Meta-analysis of QTLs controlling seed quality traits based on QTL alignment in Brassica napus

Bilgrami, Sayedehsaba; Liu, Liezhao; Farokhzadeh, Sara; Najafabadi, Ahmad Sobhani; Ramandi, Hadi Darzi; Nasiri, Najmeh; Darwish, I. H.

doi:10.1016/j.indcrop.2021.114307

Cited by 8 publications

(6 citation statements)

References 52 publications

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“…The value of these studies is greatly increased when they are taken collectively as with the MQTL approach. The integration of QTL and GWAS is an effective approach to take advantage of the power of detection of QTL and high resolution of GWAS, narrowing down the number of candidate genes (Bilgrami et al., 2022; Brown et al., 2021). Seed yield is a complex trait that is controlled by many QTL with small affects, and it is strongly affected by environmental conditions.…”

Section: Discussionmentioning

confidence: 99%

“…Since many market classes of dry beans are bred around the world, the extrapolation of information generated among breeding programs may be limited (Vandemark et al., 2015). The Meta‐QTL analysis (MQTL) is an approach that can overcome these limitations by integrating QTL from independent studies to identify and refine the most consistent QTL (Goffinet & Gerber, 2000; Izquierdo et al., 2018; Soriano & Alvaro, 2019), and the co‐localization of QTN within MQTL regions leads to the identification of candidate loci with potential use in plant breeding (Bilgrami et al., 2022; Shariatipour et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

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Combination of meta‐analysis of QTL and GWAS to uncover the genetic architecture of seed yield and seed yield components in common bean

et al. 2023

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Increasing seed yield in common bean could help to improve food security and reduce malnutrition globally due to the high nutritional quality of this crop. However, the complex genetic architecture and prevalent genotype by environment interactions for seed yield makes increasing genetic gains challenging. The aim of this study was to identify the most consistent genomic regions related with seed yield components and phenology reported in the last 20 years in common bean. A meta‐analysis of quantitative trait locus (QTL) for seed yield components and phenology (MQTL‐YC) was performed for 394 QTL reported in 21 independent studies under sufficient water and drought conditions. In total, 58 MQTL‐YC over different genetic backgrounds and environments were identified, reducing threefold on average the confidence interval (CI) compared with the CI for the initial QTL. Furthermore, 40 MQTL‐YC identified were co‐located with 210 SNP peak positions reported via genome‐wide association (GWAS), guiding the identification of candidate genes. Comparative genomics among these MQTL‐YC with MQTL‐YC reported in soybean and pea allowed the identification of 14 orthologous MQTL‐YC shared across species. The integration of MQTL‐YC, GWAS, and comparative genomics used in this study is useful to uncover and refine the most consistent genomic regions related with seed yield components for their use in plant breeding.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Combination of meta‐analysis of QTL and GWAS to uncover the genetic architecture of seed yield and seed yield components in common bean

et al. 2023

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“…Given the polyploid nature of the rapeseed genome, the gene content of the candidate QTL regions was analyzed to determine possible homoeology with the numerous QTLs. Such homoeologous QTLs were reported particularly in relation to glucosinolate content (Howell et al, 2003), yield traits (Bouchet et al, 2016b; Bilgrami et al, 2023), or resistance to stem canker (Fopa Fomeju et al, 2014). Here, 47 homoeologous gene pairs were covered in one single QTL pair (Table S8, Figure 3), illustrating conservation between A and C subgenomes.…”

Section: Discussionmentioning

confidence: 98%

Genetic control of root morphology in response to nitrogen across rapeseed diversity

Haelterman,

Louvieaux,

Chiodi

et al. 2024

Physiologia Plantarum

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Rapeseed (Brassica napus L.) is an oil‐containing crop of great economic value but with considerable nitrogen requirement. Breeding root systems that efficiently absorb nitrogen from the soil could be a driver to ensure genetic gains for more sustainable rapeseed production. The aim of this study is to identify genomic regions that regulate root morphology in response to nitrate availability. The natural variability offered by 300 inbred lines was screened at two experimental locations. Seedlings grew hydroponically with low or elevated nitrate levels. Fifteen traits related to biomass production and root morphology were measured. On average across the panel, a low nitrate level increased the root‐to‐shoot biomass ratio and the lateral root length. A large phenotypic variation was observed, along with important heritability values and genotypic effects, but low genotype‐by‐nitrogen interactions. Genome‐wide association study and bulk segregant analysis were used to identify loci regulating phenotypic traits. The first approach nominated 319 SNPs that were combined into 80 QTLs. Three QTLs identified on the A07 and C07 chromosomes were stable across nitrate levels and/or experimental locations. The second approach involved genotyping two groups of individuals from an experimental F2 population created by crossing two accessions with contrasting lateral root lengths. These individuals were found in the tails of the phenotypic distribution. Co‐localized QTLs found in both mapping approaches covered a chromosomal region on the A06 chromosome. The QTL regions contained some genes putatively involved in root organogenesis and represent selection targets for redesigning the root morphology of rapeseed.

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“…In this study, we used 487 QTLs related to VW, FW and RKN resistance for meta-QTL analysis. Recently, meta-analyses have been conducted for a variety of traits in a number of key crops [ 31 , [60] , [61] , [62] , [63] ]. Meta-QTL analysis can eliminate the influence of planting environment, population type and genetic background on research results about QTLs, and effectively integrate QTLs data from different backgrounds and environments [ 64 ].…”

Section: Discussionmentioning

confidence: 99%

Unraveling genomic regions and candidate genes for multiple disease resistance in upland cotton using meta-QTL analysis

Huo

Zhang

Ren

et al. 2023

Heliyon

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Meta-analysis of QTLs controlling seed quality traits based on QTL alignment in Brassica napus

Cited by 8 publications

References 52 publications

Combination of meta‐analysis of QTL and GWAS to uncover the genetic architecture of seed yield and seed yield components in common bean

Combination of meta‐analysis of QTL and GWAS to uncover the genetic architecture of seed yield and seed yield components in common bean

Genetic control of root morphology in response to nitrogen across rapeseed diversity

Unraveling genomic regions and candidate genes for multiple disease resistance in upland cotton using meta-QTL analysis

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