Candidate QTLs Research Articles

Genetic Factors Regulating Lung Vasculature and Immune Cell Functions Associate with Resistance to Pneumococcal Infection

Streptococcus pneumoniae is an important human pathogen responsible for high mortality and morbidity worldwide. The susceptibility to pneumococcal infections is controlled by as yet unknown genetic factors. To elucidate these factors could help to develop new medical treatments and tools to identify those most at risk. In recent years genome wide association studies (GWAS) in mice and humans have proved successful in identification of causal genes involved in many complex diseases for example diabetes, systemic lupus or cholesterol metabolism. In this study a GWAS approach was used to map genetic loci associated with susceptibility to pneumococcal infection in 26 inbred mouse strains. As a result four candidate QTLs were identified on chromosomes 7, 13, 18 and 19. Interestingly, the QTL on chromosome 7 was located within S. pneumoniae resistance QTL (Spir1) identified previously in a linkage study of BALB/cOlaHsd and CBA/CaOlaHsd F2 intercrosses. We showed that only a limited number of genes encoded within the QTLs carried phenotype-associated polymorphisms (22 genes out of several hundred located within the QTLs). These candidate genes are known to regulate TGFβ signalling, smooth muscle and immune cells functions. Interestingly, our pulmonary histopathology and gene expression data demonstrated, lung vasculature plays an important role in resistance to pneumococcal infection. Therefore we concluded that the cumulative effect of these candidate genes on vasculature and immune cells functions as contributory factors in the observed differences in susceptibility to pneumococcal infection. We also propose that TGFβ-mediated regulation of fibroblast differentiation plays an important role in development of invasive pneumococcal disease. Gene expression data submitted to the NCBI Gene Expression Omnibus Accession No: GSE49533SNP data submitted to NCBI dbSNP Short Genetic Variation http://www.ncbi.nlm.nih.gov/projects/SNP/snp_viewTable.cgi?handle=MUSPNEUMONIA.

An integrated approach to crop genetic improvement.

The balance between the supply and demand of the major food crops is fragile, fueling concerns for long-term global food security. The rising population, increasing wealth and a proliferation of non-food uses (e.g. bioenergy) has led to growing demands on agriculture, while increased production is limited by greater urbanization, and the degradation of land. Furthermore, global climate change with increasing temperatures and lower, more erratic rainfall is projected to decrease agricultural yields. There is a predicted need to increase food production by at least 70% by 2050 and therefore an urgent need to develop novel and integrated approaches, incorporating high-throughput phenotyping that will both increase production per unit area and simultaneously improve the resource use efficiency of crops. Yield potential, yield stability, nutrient and water use are all complex multigenic traits and while there is genetic variability, their complexity makes such traits difficult to breed for directly. Nevertheless molecular plant breeding has the potential to deliver substantial improvements, once the component traits and the genes underlying these traits have been identified. In addition, interactions between the individual traits must also be taken into account, a demand that is difficult to fulfill with traditional screening approaches. Identified traits will be incorporated into new cultivars using conventional or biotechnological tools. In order to better understand the relationship between genotype, component traits, and environment over time, a multidisciplinary approach must be adopted to both understand the underlying processes and identify candidate genes, QTLs and traits that can be used to develop improved crops.

New Technologies, Tools and Approaches for Improving Crop Breeding

New Technologies, Tools and Approaches for Improving Crop Breeding

Identification of several small main-effect QTLs and a large number of epistatic QTLs for drought tolerance related traits in groundnut (Arachis hypogaea L.)

Cultivated groundnut or peanut (Arachis hypogaea L.), an allotetraploid (2n = 4x = 40), is a self pollinated and widely grown crop in the semi-arid regions of the world. Improvement of drought tolerance is an important area of research for groundnut breeding programmes. Therefore, for the identification of candidate QTLs for drought tolerance, a comprehensive and refined genetic map containing 191 SSR loci based on a single mapping population (TAG 24 × ICGV 86031), segregating for drought and surrogate traits was developed. Genotyping data and phenotyping data collected for more than ten drought related traits in 2–3 seasons were analyzed in detail for identification of main effect QTLs (M-QTLs) and epistatic QTLs (E-QTLs) using QTL Cartographer, QTLNetwork and Genotype Matrix Mapping (GMM) programmes. A total of 105 M-QTLs with 3.48–33.36% phenotypic variation explained (PVE) were identified using QTL Cartographer, while only 65 M-QTLs with 1.3–15.01% PVE were identified using QTLNetwork. A total of 53 M-QTLs were such which were identified using both programmes. On the other hand, GMM identified 186 (8.54–44.72% PVE) and 63 (7.11–21.13% PVE), three and two loci interactions, whereas only 8 E-QTL interactions with 1.7–8.34% PVE were identified through QTLNetwork. Interestingly a number of co-localized QTLs controlling 2–9 traits were also identified. The identification of few major, many minor M-QTLs and QTL × QTL interactions during the present study confirmed the complex and quantitative nature of drought tolerance in groundnut. This study suggests deployment of modern approaches like marker-assisted recurrent selection or genomic selection instead of marker-assisted backcrossing approach for breeding for drought tolerance in groundnut.Electronic supplementary materialThe online version of this article (doi:10.1007/s00122-010-1517-0) contains supplementary material, which is available to authorized users.

Detection of quantitative trait loci for physical traits of cashew apple

The identification of loci controlling quantitative traits (QTL) and marker-assisted selection have attracted great interest in breeding programs for fruit quality. This study aimed to identify QTLs related to the quality of cashew apple. Physical analysis were carried out in cashew fruits of 66 F1 plants from the cross of CCP 1001 x CP 96. Seven traits related to color, size and weight of the cashew apple were tested for QTLs. Candidate QTLs were identified by the methods of interval mapping and multiple QTL mapping. Thirty QTLs associated with cashew quality were identified. The lowest number of QTL (two) was detected for the trait red intensity, and the highest (seven) for cashew apple weight. The QTLs explained between 3.15% and 21.33% of the total phenotypic variation and are being evaluated for future use in marker-assisted selection.

Mapping candidate QTLs related to plant persistency in red clover

Red clover (Trifolium pratense L.) is a diploid (2n = 14), self-incompatible legume that is widely cultivated as a forage legume in cold geographical regions. Because it is a short-lived perennial species, improvement of plant persistency is the most important objective for red clover breeding. To develop a marker-assisted selection (MAS) approach for red clover, we identified candidate QTLs related to plant persistency. Two full-sib mapping populations, 272 × WF1680 and HR × R130, were used for QTL identification. Resistance to Sclerotinia trifoliorum and Fusarium species, as well as to winter hardiness, was investigated in the laboratory and in field experiments in Moscow region (Russia), and Sapporo (Japan). With the genotype data derived from microsatellite and other DNA markers, candidate QTLs were identified by simple interval mapping (SIM), Kruskal–Wallis analysis (KW analysis) and genotype matrix mapping (GMM). A total of 10 and 23 candidate QTL regions for plant persistency were identified in the 272 × WF1680 and the HR × R130 mapping populations, respectively. The QTLs identified by multiple mapping approaches were mapped on linkage group (LG) 3 and LG6. The significant QTL interactions identified by GMM explained the higher phenotypic variation than single effect QTLs. Identification of haplotypes having positive effect QTLs in each parent were first demonstrated in this study for pseudo-testcross mapping populations in plant species using experimental data.Electronic supplementary materialThe online version of this article (doi:10.1007/s00122-009-1253-5) contains supplementary material, which is available to authorized users.

Identification of candidate genome regions controlling disease resistance in Arachis

BackgroundWorldwide, diseases are important reducers of peanut (Arachis hypogaea) yield. Sources of resistance against many diseases are available in cultivated peanut genotypes, although often not in farmer preferred varieties. Wild species generally harbor greater levels of resistance and even apparent immunity, although the linkage of agronomically un-adapted wild alleles with wild disease resistance genes is inevitable. Marker-assisted selection has the potential to facilitate the combination of both cultivated and wild resistance loci with agronomically adapted alleles. However, in peanut there is an almost complete lack of knowledge of the regions of the Arachis genome that control disease resistance.ResultsIn this work we identified candidate genome regions that control disease resistance. For this we placed candidate disease resistance genes and QTLs against late leaf spot disease on the genetic map of the A-genome of Arachis, which is based on microsatellite markers and legume anchor markers. These marker types are transferable within the genus Arachis and to other legumes respectively, enabling this map to be aligned to other Arachis maps and to maps of other legume crops including those with sequenced genomes. In total, 34 sequence-confirmed candidate disease resistance genes and five QTLs were mapped.ConclusionCandidate genes and QTLs were distributed on all linkage groups except for the smallest, but the distribution was not even. Groupings of candidate genes and QTLs for late leaf spot resistance were apparent on the upper region of linkage group 4 and the lower region of linkage group 2, indicating that these regions are likely to control disease resistance.

Enhancing genetic mapping of complex genomes through the design of highly-multiplexed SNP arrays: application to the large and unsequenced genomes of white spruce and black spruce

BackgroundTo explore the potential value of high-throughput genotyping assays in the analysis of large and complex genomes, we designed two highly multiplexed Illumina bead arrays using the GoldenGate SNP assay for gene mapping in white spruce (Picea glauca [Moench] Voss) and black spruce (Picea mariana [Mill.] B.S.P.).ResultsEach array included 768 SNPs, identified by resequencing genomic DNA from parents of each mapping population. For white spruce and black spruce, respectively, 69.2% and 77.1% of genotyped SNPs had valid GoldenGate assay scores and segregated in the mapping populations. For each of these successful SNPs, on average, valid genotyping scores were obtained for over 99% of progeny. SNP data were integrated to pre-existing ALFP, ESTP, and SSR markers to construct two individual linkage maps and a composite map for white spruce and black spruce genomes. The white spruce composite map contained 821 markers including 348 gene loci. Also, 835 markers including 328 gene loci were positioned on the black spruce composite map. In total, 215 anchor markers (mostly gene markers) were shared between the two species. Considering lineage divergence at least 10 Myr ago between the two spruces, interspecific comparison of homoeologous linkage groups revealed remarkable synteny and marker colinearity.ConclusionThe design of customized highly multiplexed Illumina SNP arrays appears as an efficient procedure to enhance the mapping of expressed genes and make linkage maps more informative and powerful in such species with poorly known genomes. This genotyping approach will open new avenues for co-localizing candidate genes and QTLs, partial genome sequencing, and comparative mapping across conifers.

Candidate genes and QTLs for fruit ripening and softening in melon

Different factors affect the quality of melon fruit and among them long shelf life is critical from the consumer's point of view. In melon, cultivars showing both climacteric and non-climacteric ripening types are found. In this study we have investigated climacteric ripening and fruit softening using a collection of near-isogenic lines (NILs) derived from the non-climacteric melon parental lines PI 161375 (SC) and "Piel de Sapo" (PS). Surprisingly, we found that QTL eth3.5 in NIL SC3-5b induced a climacteric-ripening phenotype with increased respiration and ethylene levels. Data suggest that the non-climacteric phenotypes from PI 161375 and "Piel de Sapo" may be the result of mutations in different genes. Several QTLs for fruit flesh firmness were also detected. Candidate genes putatively involved in ethylene regulation, biosynthesis and perception and cell wall degradation were mapped and some colocations with QTLs were observed. These results may provide additional data towards understanding of non-climacteric ripening in melon.

Candidate genes for quantitative resistance to Mycosphaerella pinodes in pea (Pisum sativum L.).

Partial resistance to Mycosphaerella pinodes in pea is quantitatively inherited. Genomic regions involved in resistance (QTLs) have been previously identified in the pea genome, but the molecular basis of the resistance is still unknown. The objective of this study was to map resistance gene analogs (RGA) and defense-related (DR) genes in the JI296 x DP RIL population that has been used for mapping QTLs for resistance to M. pinodes, and identify co-localizations between candidate genes and QTLs. Using degenerate oligonucleotide primers designed on the conserved motifs P-loop and GLPL of cloned resistance genes, we isolated and cloned 16 NBS-LRR sequences, corresponding to five distinct classes of RGAs. Specific second-generation primers were designed for each class. RGAs from two classes were located on the linkage group (LG) VII. Another set of PCR-based markers was designed for four RGA sequences previously isolated in pea and 12 previously cloned DR gene sequences available in databases. Out of the 16 sequences studied, the two RGAs RGA-G3A and RGA2.97 were located on LG VII, PsPRP4A was located on LG II, Peachi21, PsMnSOD, DRR230-b and PsDof1 were mapped on LG III and peabetaglu and DRR49a were located on LG VI. Two co-localizations between candidate genes and QTLs for resistance to M. pinodes were observed on LG III, between the putative transcription factor PsDof1 and the QTL mpIII-1 and between the pea defensin DRR230-b gene and the QTL mpIII-4. Another co-localization was observed on LG VII between a cluster of RGAs and the QTL mpVII-1. The three co-localizations appear to be located in chromosomal regions containing other disease resistance or DR genes, suggesting an important role of these genomic regions in defense responses against pathogens in pea.

A genetic map of candidate genes and QTLs involved in tomato fruit size and composition.

In order to screen for putative candidate genes linked to tomato fruit weight and to sugar or acid content, genes and QTLs involved in fruit size and composition were mapped. Genes were selected among EST clones in the TIGR tomato EST database (http://www.tigr.org/tdb/tgi/lgi/) or corresponded to genes preferentially expressed in the early stages of fruit development. These clones were located on the tomato map using a population of introgression lines (ILs) having one segment of Lycopersicon pennellii (LA716) in a L. esculentum (M82) background. The 75 ILs allowed the genome to be segmented into 107 bins. Sixty-three genes involved in carbon metabolism revealed 79 loci. They represented enzymes involved in the Calvin cycle, glycolysis, the TCA cycle, sugar and starch metabolism, transport, and a few other functions. In addition, seven cell-cycle-specific genes mapped into nine loci. Fourteen genes, primarily expressed during the cell division stage, and 23 genes primarily expressed during the cell expansion stage, revealed 24 and 26 loci, respectively. The fruit weight, sugars, and organic acids content of each IL was measured and several QTLs controlling these traits were mapped. Comparison between map location of QTLs and candidate gene loci indicated a few candidate genes that may influence the variation of sugar or acid contents. Furthermore, the gene/QTL locations could be compared with the loci mapped in other tomato populations.