2014
DOI: 10.1186/s12711-014-0069-1
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Accuracy of genotype imputation in Nelore cattle

Abstract: BackgroundGenotype imputation from low-density (LD) to high-density single nucleotide polymorphism (SNP) chips is an important step before applying genomic selection, since denser chips tend to provide more reliable genomic predictions. Imputation methods rely partially on linkage disequilibrium between markers to infer unobserved genotypes. Bos indicus cattle (e.g. Nelore breed) are characterized, in general, by lower levels of linkage disequilibrium between genetic markers at short distances, compared to tau… Show more

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Cited by 92 publications
(82 citation statements)
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References 26 publications
(45 reference statements)
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“…Neves et al (2014) documented the feasibility of using different genetic evaluation and validation methods for genomic selection in Nellore. Carvalheiro et al (2014) proposed the best imputation strategy for increasing the power of genomic information by predicting large SNP panels ( > 700,000) from panels as low as 15,000 SNPs. These results have prompted increased interest among breeders in adopting genomic technologies with a greater uptake expected in 2016.…”
Section: South Americamentioning
confidence: 99%
“…Neves et al (2014) documented the feasibility of using different genetic evaluation and validation methods for genomic selection in Nellore. Carvalheiro et al (2014) proposed the best imputation strategy for increasing the power of genomic information by predicting large SNP panels ( > 700,000) from panels as low as 15,000 SNPs. These results have prompted increased interest among breeders in adopting genomic technologies with a greater uptake expected in 2016.…”
Section: South Americamentioning
confidence: 99%
“…A total of 1,900 heifers and 1,129 sires were genotyped using the Illumina BovineHD BeadChip assay (770 k, Illumina Inc., San Diego, CA, USA) and 850 heifers using GeneSeek ® Genomic Profiler HDi 75 K (GeneSeek In/c., Lincoln, NE). The heifers genotyped with the lower density panel (75 K) were imputed to the HD panel using FImpute v2.2 12 with an expected imputation accuracy of 0.98, according to the imputation study performed by Carvalheiro et al 13 . FImpute was run using pedigree information as well as parentage testing option.…”
mentioning
confidence: 99%
“…Genotype imputation accuracy was higher than 97% as previously reported by Carvalheiro et al (2014). After the imputation process, markers with MAF <5% were excluded, resulting in a final dataset with 10,909 genotyped animals (7,309 males and 3,600 females) and 397,423 SNPs.…”
Section: Genotypic Datamentioning
confidence: 76%