Evidence map›Paper›PMID 25927950›Full record

ArticleGenetics, selection, evolution : GSE2014

Accuracy of genotype imputation in Nelore cattle.

Roberto Carvalheiro, Solomon A Boison, Haroldo H R Neves, Mehdi Sargolzaei, Flavio S Schenkel, Yuri T Utsunomiya, Ana Maria Pérez O'Brien, Johann Sölkner, John C McEwan, Curtis P Van Tassell and 2 more

Open access · goldAbstract read
In one paragraph

Article in Genetics, selection, evolution : GSE, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed, 1 pooled it
2.3field-weighted citation impact, top 11% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

42 citing papers in PubMed, 1 synthesis or guideline pooled it, 121 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 5 institutions in 5 countries.

Roberto CarvalheiroUNESP, Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal, São Paulo, 14884-900, Brazil. rcar@fcav.unesp.br.
Solomon A BoisonDivision of Livestock Sciences, Department of Sustainable Agricultural Systems BOKU, University of Natural Resources and Life Sciences, Vienna, A-1180, Austria. soloboan@yahoo.com.
Haroldo H R NevesUNESP, Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal, São Paulo, 14884-900, Brazil. haroldozoo@hotmail.com.
Mehdi SargolzaeiCentre for Genetic Improvement of Livestock, University of Guelph, Guelph, ON, N1G2W1, Canada. msargol@uoguelph.ca.
Flavio S SchenkelCentre for Genetic Improvement of Livestock, University of Guelph, Guelph, ON, N1G2W1, Canada. schenkel@uoguelph.ca.
Yuri T UtsunomiyaUNESP, Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal, São Paulo, 14884-900, Brazil. ytutsunomiya@gmail.com.
Ana Maria Pérez O'BrienDivision of Livestock Sciences, Department of Sustainable Agricultural Systems BOKU, University of Natural Resources and Life Sciences, Vienna, A-1180, Austria. anita_op@hotmail.com.
Johann SölknerDivision of Livestock Sciences, Department of Sustainable Agricultural Systems BOKU, University of Natural Resources and Life Sciences, Vienna, A-1180, Austria. johann.soelkner@boku.ac.at.
John C McEwanCentre for Reproduction and Genomics, AgResearch, Invermay, Mosgiel, New Zealand. john.mcewan@agresearch.co.nz.
Curtis P Van TassellUnited States Department of Agriculture, Agricultural Research Service, Bovine Functional Genomics Laboratory, Beltsville, MD, 20705, USA. curt.vantassell@ars.usda.gov.
Tad S SonstegardUnited States Department of Agriculture, Agricultural Research Service, Bovine Functional Genomics Laboratory, Beltsville, MD, 20705, USA. tad.sonstegard@ars.usda.gov.
José Fernando GarciaUNESP, Universidade Estadual Paulista, Faculdade de Ciências Agrárias e Veterinárias, Jaboticabal, São Paulo, 14884-900, Brazil. jfgarcia@fmva.unesp.br.
Universidade Estadual Paulista (Unesp) · BRBOKU University · ATAgricultural Research Service · USUniversity of Guelph · CAAgResearch · NZ

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 taurine breeds. Thus, it is important to evaluate the accuracy of imputation to better define which imputation method and chip are most appropriate for genomic applications in indicine breeds.

methodsAccuracy of genotype imputation in Nelore cattle was evaluated using different LD chips, imputation software and sets of animals. Twelve commercial and customized LD chips with densities ranging from 7 K to 75 K were tested. Customized LD chips were virtually designed taking into account minor allele frequency, linkage disequilibrium and distance between markers. Software programs FImpute and BEAGLE were applied to impute genotypes. From 995 bulls and 1247 cows that were genotyped with the Illumina® BovineHD chip (HD), 793 sires composed the reference set, and the remaining 202 younger sires and all the cows composed two separate validation sets for which genotypes were masked except for the SNPs of the LD chip that were to be tested.

resultsImputation accuracy increased with the SNP density of the LD chip. However, the gain in accuracy with LD chips with more than 15 K SNPs was relatively small because accuracy was already high at this density. Commercial and customized LD chips with equivalent densities presented similar results. FImpute outperformed BEAGLE for all LD chips and validation sets. Regardless of the imputation software used, accuracy tended to increase as the relatedness between imputed and reference animals increased, especially for the 7 K chip.

conclusionsIf the Illumina® BovineHD is considered as the target chip for genomic applications in the Nelore breed, cost-effectiveness can be improved by genotyping part of the animals with a chip containing around 15 K useful SNPs and imputing their high-density missing genotypes with FImpute.

Indexed as

Polymorphism, Single NucleotideAnimalsCattleFemaleGenomicsGenotypeGenotyping TechniquesMaleOligonucleotide Array Sequence AnalysisPedigreeQuality ControlReproducibility of ResultsSoftware

Identifiers

PMID25927950
PMCPMC4192291
OpenAlexW2156976856

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.