Yekaterinburg, Russian Federation
Abstract. Genome-wide association analysis is one of the key tools for elucidating the genetic etiology of various phenotypes, including diseases and the degree of predisposition to them. Subsequently, for statistically significant genetic markers, it is necessary to conduct validation studies on independent cohorts. These post-GWAS validation studies test genetic markers that are strongly associated with the phenotype, regardless of sample size, allowing to identify false-positive results from the initial association analysis. When choosing a genotyping technique for post-GWAS validation studies, consideration should be given to the sample size and the number of genetic markers planned to be studied, since genotyping techniques differ in throughput und cost. The aim of this paper is to describe modern methods of genotyping depending on their performance and to carry out genotyping of cattle for the SNPs rs137396952 and rs134055603, for which a high degree of association with the development of ketosis was shown in previous GWAS studies. Utilized genotyping methods include TaqMan and High-Resolution Melt Analysis; genotype analysis was performed using the SNPStats web tool. When comparing the results of genotyping using these technologies, the specifity of allelic discrimination carried out using these methods was demonstrated. Testing of the genotyping results had shown that rs134055603 does not obey the Hardy-Weinberg equilibrium in the studied cohort of animals. Scientific novelty. Obtained genotyping results will be used in further association tests with physiologically valuable parameters of dairy cattle, including resistance to diseases.
GWAS, cattle, genotyping, SNP, DNA, PCR, electrophoresis
1. Uffelmann E., Huang Q. Q., Munung N. S., de Vries J., Okada Y., Martin A. R., Martin H. C., Lappalainen T., Posthuma D. Genome-wide association studies // Nature Reviews Methods Primers. 2021. Vol. 1. No. 1. Article number 59. DOI:https://doi.org/10.1038/s43586-021-00056-9.
2. Oetting W. S., Jacobson P. A., Israni A. K. Validation Is Critical for Genome-Wide Association Study-Based Associations // American Journal of Transplantation. 2017. Vol. 17. No. 2. Pp. 318-319. DOI:https://doi.org/10.1111/ajt.14051.
3. Goodwin S., McPherson J. D., McCombie W. R. Coming of age: ten years of next-generation sequencing technologies // Nature Reviews Genetics. 2016. Vol. 17. No. 6. Pp. 333-351. DOI:https://doi.org/10.1038/nrg.2016.49.
4. Kovalchuk S. N., Arkhipova A. L. Development of TaqMan PCR assay for genotyping SNP rs211250281 of the bovine agpat6 gene // Animal Biotechnology. 2022. DOI:https://doi.org/10.1080/10495398.2022.2077742.
5. Heather J. M., Chain B. The sequence of sequencers: The history of sequencing DNA // Genomics. 2016. Vol. 107. No. 1. DOI:https://doi.org/10.1016/j.ygeno.2015.11.003.
6. Jiang J., Ma L., Prakapenka D., VanRaden P. M., Cole J. B., Da Y. A Large-Scale Genome-Wide Association Study in U.S. Holstein Cattle // Frontiers in Genetics. 2019. Vol. 10. Article number 412. DOI:https://doi.org/10.3389/fgene.2019.00412.
7. Liu Y., Jiao Y., Li P., Zan L. MALDI-TOF-MS-based high throughput genotyping of mutations associated with body measurement traits in cattle // Mammalian Genome. 2020. Vol. 31. No. 7-8. Pp. 228-239. DOI:https://doi.org/10.1007/s00335-020-09840-6.
8. Nayeri S., Schenkel F., Fleming A., Kroezen V., Sargolzaei M., Baes C., Canovas A., Squires J., Miglior F. Genome-wide association analysis for beta-hydroxybutyrate concentration in Milk in Holstein dairy cattle // BMC Genetics. 2019. Vol. 20. No. 1. Article number 58. DOI:https://doi.org/10.1186/s12863-019-0761-9.
9. Kalendar R., Khassenov B., Ramankulov Y., Samuilova O., Ivanov K. I. FastPCR: An in silico tool for fast primer and probe design and advanced sequence analysis // Genomics. 2017. Vol. 109. No. 3-4. Pp. 312-319. DOI:https://doi.org/10.1016/j.ygeno.2017.05.005.
10. Kalendar R., Lee D., Schulman A. H. Java web tools for PCR, in silico PCR, and oligonucleotide assembly and analysis // Genomics. 2011. Vol. 98. No. 2. Pp. 137-144. DOI:https://doi.org/10.1016/j.ygeno.2011.04.009.
11. Pavlo H. pavlohrab/hrmR: Pre-release version (v0.1-alpha) // Zenodo. 2021. DOI:https://doi.org/10.5281/zenodo.4491296.
12. Sole X., Guino E., Valls J., Iniesta R., Moreno V. SNPStats: a web tool for the analysis of association studies // Bioinformatics. 2006. Vol. 22. No. 15. Pp. 1928-1929. DOI:https://doi.org/10.1093/bioinformatics/btl268.
13. Chen Z., Boehnke M., Wen X., Mukherjee B. Revisiting the genome-wide significance threshold for common variant GWAS // G3 (Bethesda). 2021. Vol. 11. No. 2. Article number jkaa056. DOI:https://doi.org/10.1093/g3journal/jkaa056.
14. Qanbari S. On the Extent of Linkage Disequilibrium in the Genome of Farm Animals // Frontiers in Genetics. 2019. Vol. 10. Article number 1304. DOI:https://doi.org/10.3389/fgene.2019.01304.
15. Fabbri M. C., Dadousis C., Bozzi R. Estimation of Linkage Disequilibrium and Effective Population Size in Three Italian Autochthonous Beef Breeds // Animals (Basel). 2020. Vol. 10. No. 6. Article number 1034. DOI:https://doi.org/10.3390/ani10061034.
16. Singh A., Kumar A., Mehrotra A., Pandey A. K., Mishra B. P., Dutt T. Estimation of linkage disequilibrium levels and allele frequency distribution in crossbred Vrindavani cattle using 50K SNP data // PLoS One. 2021. Vol. 16. No. 11. Article number e0259572. DOI:https://doi.org/10.1371/journal.pone.0259572.
17. Joiret M., Mahachie John J. M., Gusareva E. S., Van Steen K. Confounding of linkage disequilibrium patterns in large scale DNA based gene-gene interaction studies // BioData Mining. 2019. Vol. 12. Article number 11. DOI:https://doi.org/10.1186/s13040-019-0199-7.
18. Gallagher M. D., Chen-Plotkin A. S. The Post-GWAS Era: From Association to Function // American Journal of Human Genetics. 2018. Vol. 102. No. 5. Pp. 717-730. DOI:https://doi.org/10.1016/j.ajhg.2018.04.002.
19. Adam Y., Samtal C., Brandenburg J. T., Falola O., Adebiyi E. Performing post-genome-wide association study analysis: overview, challenges and recommendations // F1000Research. 2021. Vol. 10. Article number 1002. DOI:https://doi.org/10.12688/f1000research.53962.1.
20. Kempfer R., Pombo A. Methods for mapping 3D chromosome architecture // Nature Reviews Genetics. 2020. Vol. 21. No. 4. Pp. 207-226. DOI:https://doi.org/10.1038/s41576-019-0195-2.
21. Cano-Gamez E., Trynka G. From GWAS to Function: Using Functional Genomics to Identify the Mechanisms Underlying Complex Diseases // Frontiers in Genetics. 2020. Vol. 11. Article number 424. DOI:https://doi.org/10.3389/fgene.2020.00424.