Genetic variance explanation of Residual Feed Intake (RFI) by SNPs discovered on transcriptome of Holstein cows

Banabazi, Mohammad Hossein; Nejati Javaremi, Ardeshir; Imumorin, Ikhide; Ghaderi Zefrei, Mostafa

doi:10.22092/asj.2017.111320

Document Type : Research Paper

Authors

Department of Animal Science, University of Yasouj, Faculty of Agriculture, 74934, Yasouj, IRAN.

https://doi.org/10.22092/asj.2017.111320

Abstract

In recent years, Single Nucleotide Polymorphisms (SNPs) has been the most important and efficient tool in animal breeding. Genome-wide Association Studies (GWAS) and Genome-enabled predictions (genomic selection) are two major applications of SNPs in animal genetics and breeding that both relay on genotyping a lot of SNPs. Some high dense (HD) SNP arrays have been applied particularly in dairy cattle for this purpose. Since transcriptome is resulted only from transcription of coding genomic regions and finally expressed into a protein, the SNPs located on transcriptome may estimate breeding values well. Based on this hypothesis, SNP discovery was done on a transcriptome assembled from aligning and mapping of RNA-Seq reads on bovine reference genome for an US Holstein cow population by samtools package. Then, the contribution of the discovered SNPs in explanation of genetic variance for Residual Feed Intake (RFI) trait in Australian Holstein was evaluated based on variance components analysis. It was discovered 53478 SNPs that the most weren’t on the current Illumina bovine SNP arrays. Only 6336 discovered SNPs were shared with Illumina bovine HD array. This reduced SNP panel was low dense and therefore could not capture genetic variance of RFI larger than Illumina bovine high dense SNP Chip. It's suggested to applying the discovered SNPs on transcritome in genomic predictions based on whole genome sequencing with no limitations for the number of shred loci with the current Illumina SNP arrays.

Keywords

References

Canovas, A., Rincon, G., Islas-Trejo, A., Wickramasinghe, S., and Medrano, J. (2010). SNP discovery in the bovine milk transcriptome using RNA-Seq technology. Mammalian Genome. 21: 592-598.

Djari, A., Esquerre, D., Weiss, B., Martins, F., Meersseman, C., Boussaha, M., et al. (2013). Gene-based single nucleotide polymorphism discovery in bovine muscle using next-generation transcriptomic sequencing. BMC Genomics. 14: 307.

Do, D.N., Janss, L.L. G., Jensen, J., and Kadarmideen, H.N. (2015). SNP annotation-based whole genomic prediction and selection: An application to feed efficiency and its component traits in pigs. Journal of Animal Science. 93: 2056-2063.

Druet, T., Macleod, I.M., and Hayes, B.J. (2014). Toward genomic prediction from whole-genome sequence data: impact of sequencing design on genotype imputation and accuracy of predictions. Heredity. 112: 39-47.

Erbe, M., Hayes, B.J., Matukumalli, L.K., Goswami, S., Bowman, P.J., Reich, C.M., et al. (2012). Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels. Journal of Dairy Science. 95: 4114-4129.

Flintoft, L. (2008). Transcriptomics: Digging deep with RNA-Seq. Nature Reviews Genetics, 9: 568-568.

Haas, B.J. and Zody, M.C. (2010). Advancing RNA-seq analysis. Nature Biotechnolog, 28: 421-423.

Hayes, B., Bowman, P., Chamberlain, A., and Goddard, M. (2009). Invited review: genomic selection in dairy cattle: progress and challenge. Journal of Dairy Science, 92: 433 - 443.

Huang, W., Nadeem, A., Zhang, B., Babar, M., Soller, M., and Khatib, H. (2012). Characterization and Comparison of the Leukocyte Transcriptomes of Three Cattle Breeds. PLoS ONE, 7: e30244.

Khansefid, M., Pryce, J.E., Bolormaa, S., Miller, S.P., Wang, Z., Li, C., and Goddard, M. E. (2014). Estimation of genomic breeding values for residual feed intake in a multibreed cattle population. Journal of Animal Science. 92: 3270-3283.

Koufariotis, L., Chen, Y.-P., Bolormaa, S., and Hayes, B. (2014). Regulatory and coding genome regions are enriched for trait associated variants in dairy and beef cattle. BMC Genomics. 15: 436.

Levin, J., Adiconis, X., Yassour, M., Thompson, D., Guttman, M., Berger, M., et al. (2010). [v1] Development and evaluation of RNA-seq methods. Genome Biology. 11(Suppl 1): P26.

Marguerat, S. and Bahler, J. (2010). RNA-seq: from technology to biology. Cellular and Molecular Life Sciences. 67: 569-579.

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

Morota, G., Abdollahi-Arpanahi, R., Kranis, A., and Gianola, D. (2014). Genome-enabled prediction of quantitative traits in chickens using genomic annotation. BMC Genomics. 15: 109.

Mortazavi, A., Williams, B.A., McCue, K., Schaeffer, L., and Wold, B. (2008). Mapping and quantifying mammalian transcriptomes by RNA-seq. Nature Methods. 5: 621-628.

Nicolazzi, E.L., Caprera, A., Nazzicari, N., Cozzi, P., Strozzi, F., Lawley, C., et al. (2015). SNPchiMp v.3: integrating and standardizing single nucleotide polymorphism data for livestock species. BMC genomics, 16: 283.

Park, K.-D., Park, J., Ko, J., Kim, B., Kim, H.-S., Ahn, K., et al. (2012). Whole transcriptome analyses of six thoroughbred horses before and after exercise using RNA-Seq. BMC Genomics. 13: 473.

Pennisi, E. (2012). ENCODE Project Writes Eulogy for Junk DNA. Science. 337: 1159-1161.

Ponting, C.P., Oliver, P.L. and Reik, W. (2009). Evolution and functions of long noncoding RNAs. Cell. 136: 629-641.

Pryce, J., Arias, J., Bowman, P., Davis, S., Macdonald, K., Waghorn, G. et al. (2012). Accuracy of genomic predictions of residual feed intake and 250-day body weight in growing heifers using 625,000 single nucleotide polymorphism markers. Journal of Dairy Science. 95: 2108 - 2119.

Schaeffer, L. (2006). Strategy for applying genome-wide selection in dairy cattle. Journal of Animal Breeding and Genetics. 123: 218-223.

Sharma, U., Banerjee, P., Joshi, J. and Vijh R.K. (2012). Identification of SNPs in Goats (Capra hircus) using RNA-Seq Analysis. International Journal of Animal and Veterinary Advances, 4: 272-283.

Wang, K.C. and Chang, H.Y. (2011). Molecular mechanisms of long noncoding RNAs. Molecular Cell. 43: 904-914.

Wang, Z., Gerstein, M., and Snyder, M. (2009). RNA-Seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics. 10: 57- 63.

Wilhelm, B.T. and Landry, J.R. (2009). RNA-seq-quantitative measurement of expression through massively parallel RNA-sequencing. Methods. 48: 249-257.

Zhang, C., Wang, G., Wang, J., Ji, Z., Liu, Z., Pi, X., and Chen, C. (2013). Characterization and Comparative Analyses of Muscle Transcriptomes in Dorper and Small-Tailed Han Sheep Using RNA-Seq Technique. PLoS ONE, 8: e72686.

Research Journal of Livestock Science

Genetic variance explanation of Residual Feed Intake (RFI) by SNPs discovered on transcriptome of Holstein cows

References

References

Volume 30, Issue 114
June 2017
Pages 169-182

Genetic variance explanation of Residual Feed Intake (RFI) by SNPs discovered on transcriptome of Holstein cows

References

References

Volume 30, Issue 114June 2017Pages 169-182

Volume 30, Issue 114
June 2017
Pages 169-182