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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

40203 - Husbandry

Projekt

<a href="/cs/project/QK1810253" target="_blank" >QK1810253: Navýšení spolehlivosti celostátního genomického hodnocení dojeného skotu zařazením krav s domácí užitkovostí do genotypované referenční populace</a><br>

Návaznosti

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

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název periodika

Czech Journal of Animal Science

ISSN

1212-1819

e-ISSN

—

Svazek periodika

64

Číslo periodika v rámci svazku

10

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

6

Strana od-do

405-410

Kód UT WoS článku

000490241000001

EID výsledku v databázi Scopus

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