Quantitative Trait Loci Hotspots Research Articles

Co-location of QTL for Sclerotinia stem rot resistance and flowering time in Brassica napus

Sclerotinia stem rot (SSR) caused by Sclerotinia sclerotiorum (Lib.) de Bary is one of the most devastating diseases of Brassica napus worldwide. Both SSR resistance and flowering time (FT) adaptation are major breeding goals in B. napus. However, early maturing rapeseed varieties, which are important for rice-rapeseed rotation in China, are often highly susceptible to SSR. Here, we found that SSR resistance was significantly negatively correlated with FT in a natural population containing 521 rapeseed inbred lines and a double haploid (DH) population with 150 individual lines, both of which had great variation in FT. Four chromosomal regions on A2, A6, C2, and C8 affecting both SSR resistance and FT were identified using quantitative trait loci (QTL) mapping after constructing a high-density genetic map based on single nucleotide polymorphism markers in the DH population. Furthermore, we aligned QTL for the two traits identified in the present and previous studies to the B. napus reference genome, and identified four colocalized QTL hotspots of SSR resistance and FT on A2 (0–7.7 Mb), A3 (0.8–7.5 Mb), C2 (0–15.2 Mb), and C6 (20.2–36.6 Mb). Our results revealed a genetic link between SSR resistance and FT in B. napus, which should facilitate the development of effective strategies in both early maturing and SSR resistance breeding and in map-based cloning of SSR resistance QTL.

Differentially expressed genes between two groups of backcross inbred lines differing in fiber length developed from Upland × Pima cotton.

Fiber length is one of the most important fiber quality traits in Upland cotton (Gossypium hirsutum L.), the most important fiber crop, and its improvement has been impeded in part by a lack of knowledge regarding its genetic basis. Introgressed backcross inbred lines (BILs) or near isogenic lines (NILs) differing in fiber length in the same genetic background, developed through advanced backcrossing between Upland cotton and extra-long staple cotton (G. barbadense L.), provide an important genomic resource for studying the molecular genetic basis of fiber length. In the present study, a long-fiber group and a short-fiber group, each with five BILs of Upland cotton, were selected from a BIL population between G. hirsutum and G. barbadense. Through amicroarray-based comparative transcriptome analysis of developing fibers at 10 days postanthesis from the two groups, 1478 differentially expressed genes (DEGs) were identified. A total of 166 DEGs were then mapped to regions of fiber length quantitative trait loci (QTL), including 12 QTL hotspots and 2 QTL identified previously in the BIL population from which the two sets of BILs were selected. Several candidate genes possibly underlying the genetic control of fiber length differences between G. barbadense and G. hirsutum, including GhACX and GhKIF, were identified in this study. These results provide a list of positional candidate genes for the fine-scale mapping and map-based cloning of fiber length QTL, which will facilitate targeted gene transfer from G. barbadense to Upland cotton to further improve fiber quality.

Identification of Soybean Genes Related to Soybean Seed Protein Content Based on Quantitative Trait Loci Collinearity Analysis.

Increasing the protein content of soybean seeds through a higher ratio of glycinin is important for soybean breeding and food processing; therefore, the integration of different quantitative trait loci (QTLs) is of great significance. In this study, we investigated the collinearity of seed protein QTLs. We identified 192 collinear protein QTLs that formed six hotspot regions. The two most important regions had seed protein 36-10 and seed protein 36-20 as hub nodes. We used a chromosome segment substitution line (CSSL) population for QTL validation and identified six CSSL materials with collinear QTLs. Five materials with higher protein and glycinin contents in comparison to the recurrent parent were analyzed. A total of 13 candidate genes related to seed protein from the QTL hotspot intervals were detected, 8 of which had high expression in mature soybean seeds. These results offer a new analysis method for molecular-assisted selection (MAS) and improvement of soybean product quality.

Development and use of chromosome segment substitution lines as a genetic resource for crop improvement

CSSLs are a complete library of introgression lines with chromosomal segments of usually a distant genotype in an adapted background and are valuable genetic resources for basic and applied research on improvement of complex traits. Chromosome segment substitution lines (CSSLs) are genetic stocks representing the complete genome of any genotype in the background of a cultivar as overlapping segments. Ideally, each CSSL has a single chromosome segment from the donor with a maximum recurrent parent genome recovered in the background. CSSL development program requires population-wide backcross breeding and genome-wide marker-assisted selection followed by selfing. Each line in a CSSL library has a specific marker-defined large donor segment. CSSLs are evaluated for any target phenotype to identify lines significantly different from the parental line. These CSSLs are then used to map quantitative trait loci (QTLs) or causal genes. CSSLs are valuable prebreeding tools for broadening the genetic base of existing cultivars and harnessing the genetic diversity from the wild- and distant-related species. These are resources for genetic map construction, mapping QTLs, genes or gene interactions and their functional analysis for crop improvement. In the last two decades, the utility of CSSLs in identification of novel genomic regions and QTL hot spots influencing a wide range of traits has been well demonstrated in food and commercial crops. This review presents an overview of how CSSLs are developed, their status in major crops and their use in genomic studies and gene discovery.

Shared Genomic Regions Underlie Natural Variation in Diverse Toxin Responses.

Phenotypic complexity is caused by the contributions of environmental factors and multiple genetic loci, interacting or acting independently. Studies of yeast and Arabidopsis often find that the majority of natural variation across phenotypes is attributable to independent additive quantitative trait loci (QTL). Detected loci in these organisms explain most of the estimated heritable variation. By contrast, many heritable components underlying phenotypic variation in metazoan models remain undetected. Before the relative impacts of additive and interactive variance components on metazoan phenotypic variation can be dissected, high replication and precise phenotypic measurements are required to obtain sufficient statistical power to detect loci contributing to this missing heritability. Here, we used a panel of 296 recombinant inbred advanced intercross lines of Caenorhabditis elegans and a high-throughput fitness assay to detect loci underlying responses to 16 different toxins, including heavy metals, chemotherapeutic drugs, pesticides, and neuropharmaceuticals. Using linkage mapping, we identified 82 QTL that underlie variation in responses to these toxins, and predicted the relative contributions of additive loci and genetic interactions across various growth parameters. Additionally, we identified three genomic regions that impact responses to multiple classes of toxins. These QTL hotspots could represent common factors impacting toxin responses. We went further to generate near-isogenic lines and chromosome substitution strains, and then experimentally validated these QTL hotspots, implicating additive and interactive loci that underlie toxin-response variation.

Genetic analysis of seedling root traits reveals the association of root trait with other agronomic traits in maize

BackgroundRoot systems play important roles in crop growth and stress responses. Although genetic mechanism of root traits in maize (Zea mays L.) has been investigated in different mapping populations, root traits have rarely been utilized in breeding programs. Elucidation of the genetic basis of maize root traits and, more importantly, their connection to other agronomic trait(s), such as grain yield, may facilitate root trait manipulation and maize germplasm improvement. In this study, we analyzed genome-wide genetic loci for maize seedling root traits at three time-points after seed germination to identify chromosomal regions responsible for both seedling root traits and other agronomic traits in a recombinant inbred line (RIL) population (Zong3 × Yu87–1).ResultsEight seedling root traits were examined at 4, 9, and 14 days after seed germination, and thirty-six putative quantitative trait loci (QTLs), accounting for 9.0–23.2% of the phenotypic variation in root traits, were detected. Co-localization of root trait QTLs was observed at, but not between, the three time-points. We identified strong or moderate correlations between root traits controlled by each co-localized QTL region. Furthermore, we identified an overlap in the QTL locations of seedling root traits examined here and six other traits reported previously in the same RIL population, including grain yield-related traits, plant height-related traits, and traits in relation to stress responses. Maize chromosomal bins 1.02–1.03, 1.07, 2.06–2.07, 5.05, 7.02–7.03, 9.04, and 10.06 were identified QTL hotspots for three or four more traits in addition to seedling root traits.ConclusionsOur identification of co-localization of root trait QTLs at, but not between, each of the three time-points suggests that maize seedling root traits are regulated by different sets of pleiotropic-effect QTLs at different developmental stages. Furthermore, the identification of QTL hotspots suggests the genetic association of seedling root traits with several other traits and reveals maize chromosomal regions valuable for marker-assisted selection to improve root systems and other agronomic traits simultaneously.

Genetic variation in phytochemicals in leaves of pepper (Capsicum) in relation to thrips resistance

Phytochemicals not only determine the taste and smell of plants and their products, they also play a crucial role in resistance against pests and pathogens. In previous work, we identified a form of resistance to thrips (Frankliniella occidentalis) expressed in leaves of pepper (Capsicum annuum). In the current study, we characterized leaves of an interspecific C. annuum × C. chinense F2 population for variation in their global phytochemical composition by an untargeted metabolomics approach. Quantitative trait locus (QTL) mapping resulted in metabolite QTLs (mQTLs) for 304 of the 674 metabolites detected. We compared the QTL mapping results to those obtained earlier on fruits in the same population. Very different QTL hotspots were found, suggesting that the metabolite composition of leaves and fruits is regulated independently. Six leaf mQTLs co-located with the major QTL for resistance to thrips, which we previously identified in the same F2 population. Four of them were significantly correlated to thrips resistance, including two diterpene glycosides and a flavonoid compound which may indicate a possible role of these metabolites in thrips resistance. If a causal role of some of these metabolites in resistance can be proven this will help in the identification of the causal gene(s) and it may provide leads for the identification of other sources of thrips resistance in Capsicum and in other species.

Comparative Aerial and Ground Based High Throughput Phenotyping for the Genetic Dissection of NDVI as a Proxy for Drought Adaptive Traits in Durum Wheat.

High-throughput phenotyping platforms (HTPPs) provide novel opportunities to more effectively dissect the genetic basis of drought-adaptive traits. This genome-wide association study (GWAS) compares the results obtained with two Unmanned Aerial Vehicles (UAVs) and a ground-based platform used to measure Normalized Difference Vegetation Index (NDVI) in a panel of 248 elite durum wheat (Triticum turgidum L. ssp. durum Desf.) accessions at different growth stages and water regimes. Our results suggest increased ability of aerial over ground-based platforms to detect quantitative trait loci (QTL) for NDVI, particularly under terminal drought stress, with 22 and 16 single QTLs detected, respectively, and accounting for 89.6 vs. 64.7% phenotypic variance based on multiple QTL models. Additionally, the durum panel was investigated for leaf chlorophyll content (SPAD), leaf rolling and dry biomass under terminal drought stress. In total, 46 significant QTLs affected NDVI across platforms, 22 of which showed concomitant effects on leaf greenness, 2 on leaf rolling and 10 on biomass. Among 9 QTL hotspots on chromosomes 1A, 1B, 2B, 4B, 5B, 6B, and 7B that influenced NDVI and other drought-adaptive traits, 8 showed per se effects unrelated to phenology.

Identification of QTL hot spots for malting quality in two elite breeding lines with distinct tolerance to abiotic stress

BackgroundBarley (Hordeum vulgare) is an important crop cultivated across the world. Drought is a major abiotic factor compromising barley yield worldwide, therefore in modern spring barley cultivars superior seed and malting quality characteristics should be combined with reasonable level of drought tolerance. Previously we have identified a number of barley lines demonstrating the superior yield performance under drought conditions. The aim of this work was to perform a QTL analysis of malting quality traits in a doubled haploid (DH) mapping population of two elite barley lines that differ in their reaction pattern to drought stress.ResultsA population of DH lines was developed by crossing two drought-tolerant elite breeding lines, Victoriana and Sofiara, exploiting distinct mechanism of drought tolerance, sustaining assimilation vs remobilization. The mapping population was assayed under field conditions at four distinct locations that differed in precipitation rate. DH lines were genotyped with the Illumina 9 K iSelect assay, and linkage map including 1782 polymorphic markers and covering a total map length of 1140 cM was constructed. The result of quantitative trait loci (QTL) analysis showed that majority of the traits were affected by several main effect QTL and/or QTL x environment (QE) interactions. In total, 57, 41, and 5 QTL were associated with yield-related traits, malting quality traits and seed quality traits, respectively. 11 and 29 of mapped QTL explained more than 10 and 5% of phenotypic variation, respectively. In several chromosomal regions co-localization between QTL for various traits were observed. The largest clusters were detected on chromosomes 3H and 4H.ConclusionsOur QTL mapping results revealed several novel consistent genomic regions controlling malting quality which could be exploited in marker assisted selection. In this context, the complex QTL region on chromosome 3H seems of particular interest, as it harbors several large effect QTL.

Genome-wide association of yield traits in a nested association mapping population of barley reveals new gene diversity for future breeding.

To explore wild barley as a source of useful alleles for yield improvement in breeding, we have carried out a genome-wide association scan using the nested association mapping population HEB-25, which contains 25 diverse exotic barley genomes superimposed on an ~70% genetic background of cultivated barley. A total of 1420 HEB-25 lines were trialled for nine yield-related grain traits for 2 years in Germany and Scotland, with varying N fertilizer application. The phenotypic data were related to genotype scores for 5398 gene-based single nucleotide polymorphism (SNP) markers. A total of 96 quantitative trait locus (QTL) regions were identified across all measured traits, the majority of which co-localize with known major genes controlling flowering time (Ppd-H2, HvCEN, HvGI, VRN-H1, and VRN-H3) and spike morphology (VRS3, VRS1, VRS4, and INT-C) in barley. Fourteen QTL hotspots, with at least three traits coinciding, were also identified, several of which co-localize with barley orthologues of genes controlling grain dimensions in rice. Most of the allele effects are specific to geographical location and/or exotic parental genotype. This study shows the existence of beneficial alleles for yield-related traits in exotic barley germplasm and provides candidate alleles for future improvement of these traits by the breeder.

Quantitative trait locus analysis of seed germination, seedling vigour and seedling‐regulated hormones in Brassica napus

AbstractGood germination and seedling vigour are major breeding targets in winter oilseed rape (Brassica napus), because seedling vigour and prewinter crop establishment are closely associated with postwinter growth and yield. Here, we identified quantitative trait loci (QTL) related to germination, seedling vigour and seedling‐regulated hormones in a doubled haploid (DH) mapping population from a cross between winter oilseed rape parents with high vigour (Express 617) and low vigour (1012‐98). By phenotyping in a climate‐controlled glasshouse, we identified a total of 13 QTL on nine chromosomes for germination and seedling‐related traits at 7 and 14 days after sowing (DAS), explaining up to 11.2% of the phenotypic variation for seedling vigour. Forty‐seven metabolic QTL on 15 chromosomes were identified for auxin, abscisic acid (ABA) and dihydrophaseic acid (DPA) at 5 and 12 DAS, explaining up to 49.4% of phenotypic variation in seedling hormone composition. Multitrait QTL hot spots contribute to our understanding of the genetics and metabolomics of germination and seeding vigour in B. napus, and represent potential targets to breed high‐vigour cultivars.

Identification of QTLs associated with heat tolerance at the heading and flowering stage in rice (Oryza sativa L.)

The ongoing rise in temperatures caused by global climate change is a critical climatic risk factor for rice production, and enhancing rice heat tolerance is an area of particular research interest. A recombinant inbred line (RIL) mapping population was developed from heat sensitive, rice cultivar IAPAR-9 crossed with heat tolerant, Liaoyan241. RIL and parental lines were exposed to high temperature at the heating and flowering stage in experiments in 2014 and 2015. As indicators of heat tolerance, the seed setting rate under natural (NS) and heat stress (HTS) conditions were measured, and the reduction rate of seed set (RRS) was calculated. Quantitative trait loci (QTL) analysis revealed eleven heat tolerance QTLs located on chromosomes 1, 3, 4, 5, and 6. Single QTL contribution rates were 4.75–13.81% and effect values were − 5.98 to 5.00. Four major QTLs (qNS1, qNS4, qNS6, and qRRS1) were stable detected in different environments in both years. Thirteen QTLs with epistatic interactions and nine QTLs with environmental interactions were also detected. Major QTLs were all involved in epistatic and environmental interactions. Three QTLs from the SSR marker interval RM471 to RM177 region of chromosome 4 (qNS4, qHTS4, and qRRS4) were all involved in epistatic and environmental interactions and contributed to phenotypic variation, indicating that this region constituted a major QTL hotspot. The major QTL for heat tolerance identified in this study will aid in breeding tolerant cultivars and facilitating investigation of the molecular underpinnings of heat tolerance in rice.

High-fidelity detection of crop biomass quantitative trait loci from low-cost imaging in the field.

Field‐based, rapid, and nondestructive techniques for assessing plant productivity are needed to accelerate the discovery of genotype‐to‐phenotype relationships in next‐generation biomass grass crops. The use of hemispherical imaging and light attenuation modeling was evaluated against destructive harvest measures with respect to their ability to accurately capture phenotypic and genotypic relationships in a field‐grown grass crop. Plant area index (PAI) estimated from below‐canopy hemispherical images, as well as a suite of thirteen traits assessed by manual destructive harvests, were measured in a Setaria recombinant inbred line mapping population segregating for aboveground productivity and architecture. A significant correlation was observed between PAI and biomass production across the population at maturity (r 2 = .60), as well as for select diverse genotypes sampled repeatedly over the growing season (r 2 = .79). Twenty‐seven quantitative trait loci (QTL) were detected for manually collected traits associated with biomass production. Of these, twenty‐one were found in four clusters of colocalized QTL. Analysis of image‐based estimates of PAI successfully identified all four QTL hot spots for biomass production. QTL for PAI had greater overlap with those detected for traits associated with biomass production than with those for plant architecture and biomass partitioning. Hemispherical imaging is an affordable and scalable method, which demonstrates how high‐throughput phenotyping can identify QTL related to biomass production of field trials in place of destructive harvests that are labor, time, and material intensive.

A meta-analysis of quantitative trait loci for abiotic and biotic stress resistance in tetraploid cotton.

The number and location of mapped quantitative trait loci (QTL) depend on genetic populations and testing environments. The identification of consistent QTL across genetic backgrounds and environments is a pre-requisite to marker-assisted selection. This study analyzed a total of 661 abiotic and biotic stress resistance QTL based on our previous work and other publications using the meta-analysis software Biomercator. It identified chromosomal regions containing QTL clusters for different resistance traits and hotspots for a particular resistance trait in cotton from 98 QTL for drought tolerance under greenhouse (DT) and 150 QTL in field conditions (FDT), 80 QTL for salt tolerance in the greenhouse conditions (ST), 201 QTL for resistance to Verticillium wilt (VW, Verticillium dahliae), 47 QTL for resistance toFusarium wilt (FW, Fusarium oxysporum f. sp. vasinfectum), and 85 QTL for resistance to root-knot nematodes (RKN, Meloiodogyne incognita) and reniform nematodes (RN, Rotylenchulus reniformis). The traits used in QTL mapping for abiotic stress tolerance included morphological traits-plant height and fresh and dry shoot and root weights, physiological traits-chlorophyll content, osmotic potential, carbon isotope ratio, stomatal conductance, photosynthetic rate, transpiration, canopy temperature, and leaf area index, agronomic traits-seedcotton yield, lint yield, boll weight, and lint percent, and fiber quality traits-fiber length, uniformity, strength, elongation, and micronaire. The results showed that resistance QTL are not uniformly distributed across the cotton genome; some chromosomes carried disproportionally more QTL, QTL clusters, or hotspots. Twenty-three QTL clusters were found on 15 chromosomes (c3, c4, c5, c6, c7, c11, c14, c15, c16, c19, c20, c23, c24, c25, and c26). Moreover, 28 QTL hotshots were associated with different resistance traits including one hotspot on c4 for Verticillium wilt resistance, two QTL hotspots on c24 for chlorophyll content measured under both drought and salt stress conditions, and three other hotspots on c19 for the resistance to Verticillium wilt and Fusarium wilt, and micronaire under drought stress conditions. This meta-analysis of stress tolerance QTL provides an important foundation for cotton breeding and further studies on the genetic mechanisms of abiotic and biotic stress resistance in cotton.

Phenotypic evaluation and QTL analysis of yield and symbiotic nitrogen fixation in a common bean population grown with two levels of phosphorus supply

Common bean is an important staple crop in Eastern Africa and Latin America. Low soil fertility is a major limitation to agronomic productivity. Symbiotic nitrogen fixation (SNF) is an important property of legumes, leading to high protein levels and high nutritional value. Nitrogen (N) metabolism and yield traits were evaluated in the common bean population DOR 364 × BAT 477 in field experiments under moderate and low phosphorus (P) soil conditions resembling environments found on farmers’ fields. Low P availability in soil severely limits seed yield, and trait correlations with yield reveal that high biomass as well as early maturity and efficient seed filling are important for good performance in low P stress, resembling drought resistance. Investigation of SNF and soil N uptake under low P stress showed reduced seed nitrogen levels and major variation in soil-derived N. In low P conditions, no significant reduction of %N derived from the atmosphere (%Ndfa) was observed; however, %Ndfa was correlated with yield, indicating that under stress SNF becomes an important asset. Significant genetic variation was observed for yield, yield components, and SNF ability suggesting that traits can be improved by breeding. Quantitative trait loci (QTLs) for %Ndfa and seed N concentration were discovered on chromosomes Pv07 and Pv02; independent yield QTLs were identified on the same chromosomes. Two QTL hotspots that affect several traits including yield components were found on Pv02 and Pv06; the latter represents a constitutive QTL hotspot independent from the environment. QTLs may be used for marker design and molecular breeding.

High-Throughput Phenotyping and QTL Mapping Reveals the Genetic Architecture of Maize Plant Growth.

With increasing demand for novel traits in crop breeding, the plant research community faces the challenge of quantitatively analyzing the structure and function of large numbers of plants. A clear goal of high-throughput phenotyping is to bridge the gap between genomics and phenomics. In this study, we quantified 106 traits from a maize (Zea mays) recombinant inbred line population (n = 167) across 16 developmental stages using the automatic phenotyping platform. Quantitative trait locus (QTL) mapping with a high-density genetic linkage map, including 2,496 recombinant bins, was used to uncover the genetic basis of these complex agronomic traits, and 988 QTLs have been identified for all investigated traits, including three QTL hotspots. Biomass accumulation and final yield were predicted using a combination of dissected traits in the early growth stage. These results reveal the dynamic genetic architecture of maize plant growth and enhance ideotype-based maize breeding and prediction.

Quantitative trait loci analysis of Verticillium wilt resistance in interspecific backcross populations of Gossypium hirsutum × Gossypium barbadense.

Background Verticillium wilt (VW) caused by Verticillium dahliae (Kleb) is one of the most destructive diseases of cotton. The identification of highly resistant QTLs or genes in the whole cotton genome is quite important for developing a VW-resistant variety and for further molecular design breeding.ResultsIn the present study, BC1F1, BC1S1, and BC2F1 populations derived from an interspecific backcross between the highly resistant line Hai1 (Gossypium barbadense L.) and the susceptible variety CCRI36 (G. hirsutum L.) as the recurrent parent were constructed. Quantitative trait loci (QTL) related to VW resistance were detected in the whole cotton genome using a high-density simple sequence repeat (SSR) genetic linkage map from the BC1F1 population, with 2292 loci covering 5115.16 centiMorgan (cM) of the cotton (AD) genome, and the data concerning VW resistance that were obtained from four dates of BC2F1 in the artificial disease nursery and one date of BC1S1 and BC2F1 in the field. A total of 48 QTLs for VW resistance were identified, and 37 of these QTLs had positive additive effects, which indicated that the G. barbadense alleles increased resistance to VW and decreased the disease index (DI) by about 2.2–10.7. These QTLs were located on 19 chromosomes, in which 33 in the A subgenome and 15 QTLs in the D subgenome. The 6 QTLs were found to be stable. The 6 QTLs were consistent with those identified previously, and another 42 were new, unreported QTLs, of which 31 QTLs were from G. barbadense. By meta-analysis, 17 QTL hotspot regions were identified and 10 of them were new, unreported hotspot regions. 29 QTLs in this paper were in 12 hotspot regions and were all from G. barbadense.ConclusionsThese stable or consensus QTL regions warrant further investigation to better understand the genetics and molecular mechanisms underlying VW resistance. This study provides useful information for further comparative analysis and marker-assisted selection in the breeding of disease-resistant cotton. It may also lay an important foundation for gene cloning and further molecular design breeding for the entire cotton genome.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-3128-x) contains supplementary material, which is available to authorized users.

Systems genetics reveals key genetic elements of drought induced gene regulation in diploid potato.

In plants, tolerance to drought stress is a result of numerous minor effect loci in which transcriptional regulation contributes significantly to the observed phenotypes. Under severe drought conditions, a major expression quantitative trait loci hotspot was identified on chromosome five in potato. A putative Nuclear factor y subunit C4 was identified as key candidate in the regulatory cascade in response to drought. Further investigation of the eQTL hotspots suggests a role for a putative Homeobox leucine zipper protein 12 in relation to drought in potato. Genes strongly co-expressed with Homeobox leucine zipper protein 12 were plant growth regulators responsive to water deficit stress in Arabidopsis thaliana, implying a possible conserved mechanism. Integrative analysis of genetic, genomic, phenotypic and transcriptomic data provided insights in the downstream functional components of the drought response. The abscisic acid- and environmental stress-inducible protein TAS14 was highly induced by severe drought in potato and acts as a reliable biomarker for the level of stress perceived by the plant. The systems genetics approach supported a role for multiple genes responsive to severe drought stress of Solanum tuberosum. The combination of gene regulatory networks, expression quantitative trait loci mapping and phenotypic analysis proved useful for candidate gene selection.

Genetic dissection of fruiting body-related traits using quantitative trait loci mapping in Lentinula edodes.

To provide a better understanding of the genetic architecture of fruiting body formation of Lentinula edodes, quantitative trait loci (QTLs) mapping was employed to uncover the loci underlying seven fruiting body-related traits (FBRTs). An improved L. edodes genetic linkage map, comprising 572 markers on 12 linkage groups with a total map length of 983.7cM, was constructed by integrating 82 genomic sequence-based insertion-deletion (InDel) markers into a previously published map. We then detected a total of 62 QTLs for seven target traits across two segregating testcross populations, with individual QTLs contributing 5.5 %-30.2 % of the phenotypic variation. Fifty-three out of the 62 QTLs were clustered in six QTL hotspots, suggesting the existence of main genomic regions regulating the morphological characteristics of fruiting bodies in L. edodes. A stable QTL hotspot on MLG2, containing QTLs for all investigated traits, was identified in both testcross populations. QTLs for related traits were frequently co-located on the linkage groups, demonstrating the genetic basis for phenotypic correlation of traits. Meta-QTL (mQTL) analysis was performed and identified 16 mQTLs with refined positions and narrow confidence intervals (CIs). Nine genes, including those encoding MAP kinase, blue-light photoreceptor, riboflavin-aldehyde-forming enzyme and cyclopropane-fatty-acyl-phospholipid synthase, and cytochrome P450s, were likely to be candidate genes controlling the shape of fruiting bodies. The study has improved our understanding of the genetic architecture of fruiting body formation in L. edodes. To our knowledge, this is the first genome-wide QTL detection of FBRTs in L. edodes. The improved genetic map, InDel markers and QTL hotspot regions revealed here will assist considerably in the conduct of future genetic and breeding studies of L. edodes.

The Dissection of Expression Quantitative Trait Locus Hotspots.

Studies of the genetic loci that contribute to variation in gene expression frequently identify loci with broad effects on gene expression: expression quantitative trait locus hotspots. We describe a set of exploratory graphical methods as well as a formal likelihood-based test for assessing whether a given hotspot is due to one or multiple polymorphisms. We first look at the pattern of effects of the locus on the expression traits that map to the locus: the direction of the effects and the degree of dominance. A second technique is to focus on the individuals that exhibit no recombination event in the region, apply dimensionality reduction (e.g., with linear discriminant analysis), and compare the phenotype distribution in the nonrecombinant individuals to that in the recombinant individuals: if the recombinant individuals display a different expression pattern than the nonrecombinant individuals, this indicates the presence of multiple causal polymorphisms. In the formal likelihood-based test, we compare a two-locus model, with each expression trait affected by one or the other locus, to a single-locus model. We apply our methods to a large mouse intercross with gene expression microarray data on six tissues.