Single-step genome-wide association study uncovers known and novel candidate genomic regions for endocrine and classical fertility traits in Swedish Red and Holstein dairy cows

Assessment of genetic diversity, inbreeding and collection completeness of Jersey bulls in the US National Animal Germplasm Program

Genomic selection and extensive use of a few elite bulls through artificial insemination are leading to reduced genetic diversity in Jersey cattle. Conservation of genetic diversity through gene banks can protect a breed's genetic diversity and genetic gain, ensuring continued genetic advancement in the future. The availability of genomic information in the US National Animal Germplasm Program (NAGP) facilitates characterization of Jersey bulls in the germplasm collection. Therefore, in this study, we compared the genetic diversity and inbreeding between Jersey bulls in the NAGP and the national cooperator database (NCD). The NCD is maintained and curated by the Council on Dairy Cattle Breeding (CDCB). We found the genetic diversity to be marginally higher in NAGP (Ho = 0.34 ± 0.17) relative to the NCD population (Ho = 0.33 ± 0.16). The average pedigree and genomic inbreeding (FPED, FGRM, FROH > 2Mb) were similar between the groups, with estimates of 7.6% with FPED, 11.07% with FGRM and 20.13% with FROH > 2Mb. An increasing trend in inbreeding was detected, and a significantly higher level of inbreeding was estimated among the older bulls in the NAGP collection, suggesting an overrepresentation of the genetics from elite bulls. Results from principal component analyses (PCA) provided evidence that the NAGP collection is representative of the genetic variation found in the NCD population and a broad majority of the loci segregating (98.2%) in the NCD population were also segregating in the NAGP. Ward's clustering was used to assess collection completeness of Jerseys in the NAGP by comparison with top 1000 sires of bulls, top 1000 sires of cow, and bulls with high Lifetime Net Merit (NM$). All the clusters were represented in the NAGP suggesting that most of the genetic diversity in the US Jersey population is represented in the NAGP and confirmed the PCA results. The decade of birth was the major driver grouping bulls into clusters, suggesting the importance of selection over time. Selection signature analysis between the historic bulls in the NAGP with the newer bulls, born in the decade after implementation of genomic selection, identified selection for milk production, fat and protein yield, fertility, health, and reproductive traits. Cluster analysis revealed that the NAGP has captured allele frequency changes over time associated with selection, validating the strategy of repeated sampling and suggests that the continuation of a repeated sampling policy is essential for the germplasm collection to maintain its future utility. While NAGP should continue to collect bulls that have large influence on the population due to selection, care should be taken to include the entire breadth of bulls, including low merit bulls.

Effect of genotyping density on the detection of runs of homozygosity and heterozygosity in cattle

Runs of homozygosity (ROHom) are contiguous stretches of homozygous regions of the genome. In contrast, runs of heterozygosity (ROHet) are heterozygosity-rich regions. The detection of these two types of genomic regions (ROHom and ROHet) is influenced by the parameters involved in their identification and the number of available single-nucleotide polymorphisms (SNPs). The present study aimed to test the effect of chip density in detecting ROHom and ROHet in the Italian Simmental cattle breed. A sample of 897 animals were genotyped at low density (50k SNP; 397 individuals), medium density (140k SNP; 348 individuals), or high density (800k SNP; 152 individuals). The number of ROHom and ROHet per animal (nROHom and nROHet, respectively) and their average length were calculated. ROHom or ROHet shared by more than one animal and the number of times a particular SNP was inside a run were also computed (SNPROHom and SNPROHet). As the chip density increased, the nROHom increased, whereas their average length decreased. In contrast, the nROHet decreased and the average length increased as the chip density increased. The most repeated ROHom harbored no genes, whereas in the most repeated ROHet four genes (SNRPN, SNURF, UBE3A, and ATP10A) previously associated with reproductive traits were found. Across the 3 datasets, 31 SNP, located on Bos taurus autosome (BTA) 6, and 37 SNP (located on BTA21) exceeded the 99th percentile in the distribution of the SNPROHom and SNPROHet, respectively. The genomic region on BTA6 mapped the SLIT2, PACRGL, and KCNIP4 genes, whereas 19 and 18 genes were mapped on BTA16 and BTA21, respectively. Interestingly, most of genes found through the ROHet analysis were previously reported to be related to health, reproduction, and fitness traits. The results of the present study confirm that the detection of ROHom is more reliable when the chip density increases, whereas the ROHet trend seems to be the opposite. Genes and quantitative trait loci (QTL) mapped in the highlighted regions confirm that ROHet can be due to balancing selection, thus related to fitness traits, health, and reproduction, whereas ROHom are mainly involved in production traits. The results of the present study strengthened the usefulness of these parameters in analyzing the genomes of livestock and their biological meaning.

Integration of Multiplied Omics, a Step Forward in Systematic Dairy Research

Due to their unique multi-gastric digestion system highly adapted for rumination, dairy livestock has complicated physiology different from monogastric animals. However, the microbiome-based mechanism of the digestion system is congenial for biology approaches. Different omics and their integration have been widely applied in the dairy sciences since the previous decade for investigating their physiology, pathology, and the development of feed and management protocols. The rumen microbiome can digest dietary components into utilizable sugars, proteins, and volatile fatty acids, contributing to the energy intake and feed efficiency of dairy animals, which has become one target of the basis for omics applications in dairy science. Rumen, liver, and mammary gland are also frequently targeted in omics because of their crucial impact on dairy animals’ energy metabolism, production performance, and health status. The application of omics has made outstanding contributions to a more profound understanding of the physiology, etiology, and optimizing the management strategy of dairy animals, while the multi-omics method could draw information of different levels and organs together, providing an unprecedented broad scope on traits of dairy animals. This article reviewed recent omics and multi-omics researches on physiology, feeding, and pathology on dairy animals and also performed the potential of multi-omics on systematic dairy research.