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Laurent, Gentzbittel
Cécile, Ben
Mélanie, Mazurier
Min-Gyoung, Shin
Todd, Lorenz
Martina, Rickauer
Paul, Marjoram
Sergey V. Nuzhdin
Tatiana V. Tatarinova
2020-01-20T07:16:01Z
2020-01-20T07:16:01Z
2019-05
Laurent, Gentzbittel. WhoGEM: An admixture-based prediction machine accurately predicts quantitative functional traits in plants [Текст] / Gentzbittel Laurent, Ben Cécile, Mazurier Mélanie, Shin Min-Gyoung, Lorenz Todd, Rickauer Martina, Marjoram Paul, Sergey V. Nuzhdin, Tatiana V. Tatarinova // Genome Biology. — 2019. — Т. 20 (№ 1).
14747596
https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1697-0
https://elib.sfu-kras.ru/handle/2311/128821
The explosive growth of genomic data provides an opportunity to make increased use of sequence variations for phenotype prediction. We have developed a prediction machine for quantitative phenotypes (WhoGEM) that overcomes some of the bottlenecks limiting the current methods. We demonstrated its performance by predicting quantitative disease resistance and quantitative functional traits in the wild model plant species, Medicago truncatula, using geographical locations as covariates for admixture analysis. The method’s prediction reliability equals or outperforms all existing algorithms for quantitative phenotype prediction. WhoGEM analysis produces evidence that variation in genome admixture proportions explains most of the phenotypic variation for quantitative phenotypes.
biogeography
phenotype prediction
genotype
SNP
medicago
WhoGEM: An admixture-based prediction machine accurately predicts quantitative functional traits in plants
Journal Article
Journal Article Postprint
34.23.37
2020-01-20T07:16:01Z
10.1186/s13059-019-1697-0
Институт фундаментальной биологии и биотехнологии
Базовая кафедра защиты и современных технологии мониторинга лесов
Genome Biology
Q1
Q1


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