Fixed-length haplotypes can improve genomic prediction accuracy in an admixed dairy cattle population

[en] Fitting covariates representing the number of haplotype alleles rather than single nucleotide polymorphism (SNP) alleles may increase genomic prediction accuracy if linkage disequilibrium between quantitative trait loci and SNPs is inadequate. The objectives of this study were to evaluate the accuracy, bias and computation time of Bayesian genomic prediction methods that fit fixed-length haplotypes or SNPs. Genotypes at 37,740 SNPs that were common to Illumina BovineSNP50 and high-density panels were phased for ~58,000 New Zealand dairy cattle. Females born before 1 June 2008 were used for training, and genomic predictions for milk fat yield (n = 24,823), liveweight (n = 13,283) and somatic cell score (n = 24,864) were validated within breed (predominantly Holstein–Friesian, predominantly Jersey, or admixed KiwiCross) in later-born females. Covariates for haplotype alleles of five lengths (125, 250, 500 kb, 1 or 2 Mb) were generated and rare haplotypes were removed at four thresholds (1, 2, 5 or 10%), resulting in 20 scenarios tested. Genomic predictions fitting covariates for either SNPs or haplotypes were calculated by using BayesA, BayesB or BayesN. This is the first study to quantify the accuracy of genomic prediction using haplotypes across the whole genome in an admixed population.

Disciplines :

Genetics & genetic processes
Animal production & animal husbandry

Author, co-author :

Hess, Melanie

Druet, Tom ; Université de Liège > Département des productions animales (DPA) > GIGA-R : Génomique animale

Hess, Andrew

Garrick, Dorian

Language :

English

Title :

Fixed-length haplotypes can improve genomic prediction accuracy in an admixed dairy cattle population

Publication date :

2017

Journal title :

Genetics, Selection, Evolution

ISSN :

0999-193X

eISSN :

1297-9686

Publisher :

EDP Sciences, Les Ulis, France

Volume :

Issue :

Pages :

Peer reviewed :

Peer Reviewed verified by ORBi

Additional URL :

http://dx.doi.org/10.1186/s12711-017-0329-y

Available on ORBi :

since 04 July 2017

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Bibliography

VanRaden PM. Efficient methods to compute genomic predictions. J Dairy Sci. 2008;91:4414-23.
Meuwissen T, Hayes B, Goddard M. Accelerating improvement of livestock with genomic selection. Annu Rev Anim Biosci. 2013;1:221-37.
Habier D, Fernando RL, Dekkers JCM. The impact of genetic relationship information on genome-Assisted breeding values. Genetics. 2007;177:2389-97.
Goddard M. Genomic selection: prediction of accuracy and maximisation of long term response. Genetica. 2009;136:245-57.
Zondervan KT, Cardon LR. The complex interplay among factors that influence allelic association. Nat Rev Genet. 2004;5:89-100.
Villumsen TM, Janss L. Bayesian genomic selection: The effect of haplotype length and priors. BMC Proc. 2009;3:S11.
Villumsen TM, Janss L, Lund MS. The importance of haplotype length and heritability using genomic selection in dairy cattle. J Anim Breed Genet. 2009;126:3-13.
Hayes BJ, Chamberlain AJ, McPartlan H, Macleod I, Sethuraman L, Goddard ME. Accuracy of marker-Assisted selection with single markers and marker haplotypes in cattle. Genet Res. 2007;89:215-20.
Boichard D, Guillaume F, Baur A, Croiseau P, Rossignol MN, Boscher MY, et al. Genomic selection in French dairy cattle. Anim Prod Sci. 2012;52:115-20.
Sun X, Su H, Boddhireddy P, Garrick D. Haplotype-based genomic prediction of breeds not in training. In: Proceedings of the plant and animal genome conference xXIV: 9-13 January 2016; San Diego. 2016.
Calus MPL, Meuwissen THE, Windig JJ, Knol EF, Schrooten C, Vereijken ALJ, et al. Effects of the number of markers per haplotype and clustering of haplotypes on the accuracy of QTL mapping and prediction of genomic breeding values. Genet Sel Evol. 2009;41:11.
Cuyabano BCD, Su GS, Lund MS. Selection of haplotype variables from a high-density marker map for genomic prediction. Genet Sel Evol. 2015;47:61.
Calus MPL, Meuwissen THE, de Roos APW, Veerkamp RF. Accuracy of genomic selection using different methods to define haplotypes. Genetics. 2008;178:553-61.
Sandor C, Li W, Coppieters W, Druet T, Charlier C, Georges M. Genetic variants in REC8, RNF212, and PRDM9 influence male recombination in cattle. PLoS Genet. 2012;8:e1002854.
Weng ZQ, Saatchi M, Schnabel RD, Taylor JF, Garrick DJ. Recombination locations and rates in beef cattle assessed from parent-offspring pairs. Genet Sel Evol. 2014;46:34.
Turner S, Armstrong LL, Bradford Y, Carlson CS, Crawford DC, Crenshaw AT, et al. Quality control procedures for genome-wide association studies. Curr Protoc Hum Genet. 2011;1(unit1):19.
VanRaden PM, Van Tassell CP, Wiggans GR, Sonstegard TS, Schnabel RD, Taylor JF, et al. Invited review: reliability of genomic predictions for North American Holstein bulls. J Dairy Sci. 2009;92:16-24.
Gianola D. Priors in whole-genome regression: The Bayesian alphabet returns. Genetics. 2013;194:573-96.
Cuyabano BCD, Su G, Rosa GJM, Lund MS, Gianola D. Bootstrap study of genome-enabled prediction reliabilities using haplotype blocks across Nordic Red cattle breeds. J Dairy Sci. 2015;98:7351-63.
Meuwissen THE, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001;157:1819-29.
Zeng J. Whole genome analyses accounting for structures in genotype data. Ames: Iowa State University; 2015.
Hickey JM, Kinghorn BP, Tier B, Clark SA, van der Werf JHJ, Gorjanc G. Genomic evaluations using similarity between haplotypes. J Anim Breed Genet. 2013;130:259-69.
de Roos APW, Schrooten C, Druet T. Genomic breeding value estimation using genetic markers, inferred ancestral haplotypes, and the genomic relationship matrix. J Dairy Sci. 2011;94:4708-14.
LIC, DairyNZ: New Zealand dairy statistics 2014-15. 2015. http://www.dairynz.co.nz/media/3136117/new-zealand-dairy-statistics-2014-15.pdf. Accessed 25 June 2017.
Harris BL. Breeding dairy cows for the future in New Zealand. N Z Vet J. 2005;53:384-90.
Vanraden PM, Wiggans GR. Derivation, calculation, and use of national animal-model information. J Dairy Sci. 1991;74:2737-46.
LIC, Your index your animal evaluation system. http://www.lic.co.nz/pdf/yourindex.pdf. Accessed 25 June 2017.
Matukumalli LK, Lawley CT, Schnabel RD, Taylor JF, Allan MF, Heaton MP, et al. Development and characterization of a high density SNP genotyping assay for cattle. PLoS One. 2009;4:e5350.
Matukumalli L, Schroeder S, DeNise S, Sonstegard T, Lawley C, Georges M, et al. Analyzing LD blocks and CNV segments in cattle: novel genomic features identified using the BovineHD BeadChip. Pub No 370-2011-002. Illumina Inc.: San Diego.
Druet T, Georges M. LINKPHASE3: An improved pedigree-based phasing algorithm robust to genotyping and map errors. Bioinformatics. 2015;31:1677-9.
Druet T, Georges M. A hidden Markov model combining linkage and linkage disequilibrium information for haplotype reconstruction and quantitative trait locus fine mapping. Genetics. 2010;184:779-98.
Browning SR, Browning BL. Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am J Hum Genet. 2007;81:1084-97.
Garrick DJ, Fernando RL. Implementing a QTL detection study (GWAS) using genomic prediction methodology. Methods Mol Biol. 2013;1019:275-98.
DairyNZ: New Zealand dairy herd improvement database review. 2009. http://www.dairynz.co.nz/media/532738/anderson-report.pdf. Accessed 25 June 2017.
Meuwissen T, Goddard M. Accurate prediction of genetic values for complex traits by whole-genome resequencing. Genetics. 2010;185:623-31.
Su G, Brondum RF, Ma P, Guldbrandtsen B, Aamand GR, Lund MS. Comparison of genomic predictions using medium-density (similar to 54,000) and high-density (similar to 777,000) single nucleotide polymorphism marker panels in Nordic Holstein and Red Dairy cattle populations. J Dairy Sci. 2012;95:4657-65.
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. 2014;97:4114-29.
van Binsbergen R, Calus MPL, Bink M, van Eeuwijk FA, Schrooten C, Veerkamp RF. Genomic prediction using imputed whole-genome sequence data in Holstein Friesian cattle. Genet Sel Evol. 2015;47:71.
Heidaritabar M, Calus MPL, Megens HJ, Vereijken A, Groenen MAM, Bastiaansen JWM. Accuracy of genomic prediction using imputed whole-genome sequence data in white layers. J Anim Breed Genet. 2016;133:167-79.
Arias JA, Keehan M, Fisher P, Coppieters W, Spelman R. A high density linkage map of the bovine genome. BMC Genet. 2009;10:18.
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.
Daetwyler HD, Pong-Wong R, Villanueva B, Woolliams JA. The impact of genetic architecture on genome-wide evaluation methods. Genetics. 2010;185:1021-31.
Kizilkaya K, Fernando RL, Garrick DJ. Genomic prediction of simulated multibreed and purebred performance using observed fifty thousand single nucleotide polymorphism genotypes. J Anim Sci. 2010;88:544-51.
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.
Karim L, Takeda H, Lin L, Druet T, Arias JAC, Baurain D, et al. Variants modulating the expression of a chromosome domain encompassing PLAG1 influence bovine stature. Nat Genet. 2011;43:405-13.
Meredith BK, Kearney FJ, Finlay EK, Bradley DG, Fahey AG, Berry DP, et al. Genome-wide associations for milk production and somatic cell score in Holstein-Friesian cattle in Ireland. BMC Genet. 2012;13:21.
Habier D, Fernando RL, Kizilkaya K, Garrick DJ. Extension of the bayesian alphabet for genomic selection. BMC Bioinformatics. 2011;12:186.
Falconer DS, Mackay TFC. Introduction to quantitative genetics. 4th ed. London: Longman; 1996.
Rat Genome Sequencing and Mapping Consortium, Baud A, Hermsen R, Guryev V, Stridh P, Graham D, et al. Combined sequence-based and genetic mapping analysis of complex traits in outbred rats. Nat Genet. 2013;45:767-75.
Schopen GCB, Schrooten C. Reliability of genomic evaluations in Holstein-Friesians using haplotypes based on the BovineHD Bead chip. J Dairy Sci. 2013;96:7945-51.
Ferdosi MH, Henshall J, Tier B. Study of the optimum haplotype length to build genomic relationship matrices. Genet Sel Evol. 2016;48:75.
Winkelman AM, Johnson DL, Harris BL. Application of genomic evaluation to dairy cattle in New Zealand. J Dairy Sci. 2015;98:659-75.
Saatchi M, Schnabel RD, Taylor JF, Garrick DJ. Large-effect pleiotropic or closely linked QTL segregate within and across ten US cattle breeds. BMC Genomics. 2014;15:442.
Kachman SD, Spangler ML, Bennett GL, Hanford KJ, Kuehn LA, Snelling WM, et al. Comparison of molecular breeding values based on within-and across-breed training in beef cattle. Genet Sel Evol. 2013;45:30.
Brondum RF, Rius-Vilarrasa E, Stranden I, Su G, Guldbrandtsen B, Fikse WF. Reliabilities of genomic prediction using combined reference data of the Nordic Red dairy cattle populations. J Dairy Sci. 2011;94:4700-7.
de Roos APW, Hayes BJ, Spelman RJ, Goddard ME. Linkage disequilibrium and persistence of phase in Holstein-Friesian, Jersey and Angus cattle. Genetics. 2008;179:1503-12.
Nachman MW. Variation in recombination rate across the genome: evidence and implications. Curr Opin Genet Dev. 2002;12:657-63.
Beissinger TM, Rosa GJ, Kaeppler SM, Gianola D, de Leon N. Defining window-boundaries for genomic analyses using smoothing spline techniques. Genet Sel Evol. 2015;47:30.