Genome-wide association study for carcass weight in pasture-finished beef cattle in Hawai'i

Mandeep Adhikari; Michael B Kantar; Ryan J Longman; C N Lee; Melelani Oshiro; Kyle Caires; Yanghua He

doi:10.3389/fgene.2023.1168150

Genome-wide association study for carcass weight in pasture-finished beef cattle in Hawai'i

Front Genet. 2023 May 9:14:1168150. doi: 10.3389/fgene.2023.1168150. eCollection 2023.

Authors

Mandeep Adhikari¹, Michael B Kantar², Ryan J Longman^{3

4}, C N Lee⁵, Melelani Oshiro⁵, Kyle Caires⁵, Yanghua He^{1

5}

Affiliations

¹ Department of Molecular Biosciences and Bioengineering, University of Hawai'i at Mānoa, Honolulu, HI, United States.
² Department of Tropical Plant and Soil Sciences, University of Hawai'i at Mānoa, Honolulu, HI, United States.
³ East West Center, Honolulu, HI, United States.
⁴ Department of Geography and Environment, University of Hawai'i at Mānoa, Honolulu, HI, United States.
⁵ Department of Human Nutrition, Food, and Animal Sciences, University of Hawai'i at Mānoa, Honolulu, HI, United States.

Abstract

Introduction: Genome-wide association studies (GWAS) have identified genetic markers for cattle production and reproduction traits. Several publications have reported Single Nucleotide Polymorphisms (SNPs) for carcass-related traits in cattle, but these studies were rarely conducted in pasture-finished beef cattle. Hawai'i, however, has a diverse climate, and 100% of its beef cattle are pasture-fed. Methods: Blood samples were collected from 400 cattle raised in Hawai'i islands at the commercial harvest facility. Genomic DNA was isolated, and 352 high-quality samples were genotyped using the Neogen GGP Bovine 100 K BeadChip. SNPs that did not meet the quality control standards were removed using PLINK 1.9, and 85 k high-quality SNPs from 351 cattle were used for association mapping with carcass weight using GAPIT (Version 3.0) in R 4.2. Four models were used for the GWAS analysis: General Linear Model (GLM), the Mixed Linear Model (MLM), the Fixed and Random Model Circulating Probability Unification (FarmCPU), the Bayesian-Information and Linkage-Disequilibrium Iteratively Nested Keyway (BLINK). Results and Discussion: Our results indicated that the two multi-locus models, FarmCPU and BLINK, outperformed single-locus models, GLM and MLM, in beef herds in this study. Specifically, five significant SNPs were identified using FarmCPU, while BLINK and GLM each identified the other three. Also, three of these eleven SNPs ("BTA-40510-no-rs", "BovineHD1400006853", and "BovineHD2100020346") were shared by multiple models. The significant SNPs were mapped to genes such as EIF5, RGS20, TCEA1, LYPLA1, and MRPL15, which were previously reported to be associated with carcass-related traits, growth, and feed intake in several tropical cattle breeds. This confirms that the genes identified in this study could be candidate genes for carcass weight in pasture-fed beef cattle and can be selected for further breeding programs to improve the carcass yield and productivity of pasture-finished beef cattle in Hawai'i and beyond.

Keywords: GWAS; Hawai’i; SNPs; carcass weight; pasture-finished beef.

Grants and funding

This work was supported by the National Institute of Food and Agriculture (NIFA) through the Hatch research agencies under the United States Department of Agriculture (USDA) (HAW02062-H) and administered by the College of Tropical Agriculture and Human Resources (CTAHR) at the University of Hawai’i at Mānoa. The authors would like to extend their gratitude to Hawai’i Meats LLC (Kapolei, Oahu, Hawai’i) and its staff, particularly Kamuela Barr and Christopher Cravalho, for their invaluable contribution to this study during the period of July-December 2021. Their support in providing essential samples was crucial for the successful completion of the research, and we appreciate their cooperation and dedication to the project. The technical support and advanced computing resources from the University of Hawaii Information Technology Services – Cyberinfrastructure, funded in part by the National Science Foundation CC* awards # 2201428 and # 2232862 are gratefully acknowledged.