Impact of reference population and marker density on accuracy of population imputation

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00027014%3A_____%2F19%3AN0000168" target="_blank" >RIV/00027014:_____/19:N0000168 - isvavai.cz</a>

Result on the web

<a href="https://www.agriculturejournals.cz/publicFiles/148_2019-CJAS.pdf" target="_blank" >https://www.agriculturejournals.cz/publicFiles/148_2019-CJAS.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.17221/148/2019-CJAS" target="_blank" >10.17221/148/2019-CJAS</a>

Result language

angličtina

Original language name

Impact of reference population and marker density on accuracy of population imputation

Original language description

The effect of the reference population size and the number of missing single nucleotide polymorphisms (SNPs) on imputation accuracy was determined. The population imputation method using the FImpute software was applied. The dataset used for the purpose of this study was taken from the database of the Holstein Cattle Breeders Association of the Czech Republic. It contains 1000 animals genotyped with the Illumina BovineSNP50 v.2 BeadChip. Two datasets were created, the first containing the original genotypes, including the missing SNPs, the second containing the same genotypes modified to avoid missing data. In these datasets, animals were randomly selected for a reference population (10, 25, 50 and 75%) and there were randomly selected SNPs for deletion (15, 30, 55, 70, and 95%) in animals that were not used as the reference population. Subsequently, the data accuracy was determined by two parameters: correlation between original and imputed SNPs and percentage of correctly imputed SNPs. Since animals and SNPs were randomly selected, the process including data imputation was repeated 100 times. Accuracy was determined as the average accuracy over all repetitions. It was found that the imputation accuracy is influenced by both parameters. If the size of the reference population is sufficient, the imputation accuracy is higher despite the large number of missing SNPs.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

40203 - Husbandry

Project

<a href="/en/project/QK1810253" target="_blank" >QK1810253: Improvement of national-wide genomic evaluation of dairy cattle by including cows with domestic production records into genotyped reference population</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2019

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Czech Journal of Animal Science

ISSN

1212-1819

e-ISSN

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Volume of the periodical

64

Issue of the periodical within the volume

10

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

6

Pages from-to

405-410

UT code for WoS article

000490241000001

EID of the result in the Scopus database

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