Evaluation of the efficiency of genomic versus pedigree predictions for growth and wood quality traits in Scots pine

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41210%2F20%3A84470" target="_blank" >RIV/60460709:41210/20:84470 - isvavai.cz</a>

Výsledek na webu

<a href="https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-020-07188-4" target="_blank" >https://bmcgenomics.biomedcentral.com/articles/10.1186/s12864-020-07188-4</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s12864-020-07188-4" target="_blank" >10.1186/s12864-020-07188-4</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Evaluation of the efficiency of genomic versus pedigree predictions for growth and wood quality traits in Scots pine

Popis výsledku v původním jazyce

Background. Genomic selection (GS) or genomic prediction is a promising approach for tree breeding to obtain higher genetic gains by shortening time of progeny testing in breeding programs. As proof of concept for Scots pine (Pinus sylvestris L.), a genomic prediction study was conducted with 694 individuals representing 183 full sib families that were genotyped with genotyping by sequencing (GBS) and phenotyped for growth and wood quality traits. 8719 SNPs were used to compare different genomic with pedigree prediction models. Additionally, four prediction efficiency methods were used to evaluate the impact of genomic breeding value estimations by assigning diverse ratios of training and validation sets, as well as several subsets of SNP markers. Results. Genomic Best Linear Unbiased Prediction (GBLUP) and Bayesian Ridge Regression (BRR) combined with expectation maximization (EM) imputation algorithm showed slightly higher prediction efficiencies than Pedigree Best Linear Unbiased Prediction (PBLUP

Název v anglickém jazyce

Evaluation of the efficiency of genomic versus pedigree predictions for growth and wood quality traits in Scots pine

Popis výsledku anglicky

Background. Genomic selection (GS) or genomic prediction is a promising approach for tree breeding to obtain higher genetic gains by shortening time of progeny testing in breeding programs. As proof of concept for Scots pine (Pinus sylvestris L.), a genomic prediction study was conducted with 694 individuals representing 183 full sib families that were genotyped with genotyping by sequencing (GBS) and phenotyped for growth and wood quality traits. 8719 SNPs were used to compare different genomic with pedigree prediction models. Additionally, four prediction efficiency methods were used to evaluate the impact of genomic breeding value estimations by assigning diverse ratios of training and validation sets, as well as several subsets of SNP markers. Results. Genomic Best Linear Unbiased Prediction (GBLUP) and Bayesian Ridge Regression (BRR) combined with expectation maximization (EM) imputation algorithm showed slightly higher prediction efficiencies than Pedigree Best Linear Unbiased Prediction (PBLUP

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

2020

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

21

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

17

Strana od-do

0-0

Kód UT WoS článku

000594316500005

EID výsledku v databázi Scopus

2-s2.0-85096037889