Selection signatures of Qinchuan cattle based on whole-genome sequences

Qinchuan cattle has gradually improved in body shape and growth rate in the long-term breeding process from the draft cattle to beef cattle. As the head of the five local yellow cattle in China, the Qinchuan cattle has been designated as a specialized beef cattle breed. We investigated the selection signatures using whole genome sequencing data in Qinchuan cattle. Based on Fst, we detected hundreds of candidate genes under selection across Qinchuan, Red Angus, and Japanese Black cattle. Through protein-protein interaction analysis and functional annotation of candidate genes, the results revealed that KMT2E, LTBP1 and NIPBL were related to brain size, body characteristics, and limb development, respectively, suggesting that these potential genes may affect the growth and development traits in Qinchuan cattle. ARIH2, DACT1 and DNM2, et al. are related to meat quality. Meanwhile, TBXA2R can be used as a gene associated with reproductive function, and USH2A affect coat color. This provided a glimpse into the formation of breeds and molecular genetic breeding. Our findings will promote genome-assisted breeding to improve animal production and health.

Embryo biopsies for genomic selection in tropical dairy cattle

Genomic selection has transformed the livestock industry, enabling early-life selection of animals. Biopsy sampling of pre-implantation embryos has been described since 1968. However, it was only after 2010, with the advancement of molecular biology techniques such as whole genomic amplification and SNP Chips, that next-generation sequencing became commercially available for bovine embryos. It is now possible to make decisions about which embryos to transfer not only based on recipients' availability or embryo morphology but also on genomic estimates. This technology can be implemented for a wide spectrum of applications in livestock. In this review, we discuss the use of embryo biopsy for genomic selection and share our experience with Gir and Girolando Brazilian breeding programs, as well as future goals for implementing it in Brazilian bovine in vitro embryo production practices.

Development of an inclusive 580K SNP array and its application for genomic selection and genome-wide association studies in rice

Rice is a globally cultivated crop and is primarily a staple food source for more than half of the world's population. Various single-nucleotide polymorphism (SNP) arrays have been developed and utilized as standard genotyping methods for rice breeding research. Considering the importance of SNP arrays with more inclusive genetic information for GWAS and genomic selection, we integrated SNPs from eight different data resources: resequencing data from the Korean World Rice Collection (KRICE) of 475 accessions, 3,000 rice genome project (3 K-RGP) data, 700 K high-density rice array, Affymetrix 44 K SNP array, QTARO, Reactome, and plastid and GMO information. The collected SNPs were filtered and selected based on the breeder's interest, covering all key traits or research areas to develop an integrated array system representing inclusive genomic polymorphisms. A total of 581,006 high-quality SNPs were synthesized with an average distance of 200 bp between adjacent SNPs, generating a 580 K Axiom Rice Genotyping Chip (580 K _ KNU chip). Further validation of this array on 4,720 genotypes revealed robust and highly efficient genotyping. This has also been demonstrated in genome-wide association studies (GWAS) and genomic selection (GS) of three traits: clum length, heading date, and panicle length. Several SNPs significantly associated with cut-off, -log₁₀ p-value >7.0, were detected in GWAS, and the GS predictabilities for the three traits were more than 0.5, in both rrBLUP and convolutional neural network (CNN) models. The Axiom 580 K Genotyping array will provide a cost-effective genotyping platform and accelerate rice GWAS and GS studies.

Pre-selecting markers based on fixation index scores improved the power of genomic evaluations in a combined Yorkshire pig population

Combining different swine populations in genomic prediction can be an important tool, leading to an increased accuracy of genomic prediction using single nucleotide polymorphism (SNP) chip data compared with within-population genomic. However, the expected higher accuracy of multi-population genomic prediction has not been realized. This may be due to an inconsistent linkage disequilibrium (LD) between SNPs and quantitative trait loci (QTL) across populations, and the weak genetic relationships across populations. In this study, we determined the impact of different genomic relationship matrices, SNP density and pre-selected variants on prediction accuracy using a combined Yorkshire pig population. Our objective was to provide useful strategies for improving the accuracy of genomic prediction within a combined population. Results showed that the accuracy of genomic best linear unbiased prediction (GBLUP) using imputed whole-genome sequencing (WGS) data in the combined population was always higher than that within populations. Furthermore, the use of imputed WGS data always resulted in a higher accuracy of GBLUP than the use of 80K chip data for the combined population. Additionally, the accuracy of GBLUP with a non-linear genomic relationship matrix was markedly increased (0.87% to 15.17% for 80K chip data, and 0.43% to 4.01% for imputed WGS data) compared with that obtained with a linear genomic relationship matrix, except for the prediction of XD population in the combined population using imputed WGS data. More importantly, the application of pre-selected variants based on fixation index (Fst) scores improved the accuracy of multi-population genomic prediction, especially for 80K chip data. For BLUP|GA (BLUP approach given the genetic architecture), the use of a linear method with an appropriate weight to build a weight-relatedness matrix led to a higher prediction accuracy compared with the use of only pre-selected SNPs for genomic evaluations, especially for the total number of piglets born. However, for the non-linear method, BLUP|GA showed only a small increase or even a decrease in prediction accuracy compared with the use of only pre-selected SNPs. Overall, the best genomic evaluation strategy for reproduction-related traits for a combined population was found to be GBLUP performed with a non-linear genomic relationship matrix using variants pre-selected from the 80K chip data based on Fst scores.

Climate Adaptation of Tropical Cattle

There is sustained growth in the number of tropical cattle, which represent more than half of all cattle worldwide. By and large, most research in tropical areas is still focused on breeds of cattle, their particular advantages or disadvantages in tropical areas, and the tropical forages or feeds that could be usefully fed to them. A consistent issue for adaptation to climate is the heat of tropical environments. Changing the external characteristics of the animal, such as color and coat characteristics, is one way to adapt, and there are several major genes for these traits. However, further improvement in heat tolerance and other adaptation traits will need to use the entire genome and all physical and physiological systems. Apart from the response to heat, climate forcing through methane emission identifies dry season weight loss as an important if somewhat neglected trait in climate adaptation of cattle. The use of genome-estimated breeding values in tropical areas is in its infancy and will be difficult to implement, but will be essential for rapid, coordinated genetic improvement. The difficulty of implementation cannot be exaggerated and may require major improvements in methodology.