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Genomic Variants Revealed by Invariably Missing Genotypes in Nelore Cattle.

da Silva JM, Giachetto PF, da Silva LO, Cintra LC, Paiva SR, Caetano AR, Yamagishi ME - PLoS ONE (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Faculdade de Ciências Agrárias, Biológicas e Sociais Aplicadas, Universidade do Estado de Mato Grosso (UNEMAT), Nova Xavantina, Mato Grosso, Brazil; Programa de Pós-Graduação em Genética e Biologia Molecular-Instituto de Biologia, Universidade Estadual de Campinas (UNICAMP), Campinas, São Paulo, Brazil.

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.

No MeSH data available.


Regions defined for obtaining estimates of genomic variation.P1 represents the 50bp Illumina probe target sequence. P2 corresponds to the 50bp adjacent to P1 on the distal side of the assayed SNP. S1 and S2 are symmetrical to P1 and P2, respectively.
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pone.0136035.g001: Regions defined for obtaining estimates of genomic variation.P1 represents the 50bp Illumina probe target sequence. P2 corresponds to the 50bp adjacent to P1 on the distal side of the assayed SNP. S1 and S2 are symmetrical to P1 and P2, respectively.

Mentions: The complete set of the Illumina BovineHD 50bp probe sequences was downloaded from the manufacturer’s website. Each one of the 50bp probe sequences was blasted against the UMD3.1 reference bovine genome. This procedure was necessary for the acquisition of both the probes’ genomic start and end positions and their strand orientation. A C++ program was developed to integrate all the aforementioned information and to classify observed genomic variations according to their position in relation to each SFNB: 50bp Illumina probe target sequence (P1), 50bp adjacent to P1 on the distal side of the assayed SNP, and the symmetrical regions to P1 (S1) and P2 (S2) (see Fig 1).


Genomic Variants Revealed by Invariably Missing Genotypes in Nelore Cattle.

da Silva JM, Giachetto PF, da Silva LO, Cintra LC, Paiva SR, Caetano AR, Yamagishi ME - PLoS ONE (2015)

Regions defined for obtaining estimates of genomic variation.P1 represents the 50bp Illumina probe target sequence. P2 corresponds to the 50bp adjacent to P1 on the distal side of the assayed SNP. S1 and S2 are symmetrical to P1 and P2, respectively.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4549312&req=5

pone.0136035.g001: Regions defined for obtaining estimates of genomic variation.P1 represents the 50bp Illumina probe target sequence. P2 corresponds to the 50bp adjacent to P1 on the distal side of the assayed SNP. S1 and S2 are symmetrical to P1 and P2, respectively.
Mentions: The complete set of the Illumina BovineHD 50bp probe sequences was downloaded from the manufacturer’s website. Each one of the 50bp probe sequences was blasted against the UMD3.1 reference bovine genome. This procedure was necessary for the acquisition of both the probes’ genomic start and end positions and their strand orientation. A C++ program was developed to integrate all the aforementioned information and to classify observed genomic variations according to their position in relation to each SFNB: 50bp Illumina probe target sequence (P1), 50bp adjacent to P1 on the distal side of the assayed SNP, and the symmetrical regions to P1 (S1) and P2 (S2) (see Fig 1).

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Faculdade de Ciências Agrárias, Biológicas e Sociais Aplicadas, Universidade do Estado de Mato Grosso (UNEMAT), Nova Xavantina, Mato Grosso, Brazil; Programa de Pós-Graduação em Genética e Biologia Molecular-Instituto de Biologia, Universidade Estadual de Campinas (UNICAMP), Campinas, São Paulo, Brazil.

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.

No MeSH data available.