Effect of single-nucleotide polymorphisms on the breeding value of fertility and breeding value of beef in Hungarian Simmental cattle

Single-step genome-wide association studies and post-GWAS analyses for the number of oocytes and embryos in Gir cattle

Genome-Wide Association Studies (GWAS) are used for identification of quantitate trait loci (QTL) and genes associated with several traits. We aimed to identify genomic regions, genes, and biological processes associated with number of total and viable oocytes, and number of embryos in Gir dairy cattle. A dataset with 17,526 follicular aspirations, including the following traits: number of viable oocytes (VO), number of total oocytes (TO), and number of embryos (EMBR) from 1641 Gir donors was provided by five different stock farms. A genotype file with 2093 animals and 395,524 SNP markers was used to perform a single-step GWAS analysis for each trait. The top 10 windows with the highest percentage of additive genetic variance explained by 100 adjacent SNPs were selected. The genomic regions identified in our work were overlapped with QTLs from QTL database on chromosomes 1, 2, 5, 6, 7, 8, 9, 13, 17, 18, 20, 21, 22, 24, and 29. These QTLs were classified as External, Health, Meat and carcass, Production or Reproduction traits, and about 38% were related to Reproduction. In total, 117 genes were identified, of which 111 were protein-coding genes. Exclusively associations were observed for 42 genes with EMBR, and 1 with TO. Also, 42 genes were in common between VO and TO, 28 between VO and EMBR, and four genes were in common among all traits. In conclusion, great part of the identified genes plays a functional role in initial embryo development or general cell functions. The protein-coding genes ARNT, EGR1, HIF1A, AHR, and PAX2 are good markers for the production of oocytes and embryos in Gir cattle.

Use of a graph neural network to the weighted gene co-expression network analysis of Korean native cattle

In the general framework of the weighted gene co-expression network analysis (WGCNA), a hierarchical clustering algorithm is commonly used to module definition. However, hierarchical clustering depends strongly on the topological overlap measure. In other words, this algorithm may assign two genes with low topological overlap to different modules even though their expression patterns are similar. Here, a novel gene module clustering algorithm for WGCNA is proposed. We develop a gene module clustering network (gmcNet), which simultaneously addresses single-level expression and topological overlap measure. The proposed gmcNet includes a “co-expression pattern recognizer” (CEPR) and “module classifier”. The CEPR incorporates expression features of single genes into the topological features of co-expressed ones. Given this CEPR-embedded feature, the module classifier computes module assignment probabilities. We validated gmcNet performance using 4,976 genes from 20 native Korean cattle. We observed that the CEPR generates more robust features than single-level expression or topological overlap measure. Given the CEPR-embedded feature, gmcNet achieved the best performance in terms of modularity (0.261) and the differentially expressed signal (27.739) compared with other clustering methods tested. Furthermore, gmcNet detected some interesting biological functionalities for carcass weight, backfat thickness, intramuscular fat, and beef tenderness of Korean native cattle. Therefore, gmcNet is a useful framework for WGCNA module clustering.

Boosting the potential of cattle breeding using molecular biology, genetics, and bioinformatics approaches – a review

Cattle are among the most important farm animals that underwent an intense selection with the aim to increase milk production and to improve growth and meat properties, meanwhile reducing the generation interval allowing for a faster herd turnover. Recently, a shift from traditional breeding methods to breeding based on genetic testing has been observed. In this perspective, we review the techniques of molecular biology, genetics, and bioinformatics that are expected to further boost the agricultural potential of cattle. We discuss embryo selection based on next-generation and Nanopore sequencing and in vitro embryo production, boosting the potential of genetically superior animals. Gene editing of embryos could further speed up the selection process, essentially introducing a change in a single generation. Lastly, we discuss the host-microbiome co-evolution and adaptation. For example, cattle already adapted to low-quality low-cost fodder could be bred to achieve desired properties for the beef and dairy industry. The challenge of breeding and genetic editing is to accompany the selection on desired consumer-oriented traits with the push for sustainability and the adaptation to a changing climate while remaining economically viable. We propose that we are yet to see the limits of what is possible to achieve with modern technology for the cattle of the future; the ultimate goal will be to produce and maintain genetically elite individuals that can sustain the growing demands on the production.

Identification of markers associated with estimated breeding value and horn colour in Hungarian Grey cattle

OBJECTIVE

This study was conducted to estimate effect of single nucleotide polymorphisms (SNP) on the estimated breeding value of Hungarian Grey (HG) bulls and to find markers associated with horn colour.

METHODS

Genotypes 136 HG animals were determined on Geneseek high-density Bovine SNP 150K BeadChip. A multi-locus mixed-model was applied for statistical analyses.

RESULTS

Six SNPs were identified to be associated (-log10P>10) with green and white horn. These loci are located on chromosome 1, 3, 9, 18, and 25. Seven loci (on chromosome 1, 3, 6, 9, 10, 28) showed considerable association (-log10P>10) with the estimated breeding value.

CONCLUSION

Analysis provides markers for further research of horn colour and supplies markers to achieve more effective selection work regarding estimated breeding value of HG.

Genetic position of Hungarian Grey among European cattle and identification of breed-specific markers

Hungarian Grey is an indigenous cattle breed that is one of the national symbols of Hungary. However, genetic description of the Hungarian Grey cattle has not yet been conducted based on whole-genome screening. Using the GeneSeek high-density Bovine SNP (single nucleotide polymorphism) 150 K BeadChip, we sampled the genome of 36 Hungarian Grey, 12 Maremmana, 13 Hungarian Fleckvieh and 5 Holstein-Friesian cattle for population studies and used data of 139 other cattle from an additional dataset created on European cattle breeds (Upadhyay et al.2017. Heredity 118, 169-176). The performance of a multidimensional scaling plot showed that Hungarian Grey clustered independently from other European cattle. The number and total length of runs of homozygosity (ROH) is similar or slightly below the value of other European cattle; FROH coefficients (proportion of the autosomal genome covered by ROH) are similar to Maremmana and Maronesa. The frequency of ROH does not show increased values as it can be noticed in Heck and Maltese. These results indicate that the Hungarian Grey cattle have been successfully maintained avoiding negative genetic effects, and reflect the uniqueness among European cattle. The identification of breed-specific loci has been aimed at differentiating Hungarian Grey (n = 136 in this case) from other cattle breeds (n = 169). Ten loci (-log10P > 5) were identified as markers capable for differentiation of Hungarian Grey. These markers are located on chromosomes 6, 14, 15, 16, 20 and 24.