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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.


Distribution of SFNBs across bovine chromosomes.
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pone.0136035.g003: Distribution of SFNBs across bovine chromosomes.

Mentions: A total of 3,200 SFNBs were identified in all of the 1,709 Nelore samples evaluated (Fig 2). The number of SNPs observed to be missing in only part of the genotyped samples was minimal. The number of observed SFNBs was not found to be evenly distributed across chromosomes (Fig 3), and the correlation with chromosome size was estimated to be 0.58. Mean concordance observed between genotype calls obtained from the Bovine HD BeadChip and WGS data from eight animals was 99.5%.


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)

Distribution of SFNBs across bovine chromosomes.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0136035.g003: Distribution of SFNBs across bovine chromosomes.
Mentions: A total of 3,200 SFNBs were identified in all of the 1,709 Nelore samples evaluated (Fig 2). The number of SNPs observed to be missing in only part of the genotyped samples was minimal. The number of observed SFNBs was not found to be evenly distributed across chromosomes (Fig 3), and the correlation with chromosome size was estimated to be 0.58. Mean concordance observed between genotype calls obtained from the Bovine HD BeadChip and WGS data from eight animals was 99.5%.

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.