Combined linkage and association mapping identifies a major QTL (qRtsc8-1), conferring tar spot complex resistance in maize

Abstract

A major QTL ( qRtsc8 - 1 ) conditioning resistance to tar spot complex of maize and occurring at a frequency of 3.5 % across 890 maize inbred lines. Tar spot complex (TSC) is a highly destructive disease of maize found in some countries in America. Identification of TSC resistant germplasm and elucidating the genetic mechanism of resistance is crucial for the use of host resistance to manage this disease. We evaluated 890 elite maize inbred lines in multiple environments and used genome wide association analysis (GWAS) with genotypic data from Illumina MaizeSNP50 BeadChip containing 56 K SNPs to dissect the genetics of TSC resistance. GWAS results were validated through linkage analysis in three bi-parental populations derived from different resistant and susceptible parents. Through GWAS, three TSC resistance loci were identified on chromosome 2, 7 and 8 (-log10 (p)> 5.99). A major quantitative resistance locus (QTL) designated qRtsc8-1, was detected on maize chromosome bin 8.03. qRtsc8-1, was confirmed in three independent bi-parental populations and it accounted for 18-43 % of the observed phenotypic variation for TSC. A rare haplotype within the qRtsc8-1 region, occurring at a frequency of 3.5 % increased TSC resistance by 14 %. Candidate gene analysis revealed that a leucine-rich repeat receptor-like protein (LRR-RLKs) gene family maybe the candidate gene for qRtsc8-1. Identification and localization of a major locus conditioning TSC resistance provides the foundation for fine mapping qRtsc8-1 and developing functional markers for improving TSC resistance in maize breeding programs. To the best of our knowledge, this is the first report of a major QTL for TSC resistance.

Genome‐Wide Analysis of Tar Spot Complex Resistance in Maize Using Genotyping‐by‐Sequencing SNPs and Whole‐Genome Prediction

“…Best linear unbiased prediction (BLUP) value of genotypes, variance components, and broad‐sense heritability were obtained. A previous study (Mahuku et al, 2016) indicated that response to TSC was negatively correlated with maturity. Thus, anthesis date (data not shown) was used as a covariate to correct maturity effect and calculate the final TSC disease response BLUP.…”

“…Each plot consisted of a single 2‐m row with 10 plants per row. Disease evaluation was performed as described by Mahuku et al (2016). Briefly, disease scores were first taken at 2 wk after flowering and repeated two times at 7‐d intervals.…”

“…Disease severity was recorded using a 1‐to‐5 scale with a 0.5 increment, where 1 indicates highly resistant (HR), no visible disease symptoms or lesions identifiable on any of the leaves; 2 indicates resistant (R), moderate lesion development below the leaf subtending the ear or disease symptoms covering ∼30% of the leaf area; 3 indicates moderately susceptible (MS), heavy lesion development on and below the leaf subtending the ear and a few lesions above it or 50% of the leaf surface have disease symptoms; 4 indicates susceptible (S), many or severe lesions on all but the uppermost leaves, which may have a few lesions, lesions have coalesced and blighted, or 70% of leaf surface has disease symptoms; and 5 indicates highly susceptible (HS), all leaves are dead, no green leaf tissue remaining or disease symptoms on >80% of the leaf surface. Figures showing the symptoms and development of TSC on maize leaves and cobs was also provided in the reference of Mahuku et al (2016). For each plot, the average score across the three evaluations was used for further analyses.…”

Linkage and association mapping reveals the genetic basis of brown fibre (Gossypium hirsutum)

“…Furthermore, this strategy allows the identification of many new loci in the complex genetic background of crops (Li et al, 2016a;Motamayor et al, 2013). Hence, it is preferable to perform combined analyses of multiple populations to dissect the genetic basis of a trait (Lee et al, 2014;Mahuku et al, 2016;Motte et al, 2014;Sun et al, 2016;Wu et al, 2016).…”

Toward Genomics-Based Breeding in C3 Cool-Season Perennial Grasses

“…The Illumina GoldenGate has been the most widely used platform for SNP genotyping, while Sequenom MassARRAY platform-based SNP-typing assays are becoming popular as well (Oliver et al, 2011; Chagné et al, 2015). SNP-based high density linkage and transcriptome map construction using NGS data has been successful in wheat (Wu et al, 2015; Holtz et al, 2016), rice (Xie et al, 2010; Zhang et al, 2015), maize (Liu et al, 2010; Mahuku et al, 2016), barley (Chutimanitsakun et al, 2011; Obsa et al, 2016), perennial ryegrass (Pfender et al, 2011; Paina et al, 2016; Velmurugan et al, 2016), orchardgrass (Zhao et al, 2016), intermediate wheatgrass (Kantarski et al, 2017), and zoysia grass ( Zoysia japonica ) (Wang F. et al, 2015). NGS-derived SNP-based genetic maps are useful for comparative mapping and have great potential for cool-season perennial grasses with limited genomic information available.…”