A computationally efficient algorithm for genomic prediction using a Bayesian model

Genetics Selection Evolution

Volumn 47, Issue 1, 2015, Pages

  Wang, Tingting ^a,b,c   Chen, Yi Ping Phoebe ^a   Goddard, Michael E ^b,c,d   Meuwissen, Theo HE ^e   Kemper, Kathryn E ^d   Hayes, Ben J ^a,b,c  

^a LA TROBE UNIVERSITY   (Australia)

^b DEPARTMENT OF PRIMARY INDUSTRIES   (Australia)

^c Department of Civil and Environmental Engineering   (Australia)

^d UNIVERSITY OF MELBOURNE   (Australia)

^e NORWEGIAN UNIVERSITY OF LIFE SCIENCES   (Norway)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; BAYESIAN ANALYSIS; BREEDING; CROP; DATA SET; ERROR CORRECTION; GENOTYPE; HEALTH RISK; LIVESTOCK; MARKOV CHAIN;

BOS;

ALGORITHM; ANIMAL; BAYES THEOREM; BIOLOGICAL MODEL; BOVINE; BREEDING; GENOMICS; MALE; PROCEDURES; SINGLE NUCLEOTIDE POLYMORPHISM; STATISTICAL MODEL;

ALGORITHMS; ANIMALS; BAYES THEOREM; BREEDING; CATTLE; GENOMICS; MALE; MODELS, GENETIC; MODELS, STATISTICAL; POLYMORPHISM, SINGLE NUCLEOTIDE;

EID: 84928533525     PISSN: 0999193X     EISSN: 12979686     Source Type: Journal    
DOI: 10.1186/s12711-014-0082-4     Document Type: Article

References (43)

1. Meuwissen TH, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001;157:1819-29.
2. VanRaden PM. Efficient methods to compute genomic predictions. J Dairy Sci. 2008;91:4414-23.
3. Yang J, Benyamin B, McEvoy BP, Gordon S, Henders AK, Nyholt DR, et al. Common SNPs explain a large proportion of heritability for human height. Nat Genet. 2010;42:565-9.
4. Habier D, Fernando RL, Kizilkaya K, Garrick D. Extension of the Bayesian alphabet for genomic selection. BMC Bioinformatics. 2011;12:186.
5. Erbe M, Hayes BJ, Matukumalli LK, Goswami S, Bowman PJ, Reich CM, et al. Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels. J Dairy Sci. 2012;95:4114-29.
6. Park T, Casella G. The Bayesian Lasso. J Am Stat Assoc. 2008;103:681-6.
7. Habier D, Tetens J, Seefried FR, Lichtner P, Thaller G. The impact of genetic relationship information on genomic breeding values in German Holstein cattle. Genet Sel Evol. 2010;42:5.
8. Daetwyer HD, Swan AA, van der Werf JH, Hayes BJ. Accuracy of pedigree and genomic predictions of carcass and novel meat quality traits in multi-breed sheep data assessed by cross-validation. Genet Sel Evol. 2012;44:33.
9. Pryce JE, Arias J, Bowman PJ, Davis SR, Macdonald KA, Waghorn GC, et al. Accuracy of genomic predictions of residual feed intake and 250-day body weight in growing heifers using 625,000 single nucleotide polymorphism markers. J Dairy Sci. 2012;95:2108-19.
10. Gao H, Lund MS, Zhang Y, Su G. Accuracy of genomic prediction using different models and response variables in the Nordic Red cattle population. J Anim Breed Genet. 2013;130:333-40.
11. Wimmer V, Lehermeier C, Albrecht T, Auinger HJ, Wang Y, Schön CC. Genome-wide prediction of traits with different genetic architecture through efficient variable selection. Genetics. 2013;195:573-87.
12. Strandén I, Garrick DJ. Technical note: Derivation of equivalent computing algorithms for genomic predictions and reliabilities of animal merit. J Dairy Sci. 2009;92:2971-5.
13. Misztal I, Legarra A, Aguilar I. Using recursion to compute the inverse of the genomic relationship matrix. J Dairy Sci. 2014;97:3943-52.
14. Aguilar I, Misztal I, Legarra A, Tsuruta S. Efficient computation of the genomic relationship matrix and other matrices used in single-step evaluation. J Anim Breed Genet. 2011;128:422-8.
15. Hayes BJ, Pryce J, Chamberlain AJ, Bowman PJ, Goddard ME. Genetic Architecture of complex traits and accuracy of genomic prediction: coat colour, milk-fat percentage, and type in Holstein cattle as contrasting model traits. PLoS Genet. 2010;6:e1001139.
16. Verbyla KL, Bowman PJ, Hayes BJ, Goddard ME. Sensitivity of genomic selection to using different prior distributions. BMC Proceedings. 2010;4:S5.
17. Riedelsheimer C, Technow F, Melchinger AE. Comparison of whole-genome prediction models for traits with contrasting genetic architecture in a diversity panel of maize inbred lines. BMC Genomics. 2012;13:452.
18. Daetwyler HD, Calus MP, Pong-Wong R, de Los CG, Hickey JM. Genomic prediction in animal and plants: Simulation of data, validation, reporting and benchmarking. Genetics. 2012;193:347-65.
19. Kemper KE, Reich CM, Bowman PJ, Vander Jagt CJ, Chamberlain AJ, Mason BA, et al. Improved precision of QTL mapping using a nonlinear Bayesian method in a multi-breed population leads to greater accuracy of across-breed genomic predictions. Genet Select Evol. 2014;47:29.
20. MacLeod IM, Hayes BJ, Vander Jagt CJ, Kemper KE, Haile-Mariam M, Bowman PJ, et al. A Bayesian analysis to exploit imputed sequence variants for QTL discovery. In: Proceedings of the 10th World Congress of Genetics Applied to Livestock Production: 17-22 August 2014; Vancouver. 2014.
21. MacLeod IM, Hayes BJ, Goddard ME. The effects of demography and long term selection on the accuracy of genomic prediction with sequence data. Genetics. 2014;198:1671-84.
22. Bolormaa S, Pryce JE, Kemper K, Savin K, Hayes BJ, Barendse W, et al. Accuracy of prediction of genomic breeding values for residual feed intake and carcass and meat quality traits in Bos taurus, Bos indicus, and composite beef cattle. J Anim Sci. 2013;91:3088-104.
23. Mäntysaari EA. Challenges in industry application of genomic prediction experiences from dairy cattle. In: Proceedings of the 10th World Congress of Genetics Applied to Livestock Production: 17-22 August 2014; Vancouver. 2014.
24. Aguilar I, Misztal I, Johnson DL, Legarra A, Tsuruta S, Lawlor TJ. Hot topic: A unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. J Dairy Sci. 2010;93:743-52.
25. Christensen OF, Lund MS. Genomic prediction when some animals are not genotyped. Genet Sel Evol. 2010;42:2.
26. Clark SA, Hickey JM, van der Werf JH. Different models of genetic variation and their effect on genomic evaluation. Genet Sel Evol. 2011;43:18.
27. Meuwissen T, Goddard M. Accurate prediction of genetic value for complex traits by whole-genome resequencing. Genetics. 2010;185:623-31.
28. VanRaden PM. Genomic measures of relationship and inbreeding. Interbull Bull. 2007;37:33-6.
29. Meuwissen TH, Solberg TR, Shepherd R, Woolliams JA. A fast algorithm for BayesB type of prediction of genome-wide estimates of genetic value. Genet Sel Evol. 2009;41:2.
30. Gianola D. Priors in whole-genome regression: The Bayesian alphabet returns. Genetics. 2013;194:573-96.
31. Shepherd R, Meuwissen TH, Woolliams JA. Genomic selection and complex trait prediction using a fast EM algorithm applied to genome-wide markers. BMC Bioinformatics. 2010;11:529.
32. Seber GAF, Lee AJ. Linear Regression Analysis. Hoboken: John Wiley and Sons; 2002.
33. Gilmour AR, Gogel BJ, Cullis BR, Welham SI, Thompson R: ASReml User Guide Release 2.0. In. Hemel Hempsted, UK: VSN International Ltd.; 2006.
34. Browning BL, Browning SR. A unified approach to genotype imputation and haplotype-phase inference for large data sets of trios and unrelated individuals. Am J Hum Genet. 2009;84:210-23.
35. Grisart B, Coppieters W, Farnir F, Karim L, Ford C, Berzi P, et al. Positional candidate cloning of a QTL in dairy cattle: identification of a missense mutation in the bovine DGAT1 gene with major effect on milk yield and composition. Genome Res. 2002;12:222-31.
36. Blott S, Kim JJ, Moisio S, Schmidt-Küntzel A, Cornet A, Berzi P, et al. Molecular dissection of a quantitative trait locus: a phenylalanine-to-tyrosine substitution in the transmembrane domain of the bovine growth hormone receptor is associated with a major effect on milk yield and composition. Genetics. 2003;163:253-66.
37. Hayashi T, Iwata H. EM algorithm for Bayesian estimation of genomic breeding values. BMC Genet. 2010;11:3.
38. Sun X, Qu L, Garrick DJ, Dekkers JCM, Fernando RL. A fast EM algorithm for BayesA-Like prediction of genomic breeding values. PLoS ONE. 2012;7:e49157.
39. Yu X, Meuwissen TH. Using the pareto principle in genome-wide breeding value estimation. Genet Sel Evol. 2011;43:35.
40. VanRaden PM, Null DJ, Sargolzaei M, Wiggans GR, Tooker ME, Cole JB, et al. Genomic imputation and evaluation using high-density Holstein genotypes. J Dairy Sci. 2013;96:668-78.
41. Su G, Brøndum RF, Ma P, Guldbrandtsen B, Aamand GP, Lund MS. Comparison of genomic predictions using medium-density (∼54,000) and high-density (∼777,000) single nucleotide polymorphism marker panels in Nordic Holstein and Red Dairy cattle populations. J Dairy Sci. 2012;95:4657-65.
42. Liu Z, Goddard ME, Reinhardt F, Reents R. A single-step genomic model with direct estimation of marker effects. J Dairy Sci. 2014;97:5833-50.
43. Fernando RL, Dekkers JCM, Garrick DJ. A class of Bayesian methods to combine large numbers of genotyped and non-genotyped animals for whole-genome analyses. Genet Sel Evol. 2014;46:50.