Comparison of Methods to Select Candidates for High-Density Genotyping; Practical Observations in a Cattle Breeding Program

Rudi A. McEwin, Michelle L. Hebart, Helena Oakey, Rick Tearle, Joe Grose, Greg Popplewell and Wayne S. Pitchford
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Rudi A. McEwin: Davies Livestock Research Centre, School of Animal and Veterinary Sciences, University of Adelaide, Roseworthy, SA 5371, Australia
Michelle L. Hebart: Davies Livestock Research Centre, School of Animal and Veterinary Sciences, University of Adelaide, Roseworthy, SA 5371, Australia
Helena Oakey: Robinson Research Institute, Adelaide Medical School, University of Adelaide, North Adelaide, SA 5006, Australia
Rick Tearle: Davies Livestock Research Centre, School of Animal and Veterinary Sciences, University of Adelaide, Roseworthy, SA 5371, Australia
Joe Grose: 3D Genetics Pty LTD, 939 Pukawidgi Rd, Bukkulla, NSW 2360, Australia
Greg Popplewell: Popplewell Genetics, 33 Tom Schmidt Court, Mount Samson, QLD 4520, Australia
Wayne S. Pitchford: Davies Livestock Research Centre, School of Animal and Veterinary Sciences, University of Adelaide, Roseworthy, SA 5371, Australia

Agriculture, 2022, vol. 12, issue 2, 1-10

Abstract: Imputation can be used to obtain a large number of high-density genotypes at the cost of procuring low-density panels. Accurate imputation requires a well-formed reference population of high-density genotypes to enable statistical inference. Five methods were compared using commercial Wagyu genotype data to identify individuals to produce a “well-formed” reference population. Two methods utilised a relationship matrix (MCG and MCA), two of which utilised a haplotype block library (AHAP2 and IWS), and the last selected high influential sires with greater than 10 progeny (PROG). The efficacy of the methods was assessed based on the total proportion of genetic variance accounted for and the number of haplotypes captured, as well as practical considerations in implementing these methods. Concordance was high between the MCG and MCA and between AHAP2 and IWS but was low between these groupings. PROG-selected animals were most similar to MCA. MCG accounted for the greatest proportion of genetic variance in the population (35%, while the other methods accounted for approximately 30%) and the greatest number of unique haplotypes when a frequency threshold was applied. MCG was also relatively simple to implement, although modifications need to be made to account for DNA availability when running over a whole population. Of the methods compared, MCG is the recommended starting point for an ongoing sequencing project.

Keywords: high density genotyping; imputation; sequencing; reference population (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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