A forward genetics approach integrating genome‐wide association study and expression quantitative trait locus mapping to dissect leaf development in maize (Zea mays)

Abstract

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.

Can we improve the chilling tolerance of maize photosynthesis through breeding?

“…SNPs are particularly useful for performing genome-wide association studies (GWAS) which can identify QTL and increase the resolution of QTL mapping ( Hu et al , 2017 ; Frascaroli and Revilla, 2019 ; Li et al , 2021 ). Genetic mapping performed using SNPs can also be used for marker-assisted selection and to identify candidate genes ( Miculan et al , 2021 ; Waqas et al , 2021 ). Once identified, candidate genes relating to physiological traits of interest may be classified according to functional characteristics using Gene Ontology (GO) terms; GO term annotations are now available for all of the protein-coding genes in maize ( Wimalanathan et al , 2018 ).…”

Integrating Phenotypic and Gene Expression Linkage Mapping to Dissect Rust Resistance in Chickling Pea

“…The found genomic regions controlling gene expression are referred to as expression-QTLs (eQTLs). Previous studies have reported that distant or trans-eQTLs may explain a higher proportion of expression variance than local (at the same locus as the structural gene) or cis -eQTLs ( Liu et al, 2017 ; Carrasco-Valenzuela et al, 2019 ; Fauteux et al, 2019 ; Miculan et al, 2021 ). Hotspots of trans-eQTL may act as key regulators of phenotypes, whereas cis -eQTLs display local gene expression regulation, with co-regulated gene clusters ( Wang et al, 2018 ; Miculan et al, 2021 ).…”

“…Previous studies have reported that distant or trans-eQTLs may explain a higher proportion of expression variance than local (at the same locus as the structural gene) or cis -eQTLs ( Liu et al, 2017 ; Carrasco-Valenzuela et al, 2019 ; Fauteux et al, 2019 ; Miculan et al, 2021 ). Hotspots of trans-eQTL may act as key regulators of phenotypes, whereas cis -eQTLs display local gene expression regulation, with co-regulated gene clusters ( Wang et al, 2018 ; Miculan et al, 2021 ). Several studies have been using a hybrid approach (pQTL and eQTL analyses) to better understand the gene networks underlying traits of interest in plants ( Lima et al, 2018 ; Carrasco-Valenzuela et al, 2019 ; Fauteux et al, 2019 ; Miculan et al, 2021 ).…”

“…Hotspots of trans-eQTL may act as key regulators of phenotypes, whereas cis -eQTLs display local gene expression regulation, with co-regulated gene clusters ( Wang et al, 2018 ; Miculan et al, 2021 ). Several studies have been using a hybrid approach (pQTL and eQTL analyses) to better understand the gene networks underlying traits of interest in plants ( Lima et al, 2018 ; Carrasco-Valenzuela et al, 2019 ; Fauteux et al, 2019 ; Miculan et al, 2021 ).…”

BREEDIT: A novel multiplex genome editing strategy to improve complex quantitative traits in maize (Zea mays L.)

“…Although these marker-assisted breeding technologies have a major impact on the accuracy and speed of crop breeding, the genes underlying the QTLs are in many cases unknown. In recent years, technological advances have combined GWAS with molecular -omics phenotypes that go beyond the genomic information, so that molecular networks start to emerge in molecular breeding (Baute et al, 2015; Baute et al, 2016; Xiao et al, 2016; Miculan et al, 2021).…”