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WhoGEM: An admixture-based prediction machine accurately predicts quantitative functional traits in plants
Автор | Laurent, Gentzbittel | |
Автор | Cécile, Ben | |
Автор | Mélanie, Mazurier | |
Автор | Min-Gyoung, Shin | |
Автор | Todd, Lorenz | |
Автор | Martina, Rickauer | |
Автор | Paul, Marjoram | |
Автор | Sergey V. Nuzhdin | |
Автор | Tatiana V. Tatarinova | |
Дата внесения | 2020-01-20T07:16:01Z | |
Дата, когда ресурс стал доступен | 2020-01-20T07:16:01Z | |
Дата публикации | 2019-05 | |
Библиографическое описание | 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). | |
ISSN | 14747596 | |
URI (для ссылок/цитирований) | https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1697-0 | |
URI (для ссылок/цитирований) | https://elib.sfu-kras.ru/handle/2311/128821 | |
Аннотация | 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. | |
Тема | biogeography | |
Тема | phenotype prediction | |
Тема | genotype | |
Тема | SNP | |
Тема | medicago | |
Название | WhoGEM: An admixture-based prediction machine accurately predicts quantitative functional traits in plants | |
Тип | Journal Article | |
Тип | Journal Article Postprint | |
ГРНТИ | 34.23.37 | |
Дата обновления | 2020-01-20T07:16:01Z | |
DOI | 10.1186/s13059-019-1697-0 | |
Институт | Институт фундаментальной биологии и биотехнологии | |
Подразделение | Базовая кафедра защиты и современных технологии мониторинга лесов | |
Журнал | Genome Biology | |
Квартиль журнала в Scopus | Q1 | |
Квартиль журнала в Web of Science | Q1 |