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A forward genetics approach integrating genome‐wide association study and expression quantitative trait locus mapping to dissect leaf development in maize ( Zea mays )
Author(s) -
Miculan Mara,
Nelissen Hilde,
Ben Hassen Manel,
Marroni Fabio,
Inzé Dirk,
Pè Mario Enrico,
Dell’Acqua Matteo
Publication year - 2021
Publication title -
the plant journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.058
H-Index - 269
eISSN - 1365-313X
pISSN - 0960-7412
DOI - 10.1111/tpj.15364
Subject(s) - biology , quantitative trait locus , genetics , candidate gene , gene , single nucleotide polymorphism , genome wide association study , association mapping , locus (genetics) , expression quantitative trait loci , inbred strain , trait , genetic architecture , genetic association , phenotype , family based qtl mapping , gene mapping , computational biology , genotype , chromosome , computer science , programming language
SUMMARY The characterization of the genetic basis of maize ( Zea mays ) leaf development may support breeding efforts to obtain plants with higher vigor and productivity. In this study, a mapping panel of 197 biparental and multiparental maize recombinant inbred lines (RILs) was analyzed for multiple leaf traits at the seedling stage. RNA sequencing was used to estimate the transcription levels of 29 573 gene models in RILs and to derive 373 769 single nucleotide polymorphisms (SNPs), and a forward genetics approach combining these data was used to pinpoint candidate genes involved in leaf development. First, leaf traits were correlated with gene expression levels to identify transcript–trait correlations. Then, leaf traits were associated with SNPs in a genome‐wide association (GWA) study. An expression quantitative trait locus mapping approach was followed to associate SNPs with gene expression levels, prioritizing candidate genes identified based on transcript–trait correlations and GWAs. Finally, a network analysis was conducted to cluster all transcripts in 38 co‐expression modules. By integrating forward genetics approaches, we identified 25 candidate genes highly enriched for specific functional categories, providing evidence supporting the role of vacuolar proton pumps, cell wall effectors, and vesicular traffic controllers in leaf growth. These results tackle the complexity of leaf trait determination and may support precision breeding in maize.