Abstract
High density genotyping panels have been used in a wide range of applications. From population genetics to genome-wide association studies, this technology still offers the lowest cost and the most consistent solution for generating SNP data. However, in spite of the application, part of the generated data is always discarded from final datasets based on quality control criteria used to remove unreliable markers. Some discarded data consists of markers that failed to generate genotypes, labeled as missing genotypes. A subset of missing genotypes that occur in the whole population under study may be caused by technical issues but can also be explained by the presence of genomic variations that are in the vicinity of the assayed SNP and that prevent genotyping probes from annealing. The latter case may contain relevant information because these missing genotypes might be used to identify population-specific genomic variants. In order to assess which case is more prevalent, we used Illumina HD Bovine chip genotypes from 1,709 Nelore (Bos indicus) samples. We found 3,200 missing genotypes among the whole population. NGS re-sequencing data from 8 sires were used to verify the presence of genomic variations within their flanking regions in 81.56% of these missing genotypes. Furthermore, we discovered 3,300 novel SNPs/Indels, 31% of which are located in genes that may affect traits of importance for the genetic improvement of cattle production.
Highlights
Despite the strong lasting trend of decreasing costs associated with DNA sequencing caused by the continuing development of Generation Sequencing (NGS) technologies, SNP genotyping with DNA chips still offers the lowest cost and the most consistent solution for generating highly repeatable High-Density (HD) SNP data[1]
We present results confirming that SFNBs reveal divergent genomic variants between the Bos taurus and Bos indicus subspecies, and that these genomic variants observed in Nelore cattle (GVON)s can be found within genes that may affect production traits of importance for genetic improvement in cattle
The analysis revealed that 2,068 SNPs (64.97%) are located within intergenic regions (Fig 4) while 1,113 SNPs are located in intragenic regions: 751 SNPs (23.59%) are located within introns, 167 (5.25%) are upstream and 140 (4.4%) are downstream of assayed SNPs, 21 (0.66%) are non-synonymous variants, 20 (0.63%) are synonymous variants, 9 (0.28%) are located on 3’ UTR regions, 3 (0.09%) are located on 5’ UTR regions, 2 (0.06%) result in stop loss variants and 2 (0.06%) were found to be located on noncoding transcripts
Summary
Despite the strong lasting trend of decreasing costs associated with DNA sequencing caused by the continuing development of Generation Sequencing (NGS) technologies, SNP genotyping with DNA chips still offers the lowest cost and the most consistent solution for generating highly repeatable High-Density (HD) SNP data[1]. HD SNP data has been used in a wide range of applications, including population genetics, case-control and genome-wide association studies (GWAS), genomic evaluation and selection, and more recently copy number variation (CNV) studies [7]. As expected, genotyping probes cannot consistently anneal in the presence of any genomic variations (SNPs, deletions, insertions, etc) within target sequences and fail to produce accurate genotypes, or in some cases continually generate no genotypes at all, the so-called missing genotypes. A recent study [8] has indicated that this issue may be more complex than previously thought because genomic variations outside target regions can prevent probes from properly annealing and performing their function as well. Any genomic variation within flanking regions, even those outside probe target sequences, might hamper accurate genotyping
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