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
+ 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 59(1) 48-54 https://doi.org/10.1071/AN16821
Submitted: 17 December 2016 Accepted: 11 September 2017 Published: 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.
References
Abdollahi-Arpanahi R, Nejati-Javaremi A, Pakdel A, Moradi-Shahrbabak M, Morota G, Valente B, Kranis A, Rosa G, Gianola D (2014a) Effect of allele frequencies, effect sizes and number of markers on prediction of quantitative traits in chickens. Journal of Animal Breeding and Genetics 131, 123–133.| Effect of allele frequencies, effect sizes and number of markers on prediction of quantitative traits in chickens.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC2cXktlChtLw%3D&md5=f7d666ad89695a20660a2770966034e0CAS |
Abdollahi-Arpanahi R, Pakdel A, Nejati-Javaremi A, Moradi-Shahrbabak M, Morota G, Valente B, Kranis A, Rosa G, Gianola D (2014b) Dissection of additive genetic variability for quantitative traits in chickens using SNP markers. Journal of Animal Breeding and Genetics 131, 183–193.
| Dissection of additive genetic variability for quantitative traits in chickens using SNP markers.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC2cXot1yhsLo%3D&md5=f5c2f98e3096e53b729752bb8f790188CAS |
Chen L, Li C, Sargolzaei M, Schenkel F (2014) Impact of genotype imputation on the performance of GBLUP and Bayesian methods for genomic prediction. PLoS One 9, 1–7.
Costa RB, Camargo GMF, Diaz IDPS, Irano N, Dias MM, Carvalheiro R, Boligon AA, Baldi F, Oliveira HN, Tonhati H, Albuquerque LG (2015) Genome-wide association study of reproductive traits in Nellore heifers using Bayesian inference. Genetics, Selection, Evolution. 47, 1–9.
Daetwyler HD, Calus MPL, Pong-Wong R, de los Campos G, Hickey JM (2013) Genomic prediction in animals and plants: Simulation of data, validation, reporting, and benchmarking. Genetics 193, 347–365.
| Genomic prediction in animals and plants: Simulation of data, validation, reporting, and benchmarking.Crossref | GoogleScholarGoogle Scholar |
de los Campos G, Hickey JM, Pong-Wong R, Daetwyler HD, Calus MPL (2013) Whole-genome regression and prediction methods applied to plant and animal breeding. Genetics 193, 327–345.
| Whole-genome regression and prediction methods applied to plant and animal breeding.Crossref | GoogleScholarGoogle Scholar |
Edriss V, Guldbrandtsen B, Lund MS, Su G (2013) Effect of marker-data editing on the accuracy of genomic prediction. Journal of Animal Breeding and Genetics 130, 128–135.
| Effect of marker-data editing on the accuracy of genomic prediction.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3sXnslWrtL4%3D&md5=39621ade5d17e66672b0bf3c3abc82e0CAS |
Espigolan R, Baldi F, Boligon AA, Souza FRP, Gordo DGM, Tonussi RL, Cardoso DF, Oliveira HN, Tonhati H, Sargolzaei M, Schenkel FS, Carvalheiro R, Ferro JA, Albuquerque LG (2013) Study of whole genome linkage disequilibrium in Nellore cattle. BMC Genomics 14, 305
| Study of whole genome linkage disequilibrium in Nellore cattle.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3sXptlyhsr4%3D&md5=843c0f000f1d94a8f1b7d347e2115c2fCAS |
Habier D, Tetens J, Seefried FR, Lichtner P, Thaller G (2010) The impact of genetic relationship information on genomic breeding values in German Holstein cattle. Genetics, Selection, Evolution 42, 5
| The impact of genetic relationship information on genomic breeding values in German Holstein cattle.Crossref | GoogleScholarGoogle Scholar |
Habier D, Fernando RL, Kizilkaya K, Garrick DJ (2011) Extension of the bayesian alphabet for genomic selection. BMC Bioinformatics 12, 186
| Extension of the bayesian alphabet for genomic selection.Crossref | GoogleScholarGoogle Scholar |
Habier D, Fernando RL, Garrick DJ (2013) Genomic BLUP decoded: a look into the black box of genomic prediction. Genomic Selection 194, 597–607.
Harris BL, Johnson DL (2010) Genomic predictions for New Zealand dairy bulls and integration with national genetic evaluation. Journal of Dairy Science 93, 1243–1252.
| Genomic predictions for New Zealand dairy bulls and integration with national genetic evaluation.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXitlOis78%3D&md5=d72c8aad3d6a45b3dbd5ed6fbf6e4438CAS |
Hayes BJ, Bowman PJ, Chamberlain AJ, Goddard ME (2009) Genomic selection in dairy cattle: Progress and challenges. Journal of Dairy Science 92, 433–443.
| Genomic selection in dairy cattle: Progress and challenges.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXit1Kju7s%3D&md5=e00c3a479b7580a9ff30e7afa08daa46CAS |
Legarra A, Misztal I (2008) Technical note: computing strategies in genome-wide selection. Journal of Dairy Science 91, 360–366.
| Technical note: computing strategies in genome-wide selection.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXhsVGitA%3D%3D&md5=3fa6a92472b71b8154d1296a394517a4CAS |
Legarra A, Ricard A, Filangi O (2011) GS3: genomic selection, gibbs sampling, Gauss-Seidel (and BayesCpi). Available at http://snp.toulouse.inra.fr/~alegarra/ [Verified 6 January 2015]
Lettre G (2011) Recent progress in the study of the genetics of height. Human Genetics 129, 465–472.
| Recent progress in the study of the genetics of height.Crossref | GoogleScholarGoogle Scholar |
Lu D, Akanno EC, Crowley JJ, Schenkel F, Li H, De Pauw M, Moore SS, Wang Z, Li C, Stothard P, Plastow G, Miller SP, Basarab JA (2016) Accuracy of genomic predictions for feed efficiency traits of beef cattle using 50K and imputed HD genotypes. Journal of Animal Science 94, 1342–1353.
| Accuracy of genomic predictions for feed efficiency traits of beef cattle using 50K and imputed HD genotypes.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC28XhsVGnurzL&md5=b1c35661a5a691021ba6f11d5481450bCAS |
Makowsky R, Pajewski NM, Klimentidis YC, Vazquez AI, Duarte CW, Allison DB, de Los Campos G (2011) Beyond missing heritability: Prediction of complex traits. PLOS Genetics 7, e1002051
| Beyond missing heritability: Prediction of complex traits.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXlsl2gu74%3D&md5=4be08879797a1d9edbf4bf33215d7430CAS |
Meuwissen T, Goddard M (2010) Accurate prediction of genetic values for complex traits by whole-genome resequencing. Genetics 185, 623–631.
Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829.
Moser G, Khatkar MS, Hayes BJ, Raadsma HW (2010) Accuracy of direct genomic values in Holstein bulls and cows using subsets of SNP markers. Genetics, Selection, Evolution. 42, 37
| Accuracy of direct genomic values in Holstein bulls and cows using subsets of SNP markers.Crossref | GoogleScholarGoogle Scholar |
Neves HHR, Carvalheiro R, O’brien AMP, Utsunomiya YT, Carmo AS, Schenkel FS, Sölkner J, Mcewan JC, Tassell CPV, Cole JB, Silva MVGB, 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 |
Ni G, Cavero D, Fangmann A, Erbe M, Simianer H (2017) Whole-genome sequence based genomic prediction in laying chickens with different genomic relationship matrices to account for genetic architecture. Genetics, Selection, Evolution. 49, 8
| Whole-genome sequence based genomic prediction in laying chickens with different genomic relationship matrices to account for genetic architecture.Crossref | GoogleScholarGoogle Scholar |
Park JH, Gail MH, Weinberg CR, Carroll RJ, Chung CC, Wang Z, Chanock SJ, Fraumeni JF, Chatterjee N (2011) Distribution of allele frequencies and effect sizes and their interrelationships for common genetic susceptibility variants. Proceedings of the National Academy of Sciences of the United States of America 108, 18026–18031.
| Distribution of allele frequencies and effect sizes and their interrelationships for common genetic susceptibility variants.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXhsVOktLbM&md5=14ee9f28909e13df566963de1a0583f9CAS |
Pryce JE, Arias J, Bowman PJ, Davis SR, Macdonald KA, Waghorn GC, Wales WJ, Willians YJ, Spelman RJ, Hayes BJ (2012) Accuracy of genomic predictions of residual feed intake and 250-day body weight in growing heifers using 625000 single nucleotide polymorphism markers. Journal of Dairy Science 95, 2108–2119.
| Accuracy of genomic predictions of residual feed intake and 250-day body weight in growing heifers using 625000 single nucleotide polymorphism markers.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38XkslejtLg%3D&md5=90daedb445719a2e2441d8701bf139ecCAS |
Pszczola M, Strabel T, Mulder HA, Calus MPL (2012) Reliability of direct genomic values for animals with different relationships within and to the reference population. Journal of Dairy Science 95, 389–400.
| Reliability of direct genomic values for animals with different relationships within and to the reference population.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXhs1OlsrnE&md5=f2f7ee94aecc97b57a0e81d0357c1830CAS |
R Core Team (2015) R: a language and environment for statistical computing. Vienna, Austria, Available at http://www.R-project.org/ [Verified 12 June 2015]
Shifman S, Kuypers J, Kokoris M, Yakir B, Darvasi A (2003) Linkage disequilibrium patterns of the human genome across populations. Human Molecular Genetics 12, 771–776.
| Linkage disequilibrium patterns of the human genome across populations.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD3sXjt12qs7c%3D&md5=2da360cdf400067588e84f2141b40216CAS |
Su G, Brøndum RF, Ma P, Guldbrandtsen B, Aamand GP, Lund MS (2012) Comparison of genomic predictions using medium-density (~54000) and high-density (~777000) single nucleotide polymorphism marker panels in Nordic Holstein and Red Dairy Cattle populations. Journal of Dairy Science 95, 4657–4665.
| Comparison of genomic predictions using medium-density (~54000) and high-density (~777000) single nucleotide polymorphism marker panels in Nordic Holstein and Red Dairy Cattle populations.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38XhtVOls7%2FM&md5=a839bdd5aebaf67e91b363ac94c9a691CAS |
Toosi A, Fernando R, Dekkers J (2010) Genomic selection in admixed and crossbred populations. Journal of Animal Science 88, 32–46.
| Genomic selection in admixed and crossbred populations.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXls1ylug%3D%3D&md5=b3aac455a05fba20760d18e16f06cd63CAS |
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 |
van Binsbergen R, Calus MPL, Bink MCAM, Van Eeuwijk FA, Schrooten C, Veerkamp RF (2015) Genomic prediction using imputed whole-genome sequence data in Holstein Friesian cattle. Genetics, Selection, Evolution. 47, 71
| Genomic prediction using imputed whole-genome sequence data in Holstein Friesian cattle.Crossref | GoogleScholarGoogle Scholar |
VanRaden PM (2008) Efficient methods to compute genomic predictions. Journal of Dairy Science 91, 4414–4423.
| Efficient methods to compute genomic predictions.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1cXhtlajtLzO&md5=b65a086feb49280dd1dce5d84df452deCAS |
VanRaden PM, O’Connell JR, Wiggans GR, Weigel KA (2011) Genomic evaluations with many more genotypes. Genetics, Selection, Evolution. 43, 10
| Genomic evaluations with many more genotypes.Crossref | GoogleScholarGoogle Scholar |
Vazquez AI, Rosa GJM, Weigel KA, de Los Campos G, Gianola D, Allison DB (2010) Predictive ability of subsets of single nucleotide polymorphisms with and without parent average in US Holsteins. Journal of Dairy Science 93, 5942–5949.
| Predictive ability of subsets of single nucleotide polymorphisms with and without parent average in US Holsteins.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC2MXotVWjsr8%3D&md5=86c30fdc5a69a50e617ad3a3cb08cc0bCAS |
Villa-Angulo R, Matukumalli L, Gill C, Choi J, Van Tassell C, Grefenstette J (2009) High-resolution haplotype block structure in the cattle genome. BMC Genetics 10, 19–31.
| High-resolution haplotype block structure in the cattle genome.Crossref | GoogleScholarGoogle Scholar |
Weigel KA, de Los Campos G, González-Recio O, Naya H, Wu XL, Long N, Rosa GJM, Gianola D (2009) Predictive ability of direct genomic values for lifetime net merit of Holstein. Journal of Dairy Science 92, 5248–5257.
| Predictive ability of direct genomic values for lifetime net merit of Holstein.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXhtF2qt77P&md5=b665a29a1af5b396eec07d5dfff78ec2CAS |
Weng Z, Su H, Saatchi M, Lee J, Thomas MG, Dunkelberger JR, Garrick DJ (2016) Genome-wide association study of growth and body composition traits in Brangus beef cattle. Livestock Science 183, 4–11.
| Genome-wide association study of growth and body composition traits in Brangus beef cattle.Crossref | GoogleScholarGoogle Scholar |
Wiggans GR, Sonstegard TS, Vanraden PM, Matukumalli LK, Schnabel RD, Taylor JF, Schenkel FS, Tassell CPV (2009) Selection of single-nucleotide polymorphisms and quality of genotypes used in genomic evaluation of dairy cattle in the United States and Canada. Journal of Dairy Science 92, 3431–3436.
| Selection of single-nucleotide polymorphisms and quality of genotypes used in genomic evaluation of dairy cattle in the United States and Canada.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD1MXnslGjurs%3D&md5=fa7d86526a77c263ca7cae41113090f0CAS |
Zhang Z, Ding X, Liu J, Zhang Q, Koning D (2011) Accuracy of genomic prediction using low-density marker panels. Journal of Dairy Science 94, 3642–3650.
| Accuracy of genomic prediction using low-density marker panels.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXnvFahsbc%3D&md5=93a2752052520f54b5a8cc75de850307CAS |
Zhu B, Zhang J, Niu H, Guan L, Guo P, Xu L, Chen Y, Zhang L, Gao H, Gao X, Li J (2017) Effects of marker density and minor allele frequency on genomic prediction for growth traits in Chinese Simmental beef cattle. Journal of Integrative Agriculture 16, 911–920.
| Effects of marker density and minor allele frequency on genomic prediction for growth traits in Chinese Simmental beef cattle.Crossref | GoogleScholarGoogle Scholar |
