Supplementing selection decisions in a hybrid wheat breeding program by using F2 yield as a proxy of F1 performance

Adhikari, Anil; Ibrahim, Amir M. H.; Rudd, Jackie C.; Baenziger, P. Stephen; Easterly, Amanda C.; Garst, Nick; Belamkar, Vikas; Sarazin, Jean-Benoit

doi:10.1007/s10681-020-02664-0

Cited by 5 publications

(3 citation statements)

References 38 publications

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“…However, these assessments are still expensive, laborious, and require larger quantities of seeds that are not often available at the early stages of a breeding cycle. Identification of markers linked to QTL underlying the end‐use quality traits will allow the breeder to understand the genetic architecture, target loci within the defined breeding germplasm lines, and assign heterotic pool in a hybrid wheat breeding program (Adhikari et al., 2020; Adhikari, Ibrahim, Rudd, Baenziger, & Sarazin, 2020b).…”

Section: Introductionmentioning

confidence: 99%

Genetic dissection of end‐use quality traits in two widely adapted wheat cultivars ‘TAM 111’ and ‘TAM 112’

et al. 2021

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Quantitative trait loci (QTL) analysis genetically dissects complex traits, discerns their genetic control and genotype‐by‐environment interactions, and ultimately helps marker development for assisted breeding selection. A mapping population of 124 F5:7 recombinant inbred lines (RILs) derived from the cross of ‘TAM 112’ × ‘TAM 111’ was grown under seven diverse environments and evaluated for end‐use quality traits including kernel, flour, and dough‐mixing characteristics. The objective of this study was to detect QTL associated with end‐use quality traits in these two cultivars. Through 5,948 single nucleotide polymorphisms (SNPs), 39 QTL regions were consistently identified in two or more environments or QTL analyses. Thirteen QTL regions associated to two or more traits were also identified. Among them, 11 QTL regions were in common on chromosomes 1A, 1B, 1D, 2D, and 4D associated to kernel hardness (HARD), kernel diameter (DIAM), single kernel weight (SKW), flour yield (FYLD), midline peak time (MLPT) with logarithm of odds (LOD) up to 42.0, and explained up to 49.3% of the phenotypic variation. Notably, Glu‐D1 loci at 414.3 Mb on chromosome 1D strongly influenced dough rheology and explained up to 54.6% of the trait variation for MLPT, with the favorable allele derived from TAM 112. Novel QTL for HARD, FYLD, and ASH were also identified on the chromosome 1A and 2A, 6B, and 7D, respectively. This study confirmed previously identified loci at major genes, some newly identified QTL on chromosomes, and the favorable alleles carried forward for improved end‐use quality.

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Section: Introductionmentioning

confidence: 99%

Genetic dissection of end‐use quality traits in two widely adapted wheat cultivars ‘TAM 111’ and ‘TAM 112’

et al. 2021

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“…Validated diagnostic markers associated with targeted QTL can be used in marker-assisted selection. Particularly, breeder-friendly markers linked to QTL associated with agronomic traits will allow breeders to understand the genetic architecture of germplasms, target interested gene loci, and assign heterotic pools in hybrid wheat breeding programs ( Adhikari et al, 2020a ; Adhikari et al, 2020b ).…”

Section: Introductionmentioning

confidence: 99%

Genome-wide QTL mapping of yield and agronomic traits in two widely adapted winter wheat cultivars from multiple mega-environments

Dhakal¹,

Liu²,

Chu

et al. 2021

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Quantitative trait loci (QTL) analysis could help to identify suitable molecular markers for marker-assisted breeding (MAB). A mapping population of 124 F5:7recombinant inbred lines derived from the cross ‘TAM 112’/‘TAM 111’ was grown under 28 diverse environments and evaluated for grain yield, test weight, heading date, and plant height. The objective of this study was to detect QTL conferring grain yield and agronomic traits from multiple mega-environments. Through a linkage map with 5,948 single nucleotide polymorphisms (SNPs), 51 QTL were consistently identified in two or more environments or analyses. Ten QTL linked to two or more traits were also identified on chromosomes 1A, 1D, 4B, 4D, 6A, 7B, and 7D. Those QTL explained up to 13.3% of additive phenotypic variations with the additive logarithm of odds (LOD(A)) scores up to 11.2. The additive effect increased yield up to 8.16 and 6.57 g m−2 and increased test weight by 2.14 and 3.47 kg m−3 with favorable alleles from TAM 111 and TAM 112, respectively. Seven major QTL for yield and six for TW with one in common were of our interest on MAB as they explained 5% or more phenotypic variations through additive effects. This study confirmed previously identified loci and identified new QTL and the favorable alleles for improving grain yield and agronomic traits.

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“…The S1 of an MSFT involves the evaluation of a great number of lines. At this early stage, a lower number of replications, for example, two replications, or unreplicated designs, such as the augmented or p‐rep design (Federer, 1956; Williams et al., 2011), and lower plot size are normally used due to the low availability of seeds (Adhikari et al., 2020; Woyann et al., 2019; Wu et al., 2004). At S1, 50% or less of lines are selected to be evaluated in the next stage of testing (S2).…”

Section: Introductionmentioning

confidence: 99%

Analysis of advanced generation multistage field trials data in autogamous plant breeding: An evaluation in common Bean

et al. 2023

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The performance of inbred lines in advanced endogamous generation is commonly evaluated in successive generations of testing and selection, which we defined as "multistage field trials" (MSFT). MSFT data routinely exhibit heterogeneity of (co)variances at several levels due to genetic and/or statistical imbalance. Nowadays, mixed models have been widely used to deal with unbalanced data. However, few studies on common bean have addressed the use of a mixed model approach with modeling of (co)variance structures for random effects in MSFT. Furthermore, factor analysis and genotype-ideotype distance (FAI-BLUP) selection index was originally proposed using best linear unbiased predictions from individual analysis. In this regard, we aimed to study the implications of modeling (co)variance structures for random effects in the estimation of genetic parameters and evaluate the accuracy and efficiency of inbred line selection by the modeled FAI-BLUP approach. A total of five trials were evaluated from 2018 to 2020. The results revealed that the unstructured covariance matrix fitted better for grain yield, whereas the matrix with uniform correlation and heterogeneity of variances fitted better for grain aspect and plant architecture. The modeled FAI-BLUP approach increased the values of selection accuracy and selection efficiency. Our results suggest that modeling the different structures of (co)variances and selecting the best-performing genotypes by modeled FAI-BLUP approach should be used in common bean assays involving unbalanced data.

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Supplementing selection decisions in a hybrid wheat breeding program by using F2 yield as a proxy of F1 performance

Cited by 5 publications

References 38 publications

Genetic dissection of end‐use quality traits in two widely adapted wheat cultivars ‘TAM 111’ and ‘TAM 112’

Genetic dissection of end‐use quality traits in two widely adapted wheat cultivars ‘TAM 111’ and ‘TAM 112’

Genome-wide QTL mapping of yield and agronomic traits in two widely adapted winter wheat cultivars from multiple mega-environments

Analysis of advanced generation multistage field trials data in autogamous plant breeding: An evaluation in common Bean

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