GBS-Based Genomic Selection for Pea Grain Yield under Severe Terminal Drought

Terminal drought is the main stress that limits pea ( L.) grain yield in Mediterranean-climate regions. This study provides an unprecedented assessment of the predictive ability of genomic selection (GS) for grain yield under severe terminal drought using genotyping-by-sequencing (GBS) data. Additio...

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Main Authors: Paolo Annicchiarico, Nelson Nazzicari, Luciano Pecetti, Massimo Romani, Barbara Ferrari, Yanling Wei, E. Charles Brummer
Format: Article
Language:English
Published: Wiley 2017-07-01
Series:The Plant Genome
Online Access:https://dl.sciencesocieties.org/publications/tpg/articles/10/2/plantgenome2016.07.0072
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spelling doaj-600c6f9782c04373ae48552c5cb9f6fb2020-11-25T03:16:23ZengWileyThe Plant Genome1940-33722017-07-0110210.3835/plantgenome2016.07.0072GBS-Based Genomic Selection for Pea Grain Yield under Severe Terminal DroughtPaolo AnnicchiaricoNelson NazzicariLuciano PecettiMassimo RomaniBarbara FerrariYanling WeiE. Charles BrummerTerminal drought is the main stress that limits pea ( L.) grain yield in Mediterranean-climate regions. This study provides an unprecedented assessment of the predictive ability of genomic selection (GS) for grain yield under severe terminal drought using genotyping-by-sequencing (GBS) data. Additional aims were to assess the GS predictive ability for different GBS data quality filters and GS models, comparing intrapopulation with interpopulation GS predictive ability and to perform genome-wide association (GWAS) studies. The yield and onset of flowering of 315 lines from three recombinant inbred line (RIL) populations issued by connected crosses between three elite cultivars were assessed under a field rainout shelter. We defined an adjusted yield, which is associated with intrinsic drought tolerance, as the yield deviation from the value expected as a function of onset of flowering (which correlated negatively with grain yield). Total polymorphic markers ranged from approximately 100 (minimum of eight reads per locus, maximum 10% genotype missing data) to over 7500 markers (minimum of four reads, maximum 50% missing rate). Best predictions were provided by Bayesian Lasso (BL) or ridge regression best linear unbiased prediction (rrBLUP), rather than support vector regression (SVR) models, with at least 400–500 markers. Intrapopulation GS predictive ability exceeded 0.5 for yield and onset of flowering in all populations and approached 0.4 for the adjusted yield of a population with high trait variation. Genomic selection was preferable to phenotypic selection in terms of predicted yield gains. Interpopulation GS predictive ability varied largely depending on the pair of populations. GWAS revealed extensive colocalization of markers associated with high yield and early flowering and suggested that they are concentrated in a few genomic regions.https://dl.sciencesocieties.org/publications/tpg/articles/10/2/plantgenome2016.07.0072
collection DOAJ
language English
format Article
sources DOAJ
author Paolo Annicchiarico
Nelson Nazzicari
Luciano Pecetti
Massimo Romani
Barbara Ferrari
Yanling Wei
E. Charles Brummer
spellingShingle Paolo Annicchiarico
Nelson Nazzicari
Luciano Pecetti
Massimo Romani
Barbara Ferrari
Yanling Wei
E. Charles Brummer
GBS-Based Genomic Selection for Pea Grain Yield under Severe Terminal Drought
The Plant Genome
author_facet Paolo Annicchiarico
Nelson Nazzicari
Luciano Pecetti
Massimo Romani
Barbara Ferrari
Yanling Wei
E. Charles Brummer
author_sort Paolo Annicchiarico
title GBS-Based Genomic Selection for Pea Grain Yield under Severe Terminal Drought
title_short GBS-Based Genomic Selection for Pea Grain Yield under Severe Terminal Drought
title_full GBS-Based Genomic Selection for Pea Grain Yield under Severe Terminal Drought
title_fullStr GBS-Based Genomic Selection for Pea Grain Yield under Severe Terminal Drought
title_full_unstemmed GBS-Based Genomic Selection for Pea Grain Yield under Severe Terminal Drought
title_sort gbs-based genomic selection for pea grain yield under severe terminal drought
publisher Wiley
series The Plant Genome
issn 1940-3372
publishDate 2017-07-01
description Terminal drought is the main stress that limits pea ( L.) grain yield in Mediterranean-climate regions. This study provides an unprecedented assessment of the predictive ability of genomic selection (GS) for grain yield under severe terminal drought using genotyping-by-sequencing (GBS) data. Additional aims were to assess the GS predictive ability for different GBS data quality filters and GS models, comparing intrapopulation with interpopulation GS predictive ability and to perform genome-wide association (GWAS) studies. The yield and onset of flowering of 315 lines from three recombinant inbred line (RIL) populations issued by connected crosses between three elite cultivars were assessed under a field rainout shelter. We defined an adjusted yield, which is associated with intrinsic drought tolerance, as the yield deviation from the value expected as a function of onset of flowering (which correlated negatively with grain yield). Total polymorphic markers ranged from approximately 100 (minimum of eight reads per locus, maximum 10% genotype missing data) to over 7500 markers (minimum of four reads, maximum 50% missing rate). Best predictions were provided by Bayesian Lasso (BL) or ridge regression best linear unbiased prediction (rrBLUP), rather than support vector regression (SVR) models, with at least 400–500 markers. Intrapopulation GS predictive ability exceeded 0.5 for yield and onset of flowering in all populations and approached 0.4 for the adjusted yield of a population with high trait variation. Genomic selection was preferable to phenotypic selection in terms of predicted yield gains. Interpopulation GS predictive ability varied largely depending on the pair of populations. GWAS revealed extensive colocalization of markers associated with high yield and early flowering and suggested that they are concentrated in a few genomic regions.
url https://dl.sciencesocieties.org/publications/tpg/articles/10/2/plantgenome2016.07.0072
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