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GrainGenes Reference Report: PLO-13:e0190162

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Reference	PLO-13:e0190162
Title	Genetic dissection of the relationships between grain yield components by genome-wide association mapping in a collection of tetraploid wheats
Journal	PLoS One
Year	2018
Volume	13:e019016
Author	Mangini G [ Show all 11 ]
Abstract	Increasing grain yield potential in wheat has been a major target of most breeding programs. Genetic advance has been frequently hindered by negative correlations among yield components that have been often observed in segregant populations and germplasm collections. A tetraploid wheat collection was evaluated in seven environments and genotyped with a 90K SNP assay to identify major and stable quantitative trait loci (QTL) for grain yield per spike (GYS), kernel number per spike (KNS) and thousand-kernel weight (TKW), and to analyse the genetic relationships between the yield components at QTL level. The genome-wide association analysis detected eight, eleven and ten QTL for KNS, TKW and GYS, respectively, significant in at least three environments or two environments and the mean across environments. Most of the QTL for TKW and KNS were found located in different marker intervals, indicating that they are genetically controlled independently by each other. Out of eight KNS QTL, three were associated to significant increases of GYS, while the increased grain number of five additional QTL was completely or partially compensated by decreases in grain weight, thus producing no or reduced effects on GYS. Similarly, four consistent and five suggestive TKW QTL resulted in visible increase of GYS, while seven additional QTL were associated to reduced effects in grain number and no effects on GYS. Our results showed that QTL analysis for detecting TKW or KNS alleles useful for improving grain yield potential should consider the pleiotropic effects of the QTL or the association to other QTLs.
External Databases	https://doi.org/10.1371/journal.pone.0190162
QTL	[ Hide all but 1 of 144 ] QGys.AABB2018-1A QGys.AABB2018-1B.1 QGys.AABB2018-1B.2 QGys.AABB2018-2A.1 QGys.AABB2018-2A.2 QGys.AABB2018-2B QGys.AABB2018-3B.1 QGys.AABB2018-3B.2 QGys.AABB2018-4A.1 QGys.AABB2018-4A.2 QGys.AABB2018-4B QGys.AABB2018-5A.1 QGys.AABB2018-5A.2 QGys.AABB2018-5A.3 QGys.AABB2018-5B QGys.AABB2018-6A.1 QGys.AABB2018-6A.2 QGys.AABB2018-6A.3 QGys.AABB2018-6B.1 QGys.AABB2018-6B.2 QGys.AABB2018-7A.1 QGys.AABB2018-7A.2 QGys.AABB2018-7B.1 QGys.AABB2018-7B.2 QTgw.AABB2018-1A QTgw.AABB2018-1B.1 QTgw.AABB2018-1B.2 QTgw.AABB2018-2A.1 QTgw.AABB2018-2A.2 QTgw.AABB2018-2B QTgw.AABB2018-3B.1 QTgw.AABB2018-3B.2 QTgw.AABB2018-4A.1 QTgw.AABB2018-4A.2 QTgw.AABB2018-4B QTgw.AABB2018-5A.1 QTgw.AABB2018-5A.2 QTgw.AABB2018-5A.3 QTgw.AABB2018-5B QTgw.AABB2018-6A.1 QTgw.AABB2018-6A.2 QTgw.AABB2018-6A.3 QTgw.AABB2018-6B.1 QTgw.AABB2018-6B.2 QTgw.AABB2018-7A.1 QTgw.AABB2018-7A.2 QTgw.AABB2018-7B.1 QTgw.AABB2018-7B.2 QKps.AABB2018-1A QKps.AABB2018-1B.1 QKps.AABB2018-1B.2 QKps.AABB2018-2A.1 QKps.AABB2018-2A.2 QKps.AABB2018-2B QKps.AABB2018-3B.1 QKps.AABB2018-3B.2 QKps.AABB2018-4A.1 QKps.AABB2018-4A.2 QKps.AABB2018-4B QKps.AABB2018-5A.1 QKps.AABB2018-5A.2 QKps.AABB2018-5A.3 QKps.AABB2018-5B QKps.AABB2018-6A.1 QKps.AABB2018-6A.2 QKps.AABB2018-6A.3 QKps.AABB2018-6B.1 QKps.AABB2018-6B.2 QKps.AABB2018-7A.1 QKps.AABB2018-7A.2 QKps.AABB2018-7B.1 QKps.AABB2018-7B.2 QTL0668_KWS-Mangini_et_al__2018 QTL0670_KWS-Mangini_et_al__2018 QTL0673_KWS-Mangini_et_al__2018 QTL0676_KWS-Mangini_et_al__2018 QTL0679_KWS-Mangini_et_al__2018 QTL0682_KWS-Mangini_et_al__2018 QTL0685_KWS-Mangini_et_al__2018 QTL0688_KWS-Mangini_et_al__2018 QTL0691_KWS-Mangini_et_al__2018 QTL0694_KWS-Mangini_et_al__2018 QTL0697_KWS-Mangini_et_al__2018 QTL0700_KWS-Mangini_et_al__2018 QTL0704_KWS-Mangini_et_al__2018 QTL0705_KWS-Mangini_et_al__2018 QTL0709_KWS-Mangini_et_al__2018 QTL0712_KWS-Mangini_et_al__2018 QTL0715_KWS-Mangini_et_al__2018 QTL0718_KWS-Mangini_et_al__2018 QTL0721_KWS-Mangini_et_al__2018 QTL0724_KWS-Mangini_et_al__2018 QTL0727_KWS-Mangini_et_al__2018 QTL0730_KWS-Mangini_et_al__2018 QTL0733_KWS-Mangini_et_al__2018 QTL0736_KWS-Mangini_et_al__2018 QTL0666_TKW-Mangini_et_al__2018 QTL0671_TKW-Mangini_et_al__2018 QTL0674_TKW-Mangini_et_al__2018 QTL0677_TKW-Mangini_et_al__2018 QTL0680_TKW-Mangini_et_al__2018 QTL0683_TKW-Mangini_et_al__2018 QTL0686_TKW-Mangini_et_al__2018 QTL0689_TKW-Mangini_et_al__2018 QTL0692_TKW-Mangini_et_al__2018 QTL0695_TKW-Mangini_et_al__2018 QTL0698_TKW-Mangini_et_al__2018 QTL0701_TKW-Mangini_et_al__2018 QTL0703_TKW-Mangini_et_al__2018 QTL0706_TKW-Mangini_et_al__2018 QTL0710_TKW-Mangini_et_al__2018 QTL0713_TKW-Mangini_et_al__2018 QTL0716_TKW-Mangini_et_al__2018 QTL0719_TKW-Mangini_et_al__2018 QTL0722_TKW-Mangini_et_al__2018 QTL0725_TKW-Mangini_et_al__2018 QTL0728_TKW-Mangini_et_al__2018 QTL0731_TKW-Mangini_et_al__2018 QTL0734_TKW-Mangini_et_al__2018 QTL0737_TKW-Mangini_et_al__2018 QTL0667_KNS-Mangini_et_al__2018 QTL0669_KNS-Mangini_et_al__2018 QTL0672_KNS-Mangini_et_al__2018 QTL0675_KNS-Mangini_et_al__2018 QTL0678_KNS-Mangini_et_al__2018 QTL0681_KNS-Mangini_et_al__2018 QTL0684_KNS-Mangini_et_al__2018 QTL0687_KNS-Mangini_et_al__2018 QTL0690_KNS-Mangini_et_al__2018 QTL0693_KNS-Mangini_et_al__2018 QTL0696_KNS-Mangini_et_al__2018 QTL0699_KNS-Mangini_et_al__2018 QTL0702_KNS-Mangini_et_al__2018 QTL0707_KNS-Mangini_et_al__2018 QTL0708_KNS-Mangini_et_al__2018 QTL0711_KNS-Mangini_et_al__2018 QTL0714_KNS-Mangini_et_al__2018 QTL0717_KNS-Mangini_et_al__2018 QTL0720_KNS-Mangini_et_al__2018 QTL0723_KNS-Mangini_et_al__2018 QTL0726_KNS-Mangini_et_al__2018 QTL0729_KNS-Mangini_et_al__2018 QTL0732_KNS-Mangini_et_al__2018 QTL0735_KNS-Mangini_et_al__2018
Trait Study	Grain yield components, tetraploid collection, Mangini2018

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