9.7 (multi-calving cows) percent points compared to pedigree-based BLUP accuracy [40]. Lourenco et al [25] also compared the genomic prediction accuracy for dairy productivity traits in cows obtained using several methods (Bayesian regression, genomic BLUP, single-step GBLUP and weighted single-step GBLUP).…”

“…The most accurate marker weights in the study by Atashi et al [40] were obtained at the second iteration of the wssGBLUP procedure. Similar results for SNP marker weight accuracy assessment at the second iteration were obtained in the study by Mehrban et al (2021) using a non-linear calculation method for meat traits and carcass quality in beef cattle.…”

Genome-wide association study for reproduction traits in a Brazilian Holstein population

“…The "SNP moving average" option from the postGSF90 package (Aguilar et al, 2014) was used to back-solve the genomic estimated breeding values and to obtain SNP effects for each trait separately. The window size of 10 SNPs was defined based on the average linkage disequilibrium decay in Holstein cattle and on the density of the SNP panel used, following similar studies (Atashi et al, 2020;Nayeri et al, 2016;Qanbari et al, 2009). AFC and CI were analyzed using an animal model, described as: y = Xb+ Za + e where y is the vector of phenotypic observations for genotyped and non-genotyped animals; X is the incidence matrix linking the phenotypic records to the fixed effects; b is the vector of fixed effects, which included age at the measurement only for CI and the contemporary group; Z is the incidence matrix linking the phenotypic records to each animal; a is the vector of animal additive genetic effects; and e is the vector of residual effects.…”