The assignment success for 22 horse breeds registered in the Czech Republic: The machine learning perspective

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F21%3APU138702" target="_blank" >RIV/00216305:26220/21:PU138702 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/62156489:43210/21:43919188

Výsledek na webu

<a href="https://www.agriculturejournals.cz/web/cjas.htm?type=article&id=120_2020-CJAS" target="_blank" >https://www.agriculturejournals.cz/web/cjas.htm?type=article&id=120_2020-CJAS</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.17221/120/2020-CJAS" target="_blank" >10.17221/120/2020-CJAS</a>

Jazyk výsledku

angličtina

Název v původním jazyce

The assignment success for 22 horse breeds registered in the Czech Republic: The machine learning perspective

Popis výsledku v původním jazyce

The paper demonstrates the dependability of assignment testing in the identification of an appropriate breed to monitor comprehensive genetic information from molecular markers to analyse the collection of real population data covering 22 horse breeds registered in the Czech Republic, including native breeds and genetic resources. If 17 microsatellites are used, the mean number of alleles per locus corresponds to 10.4. The count of alleles at the individual loci ranges between five (HTG07) and 17 (ASB17). The loci ASB02, ASB23, HMS03, HTG10, and VHL20 exhibit the highest gene diversity and observed heterozygosity (both above 80%), with the mean value of 0.77 and 0.73, respectively. The moderate total inbreeding coefficient (5.2%) is estimated across all the loci and breeds. The levels of apparent breed differentiation span from zero between the Czech Warmblood and Slovak Warmblood to 0.15 between the Shetland Pony and Standardbred. The phylogenetic breed relationships are revealed via the NeighbourNet dendrogram constructed from Reynolds’ genetic distances, which clearly separate the Coldblood draught, Hot/Warmblood, and Pony horses. Our results reveal that the Bayesian approach (the Rannala and Mountain technique) provides the most intensive prediction power (83.6%) out of the GeneClass tools and that the Bayes Net algorithm exhibits the best efficiency (78.4%) from the WEKA machine learning workbench options, considering the use of the five-fold cross validation technique. The algorithms could be trained on large real reference data sets, and thus there appears another viable perspective for machine learning in horse ancestry testing. In this context, it is also important to stress the fact that innovated computational tools will potentially lead towards structuring a novel web server to allow the identification of horse breeds.

Název v anglickém jazyce

The assignment success for 22 horse breeds registered in the Czech Republic: The machine learning perspective

Popis výsledku anglicky

The paper demonstrates the dependability of assignment testing in the identification of an appropriate breed to monitor comprehensive genetic information from molecular markers to analyse the collection of real population data covering 22 horse breeds registered in the Czech Republic, including native breeds and genetic resources. If 17 microsatellites are used, the mean number of alleles per locus corresponds to 10.4. The count of alleles at the individual loci ranges between five (HTG07) and 17 (ASB17). The loci ASB02, ASB23, HMS03, HTG10, and VHL20 exhibit the highest gene diversity and observed heterozygosity (both above 80%), with the mean value of 0.77 and 0.73, respectively. The moderate total inbreeding coefficient (5.2%) is estimated across all the loci and breeds. The levels of apparent breed differentiation span from zero between the Czech Warmblood and Slovak Warmblood to 0.15 between the Shetland Pony and Standardbred. The phylogenetic breed relationships are revealed via the NeighbourNet dendrogram constructed from Reynolds’ genetic distances, which clearly separate the Coldblood draught, Hot/Warmblood, and Pony horses. Our results reveal that the Bayesian approach (the Rannala and Mountain technique) provides the most intensive prediction power (83.6%) out of the GeneClass tools and that the Bayes Net algorithm exhibits the best efficiency (78.4%) from the WEKA machine learning workbench options, considering the use of the five-fold cross validation technique. The algorithms could be trained on large real reference data sets, and thus there appears another viable perspective for machine learning in horse ancestry testing. In this context, it is also important to stress the fact that innovated computational tools will potentially lead towards structuring a novel web server to allow the identification of horse breeds.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10603 - Genetics and heredity (medical genetics to be 3)

Projekt

<a href="/cs/project/QH92277" target="_blank" >QH92277: Genetická diverzita a její uchování ve vybraných populacích koní v ČR</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

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

1805-9309

Svazek periodika

66

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

13

Strana od-do

1-12

Kód UT WoS článku

000613949300001

EID výsledku v databázi Scopus

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