The identification and analysis of notable QTL regions associated with different traits has been one of the cornerstones of modern molecular breeding for plant genetic improvement. In Capsicum spp., previous studies focused on identifying QTL linked to diverse traits including but not limited to resistance to major diseases such as chile pepper blight caused by the oomycete P. capsici, yield and yield components, capsaicin (heat) content, and agronomic traits, among others (Table 1). Given this wealth of information from previous QTL studies, it would be necessary to re-evaluate results from linkage mapping using a meta-analysis approach to refine genomic regions associated with important traits resulting in a more efficient implementation of MAS in chile pepper breeding programs. QTL meta-analysis for different traits in Capsicum remains lacking, where a major focus in the past has been the identification of meta-QTL for resistance to P. capsici24. This status quo of meta-studies for chile peppers has thus driven us to explore meta-QTL for the Capsicum QTLome for diverse traits, i.e. not only for those QTL related with disease resistance, but also for those loci linked with other important yield and agronomic characters in chiles. Here, we report the first known meta-analysis of the chile pepper QTLome rendering deeper insights into the genetic architecture of diverse sets of traits for this valuable crop.
We employed a relatively stringent method in declaring a QTL cluster as a meta-QTL: (1) each meta-QTL should be composed of at least two different QTL; and (2) these QTL should come from at least two independent studies. Accordingly, from an initial set of 39 meta-QTL, only 30 were regarded to be ‘true’ meta-QTL across the 12 chromosomes of chile peppers, with confidence intervals between 0.55 cM (MQTL5.2) and 14.90 cM (MQTL12.3). These criteria were therefore relevant for a more accurate representation of the meta-QTL identified for the chile pepper QTLome. In other crop species, varying numbers of meta-QTL have been identified. Only 11 meta-QTL were detected for seedling stage salinity tolerance in rice6, whereas 60 meta-QTL were identified for Fusarium head blight resistance in wheat25. In another meta-study of QTL in pea plants, 27 meta-QTL were resolved for seed protein content and yield-related traits26. Such differences could be a consequence of the genome size, reliability of the consensus map used for meta-analysis, number of QTL regions identified, as well as the intrinsic properties of the reported QTL, such as phenotypic variation explained and LOD scores. As precision in QTL positions are dependent on population size and trait variation explained7, re-calculating positions based on the type of mapping populations used for analyses could facilitate a better representation of the genetic positions for each of the QTL evaluated.
One of the objectives of a meta-QTL study is to delimit the region of a QTL using information from multiple linkage-mapping studies. Chromosome P5 represents a major chromosome for P. capsici resistance in chile peppers, with large-effect QTL reported in previous studies27− 29. In the current study, we reported two meta-QTL regions in chromosome P5, namely MQTL5.1, and MQTL5.2 delimited to < 1.0 cM confidence interval, i.e. 0.79 cM and 0.55 cM, respectively, comprised of QTL mapped for P. capsici fruit and root rot resistance. These corresponded to the genomic regions having the most refined genetic distance among all the meta-QTL identified in the present work. Similarly, in peanut (Arachis hypogea), a recent meta-analysis of QTL for late leaf spot resistance delimited a region to 0.38 cM and 0.70 cM30, whereas in wheat, genomic regions associated with Fusarium head blight resistance and root-related traits were narrowed to 0.82 cM25 and 0.50 cM intervals31, respectively. Capsicum spp. MQTL5.1 and MQTL5.2 consisted largely of major effect QTL, with percent variation explained ranging between 10 and 52.7% (MQTL5.1) and 8.9 and 67.7% (MQTL5.2) identified from five independent QTL mapping studies, with N ranging between 63 and 297 individuals. Notably, these constituent QTL also represent those with the highest phenotypic trait variation explained in the Capsicum QTLome evaluated; this could be a reason for a more refined meta-QTL region for disease resistance. Furthermore, MQTL5.2 consisted of 24 QTL, which was next to MQTL1.3 identified to having the highest number of individual QTL. Among the criteria for choosing a meta-QTL for selection are (1) a small confidence interval, (2) a high number individual QTL comprising the meta-QTL, and (3) a high trait variation explained of initial QTL7. Considering these factors, MQTL5.1 and MQTL5.2 could serve as potential targets for marker-assisted breeding and selection for improved P. capsici resistance in chile peppers. The identification of meta-QTL linked DNA-based markers will help prioritize different QTL for introgression through MAS in plant breeding programs3,31. In this regard, information from the flanking sequences for MQTL5.1 and MQTL5.2 identified in chromosome P5 will be utilized for the development of Kompetitive allele specific (KASP®)32 SNP assays for marker-assisted breeding. These KASP assays will be further validated using a recombinant inbred line population previously developed at New Mexico State University33, and on a diverse population of New Mexican chile peppers to screen for resistance to different races of P. capsici.
The power of meta-QTL analysis lies in determining genomic regions that are most frequently involved in phenotypic variation and in delimiting the QTL intervals, therefore enabling candidate gene identification for positional cloning31. Also, meta-QTL are potentially genomic regions that are highly rich in genes25 thereby facilitating pyramiding or stacking of important loci. Putative blight resistant protein homologues and leucine rich repeat (LRR) receptor-like serine/threonine protein kinases have been previously identified as candidate genes for P. capsici resistance in chile peppers34,35. In the current study, candidate gene analysis using sequences for markers flanking MQTL5.1 and MQTL 5.2 in chromosome P5 identified genes with diverse biological functions related to disease resistance, including DNA repair, DNA strand renaturation, ion transport, and several epigenetic mechanisms such as DNA, RNA, and histone methylation/demethylation, indicating the possible function of epigenetics in controlling gene expression for disease resistance in chile peppers. Epigenetics and its relationship with conferring disease resistance has been well recognized in other crops such as Arabidopsis36, rice37, and maize38. The denser cytosine methylation profile of the Capsicum genome relative to that of the tomato and potato genomes39 could indicate the relevance of epigenetics for the expression of different genes in peppers. Accordingly, identifying epialleles near the meta-QTL regions in chromosome P5 could be important in breeding towards improving resistance to P. capsici in chile peppers. Nevertheless, while the candidate genes identified here represent promising targets for future breeding, it is not known whether they are the true functional regulators of the detected meta-QTL, as many other genes could be present within the meta-QTL regions5. It would therefore be relevant to perform functional validation of the effects of these candidate genes using different chile pepper germplasm. Overall, meta-QTL analysis confirmed the relevance of chromosome P5 as a major genomic region harboring QTL and different candidate genes for P. capsici resistance in Capsicum.
Chile peppers are unique among the members of family Solanaceae due to their ability to produce capsaicinoids which render distinct flavors and heat profiles. Previously, gene mapping, allele sequence data, and expression profile analyses collectively identified the pungency gene Pun1 in chromosome P2 responsible for the biosynthesis of capsaicinoids in chile peppers40. More recently, the quantitative nature of capsaicinoid levels in chile peppers have also been demonstrated through linkage mapping which identified heat level-related QTL on chromosomes P1, P6, and P1041–43. Several meta-QTL (e.g. MQTL2.1, MQTL2.2) identified in the present study have pungency-related QTL co-localized with QTL for agronomic and disease resistance traits such as fruit wall (pericarp) thickness, biomass, number of leaves on primary axis, and anthracnose resistance, among others. This demonstrated potential pleiotropy and/or effects of close linkage between the underlying QTL44. Such colocation of QTL related with diverse sets of traits for the identified meta-QTL across different chromosomes of chile pepper indicates the possibility of multi-trait improvement using genomic information from multiple linkage mapping studies.
In the current study, meta-QTL analysis was used to dissect the genetic architecture of diverse traits in Capsicum. Genomic regions for disease resistance to P. capsici in chile peppers were refined, and the role of chromosome P5 as a major genomic region harboring disease resistance QTL has been confirmed. Two meta-QTL, MQTL5.1 and MQTL5.2, in chromosome P5 have been delimited to < 1.0 cM intervals. Analysis of candidate genes for these meta-QTL revealed biological functions related to DNA repair, response to bacterial and fungal infection, and DNA, RNA, and histone methylation, which demonstrate the potential role of epigenetics on resistance to P. capsici. The colocalization of several unrelated QTL on similar chromosomal regions demonstrates potential pleiotropic effects and the effect of linkage due to location. SNP assays will be developed for these meta-QTL and will be used for MAS for resistance to pepper blight. This study by far is the largest reported meta-analysis of different traits and the first known study of the Capsicum QTLome. The information presented here could serve as a valuable resource for the genomic improvement of diverse sets of traits in chile peppers.