TY - JOUR
T1 - Effect of quality control, density and allele frequency of markers on the accuracy of genomic prediction for complex traits in Nellore cattle
AU - Bresolin, Tiago
AU - Rosa, Guilherme Jordão De Magalhães
AU - Valente, Bruno Dourado
AU - Espigolan, Rafael
AU - Gordo, Daniel Gustavo Mansan
AU - Braz, Camila Urbano
AU - Fernandes, Gerardo Alves
AU - Magalhães, Ana Fabrícia Braga
AU - Garcia, Diogo Anastacio
AU - Frezarim, Gabriela Bonfá
AU - Leão, Guilherme Fonseca Carneiro
AU - Carvalheiro, Roberto
AU - Baldi, Fernando
AU - Nunes De Oliveira, Henrique
AU - De Albuquerque, Lucia Galvão
N1 - Funding Information:
The authors thank Sao Paulo Research Foundation - FAPESP (grants #2009/161188-5 and #2013/26264-4) for financial support and the breeding programs, DeltaGen and CRV Pait, for supplying part of the data
Funding Information:
The authors thank São Paulo Research Foundation – FAPESP (grants #2009/ 161188–5 and #2013/26264–4) for financial support and the breeding programs, DeltaGen and CRV Pait, for supplying part of the data.
Publisher Copyright:
© 2019 CSIRO.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Accuracy of prediction
KW - Beef cattle
KW - Marker density
KW - Marker editing
KW - Marker effects
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U2 - 10.1071/AN16821
DO - 10.1071/AN16821
M3 - Article
AN - SCOPUS:85049235553
SN - 1836-0939
VL - 59
SP - 48
EP - 54
JO - Animal Production Science
JF - Animal Production Science
IS - 1
ER -