Genomic prediction by considering genotype × environment interaction using different genomic architectures

Mehdi Bohlouli 1 , Sadegh Alijani 1 , Ardashir Nejati Javaremi 2 , Sven König 3  and Tong Yin 3
  • 1 Department of Animal Science, University of Tabriz, , Tabriz, Iran
  • 2 Department of Animal Science, University College of Agriculture and Natural Resources, University of Tehran, , Karaj, Iran
  • 3 Institute of Animal Breeding and Genetics, University of Giessen, , Giessen, Germany


In this study, accuracies of genomic prediction across various scenarios were compared using single- trait and multiple-trait animal models to detect genotype × environment (G × E) interaction based on REML method. The simulated high and low linkage disequilibrium (HLD and LLD) genome consisted of 15,000 and 50,000 SNP chip applications with 300 and 600 QTLs controlling the trait of interest. The simulation was done to create the genetic correlations between the traits in 4 environments and heritabilities of the traits were 0.20, 0.25, 0.30 and 0.35 in environments 1, 2, 3 and 4, respectively. Two strategies were used to predict the accuracy of genomic selection for cows without phenotypes. In the first strategy, phenotypes for cows in three environments were kept as a training set and breeding values for all animals were estimated using three-trait model. In the second one, only 25, 50 or 75% of records in the fourth environment and all the records in the other three environments were used to predict GBV for non-phenotyped cows in the environment 4. For the first strategy, the highest accuracy of 0.695 was realized in scenario HLD with 600 QTL and 50K SNP chip for the fourth environment and the lowest accuracy of 0.495 was obtained in scenario LLD with 600QTL and 15K SNP chips for the first environment. Generally, the accuracy of prediction increased significantly (P<0.05) with increasing the number of markers, heritability and the genetic correlation between the traits, but no significant difference was observed between scenarios with 300 and 600 QTL. In comparison with models without G × E interaction, accuracies of the GBV for all environments increased when using multi-trait models. The results showed that the level of LD, number of animals in training set and genetic correlation across environments play important roles if G × E interaction exists. In conclusion, G × E interaction contributes to understanding variations of quantitative trait and increasing accuracy of genomic prediction. Therefore, the interaction should be taken into account in conducting selection in various environments or across different genotypes.

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  • Aguilar I., Misztal I., Johnson D.L., Legarra A., Tsuruta S., Lawlor T.J. (2010). Hot topic: Aunified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. J. Dairy Sci., 93: 743-752.

  • Aguilar I., Misztal I., Legarra A., Tsuruta S. (2011). Efficient computation of the genomic relationship matrix and other matrices used in single-step evaluation. J. Anim. Breed. Genet., 128: 422-428.

  • Bastiaansen J.W.M., Bovenhuis H., Lopes M.S., Silva F.F., Megens H.J., Calus M.P.L. (2014). SNPeffects depend on genetic and environmental context. Proc. 10th World Congress on Genetics Applied to Livestock Production, 17-22.08.2014, Vancouver, Canada.

  • Bohlouli M., Shodja J., Alijani S., Eghbal A. (2013). The relationship between temperature- humidity index and test-day milk yield of Iranian Holstein dairy cattle using random regression model. Livest. Sci., 157: 414-420.

  • Bohlouli M., Shodja J., Alijani S., Pirany N. (2014). Interaction between genotype and geographical region for milk production traits of Iranian Holstein dairy cattle. Livest. Sci., 169: 1-9.

  • Brito F.V., Neto J.B., Sargolzaei M., Cobuci J.A., Schenkel F.S. (2011). Accuracy of genomic selection in simulated populations mimicking the extent of linkage disequilibrium in beef cattle. BMC Genetics, 12: 80.

  • Brügemann K., Gernand E., von Borstel U.U., König S. (2011). Genetic analyses of protein yield in dairy cows applying random regression models with time-dependent and temperature × humidity-dependent covariates. J. Dairy Sci., 94: 4129-4139.

  • Calus M.P.L., Veerkamp R.F. (2011). Accuracy of multi-trait genomic selection using different methods. Genet. Sel. Evol., 43: 1-14.

  • Calus M.P.L., Groen A.F., De Jong G. (2002). Genotype by environment interaction for protein yield in Dutch dairy cattle as quantified by different models. J. Dairy Sci., 85: 3115-3123.

  • Calus M.P.L.,de Haas Y., Pszczola M., Veerkamp R.F. (2013). Predicted accuracy of and response to genomic selection for new traits in dairy cattle. Animal, 7: 183-191.

  • Ceron-unoz M., Tonhati F.H., Costa C.N., Rojas- Sarmiento D., Echeverri D.M. (2004). Factors that cause genotype by environment interaction and use ofamultiple-trait herd-cluster model for milk yield of Holstein cattle from Brazil and Colombia. J. Dairy Sci., 87: 2687-2692.

  • Clark S.A., Hickey J.M., van der Werf J.H.J. (2011). Different models of genetic variation and their effect on genomic evaluation. Genet. Sel. Evol., 43: 18.

  • Daetwyler H.D., Villanueva B., Woolliams J.A. (2008). Accuracy of predicting the genetic risk of disease usingagenome-wide approach. PLo S ONE, 3: e3395.

  • Daetwyler H.D., Pong - Wong R., Villanueva B., Woolliams J.A. (2010). The impact of genetic architecture on genome-wide evaluation methods. Genetics, 185: 1021-1031.

  • De Roos A.P.W., Hayes B.J., Goddard M.E. (2009). Reliability of genomic predictions across multiple populations. Genetics, 183: 1545-1553.

  • Dekkers J.C.M. (2007). Prediction of response to marker-assisted and genomic selection using selection index theory. J. Anim. Breed. Genet., 124: 331-341.

  • Falconer D.S., Mac Kay T.F.C. (1996). Introduction to Quantitative Genetics, 4th ed., Longman Group, Essex, UK.

  • Goddard M. (2009). Genomic selection: Prediction of accuracy and maximisation of long term response. Genetica, 136: 245-257.

  • Guo G., Zhao F., Wang Y., Zhang Y., Du L., Su G. (2014). Comparison of single-trait and multiple-trait genomic prediction models. Genetics, 15: 30.

  • Habier D., Fernando R.L., Dekkers J.C.M. (2009). Genomic selection using low-density marker panels. Genetics, 182: 343-353.

  • Haile-Mariam M., Pryce J.E., Schrooten C., Hayes B.J. (2015). Including overseas performance information in genomic evaluations of Australian dairy cattle. J. Dairy Sci., 98: 1-17.

  • Hammami H., Rekik B., Bastin C., Soyeurt H., Bormann J., Stoll J., Gengler N. (2009). Environmental sensitivity for milk yield in Luxembourg and Tunisian Holsteins by herd management level. J. Dairy Sci., 92: 4604-4612.

  • Hayashi T., Iwata H. (2013). A Bayesian method and its variational approximation for prediction of genomic breeding values in multiple traits. BMC Bioinformatics, 14: 1-14.

  • Hayes B.J., Bowman P.J., Chamberlain A.J., Goddard M.E. (2009). Invited review: Genomic selection in dairy cattle: progress and challenges. J. Dairy Sci., 92: 433-443.

  • Hayes B.J., Daetwyler H.D., Goddard M.E. (2016). Models for genome × environment interaction: examples in livestock. Crop Sci., 56: 2251-2259.

  • Hickey J.M., Gorjanc G. (2012). Simulated data for genomic selection and genome-wide association studies usingacombination of coalescent and gene drop methods. G3, 2: 425-427.

  • Hill W.G., Robertson A. (1968). Linkage disequilibrium in finite populations. Theor. Appl. Genet., 6: 226-231.

  • Hozé C., Fritz S., Phocas F., Boichard D., Ducrocq V., Croiseau P. (2014). Efficiency of multi-breed genomic selection for dairy cattle breeds with different sizes of reference population. J. Dairy Sci., 97: 3918-3929.

  • Jia Y., Jannink J.L. (2012). Multiple-trait genomic selection methods increase genetic value prediction accuracy. Genetics, 192: 1513-1522.

  • Jiang J., Zhang Q., Ma L., Li J., Wang Z., Liu J.F. (2015). Joint prediction of multiple quantitative traits usinga Bayesian multivariate antedependence model. Heredity, 115: 29-36.

  • Jiménez-Montero J.A., González- Recio O., Alenda R. (2013). Comparison of methods for the implementation of genome-assisted evaluation of Spanish dairy cattle. J. Dairy Sci., 96: 625-634.

  • Karoui S., Carabaño M.J., Díaz C., Legarra A. (2012). Joint genomic evaluation of French dairy cattle breeds using multiple-trait models. Genet. Sel. Evol., 44: 39.

  • Kolmodin R., Strandberg E., Madsen P., Jensen J., Jorjani H. (2002). Genotype by environment interaction in Nordic dairy cattle studied by use of reaction norms. Acta Agric. Scand. A Anim. Sci., 52: 11-24.

  • König S., Simianer H., Willam A. (2009). Economic evaluation of genomic breeding programs. J. Dairy Sci., 92: 382-391.

  • Lillehammer M., Ødegard J., Meuwissen T.H.E. (2007). Random regression models for detection of gene by environment interaction. Genet. Sel. Evol., 39: 105-121.

  • Lillehammer M., Goddard M.E., Nilsen H., Sehested E., Olsen H.G., Lien S., Meuwissen T.H.E. (2008). Quantitative trait locus-by-environment interaction for milk yield traits on Bos taurus autosome 6. Genetics, 179: 1539-1546.

  • Lillehammer M., Hayes B.J., Meuwissen T.H.E., Goddard M.E. (2009). Gene by environment interactions for production traits in Australian dairy cattle. J. Dairy Sci., 92: 4008-4017.

  • Lund M.S., Su G., Janss L., Guldbrandtsen B., Brøndum R.F. (2014). Genomic evaluation of cattle inamulti-breed context. Livest. Sci., 166: 101-110.

  • Meuwissen T.H.E., Hayes B., Goddard M.E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics, 157: 1819-1829.

  • Misztal I., Tsuruta S., Strabel T., Auvray B., Druet T., Lee D.H. (2002). BLUPF90 and related programs. Communication no. 28-07. Proc. 7th World Congress on Genetics Applied to Livestock Production, Montpellier, France.

  • Moser G., Khatkar M.S., Hayes B.J., Raadsma H.W. (2012). Accuracy of direct genomic values in Holstein bulls and cows using subsets of SNPmarkers. Genet. Sel. Evol., 42: 37.

  • Muir W.M. (2007). Comparison of genomic and traditional BLUP-estimated breeding value accuracy and selection response under alternative trait and genomic parameters. J. Anim. Breed. Genet., 124: 342-355.

  • Nejati - Javaremi A., Smith C., Gibson J. (1997). Effect of total allelic relationship on accuracy of evaluation and response to selection. J. Anim. Sci., 75: 1738-1745.

  • Olson K.M., Van Raden P.M., Tooker M.E. (2012). Multibreed genomic evaluations using purebred Holsteins, Jerseys, and Brown Swiss. J. Dairy Sci., 95: 5378-5383.

  • Pimentel E.C.G., Wensch - Dorendorf M., König S., Swalve H.H. (2013). Enlarging a training set for genomic selection by imputation of un-genotyped animals in populations of varying genetic architecture. Genet. Sel. Evol., 45: 12.

  • Purcell S., Neale B., Todd - Brown K., Thomas L., Ferreira M.A.R., Bender D., Maller J., Sklar P.,de Bakker P.I.W., Daly M.J., Sham P.C. (2007). PLINK: Atool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet., 81: 559-575.

  • Robertson A. (1959). The sampling variance of the genetic correlation coefficient. Biometrics, 15: 469-485.

  • Sargolzaei M., Schenkel F.S. (2009). QMSim:alarge-scale genome simulator for livestock. Bioinformatics, 25: 680-681.

  • Solberg T.R., Sonesson A.K., Woolliams J.A., Meuwissen T.H.E. (2008). Genomic selection using different marker types and densities. J. Anim. Sci., 86: 2447-2454.

  • Van Raden P.M. (2008). Efficient methods to compute genomic predictions. J. Dairy Sci., 91: 4414-4423.

  • Wientjes Y.C.J., Calus M.P.L., Goddard M.E., Hayes B.J. (2015). Impact of QTLproperties on the accuracy of multi-breed genomic prediction. Genet. Sel. Evol., 47: 42.

  • Yin T., Pimentel E.C.G., König U., Borstel V., König S. (2014). Strategy for the simulation and analysis of longitudinal phenotypic and genomic data in the context ofatemperature × humidity-dependent covariate. J. Dairy Sci., 97: 2444-2454.


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