Identification of QTLs for pod and kernel traits in cultivated peanut by bulked segregant analysis

Selvaraj, Michael Gomez; Manivannan, N.; Schubert, A. M.; Ayers, J. L.; Baring, Michael R.; Burow, Mark D.

doi:10.2225/vol12-issue2-fulltext-13

Cited by 50 publications

(64 citation statements)

References 26 publications

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“…Further, four associated markers namely TC4G10 (40.36% PV), TC11A04 (28.7% PV), Seq7G02 (28.65% PV) and Seq3B05 (22.3% PV) showed high PV and, hence, their deployment may be considered in developing cultivars with high OC. Although, earlier studies identified so far seven QTLs for OC but they showed very low PV (1.5–9.5%) [30], [32], [33]. Therefore, the MTAs identified in the present study will have more impact in increasing OC in peanut.…”

Section: Resultscontrasting

confidence: 47%

Genomewide Association Studies for 50 Agronomic Traits in Peanut Using the ‘Reference Set’ Comprising 300 Genotypes from 48 Countries of the Semi-Arid Tropics of the World

et al. 2014

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Peanut is an important and nutritious agricultural commodity and a livelihood of many small-holder farmers in the semi-arid tropics (SAT) of world which are facing serious production threats. Integration of genomics tools with on-going genetic improvement approaches is expected to facilitate accelerated development of improved cultivars. Therefore, high-resolution genotyping and multiple season phenotyping data for 50 important agronomic, disease and quality traits were generated on the ‘reference set’ of peanut. This study reports comprehensive analyses of allelic diversity, population structure, linkage disequilibrium (LD) decay and marker-trait association (MTA) in peanut. Distinctness of all the genotypes can be established by using either an unique allele detected by a single SSR or a combination of unique alleles by two or more than two SSR markers. As expected, DArT features (2.0 alleles/locus, 0.125 PIC) showed lower allele frequency and polymorphic information content (PIC) than SSRs (22.21 alleles /locus, 0.715 PIC). Both marker types clearly differentiated the genotypes of diploids from tetraploids. Multi-allelic SSRs identified three sub-groups (K = 3) while the LD simulation trend line based on squared-allele frequency correlations (r2) predicted LD decay of 15–20 cM in peanut genome. Detailed analysis identified a total of 524 highly significant MTAs (pvalue >2.1×10–6) with wide phenotypic variance (PV) range (5.81–90.09%) for 36 traits. These MTAs after validation may be deployed in improving biotic resistance, oil/ seed/ nutritional quality, drought tolerance related traits, and yield/ yield components.

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Section: Resultscontrasting

confidence: 47%

Genomewide Association Studies for 50 Agronomic Traits in Peanut Using the ‘Reference Set’ Comprising 300 Genotypes from 48 Countries of the Semi-Arid Tropics of the World

et al. 2014

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“…Peanut is an important oil crop and has a key role in human nutrition [1]. Improving yield has been one of the major objectives in peanut breeding programs, which is directly influenced by pod-related traits (PRTs) [2–4]. Quantitative traits, including PRTs, show complex interaction with environment leading to varied productivity under different environments.…”

Section: Introductionmentioning

confidence: 99%

Co-localization of major quantitative trait loci for pod size and weight to a 3.7 cM interval on chromosome A05 in cultivated peanut (Arachis hypogaea L.)

Luo

Ren

et al. 2017

BMC Genomics

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BackgroundCultivated peanut (Arachis hypogaea L.), an important source of edible oil and protein, is widely grown in tropical and subtropical areas of the world. Genetic improvement of yield-related traits is essential for improving yield potential of new peanut varieties. Genomics-assisted breeding (GAB) can accelerate the process of genetic improvement but requires linked markers for the traits of interest. In this context, we developed a recombinant inbred line (RIL) mapping population (Yuanza 9102 × Xuzhou 68-4) with 195 individuals and used to map quantitative trait loci (QTLs) associated with three important pod features, namely pod length, pod width and hundred-pod weight.ResultsQTL analysis using the phenotyping data generated across four environments in two locations and genotyping data on 743 mapped loci identified 15 QTLs for pod length, 11 QTLs for pod width and 16 QTLs for hundred-pod weight. The phenotypic variation explained (PVE) ranged from 3.68 to 27.84%. Thirteen QTLs were consistently detected in at least two environments and three QTLs (qPLA05.7, qPLA09.3 and qHPWA05.6) were detected in all four environments indicating their consistent and stable expression. Three major QTLs, detected in at least three environments, were found to be co-localized to a 3.7 cM interval on chromosome A05, and they were qPLA05.7 for pod length (16.89–27.84% PVE), qPWA05.5 for pod width (13.73–14.12% PVE), and qHPWA05.6 for hundred-pod weight (13.75–26.82% PVE). This 3.7 cM linkage interval corresponds to ~2.47 Mb genomic region of the pseudomolecule A05 of A. duranensis, including 114 annotated genes related to catalytic activity and metabolic process.ConclusionsThis study identified three major consistent and stable QTLs for pod size and weight which were co-localized in a 3.7 cM interval on chromosome A05. These QTL regions not only offer further investigation for gene discovery and development of functional markers but also provide opportunity for deployment of these QTLs in GAB for improving yield in peanut.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3456-x) contains supplementary material, which is available to authorized users.

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“…Resistance to biotic stresses Angular leaf spot QTL 22-64 Oblessuc et al, 2012;Teixeira et al, 2005 (Khedikar et al, 2010;Liang et al, 2009a;Qin et al, 2012;Sujay et al, 2012;Wang et al, 2013 (Gautami et al, 2012b;Ravi et al, 2011;Varshney et al, 2009b) Gautami et al, 2012a;Liang et al, 2009;Ravi et al, 2011;Selvaraj et al, 2009;Shirasawa et al, 2012;Varshney et al, 2009b) (Continued on next page) (Liang et al, 2009;Pandey et al, 2012c;Sarvamangala et al, 2011;Selvaraj et al, 2009;Shirasawa et al, 2012) Pea Plant height QTL 19-65 (Tullu et al, 2008;Tar'an et al, 2003b;Dirlewanger et al, 1994) (Atibalentja et al, 2005;Guo et al, 2006;Guo et al, 2008;Han et al, 2008;Huynh et al, 2010;Kazi et al, 2008;Ruben et al, 2006;Shi et al, 2008;Silva and Danielle, 2008;Vuong et al, 2008;Wang et al, 2010;Winter et al, 2007) (Continued on next page) Funatsuki et al, 2005;Githiri et al, 2006;Githiri et al, 2007;…”

Section: Common Beanmentioning

confidence: 99%

Translational Genomics in Agriculture: Some Examples in Grain Legumes

Varshney

Kudapa

Pazhamala

et al. 2014

Critical Reviews in Plant Sciences

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Identification of QTLs for pod and kernel traits in cultivated peanut by bulked segregant analysis

Cited by 50 publications

References 26 publications

Genomewide Association Studies for 50 Agronomic Traits in Peanut Using the ‘Reference Set’ Comprising 300 Genotypes from 48 Countries of the Semi-Arid Tropics of the World

Genomewide Association Studies for 50 Agronomic Traits in Peanut Using the ‘Reference Set’ Comprising 300 Genotypes from 48 Countries of the Semi-Arid Tropics of the World

Co-localization of major quantitative trait loci for pod size and weight to a 3.7 cM interval on chromosome A05 in cultivated peanut (Arachis hypogaea L.)

Translational Genomics in Agriculture: Some Examples in Grain Legumes

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