|ZENG, LIANG - Beijing Genome Institute|
|TAO, YE - Beijing Genome Institute|
|VUONG, TRI - University Of Missouri|
|WAN, JINRONG - University Of Missouri|
|BOERMA, ROGER - University Of Georgia|
|NOE, JIM - University Of Georgia|
|LI, ZENGLU - University Of Georgia|
|FINNERTY, STEVE - University Of Georgia|
|PATHAN, SAFIULLAH - University Of Missouri|
|SHANNON, GROVER - University Of Missouri|
|NGUYEN, HENRY - University Of Missouri|
Submitted to: Proceedings of the National Academy of Sciences (PNAS)
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/14/2013
Publication Date: 8/13/2013
Citation: Xu, X., Zeng, L., Tao, Y., Vuong, T., Wan, J., Boerma, R., Noe, J., Li, Z., Finnerty, S., Pathan, S.M., Shannon, G., Nguyen, H.T. 2013. Pinpointing genes underlying the quantitative trait loci for root-knot nematode resistance in palaeopolyploid soybean by whole genome resequencing. Proceedings of the National Academy of Sciences. 110(33):13469-13474.
Interpretive Summary: In this study, we developed a novel QTL mapping approach based on whole genome resequencing (WGR) for crops with a complex genome. Different from the traditional QTL mapping methods, we focus on identifying recombination events in a mapping population, defining a set of bins on chromosomes, and constructing a linkage map with bins serving as markers. This approach combines SNP discovery, SNP validation and genotyping. Using this approach, we were able to map three QTL for root-knot nematode (RKN) resistance with unprecedented accuracy, and also able to pinpoint the genes underlying a major QTL without a time-consuming and laborious fine-mapping process. The mapping resolution of the WGR approach was 16.7 to 144.5 times higher than traditional methods based on SNP array and SSR markers at the three QTL regions. Our current study mainly solved issues caused by duplicated genome and repetitive sequences in genotyping mapping population with next-generation sequencing technologies. Thus, this novel QTL mapping approach can be widely used in crops with a reference genome. Root-knot nematode is an important soybean pest that causes severe yield loss in the United States. Although enormous efforts had been directed to characterizing RKN resistance in soybean, RKN resistance genes were still elusive. Our study revealed that the RKN resistance genes found in soybean germplasm PI 438489B, more likely the Rmi1 gene, are two closely related genes encoding cell-wall-modifying enzymes, which are different from those identified in other crop species (mainly R genes). Thus, they represent a new type of RKN resistance.
Technical Abstract: The objective of this study was to utilize next-generation sequencing (NGS) technologies to dissect quantitative trait loci (QTL) for southern root-knot nematode (RKN) resistance into individual genes in soybean. Two-hundred forty-six recombinant inbred lines (RIL) derived from a cross between Magellan (susceptible) and PI 438489B (resistant) were evaluated for RKN resistance in a greenhouse and sequenced at an average of 0.19 X depth. A sequence analysis pipeline was developed to identify and validate single nucleotide polymorphisms (SNP), infer the parental source of each SNP allele, and genotype the RIL population. Based on 109,273 phased SNPs, recombination events in RILs were identified, and a total of 3,509 bins and 3,489 recombination intervals were defined. About 50.8% of bins contain one to 10 genes. A linkage map was subsequently constructed by using bins as molecular markers. Three QTL for RKN resistance were identified. Of these, one major QTL was mapped to bin 10 of chromosome 10, which is 29.7 Kb in size and harbors three true genes and two pseudogenes. Based on sequence variations and gene expression analysis, the candidate genes underlying the major QTL for RKN resistance were pinpointed. They are Glyma10g02150 and Glyma10g02160, encoding a pectin methylesterase inhibitor (PMEI) and a PMEI-pectin methylesterase, respectively. This novel QTL mapping approach not only combines SNP discovery, SNP validation, and genotyping, but also solves the issues caused by genome duplication and repetitive sequences. Hence, it can be widely used in crops with a reference genome to enhance QTL mapping accuracy.