QTL Mapping in Three Rice Populations Uncovers Major Genomic Regions Associated with African Rice Gall Midge Resistance

Abstract

African rice gall midge (AfRGM) is one of the most destructive pests of irrigated and lowland African ecologies. This study aimed to identify the quantitative trait loci (QTL) associated with AfRGM pest incidence and resistance in three independent bi-parental rice populations (ITA306xBW348-1, ITA306xTOG7106 and ITA306xTOS14519), and to conduct meta QTL (mQTL) analysis to explore whether any genomic regions are conserved across different genetic backgrounds. Composite interval mapping (CIM) conducted on the three populations independently uncovered a total of 28 QTLs associated with pest incidence (12) and pest severity (16). The number of QTLs per population associated with AfRGM resistance varied from three in the ITA306xBW348-1 population to eight in the ITA306xTOG7106 population. Each QTL individually explained 1.3 to 34.1% of the phenotypic variance. The major genomic region for AfRGM resistance had a LOD score and R2 of 60.0 and 34.1% respectively, and mapped at 111 cM on chromosome 4 (qAfrGM4) in the ITA306xTOS14519 population. The meta-analysis reduced the number of QTLs from 28 to 17 mQTLs, each explaining 1.3 to 24.5% of phenotypic variance, and narrowed the confidence intervals by 2.2 cM. There was only one minor effect mQTL on chromosome 1 that was common in the TOS14519 and TOG7106 genetic backgrounds; all other mQTLs were background specific. We are currently fine-mapping and validating the major effect genomic region on chromosome 4 (qAfRGM4). This is the first report in mapping the genomic regions associated with the AfRGM resistance, and will be highly useful for rice breeders.

Meta-QTL analysis of seed iron and zinc concentration and content in common bean (Phaseolus vulgaris L.)

“…Meta-QTL analysis compiles information from multiple studies and improves QTL location by comparing individual experiments a n d narrowing down confidence intervals obtained from individual analyses (Goffinet and Gerber, 2000). Meta-QTL analysis has been conducted in several crops for various traits, including grain size and resistance to African gall midge in rice (Daware et al, 2017;Wu et al, 2016;Yao et al, 2016), grain traits in wheat (Tyagi, 2015), oil and protein in soybean ( Van and McHale, 2017) and resistance to white mold in common bean (Vasconcellos et al, 2017). Various statistical methods have been developed for meta-QTL analysis.…”

Comparative Transcriptome Analysis Between Resistant and Susceptible Rice Cultivars Responding to Striped Stem Borer (SSB), Chilo suppressalis (Walker) Infestation

“…Among these stem borers, Chilo suppressalis (Walker), commonly known as the striped stem borer, is a widely distributed destructive pest of rice that has greatly reduced rice production in China ( Qu et al, 2003 ). Rice plant damage by C. suppressalis larvae includes boring into the stem and feeding inside, resulting in “dead hearts” (yellowing and withering of the stem) at the vegetative stage and “white heads” at the reproduction stage ( Pathak, 1968 ). Once infested, such a rice plant will fail to produce an effective panicle.…”

Putting Plant Genetic Diversity and Variability at Work for Breeding: Hybrid Rice Suitability in West Africa

“…Crossing should be made with donors that possess multiple tolerance to biophysical stresses, and off-sites should be used to select appropriate lines from segregating generations [32]. Recently the major QTL of AfRGM were located and validated [11], being the QTL with major effect (qAfRGM4) on chromosome 4. They could be easily incorporated into the parental lines of the promising hybrids through marker-aided backcrossing [33].…”

“…There are 16 quantitative trait loci (QTL) associated with host plant resistance to AfRGM, of which three are in [ITA306 × BW348-1], five in [ITA306 × TOS14519], and eight in [ITA306 × TOG7106] breeding populations. The major effect genomic region for AfRGM resistance was in the [ITA306 × TOS14519] population, which was at 111cM on chromosome 4 (qAfrGM4), had a LOD score of 60 and accounted for 34.1% of the total phenotypic variance [11]. Likewise, Rice Yellow Mottle Virus (RYMV, a Sobemo virus) is another major constraint to rice production in the continent [12] because it causes 17 to 100% grain yield loss according to both the infection date and time, and the cultivar host [13,14]).…”