Genome-wide association study for growth traits in Nelore cattle

Abstract

The objective of this study was to investigate the association of single nucleotide polymorphisms (SNPs) with birth weight, weight gain from birth to weaning and from weaning to yearling, yearling height and cow weight in Nelore cattle. Data from 5064 animals participating in the DeltaGen and PAINT breeding programs were used. The animals were genotyped with a panel of 777 962 SNPs (Illumina BovineHD BeadChip) and 412 993 SNPs remained after quality control analysis of the genomic data. A genome-wide association study was performed using a single-step methodology. The analyses were processed with the BLUPF90 family of programs. When applied to a genome-wide association studies, the single-step GBLUP methodology is an iterative process that estimates weights for the SNPs. The weights of SNPs were included in all analyses by iteratively applying the single-step GBLUP methodology and repeated twice so that the effect of the SNP and the effect of the animal were recalculated in order to increase the weight of SNPs with large effects and to reduce the weight of those with small effects. The genome-wide association results are reported based on the proportion of variance explained by windows of 50 adjacent SNPs. Considering the two iterations, only windows with an additive genetic variance >1.5% were presented in the results. Associations were observed with birth weight on BTA 14, with weight gain from birth to weaning on BTA 5 and 29, with weight gain from weaning to yearling on BTA 11, and with yearling height on BTA 8, showing the genes TMEM68 (transmembrane protein 8B) associated with birth weight and yearling height, XKR4 (XK, Kell blood group complex subunit-related family, member 4) associated with birth weight, NPR2 (natriuretic peptide receptor B) associated with yearling height, and REG3G (regenerating islet-derived 3-gamma) associated with weight gain from weaning to yearling. These genes play an important role in feed intake, weight gain and the regulation of skeletal growth.

Genome‐wide scan for runs of homozygosity in the composite Montana Tropical® beef cattle

“…The XKR4 (XK, Kell blood group complex subunitrelated family, member 4) gene has been associated with several economically important traits in the beef cattle such as intramuscular fat (Ramayo-Caldas et al, 2014) and subcutaneous rump fat thickness (Bolormaa et al, 2011;Porto Neto, Bunch, Harrison, & Barendse, 2012). This gene has also been described to have functions associated with serum prolactin concentrations in the Angus-Simmental-Charolais crossbred (Bastin et al, 2014), feed intake in crossbred steers (Lindholm-Perry et al, 2012), age at puberty in Brahman (Fortes et al, 2012), and backfat thickness (Silva et al, 2017), birth weight (Terakado et al, 2018) and meat tenderness (Magalhães et al, 2016) in the Nellore cattle. The second gene (MT1EI, metallothionein 1E) encodes a protein that exhibits antioxidant activity (Chung, Hogstrand, & Lee, 2006) and displayed a significant negative correlation with dry matter intake in beef steers (Sun, Zhao, Zhou, Chen, & Guan, 2019).…”

Weighted Single-Step Genome-Wide Association Study for Growth Traits in Chinese Simmental Beef Cattle

“…These findings implied that these windows (BTA3: 106,574,782-106,644,015 bp; BTA21: 5,941,998-5,968,820 bp; and BTA5: 77,160,030-77,212,501 bp) need more attention when selecting candidate genes for BYADG. Table 5 shows the 21 windows associated with 18MW which were located on BTA1, 3,4,5,9,10,14,20,21,22,23,25, and 26, together with 16 genes near the most important SNP within each window. The identified genomic windows explained 1.01-3.44% of the genetic variance for 18MW.…”

“…Beef cattle provide a large proportion of the meat consumed by humans throughout the world [1]. Improving the genetic process of growth traits (e.g., body weight and average daily gain) is one of the major goals in the beef cattle breeding industry, as it can increase meat production and reduce the cost of raising animals [2,3]. The key to accelerating the progress towards this goal is to genetically select elite cattle and to mine major genes that affect growth traits.…”

“…A genome-wide association study (GWAS) can detect significant single nucleotide polymorphisms (SNPs) or genomic regions that are associated with economically important traits based on the linkage disequilibrium (LD) between SNPs and possible causative mutations [4]. GWASs have recently been used to identify several quantitative trait loci (QTLs) and genes associated with growth traits in beef cattle [1,3,5]. For instance, Kim et al [6] used 602 crossbred cattle of Bos taurus (Angus) and Bos indicus (Brahman) genotyped for 417 microsatellite markers and detected a total of 35 QTLs for growth traits (e.g., birth weight and yearling weight).…”

“…This approach can make full use of genealogical information and phenotypes of genotyped and nongenotyped animals [9]. The weighted single-step approach has been successfully applied to domesticated animals, and has led to the detection of additional QTLs and candidate genes for growth traits in Nellore cattle [3], semen traits in Duroc boar pigs [10], and milk protein composition traits in Chinese Holstein dairy cattle [11]. However, to our knowledge, few of the studies examining growth traits in Simmental beef cattle have used wssGWAS.…”