Genomic Prediction and Association Analysis with Models Including Dominance Effects for Important Traits in Chinese Simmental Beef Cattle

Abstract

Non-additive effects play important roles in determining genetic changes with regard to complex traits; however, such effects are usually ignored in genetic evaluation and quantitative trait locus (QTL) mapping analysis. In this study, a two-component genome-based restricted maximum likelihood (GREML) was applied to obtain the additive genetic variance and dominance variance for carcass weight (CW), dressing percentage (DP), meat percentage (MP), average daily gain (ADG), and chuck roll (CR) in 1233 Simmental beef cattle. We estimated predictive abilities using additive models (genomic best linear unbiased prediction (GBLUP) and BayesA) and dominance models (GBLUP-D and BayesAD). Moreover, genome-wide association studies (GWAS) considering both additive and dominance effects were performed using a multi-locus mixed-model (MLMM) approach. We found that the estimated dominance variances accounted for 15.8%, 16.1%, 5.1%, 4.2%, and 9.7% of the total phenotypic variance for CW, DP, MP, ADG, and CR, respectively. Compared with BayesA and GBLUP, we observed 0.5–1.1% increases in predictive abilities of BayesAD and 0.5–0.9% increases in predictive abilities of GBLUP-D, respectively. Notably, we identified a dominance association signal for carcass weight within RIMS2, a candidate gene that has been associated with carcass weight in beef cattle. Our results suggest that dominance effects yield variable degrees of contribution to the total genetic variance of the studied traits in Simmental beef cattle. BayesAD and GBLUP-D are convenient models for the improvement of genomic prediction, and the detection of QTLs using a dominance model shows promise for use in GWAS in cattle.

Genome-Wide Association Study Using Whole-Genome Sequence Data for Fertility, Health Indicator, and Endoparasite Infection Traits in German Black Pied Cattle

“…Significant marker associations for SCS in other dairy cattle breeds were described on each chromosome [85], indicating the complex genetic architecture of the trait. In the present study, we estimated additive genetic effects by neglecting non-additive dominance effects, which could play a crucial role in the genomic architecture of complex functional traits [86]. As pointed out by Howard et al [87], dominance decreases when alleles are almost fixed due to inbreeding.…”

Genome Wide Association Study of Beef Traits in Local Alpine Breed Reveals the Diversity of the Pathways Involved and the Role of Time Stratification

“…While the latter two have already been associated respectively with growth ( Londoño-Gil et al, 2021 ) and backfat thickness in cattle ( Júnior et al, 2016 ), SLC12A1, to our knowledge, has never been associated with growth or weight traits in cattle (but see Kemter et al, 2014 , for evidence in mice). However, among the suggestively associated SNPs on BTA10 ( Table 3 ), several were within or close genes highly important for ADG, such as ALDH1A2 , FBN1, and AQP9 ( Hirano et al, 2012 ; Liu et al, 2019 ; Londoño-Gil et al, 2021 ; Zhang et al, 2021 ). Figure 9 shows that enriched pathways spanned several macro-categories ( Figure 9 ; Supplementary Figure S4E ): these results suggest that, as for BW, during the late months of the first year, a complex interplay of different biological processes takes place in growing bulls.…”

Genomic prediction with non-additive effects in beef cattle: stability of variance component and genetic effect estimates against population size

“…On the other hand, the genetic structure of the population in the present study was more uniform, as illustrated by the population structure analysis (Figure S1 ), and consequently lower estimates might be obtained. Dominance variances for carcass traits can be found in other breeds, for example, CW of Simmental (the proportion relative to the phenotypic variance was 0.158) [ 13 ] and intramuscular fat and carcass retail yield of a population consisting of multiple breeds (0.10 ± 0.03 and 0.18 ± 0.06, respectively) [ 9 ]. Dominance variances of growth traits were also reported in other breeds of beef cattle, for example, for yearling weight of Brahman and Tropical composite (up to 0.13 and 0.10, respectively) [ 14 ], 205-day body weight in synthetic populations (0.00–0.52) [ 15 ] and post-weaning gain for Limousin (0.099 ± 0.016) [ 16 ].…”