QTL mapping and QTL × environment interaction analysis of multi-seed pod in cultivated peanut (Arachis hypogaea L.)

Wang, Liang; Yang, Xinlei; Cui, Shunli; Mu, Guangqing; Sun, Xiaotao; Liu, Lifeng; Li, Zichao

doi:10.1016/j.cj.2018.11.007

Cited by 24 publications

(9 citation statements)

References 37 publications

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“…This follows previous work suggesting that alleles from QTL for seed and pod size are clustered in A07, B02 and B06 due to domestication (Fonceka et al 2012). Also, the seven QTL found in B06 confirmed previous studies, mainly the QTL found by Wang et al (2018), which found pleiotropic QTL at the end of the B06 chromosome and found candidate genes associated with yield traits, some of them related to embryo development. These findings demonstrate the consistency of QTL across different genetic backgrounds and the potential for marker assisted selection of desirable seed and pod traits.…”

Section: Discussionsupporting

confidence: 91%

“…Mapping of seed and pod traits in bi-parental populations has included QTL analyses for pod and seed length, width, weight and number of seed per pod ( Gomez Selvaraj et al 2009 ; Fonceka et al 2012 ; Shirasawa et al 2012 ; Wu et al 2014 ; Huang et al 2015 ; Chen et al 2016a , 2017 , 2019 ; Luo et al 2017 , 2018 ; Wang et al 2019 , 2018a ) as well as associations for pod and seed weight, number of seeds and pods per plant ( Gomez Selvaraj et al 2009 ; Ravi et al 2010 ; Fonceka et al 2012 ; Shirasawa et al 2012 ; Wang et al 2018a 2019 ; Chen et al 2019 ), shelling percentage ( Faye et al 2015 ; Huang et al 2015 ; Chen et al 2016a ), pod maturity ( Liang et al 2009b ; Gomez Selvaraj et al 2009 ; Fonceka et al 2012 ; Faye et al 2015 ), and morphological traits such as pod constriction, thickness or seed coat color ( Fonceka et al 2012 ; Shirasawa et al 2012 ). However, none of these studies included pod density as an indicator of pod filling on the yield components, and most of these studies were limited by the small number of markers (∼220-820 markers) ( Liang et al 2009a ; Gomez Selvaraj et al 2009 ; Ravi et al 2010 ; Fonceka et al 2012 ; Huang et al 2015 ; Chen et al 2016a 2017 ; Luo et al 2017 , 2018 ; Wang et al 2019 ), except Shirasawa et al (2012) that included 1114 SSRs and, more recently, Wang et al (2018a) which included 3630 SNPs.…”

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confidence: 99%

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Pod and Seed Trait QTL Identification To Assist Breeding for Peanut Market Preferences

Chavarro

Chu

Holbrook

et al. 2020

G3 Genes|Genomes|Genetics

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Although seed and pod traits are important for peanut breeding, little is known about the inheritance of these traits. A recombinant inbred line (RIL) population of 156 lines from a cross of Tifrunner x NC 3033 was genotyped with the Axiom_Arachis1 SNP array and SSRs to generate a genetic map composed of 1524 markers in 29 linkage groups (LG). The genetic positions of markers were compared with their physical positions on the peanut genome to confirm the validity of the linkage map and explore the distribution of recombination and potential chromosomal rearrangements. This linkage map was then used to identify Quantitative Trait Loci (QTL) for seed and pod traits that were phenotyped over three consecutive years for the purpose of developing trait-associated markers for breeding. Forty-nine QTL were identified in 14 LG for seed size index, kernel percentage, seed weight, pod weight, single-kernel, double-kernel, pod area and pod density. Twenty QTL demonstrated phenotypic variance explained (PVE) greater than 10% and eight more than 20%. Of note, seven of the eight major QTL for pod area, pod weight and seed weight (PVE >20% variance) were attributed to NC 3033 and located in a single linkage group, LG B06_1. In contrast, the most consistent QTL for kernel percentage were located on A07/B07 and derived from Tifrunner.

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Section: Discussionsupporting

confidence: 91%

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confidence: 99%

Pod and Seed Trait QTL Identification To Assist Breeding for Peanut Market Preferences

Chavarro

Chu

Holbrook

et al. 2020

G3 Genes|Genomes|Genetics

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show abstract

“…Only one QTL identified by multiexperimental joint analysis (7.93%) was also identified in individual environmental analysis (S01_1572065), which showed a high phenotypic contribution rate (11.56%). Wang et al (2019) suggested that both the QTL additive effect and QTL × environment interaction act on reducing the phenotypic variation in the multienvironmental joint analysis, since the individual environmental analysis method only estimates the additive effects of QTL, which also seems to apply in the current study. In addition to that, all QTLs showed stability across the years of experimentation, and heritability ranged from 0.37 to 0.57 (moderate to high), indicating that the selection is likely to provide a benefit for common bean breeding, and the markers could provide promising targets for application in future studies.…”

Section: Discussionmentioning

confidence: 95%

Genome-Wide Association Studies Detect Multiple QTLs for Productivity in Mesoamerican Diversity Panel of Common Bean Under Drought Stress

Valdisser

Müller

Filho³

et al. 2020

Front. Plant Sci.

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Drought stress is an important abiotic factor limiting common bean yield, with great impact on the production worldwide. Understanding the genetic basis regulating beans’ yield and seed weight (SW) is a fundamental prerequisite for the development of superior cultivars. The main objectives of this work were to conduct genome-wide marker discovery by genotyping a Mesoamerican panel of common bean germplasm, containing cultivated and landrace accessions of broad origin, followed by the identification of genomic regions associated with productivity under two water regimes using different genome-wide association study (GWAS) approaches. A total of 11,870 markers were genotyped for the 339 genotypes, of which 3,213 were SilicoDArT and 8,657 SNPs derived from DArT and CaptureSeq. The estimated linkage disequilibrium extension, corrected for structure and relatedness (r2sv), was 98.63 and 124.18 kb for landraces and breeding lines, respectively. Germplasm was structured into landraces and lines/cultivars. We carried out GWASs for 100-SW and yield in field environments with and without water stress for 3 consecutive years, using single-, segment-, and gene-based models. Higher number of associations at high stringency was identified for the SW trait under irrigation, totaling ∼185 QTLs for both single- and segment-based, whereas gene-based GWASs showed ∼220 genomic regions containing ∼650 genes. For SW under drought, 18 QTLs were identified for single- and segment-based and 35 genes by gene-based GWASs. For yield, under irrigation, 25 associations were identified, whereas under drought the total was 10 using both approaches. In addition to the consistent associations detected across experiments, these GWAS approaches provided important complementary QTL information (∼221 QTLs; 650 genes; r2 from 0.01% to 32%). Several QTLs were mined within or near candidate genes playing significant role in productivity, providing better understanding of the genetic mechanisms underlying these traits and making available molecular tools to be used in marker-assisted breeding. The findings also allowed the identification of genetic material (germplasm) with better yield performance under drought, promising to a common bean breeding program. Finally, the availability of this highly diverse Mesoamerican panel is of great scientific value for the analysis of any relevant traits in common bean.

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“…The main reason is the poor consistency of QTL effects across environments [28,29]. Therefore, environmentally stable QTL need to be identi ed and multi-environmental analysis need to be applied [29,30]. In the present study, both QTL identi cation across individual environment and multi-environmental analysis was applied, and six environmentally stable QTL and thirteen QEI were detected.…”

Section: Discussionmentioning

confidence: 88%

Identification of QTL and QEI for Fiber-related Traits in Gossypium Hirsutum L. 

Sharkh¹,

Teng²,

Yang³

et al. 2020

Preprint

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Background Cotton is one of the most important cash crops in the world, depending on fiber quality, yield, seed oil and protein content. Identifying QTL for fiber related and other agronomic traits will facilitate the genetic improvement in cotton. Results In this study, forty-seven QTL for fiber related traits were identified across four different environments and six of these QTL were detected in more than one environment, including two for lint percentage (qLP-D03-1 and qLP-D09-1), two for fiber length (qFL-A07-2 and qFL-D11-1), and two for fiber micronaire (qFM-A08-1 and qFM-D11-1), respectively. Four QTL clusters contained 12 QTL were distributed on four chromosomes including two in At subgenome and two in the Dt subgenome. Moreover, thirteen QTL by environment interactions (QEI) were recognized, including one for lint percentage, five for fiber length, three for fiber strength, two for fiber micronaire, one for fiber uniformity and one for fiber elongation, respectively. ConclusionSix QTL were detected in more than one environment and two environmentally stable QTL (qLP-D03-1 and qFM-A08-1) interacted significantly environment. The QTL detected in more than one environment could be useful for further fine-mapping and marker assisted selection in cotton.

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QTL mapping and QTL × environment interaction analysis of multi-seed pod in cultivated peanut (Arachis hypogaea L.)

Cited by 24 publications

References 37 publications

Pod and Seed Trait QTL Identification To Assist Breeding for Peanut Market Preferences

Pod and Seed Trait QTL Identification To Assist Breeding for Peanut Market Preferences

Genome-Wide Association Studies Detect Multiple QTLs for Productivity in Mesoamerican Diversity Panel of Common Bean Under Drought Stress

Identification of QTL and QEI for Fiber-related Traits in Gossypium Hirsutum L.

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QTL mapping and QTL × environment interaction analysis of multi-seed pod in cultivated peanut (Arachis hypogaea L.)

Cited by 24 publications

References 37 publications

Pod and Seed Trait QTL Identification To Assist Breeding for Peanut Market Preferences

Pod and Seed Trait QTL Identification To Assist Breeding for Peanut Market Preferences

Genome-Wide Association Studies Detect Multiple QTLs for Productivity in Mesoamerican Diversity Panel of Common Bean Under Drought Stress

Identification of QTL&nbsp;and QEI&nbsp;for Fiber-related Traits in Gossypium Hirsutum L.&nbsp;

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QTL mapping and QTL × environment interaction analysis of multi-seed pod in cultivated peanut (Arachis hypogaea L.)

Identification of QTL and QEI for Fiber-related Traits in Gossypium Hirsutum L.