Genomic evaluation of feed efficiency in US Holstein heifers. 2023

P Khanal, and J Johnson, and G Gouveia, and P Ross, and N Deeb
STgenetics, Navasota, TX 77868. Electronic address: piush.khanal@stgen.com.

There is growing interest in improving feed efficiency traits in dairy cattle. The objectives of this study were to estimate the genetic parameters of residual feed intake (RFI) and its component traits [dry matter intake (DMI), metabolic body weight (MBW), and average daily gain (ADG)] in Holstein heifers, and to develop a system for genomic evaluation for RFI in Holstein dairy calves. The RFI data were collected from 6,563 growing Holstein heifers (initial body weight = 261 ± 52 kg; initial age = 266 ± 42 d) for 70 d, across 182 trials conducted between 2014 and 2022 at the STgenetics Ohio Heifer Center (South Charleston, OH) as part of the EcoFeed program, which aims to improve feed efficiency by genetic selection. The RFI was estimated as the difference between a heifer's actual feed intake and expected feed intake, which was determined by regression of DMI against midpoint MBW, age, and ADG across each trial. A total of 61,283 SNPs were used in genomic analyses. Animals with phenotypes and genotypes were used as training population, and 4 groups of prediction population, each with 2,000 animals, were selected from a pool of Holstein animals with genotypes, based on their relationship with the training population. All traits were analyzed using univariate animal model in DMU version 6 software. Pedigree information and genomic information were used to specify genetic relationships to estimate the variance components and genomic estimated breeding values (GEBV), respectively. Breeding values of the prediction population were estimated by using the 2-step approach: deriving the prediction equation of GEBV from the training population for estimation of GEBV of prediction population with only genotypes. Reliability of breeding values was obtained by approximation based on partitioning a function of the accuracy of training population GEBV and magnitudes of genomic relationships between individuals in the training and prediction population. Heifers had DMI (mean ± SD) of 8.11 ± 1.59 kg over the trial period, with growth rate of 1.08 ± 0.25 kg/d. The heritability estimates (mean ± SE) of RFI, MBW, DMI, and growth rate were 0.24 ± 0.02, 0.23 ± 0.02, 0.27 ± 0.02, and 0.19 ± 0.02, respectively. The range of genomic predicted transmitted abilities (gPTA) of the training population (-0.94 to 0.75) was higher compared with the range of gPTA (-0.82 to 0.73) of different groups of prediction population. Average reliability of breeding values from the training population was 58%, and that of prediction population was 39%. The genomic prediction of RFI provides new tools to select for feed efficiency of heifers. Future research should be directed to find the relationship between RFI of heifers and cows, to select individuals based on their lifetime production efficiencies.

UI MeSH Term Description Entries
D001835 Body Weight The mass or quantity of heaviness of an individual. It is expressed by units of pounds or kilograms. Body Weights,Weight, Body,Weights, Body
D002417 Cattle Domesticated bovine animals of the genus Bos, usually kept on a farm or ranch and used for the production of meat or dairy products or for heavy labor. Beef Cow,Bos grunniens,Bos indicus,Bos indicus Cattle,Bos taurus,Cow,Cow, Domestic,Dairy Cow,Holstein Cow,Indicine Cattle,Taurine Cattle,Taurus Cattle,Yak,Zebu,Beef Cows,Bos indicus Cattles,Cattle, Bos indicus,Cattle, Indicine,Cattle, Taurine,Cattle, Taurus,Cattles, Bos indicus,Cattles, Indicine,Cattles, Taurine,Cattles, Taurus,Cow, Beef,Cow, Dairy,Cow, Holstein,Cows,Dairy Cows,Domestic Cow,Domestic Cows,Indicine Cattles,Taurine Cattles,Taurus Cattles,Yaks,Zebus
D004435 Eating The consumption of edible substances. Dietary Intake,Feed Intake,Food Intake,Macronutrient Intake,Micronutrient Intake,Nutrient Intake,Nutritional Intake,Ingestion,Dietary Intakes,Feed Intakes,Intake, Dietary,Intake, Feed,Intake, Food,Intake, Macronutrient,Intake, Micronutrient,Intake, Nutrient,Intake, Nutritional,Macronutrient Intakes,Micronutrient Intakes,Nutrient Intakes,Nutritional Intakes
D005260 Female Females
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000818 Animals Unicellular or multicellular, heterotrophic organisms, that have sensation and the power of voluntary movement. Under the older five kingdom paradigm, Animalia was one of the kingdoms. Under the modern three domain model, Animalia represents one of the many groups in the domain EUKARYOTA. Animal,Metazoa,Animalia
D000821 Animal Feed Foodstuff used especially for domestic and laboratory animals, or livestock. Fodder,Animal Feeds,Feed, Animal,Feeds, Animal,Fodders
D015203 Reproducibility of Results The statistical reproducibility of measurements (often in a clinical context), including the testing of instrumentation or techniques to obtain reproducible results. The concept includes reproducibility of physiological measurements, which may be used to develop rules to assess probability or prognosis, or response to a stimulus; reproducibility of occurrence of a condition; and reproducibility of experimental results. Reliability and Validity,Reliability of Result,Reproducibility Of Result,Reproducibility of Finding,Validity of Result,Validity of Results,Face Validity,Reliability (Epidemiology),Reliability of Results,Reproducibility of Findings,Test-Retest Reliability,Validity (Epidemiology),Finding Reproducibilities,Finding Reproducibility,Of Result, Reproducibility,Of Results, Reproducibility,Reliabilities, Test-Retest,Reliability, Test-Retest,Result Reliabilities,Result Reliability,Result Validities,Result Validity,Result, Reproducibility Of,Results, Reproducibility Of,Test Retest Reliability,Validity and Reliability,Validity, Face
D016678 Genome The genetic complement of an organism, including all of its GENES, as represented in its DNA, or in some cases, its RNA. Genomes
D023281 Genomics The systematic study of the complete DNA sequences (GENOME) of organisms. Included is construction of complete genetic, physical, and transcript maps, and the analysis of this structural genomic information on a global scale such as in GENOME WIDE ASSOCIATION STUDIES. Functional Genomics,Structural Genomics,Comparative Genomics,Genomics, Comparative,Genomics, Functional,Genomics, Structural

Related Publications

P Khanal, and J Johnson, and G Gouveia, and P Ross, and N Deeb
March 2020, Journal of dairy science,
P Khanal, and J Johnson, and G Gouveia, and P Ross, and N Deeb
January 1999, Journal of dairy science,
P Khanal, and J Johnson, and G Gouveia, and P Ross, and N Deeb
September 1997, Journal of dairy science,
P Khanal, and J Johnson, and G Gouveia, and P Ross, and N Deeb
January 2003, Journal of dairy science,
P Khanal, and J Johnson, and G Gouveia, and P Ross, and N Deeb
March 2022, Journal of dairy science,
P Khanal, and J Johnson, and G Gouveia, and P Ross, and N Deeb
April 2024, Journal of dairy science,
P Khanal, and J Johnson, and G Gouveia, and P Ross, and N Deeb
May 1986, Journal of dairy science,
P Khanal, and J Johnson, and G Gouveia, and P Ross, and N Deeb
March 2023, Animals : an open access journal from MDPI,
P Khanal, and J Johnson, and G Gouveia, and P Ross, and N Deeb
May 1997, Journal of dairy science,
P Khanal, and J Johnson, and G Gouveia, and P Ross, and N Deeb
May 2023, Animals : an open access journal from MDPI,
Copied contents to your clipboard!