Association mapping reveals loci associated with multiple traits that affect grain yield and adaptation in soft winter wheat

Lozada, Dennis N.; Mason, R. Esten; Babar, Ali; Carver, Brett F.; Brown-Guedira, Gina; Merrill, Keith R.; Arguello, Maria N.; Acuña, Andrea; Vieira, Lucas Vasconcelos; Holder, Amanda; Addison, Christopher K.; Moon, David E.; Miller, R. G.; Dreisigacker, Susanne

doi:10.1007/s10681-017-2005-2

Cited by 47 publications

(62 citation statements)

References 76 publications

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“…In barley, PLS did not perform well on traits with medium or high heritability and a smaller training set [83]. Overall, although the observed heritability values for grain yield in the current study were within the range of published values in wheat [4,84,85], we still observed a weak correlation (r = 0.06) between heritability and mean prediction ability across datasets for the crossvalidations. There was also no significant difference in average accuracies in using the DH datasets (where grain yield had higher heritability values) for predictions in the IP2 scenario.…”

Section: Prediction Ability For Covariate and Multivariate Modelscontrasting

confidence: 74%

“…The wealth of genomic information available for important crops has enabled the use of marker data for molecular breeding and DNA-based selection for plant improvement. In recent years, genomic approaches such as genome-wide association studies (GWAS) have been used to understand the genetic basis of important traits such as grain yield, disease resistance, and adaptation traits in wheat [1][2][3][4]. However, association mapping could not identify small effect loci and with such, the power of GWAS for the dissection of complex traits is limited [5,6].…”

Section: Introductionmentioning

confidence: 99%

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Accuracy of Single and Multi-Trait Genomic Prediction Models for Grain Yield in US Pacific Northwest Winter Wheat

2019

Crop Breed Genet Genom.

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Incorporating secondary correlated traits collected from high-throughput phenotyping in genomic selection (GS) models for complex traits has been demonstrated to improve accuracy. The prediction ability of different single and multiple trait partial least square (PLS) regression models for grain yield were assessed for winter wheat lines evaluated in US Pacific Northwest environments. Different populations including a diversity panel, F5, and double haploid breeding lines were evaluated in Lind and Pullman, WA between 2015 and 2018 and were genotyped with genotyping by sequencing-derived SNP markers. Prediction ability was assessed under cross-validations and independent predictions. Multi-trait covariate models were advantageous in obtaining optimal predictions for yield, especially when there is less genetic relatedness between the training and test populations. Adding multiple traits in the model improved predictions for environments with low heritability. Cross-validations resulted in the highest prediction ability (0.16) whereas independent predictions using the diversity panel to predict F5 and double haploid winter wheat breeding lines obtained the lowest (0.002). Relatedness between populations, heritability of the secondary traits, and the type of PLS model used were among the principal factors affecting prediction ability. Our results showed the relevance of using multi-trait GS models to achieve increased predictions. Genetic architecture of the target trait and genetic relatedness between populations should be taken into consideration when choosing which type of models to implement in the breeding program. An increased prediction ability for the multi-trait models indicates the potential to attain improved genetic gains for yield in wheat breeding programs through these GS approaches.

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Section: Prediction Ability For Covariate and Multivariate Modelscontrasting

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Accuracy of Single and Multi-Trait Genomic Prediction Models for Grain Yield in US Pacific Northwest Winter Wheat

2019

Crop Breed Genet Genom.

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“…A K-PC model with PC = 3 was previously observed to be the most reliable in identifying significant marker-trait associations (MTAs) for yield and agronomic traits in winter wheat [7], and hence, a total of three PC were included in the GWAS model. The number of significant MTAs identified in either or on both GWAS models was also compared.…”

Section: Snp Genotyping and Genomewide Association Studymentioning

confidence: 99%

“…One approach which can facilitate a more accurate estimation for the improvement of trait stability is through examining the genetic architecture underlying stability and implementing genomic-assisted breeding [4,5]. Genetic mapping through association studies and genomic selection has been widely used to dissect the architecture of important traits in wheat (Triticum aestivum L.) [6][7][8][9]. A genomewide association study (GWAS) identifies genetic loci linked to phenotypic values of trait variation through…”

Section: Introductionmentioning

confidence: 99%

“…The AMMI model uses the additive analysis of variance (ANOVA) to partition variation into genotype (G), environment (E), and GEI effects, and the multiplicative principal components analysis (PCA) of the GEI [26,27], where G and G + GE effects represent wide and specific adaptation, respectively [28,29]. AMMI further allows an understanding of complex GEI, including identifying mega-environments, determining lines to exploit narrow adaptations, and predicting performance to improve cultivar recommendations, selections, and genetic gain [7]. Other phenotypic stability parameters were subsequently derived from the AMMI.…”

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confidence: 99%

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Insights into the Genetic Architecture of Phenotypic Stability Traits in Winter Wheat

Lozada

Carter

2020

Agronomy

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Examining the architecture of traits through genomics is necessary to gain a better understanding of the genetic loci affecting important traits to facilitate improvement. Genomewide association study (GWAS) and genomic selection (GS) were implemented for grain yield, heading date, and plant height to gain insights into the genetic complexity of phenotypic stability of traits in a diverse population of US Pacific Northwest winter wheat. Analysis of variance using the Additive Main Effect and Multiplicative Interaction (AMMI) approach revealed significant genotype and genotype by environment interactions. GWAS identified 12 SNP markers distributed across 10 chromosomes affecting variation for both trait and phenotypic stability, indicating potential pleiotropic effects and signifying that similar genetic loci could be associated with different aspects of stability. The lack of stable and major effect loci affecting phenotypic variation supports the complexity of stability of traits. Accuracy of GS was low to moderate, between 0.14 and 0.66, indicating that phenotypic stability is under genetic control. The moderate to high correlation between trait and trait stability suggests the potential of simultaneous selection for trait and trait stability. Our results demonstrate the complex genetic architecture of trait stability and show the potential for improving stability in winter wheat using genomic-assisted approaches.Agronomy 2020, 10, 368 2 of 15 the strength of linkage disequilibrium (i.e., the non-random association of alleles at multiple loci) between markers and functional polymorphism across diverse germplasm [10]. In wheat, GWAS has been conducted in different traits such as grain yield, yield components (thousand kernel weight and kernel number), Fusarium head blight resistance, snow mold tolerance, plant height, and heading date [11][12][13][14][15][16]. Compared with biparental mapping, GWAS results in a higher mapping resolution as it exploits the majority of the recombination histories of the individuals belonging to the diverse population used [17,18]. The main drawback of GWAS, nonetheless, is that it might not able to capture the causative and rare variants (loci) with small effects, and therefore would be a special case of missing heritability, i.e., the portion of genetic variance that cannot be explained by all significant loci [19]. In this case, genomic selection (GS) is seen as a complementary approach to GWAS.GS uses genomewide markers to estimate the marker effects and calculate genomic estimated breeding values (GEBV), which represents the genetic "worth" or merit of an individual. A high GEBV for grain yield, for example, would mean that a line is predicted to have better grain yield compared with others. In contrast to GWAS, GS does not test for significance but rather uses genomewide marker data to calculate the GEBV. More genetic variation could be captured in GS compared with marker-assisted selection as all markers are used in estimating breeding values [20]. In GS, a prediction model ...

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Genome‐wide association analysis of coleoptile length and interaction with plant height in durum wheat

Sesiz,

Alsaleh,

Bektas

et al. 2023

Agronomy Journal

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Genotypes with longer coleoptiles can be sown in deep soil layers to reach the underground moisture needed for germination in dry areas. Developing new varieties with longer coleoptiles and shorter plant heights would be novel for wheat breeding and production. In this study, coleoptile lengths of a panel of durum wheat (Triticum turgidum ssp. durum) genotypes were determined, and 14,255 DArTseq (SNP and Silico‐DArT) markers were used to identify coleoptile length associated markers (MTAs) by Genome‐Wide Association Study (GWAS). A wide genetic variation was accounted for both coleoptile length and plant height. The genetic relationships between coleoptile length and plant height were evaluated using plant height values from five different environments. Two environmentally stable MTAs were identified, one for coleoptile length (QCol.su.4BS) and one for plant height (QPh.su.4BS). These MTAs were located on the short arm of chromosome 4B with LOD scores up to 12.00 and 17.00, respectively. A relatively high LD (r2 = 0.71) was accounted for between QCol.su.4BS and QPh.su.4BS. The LD block intervals of the MTAs overlapped with some genes with roles in plant growth and development. The functions of plausible candidate genes tell us that QCol.su.4BS may be controlling coleoptile length, whereas QPh.su.4BS may be regulating plant height. The combination of the two loci would be desirable. In conclusion, this study sheds light on the genetic control of coleoptile length and its relationship with plant height in durum wheat.This article is protected by copyright. All rights reserved

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Association mapping reveals loci associated with multiple traits that affect grain yield and adaptation in soft winter wheat

Cited by 47 publications

References 76 publications

Accuracy of Single and Multi-Trait Genomic Prediction Models for Grain Yield in US Pacific Northwest Winter Wheat

Accuracy of Single and Multi-Trait Genomic Prediction Models for Grain Yield in US Pacific Northwest Winter Wheat

Insights into the Genetic Architecture of Phenotypic Stability Traits in Winter Wheat

Genome‐wide association analysis of coleoptile length and interaction with plant height in durum wheat

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