Investigating the impact of alle frequencies on genomic variance components for body weight in Suffolk sheep using SNP data

Document Type : Research Paper

Authors

1 Agriculture And Natural Resources University Of Khouzestan

2 Khuzestan Agricultural and Natural Resources University

3 Assistant professor, Department of Animal and Poultry Science College of Aburaihan, University of Tehran 465 Pakdasht, Iran

4 Professor, School of Environmental and Rural Science, University of New England, Armidale, Australia

Abstract

Since SNP markers in genomic selection spread across the genome, they may potentially explain all of genetic variation. In this study, genotype data from Suffolk sheep, genotyped by 50k Illumina SNP chip were used. Birth weight and weaning weight traits were studied in this research. Quality control for minor allele frequency at two thresholds, one and five percent were studied. To study the association between allele frequency spectrum and captured additive genetic variance, all SNPs were partitioned in five MAF bins with the equal numbers of SNPs. The analysis were performed using GREML approach via GCTA method. Using all SNPs with MAF>0.01 estimates of genomic heritability were 0.45 and 0.19 for birth weight and weaning weight, respectively. For MAF>0.05 these values were 0.42 and 0.18 respectively. The contribution of different groups of SNPs with rare allele frequency in justifying genetic variation for the two traits was different. In general, a significant portion of the genetic variance was explained by SNPs with a MAF

Keywords

References

  • راشدی ده‌صحرائی، آ.، فیاضی، ج.، عبدالهی آرپناهی، ر.، ون درورف، ج. و روشنفکر، ه. (1396). برآورد اجزای واریانس وزن بدن گوسفند مرینوس در تولد و شیرگیری با استفاده از نشانگرهای تک نوکلئوتیدی ودو رویکرد حداکثر درست‌نمایی محدود شده و بیزی. نشریه پژوهش در نشخوارکنندگان. جلد پنجم، شماره دوم، ص ص: 29-44.
  • عبداللهی آرپناهی، ر.، پاکدل، ع. و زندی، م.ب. (1388). از مدل بی‌نهایت ژنگاه با اثرات جزئی تا انتخاب ژنومیک. فصلنامه ژنتیک نوین. سال هفتم، شماره دوم، ص ص: 105-114.
  • عمرانی، ح.، واعظ ترشیزی، ر.، مسعودی، ع. و احسانی، ع. (1396). برآورد وراثت‌پذیری ژنومی صفات رشد در نسل F2 حاصل از تلاقی لاین آرین و مرغ بومی آذربایجان به کمک تراشه 60K. نشریه علوم دامی (پژوهش و سازندگی). شماره 114، ص ص: 273-284.
  • فروتنی‌فر، ص. (1393). انتخاب ژنومی. مجله ژنتیک در هزاره سوم. سال دوازدهم. شماره 4. ص:3794-3805.
  • Abdollahi-Arpanahi, R., Pakdel, A., Nejati-Javaremi, A., Moradi Shahrbabak, M., Morota, G., Valente, B.D. et al. (2014). Dissection of additive genetic variability for quantitative traits in chickens using SNP markers. Animal Breeding and Genetics. 131: 183–193.
  • Dekkers, J.C.M. and Hospital, F. (2002). The use of molecular genetics in the improvement of agricultural populations. Nature Reviews Genetics. 3(1): 22-32.
  • Goddard, M.E., Hayes, B.J., Meuwissen, T.H. (2010). Genomic selection in livestock populations. Genetic Research (Camb). 92(5-6):413-21.
  • 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. Journal of Animal Breeding and Genetics. 130: 20–31.
  • 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 Genetics. 13, 44.
  • Lee, S.H., DeCandia, T.R., Ripke, S., Yang, J., Sullivan, P.F., Goddard, M.E. and et al. (2012). Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs. Nature Genetics. 44: 247–250.
  • Lee, S.H., Harold, D., Nyholt, D.R., Goddard, M.E., Zondervan, K.T., Williams, J. et al. (2013). Estimation and partitioning of polygenic variation captured by common SNPs for Alzheimer’s disease, multiple sclerosis and endometriosis. Human Molecular Genetics. 22: 832–841.
  • Meuwissen, T. H. E., B. J. Hayes, and M. E. Goddard. (2001). Prediction of total genetic value using genome-wide dense marker maps. Genetics. 157:1819–1829.
  • Nejati-Javaremi, A., Smith, C. and Gibson, J. (1997). Effect of total allelic relationship on accuracy of evaluation and response to selection. Journal of animal science 75, 1738-
  • Ogawa, Sh., 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. Journal of Biosciences and Medicines. 4: 89-97.
  • Park, J.H., Gail, M.H., Weinberg, C.R., Carroll, R.J., Chung, C.C., Wang, Z. et al. (2011). Distribution of allele frequencies and effect sizes and their interrelationships for common genetic susceptibility variants. Proceedings of the National Academy of Sciences of the USA. 108(44): 18026–18031.
  • Pimentel, E.C.G., Erbe, M., Konig S. and Simianer. 2011. Genome partitioning of genetic variation for milk production and composition traits in Holstein cattle. Front. Genet. 2,19.
  • Purcell, S., Neale, B., Todd-Brown, K., Thomas. , Ferreira, M.A.R., Bender, D. et al. (2007). PLINK: a tool set for whole-genome association and population-based linkage analyses. American Journal of Human Genetic. 81: 559–575.
  • Simeone, R., Misztal, I., Aguilar, I. and Legarra, A. (2011). Evaluation of the utility of diagonal elements of the genomic relationship matrix as a diagnostic tool to detect mislabeled genotyped animals in a broiler chicken population. Journal of animal breeding and genetics, 128(5): 386-393.
  • 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 Journal of Animal and Veterinary Sciences, 44: 358-365.
  • Uemoto, Y., Sasaki, Sh., 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 Genetics. (2015) 16:134.
  • Van Raden, P.M. (2008). Efficient methods to compute genomic predictions. Journal of Dairy Science. 91:4414–23.
  • Watson, C.T., Disanto, G., Breden, F., Giovannoni, G., and Ramagopalan, S.V. (2012). Estimating the proportion of variation in susceptibility to multiple sclerosis captured by common SNPs. Journal of Scientific Reports. 2:770.
  • Wray, N.R. (2005). Allele frequencies and the r2 measure of linkage disequilibrium: impact on design and interpretation of association studies. Twin Reserch and Human Genetics. 8: 87–94.
  • Yang, J., Benyamin, B., McEvoy, B.P., Gordon, S., Henders, A.K., Nyholt, D.R. et al. (2010). Common SNPs explain a large proportion of the heritability for human height. Nature Genetics. 42: 565–569.
Animal Sciences Journal
Volume 32, Issue 124
December 2019
Pages 119-130

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