Genomic prediction accuracies in space and time for height and wood density of Douglas-fir using exome capture as the genotyping platform

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41320%2F17%3A74923" target="_blank" >RIV/60460709:41320/17:74923 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1186/s12864-017-4258-5" target="_blank" >http://dx.doi.org/10.1186/s12864-017-4258-5</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s12864-017-4258-5" target="_blank" >10.1186/s12864-017-4258-5</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Genomic prediction accuracies in space and time for height and wood density of Douglas-fir using exome capture as the genotyping platform

Popis výsledku v původním jazyce

Genomic selection (GS) can offer unprecedented gains, in terms of cost efficiency and generation turnover, to forest tree selective breeding, especially for late expressing and low heritability traits. Here, we used 1) exome capture as a genotyping platform for 1372 Douglas-fir trees representing 37 full sib families growing on three sites in British Columbia, Canada and 2) height growth and wood density (EBVs), and deregressed estimated breeding values (DEBVs) as phenotypes. Representing models with (EBVs) and without (DEBVs) pedigree structure. Ridge regression best linear unbiased predictor (RRBLUP) and generalized ridge regression (GRR) were used to assess their predictive accuracies over space (within site, cross sites, multi site, and multi site to single site) and time (age to age and trait to trait). The RRBLUP and GRR models produced similar predictive accuracies across the studied traits. Within site GS prediction accuracies with models trained on EBVs were high (RRBLUP from 0,79 to 0,91 an

Název v anglickém jazyce

Genomic prediction accuracies in space and time for height and wood density of Douglas-fir using exome capture as the genotyping platform

Popis výsledku anglicky

Genomic selection (GS) can offer unprecedented gains, in terms of cost efficiency and generation turnover, to forest tree selective breeding, especially for late expressing and low heritability traits. Here, we used 1) exome capture as a genotyping platform for 1372 Douglas-fir trees representing 37 full sib families growing on three sites in British Columbia, Canada and 2) height growth and wood density (EBVs), and deregressed estimated breeding values (DEBVs) as phenotypes. Representing models with (EBVs) and without (DEBVs) pedigree structure. Ridge regression best linear unbiased predictor (RRBLUP) and generalized ridge regression (GRR) were used to assess their predictive accuracies over space (within site, cross sites, multi site, and multi site to single site) and time (age to age and trait to trait). The RRBLUP and GRR models produced similar predictive accuracies across the studied traits. Within site GS prediction accuracies with models trained on EBVs were high (RRBLUP from 0,79 to 0,91 an

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

40102 - Forestry

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název periodika

BMC GENOMICS

ISSN

1471-2164

e-ISSN

—

Svazek periodika

18

Číslo periodika v rámci svazku

930

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

16

Strana od-do

1-16

Kód UT WoS článku

000416966500001

EID výsledku v databázi Scopus

2-s2.0-85036477155