Animal Production Science Animal Production Science Society
Food, fibre and pharmaceuticals from animals
RESEARCH ARTICLE

Effect of quality control, density and allele frequency of markers on the accuracy of genomic prediction for complex traits in Nellore cattle

Tiago Bresolin A , Guilherme Jordão de Magalhães Rosa B , Bruno Dourado Valente B , Rafael Espigolan A , Daniel Gustavo Mansan Gordo A , Camila Urbano Braz A , Gerardo Alves Fernandes Júnior A , Ana Fabrícia Braga Magalhães A , Diogo Anastacio Garcia A , Gabriela Bonfá Frezarim A , Guilherme Fonseca Carneiro Leão A , Roberto Carvalheiro A C , Fernando Baldi A C , Henrique Nunes de Oliveira A C and Lucia Galvão de Albuquerque A C D
+ Author Affiliations
- Author Affiliations

A Departamento de Zootecnia, Universidade Estadual Paulista (Unesp), Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal, Via de acesso Prof. Paulo Donato Castellane, s/n, Jaboticabal, SP 14884-900, Brazil.

B Department of Animal Sciences, University of Wisconsin, 436 Animal Science Building, 1675 Observatory Drive, Madison, WI 53706, USA.

C National Counsel of Technological and Scientific Development, CNPq, SHIS QI 1 Conjunto B – Blocos A, B, C e D, CEP 71605-001, Lago Sul, Brasília, DF, Brazil.

D Corresponding author. Email: lgalb@fcav.unesp.br

Animal Production Science - https://doi.org/10.1071/AN16821
Submitted: 17 December 2016  Accepted: 11 September 2017   Published online: 1 December 2017

Abstract

This study was designed to test the impact of quality control, density and allele frequency of single nucleotide polymorphisms (SNP) markers on the accuracy of genomic predictions, using three traits with different heritabilities and two methods of prediction in a Nellore cattle population genotyped with the Illumina Bovine HD Assay. A total of 1756; 3150 and 3119 records of age at first calving (AFC); weaning weight (WW) and yearling weight (YW), respectively, were used. Three scenarios with different exclusion thresholds for minor allele frequency (MAF), deviation from Hardy–Weinberg equilibrium (HWE) and correlation between SNP pairs (r2) were constructed for all traits: (1) high rigor (S1): call rate <0.98, MAF <0.05, HWE with P <10−5, and r2 >0.999; (2) Moderate rigor (S2): call rate <0.85 and MAF <0.01; (3) Low rigor (S3): only non-autosomal SNP and those mapped on the same position were excluded. Additionally, to assess the prediction accuracy from different markers density, six panels (10K, 50K, 100K, 300K, 500K and 700K) were customised using the high-density genotyping assay as reference. Finally, from the markers available in high-density genotyping assay, six groups (G) with different minor allele frequency bins were defined to estimate the accuracy of genomic prediction. The range of MAF bins was approximately equal for the traits studied: G1 (0.000–0.009), G2 (0.010–0.064), G3 (0.065–0.174), G4 (0.175–0.325), G5 (0.326–0.500) and G6 (0.000–0.500). The Genomic Best Linear Unbiased Predictor and BayesCπ methods were used to estimate the SNP marker effects. Five-fold cross-validation was used to measure the accuracy of genomic prediction for all scenarios. There were no effects of genotypes quality control criteria on the accuracies of genomic predictions. For all traits, the higher density panel did not provide greater prediction accuracies than the low density one (10K panel). The groups of SNP with low MAF (MAF ≤0.007 for AFC, MAF ≤0.009 for WW and MAF ≤0.008 for YW) provided lower prediction accuracies than the groups with higher allele frequencies.

Additional keywords: accuracy of prediction, beef cattle, marker editing, marker density, marker effects.


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