|Heaton, Michael - Mike|
|KALBFLEISCH, THEODORE - University Of Louisville|
|Smith, Timothy - Tim|
|SIMPSON, BARRY - Geneseek Inc, A Neogen Company|
|QIU, JIANSHENG - Geneseek Inc, A Neogen Company|
Submitted to: Plant and Animal Genome Conference Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: 11/5/2015
Publication Date: 12/5/2015
Citation: Heaton, M.P., Kalbfleisch, T.S., Carnahan, J.K., Smith, T.P.L., Harhay, G.P., Simpson, B., Qiu, J. 2015. A searchable, whole genome resource designed for protein variant analysis in diverse lineages of U.S. beef cattle [abstract]. International Plant and Animal Genome XXIV Conference Proceedings, January 9-13, 2016, San Diego, CA. Poster #P0506.
Technical Abstract: A key feature of a gene's function is the variety of protein isoforms it encodes in a population. However, the genetic diversity in bovine whole genome databases tends to be underrepresented because these databases contain an abundance of sequence from the most influential sires. Our first aim was to create a unique set of 96 mapped genomes from 19 U.S. beef breeds, with bulls from distinct lineages. Our second aim was to illustrate the utility of this publicly viewable set of genomes for identifying rare protein variants encoded by a gene of interest. For each bull, the identity and quality of its mapped data set was tested by comparing more than 700k single nucleotide polymorphism (SNP) genotypes to those previously derived from other platforms. The average read depth at parentage SNPs was about 12.5, and the scoring rate and accuracy were approximately 99% each. The bovine beta-2 integrin gene (ITGB2) was used as an example of protein variant analysis and found to contain 14 missense SNPs affecting 12 codons. Rare codon mutations, not previously detected by PCR-Sanger sequencing, were observed in Simmental and Santa Gertrudis bulls. Eleven protein haplotypes were inferred by maximum parsimony, resulting in 66 possible paired combinations of ITGB2 protein variants. Thus, this searchable, diverse, whole genome resource facilitates accurate identification of protein variants in silico for U.S. beef cattle and provides a means of translating WGS data into a biological context.