Evaluation of genetic gain and inbreeding trend of simulated populations based on mating designs and different relationship matrices

Document Type : Research Paper

Authors

Kurdistan Agricultural and Natural Resources Research andEducation Center, Pasdaran street, Sanandaj

Abstract

Genomic information offers new possibilities to control the level of progeny inbreeding. Traditionally, mating plans constrain the inbreeding of predicted progeny
by using a matrix of relationships among individuals. In this study aims to improve genetic gain and restrict the inbreeding level of progeny, a founder population, with effective population size 1000 and 1000 generations, was simulated using the QMSim program to generate linkage disequilibrium. Thereafter, 500 males and 500 females were used to generate a secondary 10-generation population, with 1000 individuals per generation and its corresponding phenotype and genotype in SNP terms. Five hundred sires and 500 dams from generation 1010 were randomly selected to create generation G0 and a trait with heritability of 0.3 was simulated. The eight strategies consisting of two mating designs: minimum_inbreeding (minf) and random (rnd) and four relationship matrices as matrices of A, GRM, IBS and Weighted were used to estimate breeding value for ten generations. Comparison of strategies showed that type of mating designs and different relationship matrices had significant effect on amount of genetic gain, homozygosity and inbreeding (p < 0.05). The type of relationship matrix affected the accuracy of predictions. In strategies with weighted matrix (Gweighted), average of genetic gain during 10 generations was less than strategies using A, GRM and IBS matrices. In strategies with random mating, inbreeding rate had increasing trend. Accuracy of selection (0.63) in strategies with A matrix was lower than other strategies. In general,

Keywords

References

فرجی آروق، ه. اسلمی نژاد، ع، ا. طهمورث پور، م. رکوعی، م و شریعتی، م.م. (1393). ارزیابی روند همخونی جمعیت گاوهای شیری با تغییر نوع آمیزش، شدت انتخاب و استفاده از انتقال جنین با استفاده از شبیه‌سازی تصادفی. نشریه پژوهش در نشخوارکنندگان.
جلد دوم، شماره سوم. ص ص. 121-138.
 
Albers, G.A.A., Rattink, A. P. and Vereijken, A. L. J. (2007). The future of molecular genetics in poultry breeding. XII European Poultry Conference,Verona, Italy.
Colleau, J. J., Tual. K., de Preaumont, H. and Regaldo, D. (2009). A mating method accounting for inbreeding and multi-trait selection in dairy cattle populations. Genetics Selection Evolution. 41:1. 7.
Daetwyler, H. D., Villanueva, B., Bijma P. and Woolliams, J. A. (2007). Inbreeding in genome-wide selection. Journal Animal Breeding and Genetics. 124:369-376.
Dekkers, J. C. M. and Hospital, F. (2002). Multifactorial genetics: the use of molecular genetics in the improvement of agricultural populations. Nature Reviews Genetics. 3: 22–32.
Fernando R. L, Grossman M: (1989). Marker assisted selection using best linear unbiased prediction. Genetics Selection Evolution. 21:467–477.
Goddard, M. (2009). Genomic selection: prediction of accuracy and maximisation of long term response, Genetica. 136:2: 245-257.
Habier D, Fernando RL, Dekkers JCM: (2007). The impact of genetic relationship information on genome-assisted breeding values.Genetics, 177:2389-2397.
Haldane, J. B. S. (1919). The combination of linkage values, and the calculation of distances between the loci of linked factors. Journal of Genetics. 8:299–309.
Hayes, B. and Goddard, M. E. (2001). The distribution of the effects of genes affecting quantitative traits in livestock. Genetics Selection Evolution. 33:3: 209-29.
Hayes, B. J., Visscher, P. M., McPartlan, H. C. and Goddard, M. E. (2003). Novel multilocus measure of linkage disequilibrium to estimate past effective population size. Genome Research. 13:635–643.
Hayes, B. (2007). QTL mapping, MAS, and genomic selection. Technical report, Department of Animal Science, Iowa State University.
Hayes, B. J., Bowman, P. J., Chamberlain, A. J. and Goddard, M. E. (2009). Invited review: Genomic selection in dairy cattle: Progress and challenges. Journal of Dairy Science. 92:433–443.
Jannink, J. L. (2010). Dynamics of long-term genomic selection. Genetics Selection Evolution. 42:35–45.
Kinghorn, B. P. (2011). An algorithm for efficient constrained mate selection. Genetics Selection Evolution. 43:4.
Kirin, M., McQuillan, R., Franklin, C. S., Campbell, H., McKeigue, P. M. and Wilson, J. F. (2010). Genomic runs of homozygosity record population history and consanguinity. PLoS ONE 5:e13996
Lien, S., Gidskehaug, L., Moen, T., Hayes, B. J., Berg, P. R., Davidson, W. S., et al. (2011). A dense SNP-based linkage map for Atlantic salmon (Salmo salar) reveals extended chromosome homeologies and striking differences in sex-specific recombination patterns. BMC Genomics. 12:615.
Lillehammer, M., Meuwissen, T. H. E. and Sonesson, A. K. (2011). A comparison of dairy cattle breeding designs that use genomic selection. Journal of Dairy Science. 94: 1: 493-500.
Liu, H., Meuwissen, T. H. E., Sorensen, A. C. and Berg, P. (2014). Upweighting rare favourable alleles increases long-term genetic gain in genomic selection programs. Genetics Selection Evolution. 47:19.
Liu. H., Henryon, M. and Sørensen, A. C. (2016). Mating strategies with genomic information reduce rates of inbreeding in animal breeding schemes without compromising genetic gain. Animal. 11:4: 547–555.
Luan, T., Woolliams, J.A., Odegard, J., Dolezal, M., Roman-Ponce, S. I., Bagnato, A. and Meuwissen, T. H. E. (2012). The importance of identity-by-state information for the accuracy of genomic selection. Genetics Selection Evolution, 44:28.
Luan, T., Yu, X., Dolezal, M., Bagnato, A. and Meuwissen, T.H.E. (2014). Genomic prediction based on runs of homozygosity. Genetics Selection Evolution. 46:64.
MacLeod, I. M., Meuwissen, T. H. E., Hayes, B. J. and Goddard, M. E. (2009). A novel predictor of multilocus haplotype homozygosity: Comparison with existing predictors. Genetics Research. 91:413–426.
Mastrangelo, S., Gerlando, R. D., Tolone, M., Tortorici, L. and Sardina, M. T. (2014). Genome wide linkage disequilibrium and genetic structure in Sicilian dairy sheep breeds. Genetics, 15:108.
Meuwissen, T. H. E. (1997). Maximizing the response of selection with a predetermined rate of inbreeding. Journal Animal Science. 75:934–940.
Meuwissen, T. and Goddard, M. E. (2010). Accurate prediction of genetic values for complex traits by whole-genome resequencing. Genetics. 185:623–631.
Pryce, J. E., Goddard, M. E., Raadsma, H. W. and Hayes, B. J. (2010). Deterministic models of breeding scheme designs that incorporate genomic selection. Journal of Dairy Science. 93: 5455–5466.
Purcel, 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. and Sham, P. C. (2007). PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses. The American Journal of Human Genetics. 81:559-575.
Sargolzaei, M. and Schenkel, F. S. (2009). QMSim:a large-scale genome simulator for livestock. Genetic and population analysis. 25:5:680-681.
Schaeffer, L. R. 2006. Strategy for applying genome-wide selection in dairy cattle. Journal of Animal Breeding Genetic. 123:4: 218-223.
Smith, L. A., Cassell, B. G. and Pearson, R. E. (1998). The effects of inbreeding on lifetime performance of dairy cattle. Journal of Dairy Science. 81:2729–2737.
Sonesson, A. and Meuwissen, T. (2000). Mating schemes for optimum contribution selection with constrained rates of inbreeding, Genetics Selection Evolution. 32:3: 231-248.
Sonesson, A. K. and Meuwissen, T. H. E. (2009). Testing strategies for genomic selection in aquaculture breeding programs. Genetics Selection Evolution. 41:37.
Sonesson, A. K., Woolliams, J. A. and Meuwissen, T. H. E. (2010). Maximising genetic gain whilst controlling rates of genomic inbreeding using genomic optimum contribution selection. Proceedings of the 9th WCGALP, Leipzig, Germany, p: 150.
VanRaden, P. M. (2008). Efficient methods to compute genomic predictions. Journal Dairy Science. 91:4414–4423.
Vela-Avitúa, S., Meuwissen, T. H. E., Luan, T. and Ødegård, J. (2015). Accuracy of genomic selection for a sib-evaluated trait using identity-by-state and identity-by-descent Relationships. Genetics Selection Evolution. 47:9.
Wray, N. R., and Goddard, M. E. (1994). Increasing long term response to selection. Genetics Selection Evolution. 26:431−451.
Animal Sciences Journal
Volume 33, Issue 126
June 2020
Pages 157-174

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