Effect of Selection on the Accuracy of Genomic Selection and Genome-Wide Association Analysis
محورهای موضوعی :
A.A. Shadparvar
1
,
N. Ghavi Hossein-Zadeh
2
,
Z. Lotfi
3
,
A. Safari
4
1 - Department of Animal Science, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran
2 - Department of Animal Science, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran
3 - Department of Animal Science, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran
4 - Department of Animal Science, Faculty of Agricultural Sciences, University of Guilan, Rasht, Iran
کلید واژه: accuracy, genome-wide association, genomic selection, quantitative trait locus, reference population, simulation, validation population,
چکیده مقاله :
Several factors, such as trait heritability, marker density, distance between individuals in the reference population and selection candidates, as well as the number of phenotypic records in the reference popula-tion dataset, significantly affect the accuracy of genomic evaluation. The objective of the present study was to evaluate the effect of selection on the accuracy of genomic selection and genome-wide association analy-sis. Four selection schemes were considered: 1) no selection in both reference and validation populations. 2) selection in the reference population based on the estimated accuracy of estimated breeding values of 0.7, while no selection was applied in the validation population. 3) selection in both the reference and validation populations, comparable to scheme 2. 4) selection in both populations similar to that of scheme 3, however, the selection was done in the validation population with an accuracy of 0.5. In each scenario, a reference population and a validation population were randomly simulated using QMSim. The results indicated that the comparison of accuracy levels between the reference and validation populations showed higher accu-racy in the reference population for all schemes, heritabilities, numbers of markers, and QTL numbers. The highest genomic evaluation accuracy for both populations was achieved in scheme 1. Results suggested that the correlation coefficient between genomic evaluation accuracy in the reference and validation populations was related to the number of common single nucleotide polymorphisms (SNPs) between the reference and validation populations. Selection in the reference population led to a significant reduction in the number of markers.
Several factors, such as trait heritability, marker density, distance between individuals in the reference population and selection candidates, as well as the number of phenotypic records in the reference popula-tion dataset, significantly affect the accuracy of genomic evaluation. The objective of the present study was to evaluate the effect of selection on the accuracy of genomic selection and genome-wide association analy-sis. Four selection schemes were considered: 1) no selection in both reference and validation populations. 2) selection in the reference population based on the estimated accuracy of estimated breeding values of 0.7, while no selection was applied in the validation population. 3) selection in both the reference and validation populations, comparable to scheme 2. 4) selection in both populations similar to that of scheme 3, however, the selection was done in the validation population with an accuracy of 0.5. In each scenario, a reference population and a validation population were randomly simulated using QMSim. The results indicated that the comparison of accuracy levels between the reference and validation populations showed higher accu-racy in the reference population for all schemes, heritabilities, numbers of markers, and QTL numbers. The highest genomic evaluation accuracy for both populations was achieved in scheme 1. Results suggested that the correlation coefficient between genomic evaluation accuracy in the reference and validation populations was related to the number of common single nucleotide polymorphisms (SNPs) between the reference and validation populations. Selection in the reference population led to a significant reduction in the number of markers.
Bouquet A. and Juga J. (2013). Integrating genomic selection into dairy cattle breeding programs: A review. Animal. 7(5), 705-713.
Brito F.V., Neto J.B., Sargolzaei M., Cobuci J.A. and Schenkel F.S. (2011). Accuracy of genomic selection in simulated populations mimicking the extent of linkage disequilibrium in beef cattle. BMC Genet. 12, 1-10.
Charlesworth B., Morgan M. and Charlesworth D. (1993). The effect of deleterious mutations on neutral molecular variation. Genetics. 134(4), 1289-1303.
De los Campos G. and Pérez-Rodríguez P. (2014). Bayesian generalized linear regression. R package version 1.0.4. Avail-able at: http://CRAN.R-project.org/package=BGLR. Accessed Apr. 2016.
De Roos A., Hayes B.J., Spelman R. and Goddard M. (2008). Linkage disequilibrium and persistence of phase in Holstein–Friesian, Jersey, and Angus cattle. Genetics. 179(3), 1503-1512.
Hayes B.J., Bowman P.J., Chamberlain A.J. and Goddard M.E. (2009). Invited review: Genomic selection in dairy cattle: Progress and challenges. J. Dairy Sci. 92(2), 433-443.
Mc Hugh N., Meuwissen T., Cromie A. and Sonesson A. (2011). Use of female information in dairy cattle genomic breeding programs. J. Dairy Sci. 94(8), 4109-4118.
Meuwissen T.H., Hayes B.J. and Goddard M. (2013). Accelerating improvement of livestock with genomic selection. Annu. Rev. Anim. Biosci. 1(1), 221-237.
Meuwissen T.H., Hayes B.J. and Goddard M. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics. 157(4), 1819-1829.
Pryce J., Bolormaa S., Chamberlain A., Bowman P., Savin K., Goddard M. and Hayes B.J. (2010). A validated genome-wide association study in 2 dairy cattle breeds for milk production and fertility traits using variable length haplotypes. J. Dairy Sci. 93(7), 3331-3345.
Sargolzaei M. and Schenkel F.S. (2009). QMSim: a large-scale genome simulator for livestock. Bioinformatics. 25(5), 680-681.
Williams J. (2005). The use of marker-assisted selection in animal breeding and biotechnology. Rev. Sci. Technol. 24(1), 379-391.
