Contribution of All Single Nucleotide Polymorphisms (SNPs) and Minor Allele Frequency Groups to Genetic Variation of Quantitative Traits in Suffolk Sheep
محورهای موضوعی : Camel
A. Taheri Yeganeh
1
(Department of Agriculture, Iranian Research Organization for Science and Technology (IROST), Tehran, Iran)
M.R. Sanjabi
2
(Department of Agriculture, Iranian Research Organization for Science and Technology (IROST), Tehran, Iran)
J. Fayazi
3
(Department of Animal Science, Agricultural Science and Natural Resources University of Khuzestan, Mollasani, Khuzestan, Iran)
M. Zandi
4
(Department of Agriculture, Iranian Research Organization for Science and Technology (IROST), Tehran, Iran)
J. Van der Werf
5
(School of Environmental and Rural Science, University of New England, Armidale, Australia)
کلید واژه: genomic selection, Bayesian method, genomic variance, Suffolk sheep, joint analysis,
چکیده مقاله :
Accurate estimation of genetic and non-genetic variance components with pedigree and genomic information is essential for the correct prediction of breeding values. Single nucleotide polymorphisms (SNPs) from Australian Suffolk sheep were used in this research (50K illumine). The characteristics of birth weight, weaning weight, length, and diameter of the wool warp were investigated. To study the relationship between allelic frequency and the amount of additive genetic variance explained. The SNPs were classified into five different groups of rare allelic frequency (MAF). Two statistical models were fitted a separate and joint analysis of each SNPs group statistical analyses were performed with the Bayesian method using the Gibbs sampling technique and the RKHS model (semiparametric method). The amount of genomic heritability estimated by all SNPs in the Bayesian approach were estimated as 0.46, 0.19, 0.75, and 0.48 for the traits of birth weight, weaning weight, length, and wool diameter, respectively. The total heritability estimated for different groups of rare allele frequencies, in the combined analysis were almost similar to the value obtained from all SNPs for all traits. Although the numbers of SNPs in different groups were similar, the amounts of genetic variance explained by MAF groups were different. For the two traits of birth weight and weaning weight, the first group with an allelic frequency of 0.01-0.1 had the highest amount of genomic heritability. The amount of genomic heritability of fibre diameter was variable in five MAF groups. The highest estimated value in the fifth group with rare allele frequency was 0.4-0.5 (about 0.21), and the lowest value was in the third group (0.2-0.3), which was estimated at 0.098. The staple length trait, the genetic variance distribution pattern justified by SNPs, fluctuated between the five MAF groups, and the heritability value varied from zero in the second and fourth groups to about 0.16 in the third group.
Abdollahi-Arpanahi R., Pakdel A., Nejati-Javaremi A., Moradi Shahrbabak M., Morota G., Valente, B.D., Kranis A., Rosa G.J.M. and Gianola D. (2014). Dissection of additive genetic variability for quantitative traits in chickens using SNP markers. Anim. Breed. Genet. 131, 183-193.
Emrani H., Vaez Torshizi R., Masoudi A. and Ehsani A. (2017). Estimation of genomic heritability for growth traits in an F2 cross of Arian broiler line and Azerbaijan indigenous chicken using 60K SNP Beadchip. Anim. Sci. J. (Pajouhesh and Sazandegi). 114, 273-284.
Gu X.R., Feng C.G., Ma L., Song C., Wang Y.Q., Da Y., Li H., Chen K., Ye S., Ge C., Hu X. and Li N. (2011). Genome-wide association study of body weight in chicken F2 resource population. PLoS One. 6(7), e21872.
Haile-Mariam M., Nieuwhof G., Beard K., Konstatinov K. and Hayes B. (2013). Comparison of heritabilities of dairy traits in Australian Holstein-Friesian cattle from genomic and pedigree data and implications for genomic evaluations. J. Anim. Breed. Genet. 130, 20-31.
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, 433-443.
Jensen J., Su G. and Madsen P. (2012). Partitioning additive genetic variance into genomic and remaining polygenic components for complex traits in dairy cattle. BMC Genet. 13, 44-51.
Lee S.H., DeCandia T.R., Ripke S., Yang J., Sullivan P.F., Goddard M.E., Keller M.E., Matthew C., Visscher Peter M. and Wray Naomi R. (2012). Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs. Nat. Genet. 44, 247-250.
Lee S.H., Harold D., Nyholt D.R., Goddard M.E., Zondervan K.T., Williams J., Montgomery G.W., Wray N.R. and Visscher P.M. (2013). Estimation and partitioning of polygenic variation captured by common SNPs for Alzheimer's disease, multiple sclerosis, and endometriosis. Hum. Mol. Genet. 22, 832-841.
Meuwissen T.H., Hayes B.J. and Goddard M.E. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics. 157, 1819-1829.
Meuwissen T.H., Hayes B.J., MacLeod I. and Goddard M. (2022). Identification of genomic variants causing variation in quantitative traits: A review. Agriculture. 12(10), 1713-1721.
Ogawa S.H., Matsuda1 H., Taniguchi Y., Watanabe T., Sugimoto Y. and Iwaisaki H. (2016). Estimated genetic variance explained by single nucleotide polymorphisms of different minor allele frequencies for carcass traits in japanese black cattle. J. Biosci. Med. 4, 89-97.
Pérez P. and de los Campos G. (2013). BGLR: A statistical package for whole genome regression and prediction. R package version. Genetics. 198(2), 483-495.
Pimentel E.C.G., Erbe M., Konig S. and Simianer H. (2011). Genome partitioning of genetic variation for milk production and composition traits in Holstein cattle. Front. Genet. 2, 19-27.
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. and Sham P.C. (2007). PLINK: A tool set for whole-genome association and population-based linkage analyses. American J. Hum. Genet. 81, 559-575.
Rashedi Dehsahraei A., Fayazi J., Abdollahi Arpanahi R., Van Der Werf J. and Roshanfekr H. (2017). Estimation of variance components for the body weight of Merino sheep at birth and weaning using single nucleotide markers and REML and Bayesian approaches. J. Rumin. Res. 5(2), 29-44.
Rashedi Dehsahraei A., Fayazi J., Abdollahi Arpanahi R., Van Der Werf J. and Roshanfekr H. (2018). The effect of Prosopis fractal on the performance, some blood parameters, and immune and antioxidant system of broiler chickens under heat stress conditions. Anim. Sci. J. (Pajouhesh and Sazandegi). 120, 35-46.
Sun Y.F., Liu R.R., Zheng M.Q., Zhao G.P., Zhang L., Wu D., Hu Y.D., Li P. and Wen J. (2013). Genome-wide association study on shank length and shank girth in chicken. Chinese's J. Anim. Vet. Sci. 44, 358-365.
Taheri Yeganeh A., Sanjabi M.R., Fayazi J., Zandi M. and Van der Werf J. (2022). Estimation of variance components and genome partitioning according to minor allele frequency for quantitative traits in sheep. Res. Anim. Prod. 13, 35-42.
Uemoto Y., Sasaki S., Kojima T., Sugimoto Y. and Watanabe T. (2015). Impact of QTL minor allele frequency on genomic evaluation using real genotype data and simulated phenotypes in Japanese Black cattle. BMC Genet. 16, 134-142.
VanRaden P.M. (2008). Efficient methods to compute genomic predictions. J. Dairy Sci. 91, 4414-4423.
Yang J., Benyamin B., McEvoy B.P., Gordon S., Henders A.K., Nyholt D.R., Madden P.A., Heath A.C., Martin N.G., Montgomery G.W., Goddard M.E. and Visscher P.M. (2010). Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42, 565-569.
