Genotype matrix mapping: searching for quantitative trait loci interactions in genetic variation in complex traits

Genome-Wide Mapping of Quantitative Trait Loci for Yield-Attributing Traits of Peanut

Peanuts (Arachis hypogaea L.) are important high-protein and oil-containing legume crops adapted to arid to semi-arid regions. The yield and quality of peanuts are complex quantitative traits that show high environmental influence. In this study, a recombinant inbred line population (RIL) (Valencia-C × JUG-03) was developed and phenotyped for nine traits under two environments. A genetic map was constructed using 1323 SNP markers spanning a map distance of 2003.13 cM. Quantitative trait loci (QTL) analysis using this genetic map and phenotyping data identified seventeen QTLs for nine traits. Intriguingly, a total of four QTLs, two each for 100-seed weight (HSW) and shelling percentage (SP), showed major and consistent effects, explaining 10.98% to 14.65% phenotypic variation. The major QTLs for HSW and SP harbored genes associated with seed and pod development such as the seed maturation protein-encoding gene, serine-threonine phosphatase gene, TIR-NBS-LRR gene, protein kinase superfamily gene, bHLH transcription factor-encoding gene, isopentyl transferase gene, ethylene-responsive transcription factor-encoding gene and cytochrome P450 superfamily gene. Additionally, the identification of 76 major epistatic QTLs, with PVE ranging from 11.63% to 72.61%, highlighted their significant role in determining the yield- and quality-related traits. The significant G × E interaction revealed the existence of the major role of the environment in determining the phenotype of yield-attributing traits. Notably, the seed maturation protein-coding gene in the vicinity of major QTLs for HSW can be further investigated to develop a diagnostic marker for HSW in peanut breeding. This study provides understanding of the genetic factor governing peanut traits and valuable insights for future breeding efforts aimed at improving yield and quality.

Identification of small effect quantitative trait loci of plant architectural, flowering, and early maturity traits in reciprocal interspecific introgression population in cotton

Plant architecture, flowering time and maturity traits are important determinants of yield and fiber quality of cotton. Genetic dissection of loci determining these yield and quality components is complicated by numerous loci with alleles conferring small differences. Therefore, mapping populations segregating for smaller numbers and sizes of introgressed segments is expected to facilitate dissection of these complex quantitative traits. At an advanced stage in the development of reciprocal advanced backcross populations from crosses between elite Gossypium hirsutum cultivar 'Acala Maxxa' (GH) and G. barbadense 'Pima S6' (GB), we undertook mapping of plant architectural traits, flowering time and maturity. A total of 284 BC₄F₁ and BC₄F₂ progeny rows, 120 in GH and 164 in GB background, were evaluated for phenotype, with only 4 and 3 (of 7) traits showing significant differences among progenies. Genotyping by sequencing yielded 3,186 and 3,026 SNPs, respectively, that revealed a total of 27 QTLs in GH background and 22 in GB, for plant height, days to flowering, residual flowering at maturity and maturity. More than of 90% QTLs identified in both backgrounds had small effects (%PV < 10), supporting the merit of this population structure to reduce background noise and small effect QTLs. Germplasm developed in this study may serve as potential pre-breeding material to develop improved cotton cultivars.

Construction of a high-density genetic map and QTL analysis for yield, yield components and agronomic traits in chickpea (Cicer arietinum L.)

Unravelling the genetic architecture underlying yield components and agronomic traits is important for enhancing crop productivity. Here, a recombinant inbred line (RIL) population, developed from ICC 4958 and DCP 92–3 cross, was used for constructing linkage map and QTL mapping analysis. The RIL population was genotyped using a high-throughput Axiom^®CicerSNP array, which enabled the development of a high-density genetic map consisting of 3,818 SNP markers and spanning a distance of 1064.14 cM. Analysis of phenotyping data for yield, yield components and agronomic traits measured across three years together with genetic mapping data led to the identification of 10 major-effect QTLs and six minor-effect QTLs explaining up to 59.70% phenotypic variance. The major-effect QTLs identified for 100-seed weight, and plant height possessed key genes, such as C3HC4 RING finger protein, pentatricopeptide repeat (PPR) protein, sugar transporter, leucine zipper protein and NADH dehydrogenase, amongst others. The gene ontology studies highlighted the role of these genes in regulating seed weight and plant height in crop plants. The identified genomic regions for yield, yield components, and agronomic traits, and the closely linked markers will help advance genetics research and breeding programs in chickpea.

Development of a dense genetic map and QTL analysis for pod borer Helicoverpa armigera (Hübner) resistance component traits in chickpea (Cicer arietinum L.)

Genetic enhancement for resistance against the pod borer, Helicoverpa armigera is crucial for enhancing production and productivity of chickpea. Here we provide some novel insights into the genetic architecture of natural variation in H. armigera resistance in chickpea, an important legume, which plays a major role in food and nutritional security. An interspecific recombinant inbred line (RIL) population developed from a cross between H. armigera susceptible accession ICC 4958 (Cicer arietinum) and resistant accession PI 489777 (Cicer reticulatum) was evaluated for H. armigera resistance component traits using detached leaf assay and under field conditions. A high-throughput AxiomCicerSNP array was utilized to construct a dense linkage map comprising of 3,873 loci and spanning a distance of 949.27 cM. Comprehensive analyses of extensive genotyping and phenotyping data identified nine main-effect QTLs and 955 epistatic QTLs explaining up to 42.49% and 38.05% phenotypic variance, respectively, for H. armigera resistance component traits. The main-effect QTLs identified in this RIL population were linked with previously described genes, known to modulate resistance against lepidopteran insects in crop plants. One QTL cluster harbouring main-effect QTLs for three H. armigera resistance component traits and explaining up to 42.49% of the phenotypic variance, was identified on CaLG03. This genomic region, after validation, may be useful to improve H. armigera resistance component traits in elite chickpea cultivars.

Molecular Dissection of Quantitative Variation in Bermudagrass Hybrids (Cynodon dactylon x transvaalensis): Morphological Traits

Bermudagrass (Cynodon (L.)) is the most important warm-season grass grown for forage or turf. It shows extensive variation in morphological characteristics and growth attributes, but the genetic basis of this variation is little understood. Detection and tagging of quantitative trait loci (QTL) affecting above-ground morphology with diagnostic DNA markers would provide a foundation for genetic and molecular breeding applications in bermudagrass. Here, we report early findings regarding genetic architecture of foliage (canopy height, HT), stolon (stolon internode length, ILEN and length of the longest stolon LLS), and leaf traits (leaf blade length, LLEN and leaf blade width, LW) in 110 F1 individuals derived from a cross between Cynodon dactylon (T89) and C. transvaalensis (T574). Separate and joint environment analyses were performed on trait data collected across two to five environments (locations, and/or years, or time), finding significant differences (P < 0.001) among the hybrid progeny for all traits. Analysis of marker-trait associations detected 74 QTL and 135 epistatic interactions. Composite interval mapping (CIM) and mixed-model CIM (MCIM) identified 32 main effect QTL (M-QTL) and 13 interacting QTL (int-QTL). Colocalization of QTL for plant morphology partially explained significant correlations among traits. M-QTL qILEN-3-2 (for ILEN; R2 = 11-19%), qLLS-7-1 (for LLS; R2 = 13-27%), qLEN-1-1 (for LLEN; R2 = 10-11%), and qLW-3-2 (for LW; R2 = 10-12%) were 'stable' across multiple environments, representing candidates for fine mapping and applied breeding applications. QTL correspondence between bermudagrass and divergent grass lineages suggests opportunities to accelerate progress by predictive breeding of bermudagrass.

Plant vigour QTLs co-map with an earlier reported QTL hotspot for drought tolerance while water saving QTLs map in other regions of the chickpea genome

BACKGROUND

Terminal drought stress leads to substantial annual yield losses in chickpea (Cicer arietinum L.). Adaptation to water limitation is a matter of matching water supply to water demand by the crop. Therefore, harnessing the genetics of traits contributing to plant water use, i.e. transpiration rate and canopy development dynamics, is important to design crop ideotypes suited to a varying range of water limited environments. With an aim of identifying genomic regions for plant vigour (growth and canopy size) and canopy conductance traits, 232 recombinant inbred lines derived from a cross between ICC 4958 and ICC 1882, were phenotyped at vegetative stage under well-watered conditions using a high throughput phenotyping platform (LeasyScan).

RESULTS

Twenty one major quantitative trait loci (M-QTLs) were identified for plant vigour and canopy conductance traits using an ultra-high density bin map. Plant vigour traits had 13 M-QTLs on CaLG04, with favourable alleles from high vigour parent ICC 4958. Most of them co-mapped with a previously fine mapped major drought tolerance "QTL-hotspot" region on CaLG04. One M-QTL was found for canopy conductance on CaLG03 with the ultra-high density bin map. Comparative analysis of the QTLs found across different density genetic maps revealed that QTL size reduced considerably and % of phenotypic variation increased as marker density increased.

CONCLUSION

Earlier reported drought tolerance hotspot is a vigour locus. The fact that canopy conductance traits, i.e. the other important determinant of plant water use, mapped on CaLG03 provides an opportunity to manipulate these loci to tailor recombinants having low/high transpiration rate and plant vigour, fitted to specific drought stress scenarios in chickpea.

Quantitative trait loci associated with constitutive traits control water use in pearl millet [Pennisetum glaucum (L.) R. Br]

There is substantial genetic variation for drought adaption in pearl millet in terms of traits controlling plant water use. It is important to understand genomic regions responsible for these traits. Here, F7 recombinant inbred lines were used to identify quantitative trait loci (QTL) and allelic interactions for traits affecting plant water use, and their relevance is discussed for crop productivity in water-limited environments. Four QTL contributed to increased transpiration rate under high vapour pressure deficit (VPD) conditions, all with alleles from drought-sensitive parent ICMB 841. Of these four QTL, a major QTL (35.7%) was mapped on linkage group (LG) 6. The alleles for 863B at this QTL decreased transpiration rate and this QTL co-mapped to a previously detected LG 6 QTL, with alleles from 863B for grain weight and panicle harvest index across severe terminal drought stress environments. This provided additional support for a link between water saving from a lower transpiration rate under high VPD and drought tolerance. 863B alleles in this same genomic region also increased shoot weight, leaf area and total transpiration under well-watered conditions. One unexpected outcome was reduced transpiration under high VPD (15%) from the interaction of two alleles for high VPD transpiration (LG 6 (B), 40.7) and specific leaf mass and biomass (LG 7 (A), 35.3), (A, allele from ICMB 841, B, allele from 863B, marker position). The LG 6 QTL appears to combine alleles for growth potential, beneficial for non-stress conditions, and for saving water under high evaporative demand, beneficial under stressful conditions. Mapping QTL for water-use traits, and assessing their interactions offers considerable potential for improving pearl millet adaptation to specific stress conditions through physiology-informed marker-assisted selection.

Identification of quantitative trait loci for yield and yield related traits in groundnut (Arachis hypogaea L.) under different water regimes in Niger and Senegal

Yield under drought stress is a highly complex trait with large influence to even a minor fluctuation in the environmental conditions. Genomics-assisted breeding holds great promise for improving such complex traits more efficiently in less time, but requires markers associated with the trait of interest. In this context, a recombinant inbred line mapping population (TAG 24 × ICGV 86031) was used to identify markers associated with quantitative trait loci (QTLs) for yield and yield related traits at two important locations of West Africa under well watered and water stress conditions. Among the traits analyzed under WS condition, the harvest index (HI) and the haulm yield (HYLD) were positively correlated with the pod yield (PYLD) and showed intermediate broad sense heritability. QTL analysis using phenotyping and genotyping data resulted in identification of 52 QTLs. These QTLs had low phenotypic variance (<12 %) for all the nine traits namely plant height, primary branching, SPAD chlorophyll meter reading, percentage of sound mature kernels, 100 kernel weight, shelling percentage, HI, HYLD and PYLD. Interestingly, few QTLs identified in this study were also overlapped with previously reported QTLs detected for drought tolerance related traits identified earlier in Indian environmental conditions using the same mapping population. Accumulating these many small-effect QTLs into a single genetic background is nearly impossible through marker-assisted backcrossing and even marker-assisted recurrent selection. Under such circumstances, the deployment of genomic selection is the most appropriate approach for improving such complex traits with more precision and in less time.

New insights into the genetic networks affecting seed fatty acid concentrations in Brassica napus

BACKGROUND

Rapeseed (B. napus, AACC, 2n = 38) is one of the most important oil seed crops in the world, it is also one of the most common oil for production of biodiesel. Its oil is a mixture of various fatty acids and dissection of the genetic network for fatty acids biosynthesis is of great importance for improving seed quality.

RESULTS

The genetic basis of fatty acid biosynthesis in B. napus was investigated via quantitative trail locus (QTL) analysis using a doubled haploid (DH) population with 202 lines. A total of 72 individual QTLs and a large number pairs of epistatic interactions associated with the content of 10 different fatty acids were detected. A total of 234 homologous genes of Arabidopsis thaliana that are involved in fatty acid metabolism were found within the confidence intervals (CIs) of 47 QTLs. Among them, 47 and 15 genes homologous to those of B. rapa and B. oleracea were detected, respectively. After the QTL mapping, the epistatic and the candidate gene interaction analysis, a potential regulatory pathway controlling fatty acid biosynthesis in B. napus was constructed, including 50 enzymes encoded genes and five regulatory factors (LEC1, LEC2, FUS3, WRI1 and ABI3). Subsequently, the interaction between these five regulatory factors and the genes involved in fatty acid metabolism were analyzed.

CONCLUSIONS

In this study, a potential regulatory pathway controlling the fatty acid was constructed by QTL analysis and in silico mapping analysis. These results enriched our knowledge of QTLs for fatty acids metabolism and provided a new clue for genetic engineering fatty acids composition in B. napus.

Emerging paradigms in genomics-based crop improvement

Next generation sequencing platforms and high-throughput genotyping assays have remarkably expedited the pace of development of genomic tools and resources for several crops. Complementing the technological developments, conceptual shifts have also been witnessed in designing experimental populations. Availability of second generation mapping populations encompassing multiple alleles, multiple traits, and extensive recombination events is radically changing the phenomenon of classical QTL mapping. Additionally, the rising molecular breeding approaches like marker assisted recurrent selection (MARS) that are able to harness several QTLs are of particular importance in obtaining a "designed" genotype carrying the most desirable combinations of favourable alleles. Furthermore, rapid generation of genome-wide marker data coupled with easy access to precise and accurate phenotypic screens enable large-scale exploitation of LD not only to discover novel QTLs via whole genome association scans but also to practise genomic estimated breeding value (GEBV)-based selection of genotypes. Given refinements being experienced in analytical methods and software tools, the multiparent populations will be the resource of choice to undertake genome wide association studies (GWAS), multiparent MARS, and genomic selection (GS). With this, it is envisioned that these high-throughput and high-power molecular breeding methods would greatly assist in exploiting the enormous potential underlying breeding by design approach to facilitate accelerated crop improvement.

eQTL Epistasis - Challenges and Computational Approaches

The determination of expression quantitative trait loci (eQTL) epistasis – a form of functional interaction between genetic loci that affect gene expression – is an important step toward the thorough understanding of gene regulation. Since gene expression has emerged as an “intermediate” molecular phenotype eQTL epistasis might help to explain the relationship between genotype and higher level organismal phenotypes such as diseases. A characteristic feature of eQTL analysis is the big number of tests required to identify associations between gene expression and genetic loci variability. This problem is aggravated, when epistatic effects between eQTLs are analyzed. In this review, we discuss recent algorithmic approaches for the detection of eQTL epistasis and highlight lessons that can be learned from current methods.

Identification and mapping of leaf, stem and stripe rust resistance quantitative trait loci and their interactions in durum wheat

Leaf rust (Puccinia triticina Eriks.), stripe rust (Puccinia striiformis f. tritici Eriks.) and stem rust (Puccinia graminis f. sp. tritici) cause major production losses in durum wheat (Triticum turgidum L. var. durum). The objective of this research was to identify and map leaf, stripe and stem rust resistance loci from the French cultivar Sachem and Canadian cultivar Strongfield. A doubled haploid population from Sachem/Strongfield and parents were phenotyped for seedling reaction to leaf rust races BBG/BN and BBG/BP and adult plant response was determined in three field rust nurseries near El Batan, Obregon and Toluca, Mexico. Stripe rust response was recorded in 2009 and 2011 nurseries near Toluca and near Njoro, Kenya in 2010. Response to stem rust was recorded in field nurseries near Njoro, Kenya, in 2010 and 2011. Sachem was resistant to leaf, stripe and stem rust. A major leaf rust quantitative trait locus (QTL) was identified on chromosome 7B at Xgwm146 in Sachem. In the same region on 7B, a stripe rust QTL was identified in Strongfield. Leaf and stripe rust QTL around DArT marker wPt3451 were identified on chromosome 1B. On chromosome 2B, a significant leaf rust QTL was detected conferred by Strongfield, and at the same QTL, a Yr gene derived from Sachem conferred resistance. Significant stem rust resistance QTL were detected on chromosome 4B. Consistent interactions among loci for resistance to each rust type across nurseries were detected, especially for leaf rust QTL on 7B. Sachem and Strongfield offer useful sources of rust resistance genes for durum rust breeding.

Will genomic selection be a practical method for plant breeding?

BACKGROUND

Genomic selection or genome-wide selection (GS) has been highlighted as a new approach for marker-assisted selection (MAS) in recent years. GS is a form of MAS that selects favourable individuals based on genomic estimated breeding values. Previous studies have suggested the utility of GS, especially for capturing small-effect quantitative trait loci, but GS has not become a popular methodology in the field of plant breeding, possibly because there is insufficient information available on GS for practical use.

SCOPE

In this review, GS is discussed from a practical breeding viewpoint. Statistical approaches employed in GS are briefly described, before the recent progress in GS studies is surveyed. GS practices in plant breeding are then reviewed before future prospects are discussed.

CONCLUSIONS

Statistical concepts used in GS are discussed with genetic models and variance decomposition, heritability, breeding value and linear model. Recent progress in GS studies is reviewed with a focus on empirical studies. For the practice of GS in plant breeding, several specific points are discussed including linkage disequilibrium, feature of populations and genotyped markers and breeding scheme. Currently, GS is not perfect, but it is a potent, attractive and valuable approach for plant breeding. This method will be integrated into many practical breeding programmes in the near future with further advances and the maturing of its theory.

Quantitative trait locus analysis and construction of consensus genetic map for drought tolerance traits based on three recombinant inbred line populations in cultivated groundnut (Arachis hypogaea L.)

Groundnut (Arachis hypogaea L.) is an important food and cash crop grown mainly in semi-arid tropics (SAT) regions of the world where drought is the major constraint on productivity. With the aim of understanding the genetic basis and identification of quantitative trait loci (QTL) for drought tolerance, two new recombinant inbred line (RIL) mapping populations, namely ICGS 76 × CSMG 84-1 (RIL-2) and ICGS 44 × ICGS 76 (RIL-3), were used. After screening of 3,215 simple sequence repeat (SSR) markers on the parental genotypes of these populations, two new genetic maps were developed with 119 (RIL-2) and 82 (RIL-3) SSR loci. Together with these maps and the reference map with 191 SSR loci based on TAG 24 × ICGV 86031 (RIL-1), a consensus map was constructed with 293 SSR loci distributed over 20 linkage groups, spanning 2,840.8 cM. As all these three populations segregate for drought-tolerance-related traits, a comprehensive QTL analysis identified 153 main effect QTL (M-QTL) and 25 epistatic QTL (E-QTL) for drought-tolerance-related traits. Localization of these QTL on the consensus map provided 16 genomic regions that contained 125 QTL. A few key genomic regions were selected on the basis of the QTL identified in each region, and their expected role in drought adaptation is also discussed. Given that no major QTL for drought adaptation were identified, novel breeding approaches such as marker-assisted recurrent selection (MARS) and genomic selection (GS) approaches are likely to be the preferred approaches for introgression of a larger number of QTL in order to breed drought-tolerant groundnut genotypes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11032-011-9660-0) contains supplementary material, which is available to authorized users.

In silico polymorphism analysis for the development of simple sequence repeat and transposon markers and construction of linkage map in cultivated peanut

BACKGROUND

Peanut (Arachis hypogaea) is an autogamous allotetraploid legume (2n = 4x = 40) that is widely cultivated as a food and oil crop. More than 6,000 DNA markers have been developed in Arachis spp., but high-density linkage maps useful for genetics, genomics, and breeding have not been constructed due to extremely low genetic diversity. Polymorphic marker loci are useful for the construction of such high-density linkage maps. The present study used in silico analysis to develop simple sequence repeat-based and transposon-based markers.

RESULTS

The use of in silico analysis increased the efficiency of polymorphic marker development by more than 3-fold. In total, 926 (34.2%) of 2,702 markers showed polymorphisms between parental lines of the mapping population. Linkage analysis of the 926 markers along with 253 polymorphic markers selected from 4,449 published markers generated 21 linkage groups covering 2,166.4 cM with 1,114 loci. Based on the map thus produced, 23 quantitative trait loci (QTLs) for 15 agronomical traits were detected. Another linkage map with 326 loci was also constructed and revealed a relationship between the genotypes of the FAD2 genes and the ratio of oleic/linoleic acid in peanut seed.

CONCLUSIONS

In silico analysis of polymorphisms increased the efficiency of polymorphic marker development, and contributed to the construction of high-density linkage maps in cultivated peanut. The resultant maps were applicable to QTL analysis. Marker subsets and linkage maps developed in this study should be useful for genetics, genomics, and breeding in Arachis. The data are available at the Kazusa DNA Marker Database (http://marker.kazusa.or.jp).

Interactive Data Mining Tool for Microarray Data Analysis Using Formal Concept Analysis

The DNA microarray analysis can explain gene functions by measuring tens of thousands of gene expressions at once and analyzing gene expression profiles that are obtained from the measurement. However, gene expression profiles have such a vast amount of information and therefore most analyses work are done on the data narrowed down by statistical methods, there remains a possibility ofmissing out on genes that consist the factors of phenomena from their evaluations. This study propose a method based on a formal concept analysis to visualize all gene expression profiles and characteristic information that can be obtained from annotation information of each gene so that the user can overview them. In the formal concept analysis, a lattice structure that allows genes to be hierarchically classified and made viewable is built based on the inclusion relations of attributes from a context table in which gene is the object and the attributes are expression profiles and binarized characteristic information. With the proposed method, the user can change the overview state by adjusting the expression ratio and the binary state of characteristic information, understand the relational structure of gene expressions, and carry out analyses of gene functions. We develop software to practice the proposed method, and then ask a biologist to evaluate effectiveness of proposed method applied to a function analysis of genes related to blue light signaling of rice seedlings.

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 x 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.

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 x WF1680 and HR x 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 x WF1680 and the HR x 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.