Abstract
Common rust (CR) caused by Puccina sorghi is one of the destructive fungal foliar diseases of maize and has been reported to cause moderate to high yield losses. Providing CR resistant germplasm has the potential to increase yields. To dissect the genetic architecture of CR resistance in maize, association mapping, in conjunction with linkage mapping, joint linkage association mapping (JLAM), and genomic prediction (GP) was conducted on an association-mapping panel and five F3 biparental populations using genotyping-by-sequencing (GBS) single-nucleotide polymorphisms (SNPs). Analysis of variance for the biparental populations and the association panel showed significant genotypic and genotype x environment (GXE) interaction variances except for GXE of Pop4. Heritability (h2) estimates were moderate with 0.37–0.45 for the individual F3 populations, 0.45 across five populations and 0.65 for the association panel. Genome-wide association study (GWAS) analyses revealed 14 significant marker-trait associations which individually explained 6–10% of the total phenotypic variances. Individual population-based linkage analysis revealed 26 QTLs associated with CR resistance and together explained 14–40% of the total phenotypic variances. Linkage mapping revealed seven QTLs in pop1, nine QTL in pop2, four QTL in pop3, five QTL in pop4, and one QTL in pop5, distributed on all chromosomes except chromosome 10. JLAM for the 921 F3 families from five populations detected 18 QTLs distributed in all chromosomes except on chromosome 8. These QTLs individually explained 0.3 to 3.1% and together explained 45% of the total phenotypic variance. Among the 18 QTL detected through JLAM, six QTLs, qCR1-78, qCR1-227, qCR3-172, qCR3-186, qCR4-171, and qCR7-137 were also detected in linkage mapping. GP within population revealed low to moderate correlations with a range from 0.19 to 0.51. Prediction correlation was high with r = 0.78 for combined analysis of the five F3 populations. Prediction of biparental populations by using association panel as training set reveals positive correlations ranging from 0.05 to 0.22, which encourages to develop an independent but related population as a training set which can be used to predict diverse but related populations. The findings of this study provide valuable information on understanding the genetic basis of CR resistance and the obtained information can be used for developing functional molecular markers for marker-assisted selection and for implementing GP to improve CR resistance in tropical maize.
Highlights
Common rust caused by Puccina sorghi is one of the most destructive fungal foliar diseases in maize-growing regions predominantly in humid areas and has been reported to cause 12 to 61% yield losses in a favorable environment [1,2]. These yield losses are subject to leaf area infected and host growth stages whereby the former has been estimated to cause about 3–8% yield loss for each 10% of the total leaf area affected [3]
The main objectives of this study were to: (1) assess the phenotypic variation for Common rust (CR) by evaluating five segregating populations and an association panel in multiple environments; (2) detect stable and the novel genetic loci significantly associated with CR resistance by using quantitative trait loci (QTL) mapping, joint linkage association mapping (JLAM), and Genome-wide association study (GWAS); and (3) genomic prediction (GP) within populations and Improved maize for African Soil (IMAS) panel and among the IMAS and F3 populations in order to understand the genetic architecture of maize CR resistance
Heritability (h2) estimates were moderate with range of 0.37–0.45 for the individual F3 populations, 0.45 across the F3 populations and 0.65 for the IMAS panel
Summary
Common rust (hereafter CR) caused by Puccina sorghi is one of the most destructive fungal foliar diseases in maize-growing regions predominantly in humid areas and has been reported to cause 12 to 61% yield losses in a favorable environment [1,2]. GWAS uses natural populations that mimic historical recombinations and provide a higher resolution as compared to linkage mapping [25] This approach, has no control over relatedness and is prone to spurious associations. Another study combined linkage and association mapping on 296 tropical maize inbreds and identified 25 QTLs on chr 1, 3, 5, 6, 8, and 10 associated with CR resistance [31] These markers and candidate genes were directly or indirectly involved with plant disease responses [12,31]. The main objectives of this study were to: (1) assess the phenotypic variation for CR by evaluating five segregating populations and an association panel in multiple environments; (2) detect stable and the novel genetic loci significantly associated with CR resistance by using QTL mapping, JLAM, and GWAS; and (3) GP within populations and Improved maize for African Soil (IMAS) panel and among the IMAS and F3 populations in order to understand the genetic architecture of maize CR resistance
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