QTL mapping of grain yield and its components under normal and drought stress conditions in barley (<i>Hordeum vulgare</i> L.)

Beheshtizadeh, Hazhir; Fakheri, Barat Ali; Aghnoum, Reza; Mahdinezhad, Nafiseh; Pourdad, Seyyed Saeid; Masoudi, Bahram

doi:10.5958/0975-6906.2018.00008.1

Cited by 5 publications

(2 citation statements)

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“…For PH, six of nine MTA with known chromosomal positions were association with HT ( Table 3 and Fig 5 ), and not directly linked to the height of plants. The positions of the remaining three MTA on chromosomes 2H, 4H, and 5H were similar to those published in Rode et al (2012) [ 49 ], Pasam et al (2012) [ 29 ], and Beheshtizadeh et al (2018) [ 50 ], respectively.…”

Section: Discussionsupporting

confidence: 88%

Marker-trait associations in two-rowed spring barley accessions from Kazakhstan and the USA

et al. 2018

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In this study, phenotyping and single nucleotide polymorphism (SNP) genotyping data of 272 accessions of two-rowed spring barley from the USA along with 94 accessions from Kazakhstan were assessed in field trials at six breeding organizations in Kazakhstan to evaluate the performance of the USA samples over three years (2009–2011). The average grain yield over the six locations was not significantly higher in Kazakh accessions in comparison to the USA samples. Twenty four samples from Montana, Washington, the USDA station in Aberdeen Idaho, and the Anheuser-Busch breeding programs showed heavier average yield than the local standard cultivar “Ubagan”. Principal Coordinate analysis based on two sets of SNP data suggested that Kazakh accessions were closest to the USA accessions among eight groups of samples from different parts of the World, and within five US barley origin groups the samples from Montana and Washington perfectly matched six groups of Kazakh breeding origins. A genome-wide association study (GWAS) using data from eighteen field trials allowed the identification of ninety one marker-trait associations (MTA) in two or more environments for nine traits, including key characters such as heading time (HT), number of kernels per spike (NKS), and thousand grain weight (TGW). Our GWAS allowed the identification of eight MTA for HT and NKS, and sixteen MTA for TGW, when those MTA were linked to mapped SNPs. Based on comparisons of chromosomal positions of MTA identified in this study, and positions of known genes and quantitative trait loci for HT, NKS and TGW, it was suggested that MTA for HT on chromosome 2H (at 158.2 cM, 11_21414), MTA for NKS on 5H (at 118.6 cM, 11_20298), and two MTA for TGW on chromosome 4H (at 94.7 cM, 12_30718, and at 129.3 cM, 11_20013) were potentially new associations in barley. GWAS suggested that six MTA for HT, including two on chromosome 1H, two on chromosome 3H, and one each on chromosomes 4H and 6H, had useful pleiotropic effects for improving barley spike traits.

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Section: Discussionsupporting

confidence: 88%

Marker-trait associations in two-rowed spring barley accessions from Kazakhstan and the USA

et al. 2018

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“…However, by interacting with other loci, they can significantly affect the observed quantitative trait. The effect of epistasis (double interaction) on the observed trait has been considered and reported for, among others: wheat [ 19 ], Triticale [ 20 ], tomato [ 21 ], soybean [ 22 ], rice [ 23 ], pea [ 24 ], canola [ 25 ], maize [ 26 ] and barley [ 27 ]. However, higher-order interactions are omited, although it is difficult to imagine that a quantitative trait is not determined by the interaction of more than two genes.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Methods to Estimate Additive–by–Additive–by–Additive of QTL×QTL×QTL Interaction Effects by Monte Carlo Simulation Studies

Cyplik

Bocianowski

2023

IJMS

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The goal of the breeding process is to obtain new genotypes with traits improved over the parental forms. Parameters related to the additive effect of genes as well as their interactions (such as epistasis of gene–by–gene interaction effect and additive–by–additive–by–additive of gene–by–gene–by–gene interaction effect) can influence decisions on the suitability of breeding material for this purpose. Understanding the genetic architecture of complex traits is a major challenge in the post-genomic era, especially for quantitative trait locus (QTL) effects, QTL–by–QTL interactions and QTL–by–QTL–by–QTL interactions. With regards to the comparing methods for estimating additive–by–additive–by–additive of QTL×QTL×QTL interaction effects by Monte Carlo simulation studies, there are no publications in the open literature. The parameter combinations assumed in the presented simulation studies represented 84 different experimental situations. The use of weighted regression may be the preferred method for estimating additive–by–additive–by–additive of QTL–QTL–QTL triples interaction effects, as it provides results closer to the true values of total additive–by–additive–by–additive interaction effects than using unweighted regression. This is also indicated by the obtained values of the determination coefficients of the proposed models.

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Association between SSR Markers and Phenologic Plus Agronomic Traits in Barley (Hordeum valgare L.) Under Cold Stress Conditions

et al. 2022

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QTL mapping of grain yield and its components under normal and drought stress conditions in barley (Hordeum vulgare L.)

Cited by 5 publications

References 0 publications

Marker-trait associations in two-rowed spring barley accessions from Kazakhstan and the USA

Marker-trait associations in two-rowed spring barley accessions from Kazakhstan and the USA

A Comparison of Methods to Estimate Additive–by–Additive–by–Additive of QTL×QTL×QTL Interaction Effects by Monte Carlo Simulation Studies

Association between SSR Markers and Phenologic Plus Agronomic Traits in Barley (Hordeum valgare L.) Under Cold Stress Conditions

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