High-density genome-wide association study for residual feed intake in Holstein dairy cattle

Abstract

Improving feed efficiency (FE) of dairy cattle may boost farm profitability and reduce the environmental footprint of the dairy industry. Residual feed intake (RFI), a candidate FE trait in dairy cattle, can be defined to be genetically uncorrelated with major energy sink traits (e.g., milk production, body weight) by including genomic predicted transmitting ability of such traits in genetic analyses for RFI. We examined the genetic basis of RFI through genome-wide association (GWA) analyses and post-GWA enrichment analyses and identified candidate genes and biological pathways associated with RFI in dairy cattle. Data were collected from 4,823 lactations of 3,947 Holstein cows in 9 research herds in the United States. Of these cows, 3,555 were genotyped and were imputed to a high-density list of 312,614 SNP. We used a single-step GWA method to combine information from genotyped and nongenotyped animals with phenotypes as well as their ancestors' information. The estimated genomic breeding values from a single-step genomic BLUP were back-solved to obtain the individual SNP effects for RFI. The proportion of genetic variance explained by each 5-SNP sliding window was also calculated for RFI. Our GWA analyses suggested that RFI is a highly polygenic trait regulated by many genes with small effects. The closest genes to the top SNP and sliding windows were associated with dry matter intake (DMI), RFI, energy homeostasis and energy balance regulation, digestion and metabolism of carbohydrates and proteins, immune regulation, leptin signaling, mitochondrial ATP activities, rumen development, skeletal muscle development, and spermatogenesis. The region of 40.7 to 41.5 Mb on BTA25 (UMD3.1 reference genome) was the top associated region for RFI. The closest genes to this region, CARD11 and EIF3B, were previously shown to be related to RFI of dairy cattle and FE of broilers, respectively. Another candidate region, 57.7 to 58.2 Mb on BTA18, which is associated with DMI and leptin signaling, was also associated with RFI in this study. Post-GWA enrichment analyses used a sumbased marker-set test based on 4 public annotation databases: Gene Ontology, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, Reactome pathways, and medical subject heading (MeSH) terms. Results of these analyses were consistent with those from the top GWA signals. Across the 4 databases, GWA signals for RFI were highly enriched in the biosynthesis and metabolism of amino acids and proteins, digestion and metabolism of carbohydrates, skeletal development, mitochondrial electron transport, immunity, rumen bacteria activities, and sperm motility. Our findings offer novel insight into the genetic basis of RFI and identify candidate regions and biological pathways associated with RFI in dairy cattle.

Comprehensive analyses of 723 transcriptomes enhance genetic and biological interpretations for complex traits in cattle

“…We previously reported details of the single-marker GWAS and fine-mapping analyses for body type, reproduction, production, and health traits from 27,214 U.S. Holstein bulls (Jiang et al 2019;Freebern et al 2020 ) and for feed efficiency (i.e., RFI) from 3947 Holstein cows (Li et al 2019). Briefly, we used de-regressed breeding values (predicted transmitting abilities [PTA]) of Holstein bulls as phenotypes.…”

“…R is a diagonal matrix with R ii = 1/r 2 i − 1, where r 2 i is the reliability of phenotype for the i th individual. For RFI, we used a single-step method to conduct GWAS analysis, which was implemented in the BLUPF90 (version 2018) (Wang et al 2012;Li et al 2019).…”

Epigenomics and genotype-phenotype association analyses reveal conserved genetic architecture of complex traits in cattle and human

“…Details of the singlemarker GWAS for body type, reproduction, and production traits using 27,214 US Holstein bulls were presented previously [24]. GWAS for health traits using~10,000 bulls could be found in [76], while GWAS for feed efficiency using 3947 Holstein cows (i.e., RFI) were described by Li et al [77]. For human GWAS data, we obtained the summary statistics for 58 complex traits with an average sample size of 128,848 and an average SNP number of 5,905,874.…”

Differences in net fat oxidation, heat production, and liver mitochondrial DNA copy numbers between high and low feed-efficient dairy cows

“…A target for increasing sustainability is improving feed efficiency in dairy cows such that dairy cows produce milk at current production levels but use less feed, or conversely, produce more milk at current feed intake, while maintaining good health. Partitioning feed more effectively toward production and less toward waste is assumed to have positive economic and environmental impacts (Li et al, 2019;Guinguina et al, 2020). Improving production efficiency has been a focus in dairy cattle for many years, and previous research, as well as potential areas for improvement, have previously been summarized in review articles (Bauman et al, 1985;Drackley et al, 2006).…”

“…A negative or low RFI indicates an animal that consumes less feed than expected (i.e., more efficient) whereas a positive or high RFI indicates an animal that consumes more feed than expected (i.e., less efficient). Much of the research in RFI of ruminants has been focused on beef cattle, but research investigating RFI in dairy cattle is increasing due to its potential as a trait for genetic selection (Li et al, 2019).…”