Genetic background- and environment-independent QTL and candidate gene identification of appearance quality in three MAGIC populations of rice

Chen, Huizhen; Zhai, Laiyuan; Chen, Kai; Shen, Congcong; Zhu, Shusheng; Qu, Pingping; Tang, Jie; Liu, Jianping; He, Haohua; Xu, Jianlong

doi:10.3389/fpls.2022.1074106

Cited by 2 publications

(3 citation statements)

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“…GWAS depend on the association between molecular markers and the phenotype of the target trait. The combined analysis of QTL mapping and GWAS can improve the accuracy of QTL/gene detection and has been used in rice ( Famoso et al., 2011 ; Zhai et al., 2018 ; Pan et al., 2020 ; Chen et al., 2022 ) and maize ( Wang et al., 2019 ; Li Z. et al., 2020 ). However, in wheat, few studies using this combination analysis have been reported ( Chen et al., 2016 ), and the following genes were detected: TaFLL-5B1 for flag leaf length ( Yan et al., 2020 ), qHR-1B for herbicide resistance ( Shi et al., 2020 ), and S2B_26494801 for drought tolerance of winter wheat ( Sallam et al., 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Genetic dissection of protein and starch during wheat grain development using QTL mapping and GWAS

et al. 2023

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Protein, starch, and their components are important for wheat grain yield and end-products, which are affected by wheat grain development. Therefore, QTL mapping and a genome-wide association study (GWAS) of grain protein content (GPC), glutenin macropolymer content (GMP), amylopectin content (GApC), and amylose content (GAsC) were performed on wheat grain development at 7, 14, 21, and 28 days after anthesis (DAA) in two environments using a recombinant inbred line (RIL) population of 256 stable lines and a panel of 205 wheat accessions. A total of 29 unconditional QTLs, 13 conditional QTLs, 99 unconditional marker−trait associations (MTAs), and 14 conditional MTAs significantly associated (p < 10−4) with four quality traits were found to be distributed on 15 chromosomes, with the phenotypic variation explained (PVE) ranging from 5.35% to 39.86%. Among these genomic variations, three major QTLs [QGPC3B, QGPC2A, and QGPC(S3|S2)3B] and SNP clusters on the 3A and 6B chromosomes were detected for GPC, and the SNP TA005876-0602 was stably expressed during the three periods in the natural population. The QGMP3B locus was detected five times in three developmental stages in two environments with 5.89%–33.62% PVE, and SNP clusters for GMP content were found on the 3A and 3B chromosomes. For GApC, the QGApC3B.1 locus had the highest PVE of 25.69%, and SNP clusters were found on chromosomes 4A, 4B, 5B, 6B, and 7B. Four major QTLs of GAsC were detected at 21 and 28 DAA. Most interestingly, both QTL mapping and GWAS analysis indicated that four chromosomes (3B, 4A, 6B, and 7A) were mainly involved in the development of protein, GMP, amylopectin, and amylose synthesis. Of these, the wPt-5870–wPt-3620 marker interval on chromosome 3B seemed to be most important because it played an important role in the synthesis of GMP and amylopectin before 7 DAA, in the synthesis of protein and GMP from 14 to 21 DAA, and in the development of GApC and GAsC from 21 to 28 DAA. Using the annotation information of IWGSC Chinese Spring RefSeq v1.1 genome assembly, we predicted 28 and 69 candidate genes for major loci from QTL mapping and GWAS, respectively. Most of them have multiple effects on protein and starch synthesis during grain development. These results provide new insights and information for the potential regulatory network between grain protein and starch synthesis.

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Section: Introductionmentioning

confidence: 99%

Genetic dissection of protein and starch during wheat grain development using QTL mapping and GWAS

et al. 2023

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“…However, even fewer well characterized genes/QTL have been successfully used in breeding for enhancing grain yield. The main reasons are that the QTL mapping results largely depend on the genetic background and no QTL have been simultaneously detected in completely different genetic backgrounds in the multi-parent advanced generation inter-cross (MAGIC) populations research [15]. The QTL are mapped in populations that generally differ from breeding populations and cannot be detectable in the breeding population, thereby limiting their application in molecular breeding.…”

Section: Introductionmentioning

confidence: 99%

“…The QTL are mapped in populations that generally differ from breeding populations and cannot be detectable in the breeding population, thereby limiting their application in molecular breeding. Although association mapping using diversity maize panels can identify favorable alleles, it is difficult to directly use them in breeding due to the poor performance of accessions in terms of many important agronomic traits [15]. Therefore, to identify background-independent QTL, integrating QTL mapping with molecular breeding in the same population will largely minimize the effect of genetic background on QTL detection [15].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Genetic Background Effect on Dissecting the Genetic Basis of Kernel Traits in Reciprocal Maize Introgression Lines

Liu

Cui

Kong

et al. 2023

Genes

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Maize yield is mostly determined by its grain size. Although numerous quantitative trait loci (QTL) have been identified for kernel-related traits, the application of these QTL in breeding programs has been strongly hindered because the populations used for QTL mapping are often different from breeding populations. However, the effect of genetic background on the efficiency of QTL and the accuracy of trait genomic prediction has not been fully studied. Here, we used a set of reciprocal introgression lines (ILs) derived from 417F × 517F to evaluate how genetic background affects the detection of QTLassociated with kernel shape traits. A total of 51 QTL for kernel size were identified by chromosome segment lines (CSL) and genome-wide association studies (GWAS) methods. These were subsequently clustered into 13 common QTL based on their physical position, including 7 genetic-background-independent and 6 genetic-background-dependent QTL, respectively. Additionally, different digenic epistatic marker pairs were identified in the 417F and 517F ILs. Therefore, our results demonstrated that genetic background strongly affected not only the kernel size QTL mapping via CSL and GWAS but also the genomic prediction accuracy and epistatic detection, thereby enhancing our understanding of how genetic background affects the genetic dissection of grain size-related traits.

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Genetic background- and environment-independent QTL and candidate gene identification of appearance quality in three MAGIC populations of rice

Cited by 2 publications

References 66 publications

Genetic dissection of protein and starch during wheat grain development using QTL mapping and GWAS

Genetic dissection of protein and starch during wheat grain development using QTL mapping and GWAS

Evaluating the Genetic Background Effect on Dissecting the Genetic Basis of Kernel Traits in Reciprocal Maize Introgression Lines

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