QTL-seq reveals a major root-knot nematode resistance locus on chromosome 11 in rice (Oryza sativa L.)

Abstract

The root-knot nematode Meloidogyne graminicola is a serious pest in rice affecting production in many rice growing areas. Natural host resistance is an attractive control strategy because the speed of the parasite’s life cycle and the broad host range it attacks make other control measures challenging. Although resistance has been found in the domesticated African rice Oryza glaberrima and the wild rice species O. longistaminata, the introgression of resistance genes to Asian rice O. sativa is challenging. Resistance due to a major gene in O. sativa would greatly aid breeding. Recently two accessions resistant to M. graminicola have been identified in a screen of 332 diverse O. sativa cultivars. In this study, these two resistant cultivars, LD 24 (an indica from Sri Lanka) and Khao Pahk Maw (an aus from Thailand), were crossed with a moderately susceptible cultivar, Vialone Nano (a temperate japonica from Italy). Approximately 175 F 2 progeny of both populations were screened for susceptibility to M. graminicola infection. Between 20 and 23 individuals with highest and lowest galls per plants were pooled to make susceptible and resistant bulks which were sequenced to conduct bulked segregant analysis using the QTL-seq method. This revealed a nematode resistance locus from 23 Mbp to the bottom of rice chromosome 11 in both crosses suggesting a rare introgression of the same locus is responsible for resistance in both cultivars. While this information can be used in marker-assisted breeding, analysis of available SNP data revealed candidate loci and genes worthy of further investigation for gene identification. Electronic supplementary material The online version of this article (10.1007/s10681-019-2427-0) contains supplementary material, which is available to authorized users.

A genome-wide association study in Indian wild rice accessions for resistance to the root-knot nematode Meloidogyne graminicola

“…The negligible susceptibility of these accessions cannot be attributed to poor root growth because root weight of these accessions was comparable with that of susceptible accessions (data not shown). The differential susceptibility of 332 O. sativa accessions to M. graminicola was earlier reported via soil-based screening in which two accessions LD 24 (indica) and Khao Pahk Maw (aus) were found to be almost immune to RRKN infection [23,46]. Among 40 highly resistant wild accessions, 34 belonged to O. nivara type (33 of them collected from middle Gangetic plains agro-climatic zone) whereas 3 each belonged to O. rufipogon and O. sativa f. spontanea types.…”

“…sativa accessions to M . graminicola was earlier reported via soil-based screening in which two accessions LD 24 ( indica ) and Khao Pahk Maw ( aus ) were found to be almost immune to RRKN infection [ 23 , 46 ].…”

Next-generation sequencing-based bulked segregant analysis without sequencing the parental genomes

“…The original sequence reads were 3.9G, 3.8G, 3.4G, and 3.5G; they became 3.8G, 3.6G, 3.3G, and 3.4G after quality control, respectively, in ERR2696318 (parent LD24), ERR2696319 (parent VialoneNano), ERR2696321 (the resistant bulk), and ERR2696322 (the susceptible bulk), which correspond to 8.8×, 8.5×, 7.6×, and 7.9× coverage, respectively (12). The preprocessed sequences were used for SNP calling to generate a SNP dataset, which was analyzed using the modified significant structural variant method, the G-statistic method, and the allele frequency method with or without the aid of the parental genome sequences.…”

“…The sequencing data used in this study were generated by Lahari et al (12). Using the allele frequency method, the authors identified a single locus for root-knot nematode resistance in rice.…”

Optimization of BSA-seq experiment for QTL mapping

“…At present, however, how to make a suitable experimental design for BSA-seq is still a problem to be solved. The size of population used in the BSA-seq experiments so far varies from very small (<100; Deokar et al 2019 ; Imerovski et al 2019 ) to very large (>10,000; Yang et al 2013 ), with most being small (around 200; Das et al 2014 ; Pandey et al 2017 ; Branham et al 2018 ; Luo et al 2018 ; Arikit et al 2019 ; Lahari et al 2019 ; Ramos et al 2020 ), and some medium (around 500; Xin et al 2020 ) or large (near or over 1000; Ruangrak et al 2019 ; Lei et al 2020 ). The pool size used is also very diverse, varying from very small (only five individuals; Branham et al 2018 ) to very large (>400; Yang et al 2013 ), corresponding to a pool proportion (the ratio of pool size to population size) of ∼10% or less in most experiments.…”