Quantitative Trait Loci for Resistance to Potato Dry Rot Caused by Fusarium sambucinum

Tuber dry rot is an important disease of potato caused by soil and seed-borne pathogens of the Fusarium genus leading to losses that may reach 60% of the yield. The goal of this work was to study the inheritance of the dry rot resistance in two diploid potato hybrid populations (11-36 and 12-3) with complex pedigrees, including several wild Solanum spp. We used an aggressive isolate of F. sambucinum for phenotyping both progenies, parents, and standard potato cultivars in laboratory tuber tests, in three subsequent years. The QTL for dry rot resistance were mapped by interval mapping on existing genetic maps of both mapping populations. The most important and reproducible QTL for this trait was mapped on chromosome I and additional year- and population-specific QTL were mapped on chromosomes II, VII, IX, XI, and XII, confirming polygenic control of this resistance. This is the first study mapping the loci affecting tuber dry rot resistance in potato genome that can contribute to better understanding of potato-F. sambucinum interaction and to more efficient breeding of resistant potato cultivars.

Mapping of crown rust (Puccinia coronata f. sp. avenae) resistance gene Pc54 and a novel quantitative trait locus effective against powdery mildew (Blumeria graminis f. sp. avenae) in the oat (Avena sativa) line Pc54

The Pc54 oat line carries the crown rust resistance gene ‘Pc54’ and an unknown gene effective against powdery mildew. In this study two recombinant inbred line populations were developed to identify the genomic locations of the two genes and producing lists of molecular markers with a potential for marker assisted selection. The RILs and parents were phenotyped for crown rust and powdery mildew in a controlled environment. They were also genotyped using the 6K Illumina Infinium iSelect oat SNP chip. Multiple interval mapping placed Pc54 on the linkage group Mrg02 (chromosome 7D) and the novel powdery mildew QTL ‘QPm.18’ on Mrg18 (chromosome 1A) both in the mapping and validating population. A total of nine and 31 significant molecular markers were identified linked with the Pc54 gene and QPm.18, respectively. Reactions to crown rust inoculations have justified separate identity of Pc54 from other genes and QTL that have previously been reported on Mrg02 except for ’qPCRFd’. Pm3 is the only powdery mildew resistance gene previously mapped on Mrg18. However, the pm3 differential line, Mostyn was susceptible to the powdery mildew race used in this study suggesting that Pm3 and QPm.18 are different genes. Determining the chromosomal locations of Pc54 and QPm.18 is helpful for better understanding the molecular mechanism of resistance to crown rust and powdery mildew in oats. Furthermore, SNPs and SSRs that are closely linked with the genes could be valuable for developing PCR based molecular markers and facilitating the utilization of these genes in oat breeding programs.

Validation of molecular markers linked to Cercospora leaf spot disease resistance in mungbean (Vigna radiata [L.] Wilczek)

Cercospora leaf spot (CLS) resistance is a highly desirable trait for mungbean (Vigna radiata [L.] Wilczek) production in Thailand. ‘V4718’ is a vital resistance source that shows high and stable resistance to CLS disease. A previous study identified a major quantitative trait locus (QTL) (qCLSC72V18-1) controlling CLS resistance and found the marker (I16274) that was located closest to the resistance gene by using F2:9 and F2:10 recombinant inbred line populations derived through a cross between ‘V4718’ and the susceptible variety ‘Chai Nat 72’ (‘CN72’). Here, we evaluated three newly reported simple sequence repeat (SSR) markers and one InDel marker together with six previously identified markers that were linked to qCLSC72V18-1 to further identify the markers that were located close to this QTL. By performing bulk segregant analysis on two validation populations, we found that two SSR markers (Vr6gCLS037 and Vr6gCLS133) and one InDel marker (VrTAF5_indel) were putatively associated with CLS resistance. Of these markers, only the VrTAF5_indel marker showed a significant association with the CLS resistance gene with a logarithm of odds score > 3 across the phenotypic data for 2016 and 2018. QTL analysis with inclusive composite interval mapping revealed that the VrTAF5_indel marker was integrated into the genetic map with other previously identified markers. The I16274 and VrTAF5_indel markers flanking the QTL of interest accounted for 41.56%-60.38% of the phenotypic variation with genetic distances of 4.0 and 5.0 cM from the resistance gene, respectively. Both markers together permitted only 0.40% recombination with the CLS resistance gene in marker-assisted selection and thus could be useful in future breeding efforts for CLS resistance in mungbean.

Genetic Mapping of Quantitative Trait Loci for End-Use Quality and Grain Minerals in Hard Red Winter Wheat

To meet the demands of different wheat-based food products, traits related to end-use quality become indispensable components in wheat improvement. Thus, markers associated with these traits are valuable for the timely evaluation of protein content, kernel physical characteristics, and rheological properties. Hereunder, we report the mapping results of quantitative trait loci (QTLs) linked to end-use quality traits. We used a dense genetic map with 5199 SNPs from a 90K array based on a recombinant inbred line (RIL) population derived from ‘CO960293-2′/’TAM 111′. The population was evaluated for flour protein concentration, kernel characteristics, dough rheological properties, and grain mineral concentrations. An inclusive composite interval mapping model for individual and across-environment QTL analyses revealed 22 consistent QTLs identified in two or more environments. Chromosomes 1A, 1B, and 1D had clustered QTLs associated with rheological parameters. Glu-D1 loci from CO960293-2 and either low-molecular-weight glutenin subunits or gliadin loci on 1A, 1B, and 1D influenced dough mixing properties substantially, with up to 34.2% of the total phenotypic variation explained (PVE). A total of five QTLs associated with grain Cd, Co, and Mo concentrations were identified on 3B, 5A, and 7B, explaining up to 11.6% of PVE. The results provide important genetic resources towards understanding the genetic bases of end-use quality traits. Information about the novel and consistent QTLs provided solid foundations for further characterization and marker designing to assist selections for end-use quality improvements.

Novel QTL and Meta-QTL Mapping for Major Quality Traits in Soybean

Isoflavone, protein, and oil are the most important quality traits in soybean. Since these phenotypes are typically quantitative traits, quantitative trait locus (QTL) mapping has been an efficient way to clarify their complex and unclear genetic background. However, the low-density genetic map and the absence of QTL integration limited the accurate and efficient QTL mapping in previous researches. This paper adopted a recombinant inbred lines (RIL) population derived from ‘Zhongdou27’and ‘Hefeng25’ and a high-density linkage map based on whole-genome resequencing to map novel QTL and used meta-analysis methods to integrate the stable and consentaneous QTL. The candidate genes were obtained from gene functional annotation and expression analysis based on the public database. A total of 41 QTL with a high logarithm of odd (LOD) scores were identified through composite interval mapping (CIM), including 38 novel QTL and 2 Stable QTL. A total of 660 candidate genes were predicted according to the results of the gene annotation and public transcriptome data. A total of 212 meta-QTL containing 122 stable and consentaneous QTL were mapped based on 1,034 QTL collected from previous studies. For the first time, 70 meta-QTL associated with isoflavones were mapped in this study. Meanwhile, 69 and 73 meta-QTL, respectively, related to oil and protein were obtained as well. The results promote the understanding of the biosynthesis and regulation of isoflavones, protein, and oil at molecular levels, and facilitate the construction of molecular modular for great quality traits in soybean.

Mapping Blast Resistance Genes in Rice Varieties ‘Minghui 63’ and ‘M-202’

Rice blast disease caused by the fungus Magnaporthe oryzae (syn. M. grisea) is one of the most lethal diseases for sustainable rice production worldwide. Blast resistance mediated by major resistance genes are often broken-down after a short period of deployment, while minor blast resistance genes, each providing a small effect on disease reactions, are more durable. In the present study, we first evaluated disease reactions of two rice breeding parents ‘Minghui 63’ and ‘M-202’ with 11 US blast races, IA45, IB1, IB45, IB49, IB54, IC1, IC17, ID1, IE1, IG1, and IH1 commonly found under greenhouse conditions using a category disease rating resembling infection types under field conditions. ‘Minghui 63’ exhibited differential resistance responses in comparison with that of ‘M-202’ to the tested blast races. A recombinant inbred line (RIL) population of 275 lines from a cross between ‘Minghui 63’ and ‘M-202’ was also evaluated with the above mentioned blast races. The population was genotyped with 156 simple sequence repeat (SSR) and insertion and deletion (Indel) markers. A linkage map with a genetic distance of 1022.84 cM was constructed using inclusive composite interval mapping (ICIM) software. A total of 10 resistance QTLs, eight from ‘Minghui 63’ and two from ‘M-202’, were identified. One major QTL, qBLAST2 on chr 2, was identified by seven races/isolates. The remaining nine minor resistance QTLs were mapped on chromosome 1, 3, 6, 9, 10, 11 and 12. These findings provide useful genetic markers and resources to tag minor blast resistance genes for marker assisted selection in rice breeding program and for further studies of underlying genes.

Robust Methods for Analysing Quantitative Trait Loci

<p>The advent of new technology for extracting genetic information from tissue samples has increased the availability of suitable data for finding genes controlling complex traits in plants, animals and humans. Quantitative trait locus (QTL) analysis relies on statistical methods to interpret genetic data in the presence of phenotype data and possibly other factors such as environmental factors. The goal is to both detect the presence of QTL with significant effects on trait value as well as to estimate their locations on the genome relative to those of known markers. This thesis reviews commonly used statistical techniques for QTL mapping in experimental populations. Regression and likelihood methods are discussed. The mixture-modelling approach to QTL mapping is explored in some detail. This thesis presents new matrix formulas for exact and convenient calculation of both the Observed and Fisher information matrices in the context of Multinomial mixtures of Univariate Normal distributions. An extension to Composite Interval mapping is proposed, together with a hypothesis testing strategy which is robust enough to de- tect existing QTL in the presence of slight deviations from model assumptions while reducing false detections.</p>

Robust Methods for Analysing Quantitative Trait Loci

<p>The advent of new technology for extracting genetic information from tissue samples has increased the availability of suitable data for finding genes controlling complex traits in plants, animals and humans. Quantitative trait locus (QTL) analysis relies on statistical methods to interpret genetic data in the presence of phenotype data and possibly other factors such as environmental factors. The goal is to both detect the presence of QTL with significant effects on trait value as well as to estimate their locations on the genome relative to those of known markers. This thesis reviews commonly used statistical techniques for QTL mapping in experimental populations. Regression and likelihood methods are discussed. The mixture-modelling approach to QTL mapping is explored in some detail. This thesis presents new matrix formulas for exact and convenient calculation of both the Observed and Fisher information matrices in the context of Multinomial mixtures of Univariate Normal distributions. An extension to Composite Interval mapping is proposed, together with a hypothesis testing strategy which is robust enough to de- tect existing QTL in the presence of slight deviations from model assumptions while reducing false detections.</p>

QTL analysis in multiple sorghum mapping populations facilitates dissection of the genetic control of agronomic and yield-related traits in sorghum [Sorghum bicolor (Moench)]

The genetic architectures of agronomic and yield-related traits are expected to involve multiple loci that are unlikely all to segregate for alternative alleles in a single bi-parental population. Therefore, the identification of quantitative trait loci (QTL) that are expressed in diverse genetic backgrounds of multiple bi-parental populations provides evidence about both background-specific and common genetic variants. The purpose of this study was to map QTLs for agronomic and yield related traits using three connected mapping populations of different genetic backgrounds, to gain insight into the genomic landscape of these important traits in elite Ethiopian sorghum germplasm. The three bi-parental populations, each with 207 F 2:3 lines were evaluated using an alpha lattice design with two replications under two moisture stress environments. Data analysis was done separately for each population using composite interval mapping, finding a total of 105 QTLs. All the QTLs identified from individual populations were projected on a combined consensus map, comprising a total of 25 meta QTLs for seven traits. The consensus map allowed us to deduce locations of a larger number of markers than possible in any individual map, providing a reference for genetic studies in different genetic backgrounds. The meta QTLs identified in this study could be used for marker-assisted breeding programs in sorghum after validation. Only one trait reduced leaf senescence, showed a striking bias of allele distribution, indicating substantial standing variation among the lines that might be employed in improving drought tolerance of sorghum.

Quantitative trait locus mapping analysis of multiple traits when using genotype data with potential errors

Background Quantitative trait locus (QTL) analysis aims to locate and estimate the effects of the genes influencing quantitative traits and infer the relationship between gene variants and changes in phenotypic characteristics using statistical methods. Some methods have been developed to map QTLs of multiple traits in the case of no genotype error in a given dataset. However, practical genetic data that people use may contain some potential errors because of the limitations of biotechnology. Common genetic data correction methods can only reduce errors, but cannot calculate the degree of error. In this paper, we propose a QTL mapping strategy for multiple traits in the presence of genotype errors. Methods The additive effect, dominant effect, recombination rate, error rate, and other parameters of QTLs can be simultaneously obtained using this new method in the framework of multiple-interval mapping. Results Our simulation results show that the accuracy of parameter estimation can be improved by considering the errors of marker genotypes during the analysis of genetic data. Real data analysis also shows that the new method proposed in this paper can map the QTLs of multiple traits more accurately.