An Integrative Genomic Prediction Approach for Predicting Buffalo Milk Traits by Incorporating Related Cattle QTLs

Abstract

Background: The 90K Axiom Buffalo SNP Array is expected to improve and speed up various genomic analyses for the buffalo (Bubalus bubalis). Genomic prediction is an effective approach in animal breeding to improve selection and reduce costs. As buffalo genome research is lagging behind that of the cow and production records are also limited, genomic prediction performance will be relatively poor. To improve the genomic prediction in buffalo, we introduced a new approach (pGBLUP) for genomic prediction of six buffalo milk traits by incorporating QTL information from the cattle milk traits in order to help improve the prediction performance for buffalo. Results: In simulations, the pGBLUP could outperform BayesR and the GBLUP if the prior biological information (i.e., the known causal loci) was appropriate; otherwise, it performed slightly worse than BayesR and equal to or better than the GBLUP. In real data, the heritability of the buffalo genomic region corresponding to the cattle milk trait QTLs was enriched (fold of enrichment > 1) in four buffalo milk traits (FY270, MY270, PY270, and PM) when the EBV was used as the response variable. The DEBV as the response variable yielded more reliable genomic predictions than the traditional EBV, as has been shown by previous research. The performance of the three approaches (GBLUP, BayesR, and pGBLUP) did not vary greatly in this study, probably due to the limited sample size, incomplete prior biological information, and less artificial selection in buffalo. Conclusions: To our knowledge, this study is the first to apply genomic prediction to buffalo by incorporating prior biological information. The genomic prediction of buffalo traits can be further improved with a larger sample size, higher-density SNP chips, and more precise prior biological information.

Unveiling the Genetic Mechanism of Meat Color in Pigs through GWAS, Multi-Tissue, and Single-Cell Transcriptome Signatures Exploration

“…Earlier studies (e.g., Esfandyari et al [3], Lee et al [4], and Zha et al [5]) have suggested that meat color traits in pigs demonstrate low to moderate heritability, indicating the significant contribution of genetic factors to the variation in these traits. Therefore, through a comprehensive analysis of the genetic mechanisms underlying meat color traits and the identification of relevant candidate sites and genes, which can in turn enhance the efficiency of marker-assisted selection or genomic selection, valuable insights for the genetic improvement of the traits can be provided [6][7][8][9]. According to the release 48 version of the animal quantitative trait loci (QTLs) database [10], a total of 18,265 QTLs or associations for porcine meat quality and carcass traits have been reported in previous studies.…”

Comparing Classic and Genomic Selection for Iranian water buffaloes simulated population in the Khuzestan region for milk trait

“…The use of genomic selection in buffalo breeding (Bubalus bubalis) has been considered to improve the economic situation of small-scale producers in the world, as well as the unique characteristics of its milk. However, it has also been reported that buffalo genome research is lagging behind that of the cow, and production records are also limited, as the genomic prediction performance will be relatively poor (Hao et al 2022).…”

“…The results of one study showed that genomic selection can reduce the generation interval in the path of male-to-male selection from 9.5 years to 3. and Bayesian alphabet methods (Hao et al 2022). In the study of genomic selection with different methods in buffaloes of the Philippine, it has been reported that genomic methods (GBLUP and ssGBLUP) provide more accurate predictions compared to pBLUP (Herrera et al 2021).…”

“…Also, for this type of evaluation and selection, approximately 92% reduction in costs is reported in the progeny testing program (Schaeffer 2006). Genomic prediction is an effective approach in animal breeding to improve selection and reduce costs (Hao et al 2022) and currently, genomic selection has become the main method of dairy cattle evaluation in many countries (Hayes et al 2009; Clark et al 2011).…”