Development and evaluation of a low-density single-nucleotide polymorphism chip specific to Bos indicus cattle
J. B. S. Ferraz
A F , X. -L. Wu B C , H. Li B C , J. Xu B D , R. Ferretti B , B. Simpson B , J. Walker B , L. R. Silva B , J. F. Garcia E , R. G. Tait A Department of Veterinary Medicine, College of Animal Science and Food Engineering, University of São Paulo, Pirassununga, Rua Duque de Caxias Norte, 225, 13635-970, Pirassununga, SP, Brazil.
B Biostatistics and Bioinformatics, Neogen GeneSeek, Lincoln, NE 68504, USA.
C Department of Animal Sciences, University of Wisconsin, Madison, WI 53706, USA.
D Department of Statistics, University of Nebraska, Lincoln, NE 68583, USA.
E Department of Animal Production and Health, College of Veterinary Medicine, UNESP, 16050-680, Araçatuba, SP, Brazil.
F Corresponding author. Email: jbferraz@usp.br
Animal Production Science 60(15) 1769-1776 https://doi.org/10.1071/AN19396
Submitted: 2 August 2019 Accepted: 19 March 2020 Published: 16 June 2020
Abstract
Context: Genomic selection has been of increasing interest in the genetic improvement of Zebu cattle, particularly for quantitative traits that are difficult or expensive to measure, such as carcass traits and meat tenderness. The success of genomic selection depends on several factors, and at its core is the availability of single-nucleotide polymorphism (SNP) chips that are appropriately designed for Bos indicus cattle. However, the currently available commercial bovine SNP chips are mostly designed for Bos taurus cattle. There are two commercial Bos indicus SNP chips; namely, GeneSeek genomic profiler high-density Bos indicus (GGP-HDi) SNP chip and a low-density (LD) Bos indicus SNP chip (Z chip), but these two Bos indicus SNP chips were built with mixed contents of SNPs for Bos indicus and Bos taurus cattle, due to limited availability of genotype data from Bos indicus cattle.
Aims: To develop a new GGP indicus 35 000 SNP chip specifically for Bos indicus cattle, which has a low cost, but high accuracy of imputation to Illumina BovineHD chips.
Methods: The design of the chip consisted of 34 000 optimally selected SNPs, plus 1000 SNPs pre-reserved for those on the Y chromosome, ‘causative’ mutations for a variety of economically relevant traits, genetic health conditions and International Society for Animal Genetics globally recognised parentage markers for those breeds of cattle.
Key results: The present results showed that this new indicus LD SNP chip had considerably increased minor allele frequencies in indicus breeds than the previous Z-chip. It demonstrated with high imputation accuracy to HD SNP genotypes in five indicus breeds, and with considerable predictability on 14 growth and reproduction traits in Nellore cattle.
Conclusions: This new indicus LD chip represented a successful effort to leverage existing knowledge and genotype resources towards the public release of a cost-effective LD SNP chip specifically for Bos indicus cattle, which is expected to replace the previous GGP indicus LD chip and to supplement the existing GGP-HDi 80 000 SNP chip.
Implications: A new SNP chip specifically designed for Bos indicus, with high power of imputation to Illumina BovineHD technology and with excellent coverage of the whole genome, is now available on the market for Bos indicus cattle, and Bos indicus and Bos taurus crosses.
Additional keywords: imputation accuracy, genomic prediction, growth, reproduction, single-nucleotide polymorphism panel, Zebu cattle.
References
Boison SA, Santos DJ, Utsunomiya AH, Carvalheiro R, Neves HH, O’Brien AM, Garcia JF, Sölkner J, da Silva MV (2015) Strategies for single nucleotide polymorphism (SNP) genotyping to enhance genotype imputation in Gyr (Bos indicus) dairy cattle: comparison of commercially available SNP chips. Journal of Dairy Science 98, 4969–4989.| Strategies for single nucleotide polymorphism (SNP) genotyping to enhance genotype imputation in Gyr (Bos indicus) dairy cattle: comparison of commercially available SNP chips.Crossref | GoogleScholarGoogle Scholar | 25958293PubMed |
Boison SA, Utsunomiya ATH, Santos DJA, Neves HHR, Carvalheiro R, Mészáros G, Utsunomiya YT, do Carmo AS, Verneque RS, Machado MA, Panetto JCC, Garcia JF, Sölkner J, da Silva MVGB (2017) Accuracy of genomic predictions in Gyr (Bos indicus) dairy cattle. Journal of Dairy Science 100, 5479–5490.
| Accuracy of genomic predictions in Gyr (Bos indicus) dairy cattle.Crossref | GoogleScholarGoogle Scholar | 28527809PubMed |
Carvalheiro R, Boison SA, Neves HH, Sargolzaei M, Schenkel FS, Utsunomiya YT, O’Brien AM, Sölkner J, McEwan JC, Van Tassell CP, Sonstegard TS, Garcia JF (2014) Accuracy of genotype imputation in Nellore cattle. Genetics, Selection, Evolution. 46, 69 11p
| Accuracy of genotype imputation in Nellore cattle.Crossref | GoogleScholarGoogle Scholar | 24575732PubMed |
Cundiff LV, Thallman RM, Kuehn LA (2012) Impact of Bos indicus genetics on the global beef industry. The American Brahman Review 3, 49–52.
Fernandes Jr GA, Rosa GJM, Valente BD, Carvalheiro R, Baldi F, Garcia DA, Gordo DGM, Espigolan R, Takada L, Tonussi RL, de Andrade WBF, Magalhães AFB, Chardulo LAL, Tonhati H, de Albuquerque LG (2016) Genomic prediction of breeding values for carcass traits in Nellore cattle. Genetics, Selection, Evolution 48, 7
| Genomic prediction of breeding values for carcass traits in Nellore cattle.Crossref | GoogleScholarGoogle Scholar |
Ferraz JBS, Wu XL, Li H, Xu J, Ferreti R, Simpson B, Walker J, Silva LR, Garcia JF, Tait JRJ, Bauck S (2018) Design of a low-density SNP chip for Bos indicus: GGP indicus technical characterization and imputation accuracy to higher density SNP genotypes. In ‘Proceedings of the World Congress on Genetics Applied to Livestock Production vol. Technologies - Genotyping’, Feb 7–11, 2018, Auckland, New Zealand. pp. 174–179 (WCGALP: Auckland, New Zealand)
Gianola D (2013) Priors in whole-genome regression: the bayesian alphabet returns. Genetics 194, 573–596.
| Priors in whole-genome regression: the bayesian alphabet returns.Crossref | GoogleScholarGoogle Scholar | 23636739PubMed |
Hoerl AE, Kennard R (1970) Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12, 55–67.
| Ridge regression: biased estimation for nonorthogonal problems.Crossref | GoogleScholarGoogle Scholar |
ISAG (2016) Cattle molecular markers and parentage testing workshop. ISAG Conference 2016, Salt Lake City. Available at http://www.isag.us/committees.asp?autotry=true&ULnotkn=true [Verified 2 June 2020]
Magnabosco CU, Lopes FB, Fragoso RC, Eifert EC, Valente BD, Rosa GJ, Sainz RD (2016) Accuracy of genomic breeding values for meat tenderness in Polled Nellore cattle. Journal of Animal Science 94, 2752–2760.
| Accuracy of genomic breeding values for meat tenderness in Polled Nellore cattle.Crossref | GoogleScholarGoogle Scholar | 27482662PubMed |
Matukumalli LK, Lawley CT, Schnabel RD, Taylor JF, Allan MF, Heaton MP, O’Connell J, Moore SS, Smith TP, Sonstegard TS, Van Tassell CP (2009) Development and characterization of a high density SNP genotyping assay for cattle. PLoS One 4, e5350
| Development and characterization of a high density SNP genotyping assay for cattle.Crossref | GoogleScholarGoogle Scholar | 19390634PubMed |
Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829.
Montaldo HH, Casas E, Ferraz JBS, Vega-Murillo VE, Román-Ponce SI (2012) Opportunities and challenges from the use of genomic selection for beef cattle breeding in Latin America. Animal Frontiers 2, 23–29.
| Opportunities and challenges from the use of genomic selection for beef cattle breeding in Latin America.Crossref | GoogleScholarGoogle Scholar |
Neves HH, Carvalheiro R, Brien AMO, Utsunomiya YT, do Carmo AS, Schenkel FS, Sölkner J, McEwan JC, Van Tassell CP, Cole JB, da Silva MV, Queiroz SA, Sonstegard TS, Garcia JF (2014) Accuracy of genomic predictions in Bos indicus (Nellore) cattle. Genetics, Selection, Evolution 46, 17
| Accuracy of genomic predictions in Bos indicus (Nellore) cattle.Crossref | GoogleScholarGoogle Scholar | 24575732PubMed |
Santana ML, Eler JP, Bignardi AB, Ferraz JBS (2013) Genetic associations among average annual productivity, growth traits and stayability: a parallel between Nelore and composite beef cattle. Journal of Animal Science 91, 2566–2574.
| Genetic associations among average annual productivity, growth traits and stayability: a parallel between Nelore and composite beef cattle.Crossref | GoogleScholarGoogle Scholar | 23482586PubMed |
Santana MHA, Utsunomiya YT, Neves HH, Gomes RC, Garcia JF, Fukumasu H, Silva SL, Leme PR, Coutinho LL, Eler JP, Ferraz JB (2014) Genome-wide association study for feedlot average daily gain in Nellore cattle (Bos indicus). Journal of Animal Breeding and Genetics 131, 210–216.
| Genome-wide association study for feedlot average daily gain in Nellore cattle (Bos indicus).Crossref | GoogleScholarGoogle Scholar |
Sargolzaei M, Chesnais JP, Schenkel FS (2014) A new approach for efficient genotype imputation using information from relatives. BMC Genomics 15, 478
| A new approach for efficient genotype imputation using information from relatives.Crossref | GoogleScholarGoogle Scholar | 24935670PubMed |
Uemoto Y, Sasaki S, Kojima T, Sugimoto Y, Watanabe T (2015) Impact of QTL minor allele frequency on genomic evaluation using real genotype data and simulated phenotypes in Japanese Black cattle. BMC Genetics 16, 134
| Impact of QTL minor allele frequency on genomic evaluation using real genotype data and simulated phenotypes in Japanese Black cattle.Crossref | GoogleScholarGoogle Scholar | 26586567PubMed |
Wu X-L, Sun C, Beissinger TM, Rosa GJ, Weigel KA, Gatti Nde L, Gianola D (2012) Parallel Markov chain Monte Carlo: bridging the gap to high-performance Bayesian computation in animal breeding and genetics. Genetics, Selection, Evolution 44, 29
| Parallel Markov chain Monte Carlo: bridging the gap to high-performance Bayesian computation in animal breeding and genetics.Crossref | GoogleScholarGoogle Scholar | 23009363PubMed |
Wu X-L, Xu J, Feng G, Wiggans GR, Taylor JF, He J, Qian C, Qiu J, Simpson B, Walker J, Baucket S (2016) Optimal design of low-density SNP arrays for genomic prediction: algorithm and applications. PLoS One 11, e0161719
| Optimal design of low-density SNP arrays for genomic prediction: algorithm and applications.Crossref | GoogleScholarGoogle Scholar | 27711128PubMed |
