Mapping the genetic architecture of complex traits in experimental populations

Identification of QTL for maize grain yield and kernel-related traits

Grain yield (GY) is one of the most important and complex quantitative traits in maize (Zea mays L.) breeding practice. Quantitative trait loci (QTLs) for GY and three kernel-related traits were detected in a set of recombinant inbred lines (RILs). One hundred and seven simple sequence repeats (SSRs) and 168 insertion/deletion polymorphism markers (Indels) were used to genotype RILs. Eight QTLs were found to be associated with four yield-related traits: GY, 100-kernel weight (HKW), 10-kernel length (KL), and 10-kernel length width (KW). Each QTL explained between 5.96 (qKL2-1) and 13.05 (qKL1-1) per cent of the phenotypic variance. Notably, one common QTL, located at the marker interval between bnlg1893 and chr2- 236477 (chromosomal bin 2.09) simultaneously controlled GY and HKW; another common QTL, at bin 2.03 was simultaneously responsible for HKW and KW. Of the QTLs identified, only one pair of significant epistatic interaction involved in chromosomal region at bin 2.03 was detected for HKW; no significant QTL × environment interactions were observed. These results provide the common QTLs and for marker-assisted breeding.

QTL mapping with different genetic systems for nine non-essential amino acids of cottonseeds

Amino acid is an important nutrient resource for both human and animals. Using a set of 188 RILs population derived from an elite hybrid cross of upland cotton cultivars ‘HS46’ × ‘MARCABUCAG8US-1-88’ and their immortal F₂ (IF₂) with reciprocal backcrosses BC₁F₁ and BC₂F₁ (BC) populations in two environments, the QTLs located on the embryo genome and maternal plant genome for nine amino acids of cottonseed were studied across environments. The QTL Network-CL-2.0-seed software was used to analyze the QTLs and their genetic effects for nine amino acids. A total of 56 QTLs for nine amino acids were detected in both populations, with many having over 5% of phenotypic variation. Ten of the total QTLs could be simultaneously found in the IF₂ and BC populations. For most QTLs, the genetic effects from embryo genome were more important than those from maternal plant genome for the performance of nine amino acids. Significant embryo additive main effects and maternal additive main effect with their environment interaction effects from many QTLs were also found in present experiment. Some QTLs with larger phenotypic variation were important for improving the amino-acid contents in cottonseeds.

Genomic Prediction of Genotypic Effects with Epistasis and Environment Interactions for Yield-Related Traits of Rapeseed (Brassica napus L.)

Oilseed rape (Brassica napus) is an economically important oil crop, yet the genetic architecture of its complex traits remain largely unknown. Here, genome-wide association study was conducted for eight yield-related traits to dissect the genetic architecture of additive, dominance, epistasis, and their environment interaction. Additionally, the optimal genotype combination and the breeding value of superior line, superior hybrid and existing best line in mapping population were predicted for each trait in two environments based on the predicted genotypic effects. As a result, 17 quantitative trait SNPs (QTSs) were identified significantly for target traits with total heritability varied from 58.47 to 87.98%, most of which were contributed by dominance, epistasis, and environment-specific effects. The results indicated that non-additive effects were large contributions to heritability and epistasis, and also noted that environment interactions were important variants for oilseed breeding. Our study facilitates the understanding of genetic basis of rapeseed yield trait, helps to accelerate rapeseed breading, and also offers a roadmap for precision plant breeding via marker-assisted selection.

Genetic dissection of seed weight by QTL analysis and detection of allelic variation in Indian and east European gene pool lines of Brassica juncea

Seed weight QTL identified in different populations were synthesized into consensus QTL which were shown to harbor candidate genes by in silico mapping. Allelic variation inferred would be useful in breeding B. juncea lines with high seed weight. Seed weight is an important yield influencing trait in oilseed Brassicas and is a multigenic trait. Among the oilseed Brassicas, Brassica juncea harbors the maximum phenotypic variation wherein thousand seed weight varies from around 2.0 g to more than 7.0 g. In this study, we have undertaken quantitative trait locus/quantitative trait loci (QTL) analysis of seed weight in B. juncea using four bi-parental doubled-haploid populations. These four populations were derived from six lines (three Indian and three east European lines) with parental phenotypic values for thousand seed weight ranging from 2.0 to 7.6 g in different environments. Multi-environment QTL analysis of the four populations identified a total of 65 QTL ranging from 10 to 25 in each population. Meta-analysis of these component QTL of the four populations identified six 'consensus' QTL (C-QTL) in A3, A7, A10 and B3 by merging 33 of the 65 component Tsw QTL from different bi-parental populations. Allelic diversity analysis of these six C-QTL showed that Indian lines, Pusajaikisan and Varuna, hold the most positive allele in all the six C-QTL. In silico mapping of candidate genes with the consensus QTL localized 11 genes known to influence seed weight in Arabidopsis thaliana and also showed conserved crucifer blocks harboring seed weight QTL between the A subgenomes of B. juncea and B. rapa. These findings pave the way for a better understanding of the genetics of seed weight in the oilseed crop B. juncea and reveal the scope available for improvement of seed weight through marker-assisted breeding.

Dissecting Genetic Network of Fruit Branch Traits in Upland Cotton by Association Mapping Using SSR Markers

Genetic architecture of branch traits has large influences on the morphological structure, photosynthetic capacity, planting density, and yield of Upland cotton (Gossypium hirsutum L.). This research aims to reveal the genetic effects of six branch traits, including bottom fruit branch node number (BFBNN), bottom fruit branch length (BFBL), middle fruit branch node number (MFBNN), middle fruit branch length (MFBL), upper fruit branch node number (UFBNN), and upper fruit branch length (UFBL). Association mapping was conducted for these traits of 39 lines and their 178 F₁ hybrids in three environments. There were 20 highly significant Quantitative Trait SSRs (QTSs) detected by mixed linear model approach analyzing a full genetic model with genetic effects of additive, dominance, epistasis and their environment interaction. The phenotypic variation explained by genetic effects ranged from 32.64 ~ 91.61%, suggesting these branch traits largely influenced by genetic factors.

Comparing GWAS Results of Complex Traits Using Full Genetic Model and Additive Models for Revealing Genetic Architecture

Most of the genome-wide association studies (GWASs) for human complex diseases have ignored dominance, epistasis and ethnic interactions. We conducted comparative GWASs for total cholesterol using full model and additive models, which illustrate the impacts of the ignoring genetic variants on analysis results and demonstrate how genetic effects of multiple loci could differ across different ethnic groups. There were 15 quantitative trait loci with 13 individual loci and 3 pairs of epistasis loci identified by full model, whereas only 14 loci (9 common loci and 5 different loci) identified by multi-loci additive model. Again, 4 full model detected loci were not detected using multi-loci additive model. PLINK-analysis identified two loci and GCTA-analysis detected only one locus with genome-wide significance. Full model identified three previously reported genes as well as several new genes. Bioinformatics analysis showed some new genes are related with cholesterol related chemicals and/or diseases. Analyses of cholesterol data and simulation studies revealed that the full model performs were better than the additive-model performs in terms of detecting power and unbiased estimations of genetic variants of complex traits.

Molecular Characterization of Resistance to Soybean Rust (Phakopsora pachyrhizi Syd. & Syd.) in Soybean Cultivar DT 2000 (PI 635999)

Resistance to soybean rust (SBR), caused by Phakopsora pachyrhizi Syd. & Syd., has been identified in many soybean germplasm accessions and is conferred by either dominant or recessive genes that have been mapped to six independent loci (Rpp1 -Rpp6), but No U.S. cultivars are resistant to SBR. The cultivar DT 2000 (PI 635999) has resistance to P. pachyrhizi isolates and field populations from the United States as well as Vietnam. A F6:7 recombinant inbred line (RIL) population derived from Williams 82 × DT 2000 was used to identify genomic regions associated with resistance to SBR in the field in Ha Noi, Vietnam, and in Quincy, Florida, in 2008. Bulked segregant analysis (BSA) was conducted using the soybean single nucleotide polymorphism (SNP) USLP 1.0 panel along with simple sequence repeat (SSR) markers to detect regions of the genome associated with resistance. BSA identified four BARC_SNP markers near the Rpp3 locus on chromosome (Chr.) 6. Genetic analysis identified an additional genomic region around the Rpp4 locus on Chr. 18 that was significantly associated with variation in the area under disease progress curve (AUDPC) values and sporulation in Vietnam. Molecular markers tightly linked to the DT 2000 resistance alleles on Chrs. 6 and 18 will be useful for marker-assisted selection and backcrossing in order to pyramid these genes with other available SBR resistance genes to develop new varieties with enhanced and durable resistance to SBR.

Quantitative Trait Locus Analysis of Late Leaf Spot Resistance and Plant-Type-Related Traits in Cultivated Peanut (Arachis hypogaea L.) under Multi-Environments

Late leaf spot (LLS) is one of the most serious foliar diseases affecting peanut worldwide leading to huge yield loss. To understand the genetic basis of LLS and assist breeding in the future, we conducted quantitative trait locus (QTL) analysis for LLS and three plant-type-related traits including height of main stem (HMS), length of the longest branch (LLB) and total number of branches (TNB). Significant negative correlations were observed between LLS and the plant-type-related traits in multi-environments of a RIL population from the cross Zhonghua 5 and ICGV 86699. A total of 20 QTLs were identified for LLS, of which two QTLs were identified in multi-environments and six QTLs with phenotypic variation explained (PVE) more than 10%. Ten, seven, fifteen QTLs were identified for HMS, LLB and TNB, respectively. Of these, one, one, two consensus QTLs and three, two, three major QTLs were detected for HMS, LLB and TNB, respectively. Of all 52 unconditional QTLs for LLS and plant-type-related traits, 10 QTLs were clustered in five genetic regions, of which three clusters including five robust major QTLs overlapped between LLS and one of the plant-type-related traits, providing evidence that the correlation could be genetically constrained. On the other hand, conditional mapping revealed different numbers and different extent of additive effects of QTLs for LLS conditioned on three plant-type-related traits (HMS, LLB and TNB), which improved our understanding of interrelationship between LLS and plant-type-related traits at the QTL level. Furthermore, two QTLs, qLLSB6-7 and qLLSB1 for LLS resistance, were identified residing in two clusters of NB-LRR—encoding genes. This study not only provided new favorable QTLs for fine-mapping, but also suggested that the relationship between LLS and plant-type-related traits of HMS, LLB and TNB should be considered while breeding for improved LLS resistance in peanut.

Genetic mapping of QTL for the sizes of eight consecutive leaves below the tassel in maize (Zea mays L.)

A set of RIL population was used to detect QTL associated with the sizes of eight consecutive leaves, across different environments, and ten QTL clusters were identified as main QTLs. One of the important parameters of the maize leaf architecture that affects light penetration into the canopy, leaf size, has long attracted breeders' attention for optimizing the plant type of maize and for maximizing the grain yield (GY). In this study, we used 253 RIL lines derived from a cross between B73 and SICAU1212 to investigate the leaf widths (LWs), leaf lengths (LLs), and leaf areas (LAs) of eight consecutive leaves of maize below the tassel and GY across different environments and to identify quantitative traits loci (QTLs) controlling the above-mentioned traits, using inclusive interval mapping for single-environment analysis plus a mixed-model-based composite interval mapping for joint analysis. A total of 171 and 159 putative QTLs were detected through these two mapping methods, respectively. Single-environment mapping revealed that 39 stable QTLs explained more than 10 % of the phenotypic variance, and 35 of the 39 QTLs were also detected by joint analysis. In addition, joint analysis showed that nine of the 159 QTLs exhibited significant QTL × environment interaction and 15 significant epistatic interactions were identified. Approximately 47.17 % of the QTLs for leaf architectural traits in joint analysis were concentrated in ten main chromosomal regions, namely, bins 1.07, 2.02, 3.06, 4.09, 5.01, 5.02, 5.03-5.04, 5.07, 6.07, and 8.05. This study should provide a basis for further fine-mapping of these main genetic regions and improvement of maize leaf architecture.

A Major QTL, Which Is Co-located with cly1, and Two Minor QTLs Are Associated with Glume Opening Angle in Barley (Hordeum vulgare L.)

Cleistogamous and chasmogamous are two opposing phenomena for flowering in barley. Cleistogamy limits the rate of outcrossing, and increases the cost of producing hybrid barley seeds. Selecting chasmogamous lines with a large glume opening angle (GOA) is essential for the utilization of barley heterosis. In the current study, 247 DH lines derived from a cross between Yangnongpi7 and Yang0187 were used to identify and validate quantitative trait loci (QTLs) associated with the GOA in different environments using SSR markers. Three QTLs associated with barley GOA were mapped on chromosomes 2H and 7H. The major QTL QGOA-2H-2 was mapped on chromosome 2H with the flanking markers of KDH and GBM1498, explaining 63.92% of the phenotypic variation. The marker KDH was developed from the gene Cly1, which was the candidate gene for QGOA-2H-2. This new marker can be used to identify barley chasmogamous lines with a large GOA. The two minor QTLs were validated at all three locations across two seasons after removing DH lines carrying the candidate gene Cly1 of QGOA-2H-2.

Mapping QTLs associated with agronomic and physiological traits under terminal drought and heat stress conditions in wheat (Triticum aestivum L.)

Wheat crops frequently experience a combination of abiotic stresses in the field, but most quantitative trait loci (QTL) studies have focused on the identification of QTLs for traits under single stress field conditions. A recombinant inbred line (RIL) population derived from SeriM82 × Babax was used to map QTLs under well-irrigated, heat, drought, and a combination of heat and drought stress conditions in two years. A total of 477 DNA markers were used to construct linkage groups that covered 1619.6 cM of the genome, with an average distance of 3.39 cM between adjacent markers. Moderate to relatively high heritability estimates (0.60-0.70) were observed for plant height (PHE), grain yield (YLD), and grain per square meter (GM2). The most important QTLs for days to heading (DHE), thousand grain weight (TGW), and YLD were detected on chromosomes 1B, 1D-a, and 7D-b. The prominent QTLs related to canopy temperature were on 3B. Results showed that common QTLs for DHE, YLD, and TGW on 7D-b were validated in heat and drought trials. Three QTLs for chlorophyll content in SPAD unit (on 1A/6B), leaf rolling (ROL) (on 3B/4A), and GM2 (on 1B/7D-b) showed significant epistasis × environment interaction. Six heat- or drought-specific QTLs (linked to 7D-acc/cat-10, 1B-agc/cta-9, 1A-aag/cta-8, 4A-acg/cta-3, 1B-aca/caa-3, and 1B-agc/cta-9 for day to maturity (DMA), SPAD, spikelet compactness (SCOM), TGW, GM2, and GM2, respectively) were stable and validated over two years. The major DHE QTL linked to 7D-acc/cat-10, with no QTL × environment (QE) interaction increased TGW and YLD. This QTL (5.68 ≤ LOD ≤ 10.5) explained up to 19.6% variation in YLD in drought, heat, and combined stress trials. This marker as a candidate could be used for verification in other populations and identifying superior allelic variations in wheat cultivars or its wild progenitors to increase the efficiency of selection of high yielding lines adapted to end-season heat and drought stress conditions.

The activity of HYDROPEROXIDE LYASE 1 regulates accumulation of galactolipids containing 12-oxo-phytodienoic acid in Arabidopsis

Arabidopsis produces galactolipids containing esters of 12-oxo-phytodienoic acid (OPDA) and dinor-12-oxo-phytodienoic acid (dnOPDA). These lipids are referred to as arabidopsides and accumulate in response to abiotic and biotic stress. We explored the natural genetic variation found in 14 different Arabidopsis accessions to identify genes involved in the formation of arabidopsides. The accession C24 was identified as a poor accumulator of arabidopsides whereas the commonly used accession Col-0 was found to accumulate comparably large amounts of arabidopsides in response to tissue damage. A quantitative trait loci analysis of an F2 population created from a cross between C24 and Col-0 located a region on chromosome four strongly linked to the capacity to form arabidopsides. Expression analysis of HYDROPEROXIDE LYASE 1 (HPL1) showed large differences in transcript abundance between accessions. Transformation of Col-0 plants with the C24 HPL1 allele under transcriptional regulation of the 35S promoter revealed a strong negative correlation between HPL1 expression and arabidopside accumulation after tissue damage, thereby strengthening the view that HPL1 competes with ALLENE OXIDE SYNTHASE (AOS) for lipid-bound hydroperoxide fatty acids. We further show that the last step in the synthesis of galactolipid-bound OPDA and dnOPDA from unstable allene oxides is exclusively enzyme-catalyzed and not the result of spontaneous cyclization. Thus, the results presented here together with previous studies suggest that all steps in arabidopside biosynthesis are enzyme-dependent and apparently all reactions can take place with substrates being esterified to galactolipids.

Dissection of genetic architecture of rice plant height and heading date by multiple-strategy-based association studies

Xieyou9308 is a certified super hybrid rice cultivar with a high grain yield. To investigate its underlying genetic basis of high yield potential, a recombinant inbred line (RIL) population derived from the cross between the maintainer line XieqingzaoB (XQZB) and the restorer line Zhonghui9308 (ZH9308) was constructed for identification of quantitative trait SNPs (QTSs) associated with two important agronomic traits, plant height (PH) and heading date (HD). By re-sequencing of 138 recombinant inbred lines (RILs), a total of ~0.7 million SNPs were identified for the association studies on the PH and HD. Three association mapping strategies (including hypothesis-free genome-wide association and its two complementary hypothesis-engaged ones, QTL-based association and gene-based association) were adopted for data analysis. Using a saturated mixed linear model including epistasis and environmental interaction, we identified a total of 31 QTSs associated with either the PH or the HD. The total estimated heritability across three analyses ranged from 37.22% to 45.63% and from 37.53% to 55.96% for the PH and HD, respectively. In this study we examined the feasibility of association studies in an experimental population (RIL) and identified several common loci through multiple strategies which could be preferred candidates for further research.

Multikernel linear mixed models for complex phenotype prediction

Linear mixed models (LMMs) and their extensions have recently become the method of choice in phenotype prediction for complex traits. However, LMM use to date has typically been limited by assuming simple genetic architectures. Here, we present multikernel linear mixed model (MKLMM), a predictive modeling framework that extends the standard LMM using multiple-kernel machine learning approaches. MKLMM can model genetic interactions and is particularly suitable for modeling complex local interactions between nearby variants. We additionally present MKLMM-Adapt, which automatically infers interaction types across multiple genomic regions. In an analysis of eight case-control data sets from the Wellcome Trust Case Control Consortium and more than a hundred mouse phenotypes, MKLMM-Adapt consistently outperforms competing methods in phenotype prediction. MKLMM is as computationally efficient as standard LMMs and does not require storage of genotypes, thus achieving state-of-the-art predictive power without compromising computational feasibility or genomic privacy.

Linkages and Interactions Analysis of Major Effect Drought Grain Yield QTLs in Rice

Quantitative trait loci conferring high grain yield under drought in rice are important genomic resources for climate resilient breeding. Major and consistent drought grain yield QTLs usually co-locate with flowering and/or plant height QTLs, which could be due to either linkage or pleiotropy. Five mapping populations used for the identification of major and consistent drought grain yield QTLs underwent multiple-trait, multiple-interval mapping test (MT-MIM) to estimate the significance of pleiotropy effects. Results indicated towards possible linkages between the drought grain yield QTLs with co-locating flowering and/or plant height QTLs. Linkages of days to flowering and plant height were eliminated through a marker-assisted breeding approach. Drought grain yield QTLs also showed interaction effects with flowering QTLs. Drought responsiveness of the flowering locus on chromosome 3 (qDTY_3.2) has been revealed through allelic analysis. Considering linkage and interaction effects associated with drought QTLs, a comprehensive marker-assisted breeding strategy was followed to develop rice genotypes with improved grain yield under drought stress.

Mapping of Ppd-B1, a Major Candidate Gene for Late Heading on Wild Emmer Chromosome Arm 2BS and Assessment of Its Interactions with Early Heading QTLs on 3AL

Wheat heading date is an important agronomic trait determining maturation time and yield. A set of common wheat (Triticum aestivum var. Chinese Spring; CS)-wild emmer (T. turgidum L. subsp. dicoccoides (TDIC)) chromosome arm substitution lines (CASLs) was used to identify and allocate QTLs conferring late or early spike emergence by examining heading date. Genetic loci accelerating heading were found on TDIC chromosome arms 3AL and 7BS, while loci delaying heading were located on 4AL and 2BS. To map QTLs conferring late heading on 2BS, F2 populations derived from two cross combinations of CASL2BS × CS and CASL3AL × CASL2BS were developed and each planted at two times, constituting four F2 mapping populations. Heading date varied continuously among individuals of these four populations, suggesting quantitative characteristics. A genetic map of 2BS, consisting of 23 SSR and one single-stranded conformation polymorphism (SSCP) marker(s), was constructed using these F2 populations. This map spanned a genetic length of 53.2 cM with average marker density of 2.3 cM. The photoperiod-sensitivity gene Ppd-B1 was mapped to chromosome arm 2BS as a SSCP molecular marker, and was validated as tightly linked to a major QTL governing late heading of CASL2BS in all mapping populations. A significant dominance by additive effect of Ppd-B1 with the LUX gene located on 3AL was also detected. CS had more copies of Ppd-B1 than CASL2BS, implying that increased copy number could elevate the expression of Ppd-1 in CS, also increasing expression of LUX and FT genes and causing CS to have an earlier heading date than CASL2BS in long days.

QTL mapping based on the embryo and maternal genetic systems for non-essential amino acids in rapeseed (Brassica napus L.) meal

BACKGROUND

Non-essential amino acids are a good source of nitrogen and also very important contributors to the metabolic process. Analysis of quantitative trait locus (QTL) simultaneously located on the amphidiploid embryo and maternal plant nuclear genomes for non-essential amino acid contents in rapeseed meal across different environments was conducive to further clarify the genetic mechanism of seed quality traits.

RESULTS

Twenty-eight QTLs associated with arginine (five QTLs), histidine (four QTLs), glutamic acid (three QTLs), glycine (three QTLs), proline (three QTLs), alanine (four QTLs) and aspartic acid (six QTLs) contents were identified in present study. All of these QTLs had significant additive main effects from embryo and maternal plant nuclear genomes with eight of them showing significant embryo dominance main effects and 12 showing notable QTL × environment interaction effects. Among them, 12 QTLs were major QTLs which could explain 13.27-35.71% of the phenotypic variation. Specially, five QTL clusters associated with several QTLs related to multiple traits were distributed on chromosomes A1, A4, A5, A7 and C2.

CONCLUSION

Non-essential amino acids in rapeseed meal could be simultaneously controlled by the genetic effects from the QTLs which were located on the chromosomes both in the embryo and maternal plant genetic systems.

A major QTL corresponding to the Rk locus for resistance to root-knot nematodes in cowpea (Vigna unguiculata L. Walp.)

Genome resolution of a major QTL associated with the Rk locus in cowpea for resistance to root-knot nematodes has significance for plant breeding programs and R gene characterization. Cowpea (Vigna unguiculata L. Walp.) is a susceptible host of root-knot nematodes (Meloidogyne spp.) (RKN), major plant-parasitic pests in global agriculture. To date, breeding for host resistance in cowpea has relied on phenotypic selection which requires time-consuming and expensive controlled infection assays. To facilitate marker-based selection, we aimed to identify and map quantitative trait loci (QTL) conferring the resistance trait. One recombinant inbred line (RIL) and two F2:3 populations, each derived from a cross between a susceptible and a resistant parent, were genotyped with genome-wide single nucleotide polymorphism (SNP) markers. The populations were screened in the field for root-galling symptoms and/or under growth-chamber conditions for nematode reproduction levels using M. incognita and M. javanica biotypes. One major QTL was mapped consistently on linkage group VuLG11 of each population. By genotyping additional cowpea lines and near-isogenic lines derived from conventional backcrossing, we confirmed that the detected QTL co-localized with the genome region associated with the Rk locus for RKN resistance that has been used in conventional breeding for many decades. This chromosomal location defined with flanking markers will be a valuable target in marker-assisted breeding and for positional cloning of genes controlling RKN resistance.

Genetic Analysis in Maize Foundation Parents with Mapping Population and Testcross Population: Ye478 Carried More Favorable Alleles and Using QTL Information Could Improve Foundation Parents

The development of maize foundation parents is an important part of genetics and breeding research, and applying new genetic information to produce foundation parents has been challenging. In this study, we focused on quantitative trait loci (QTLs) and general combining ability (GCA) of Ye478, a widely used foundation parent in China. We developed three sets of populations for QTL mapping and to analyze the GCA for some agronomic traits. The assessment of 15 traits resulted in the detection of 251 QTLs in six tested environments, with 119 QTLs identified through a joint analysis across all environments. Further, analyses revealed that most favorable alleles for plant type-related traits were from Ye478, and more than half of the favorable alleles for yield-related traits were from R08, another foundation parent used in southwestern China, suggesting that different types of foundation parents carried different favorable alleles. We observed that the GCA for most traits (e.g., plant height and 100-kernel weight) was maintained in the inbred lines descended from the foundation parents. Additionally, the continuous improvement in the GCA of the descendants of the foundation parents was consistent with the main trend in maize breeding programs. We identified three significant genomic regions that were highly conserved in three Ye478 descendants, including the stable QTL for plant height. The GCA for the traits in the F₇ generation revealed that the QTLs for the given traits per se were affected by additive effects in the same way in different populations.

Mapping Quantitative Trait Loci Affecting Biochemical and Morphological Fruit Properties in Eggplant (Solanum melongena L.)

Eggplant berries are a source of health-promoting metabolites including antioxidant and nutraceutical compounds, mainly anthocyanins and chlorogenic acid; however, they also contain some anti-nutritional compounds such as steroidal glycoalkaloids (SGA) and saponins, which are responsible for the bitter taste of the flesh and with potential toxic effects on humans. Up to now, Quantitative Trait Loci (QTL) for the metabolic content are far from being characterized in eggplant, thus hampering the application of breeding programs aimed at improving its fruit quality. Here we report on the identification of some QTL for the fruit metabolic content in an F2 intraspecific mapping population of 156 individuals, obtained by crossing the eggplant breeding lines "305E40" × "67/3." The same population was previously employed for the development of a RAD-tag based linkage map and the identification of QTL associated to morphological and physiological traits. The mapping population was biochemically characterized for both fruit basic qualitative data, like dry matter, °Brix, sugars, and organic acids, as well as for health-related compounds such chlorogenic acid, (the main flesh monomeric phenol), the two peel anthocyanins [i.e., delphinidin-3-rutinoside (D3R) and delphinidin-3-(p- coumaroylrutinoside)-5-glucoside (nasunin)] and the two main steroidal glycoalkaloids, solasonine, and solamargine. For most of the traits, one major QTL (PVE ≥10%) was spotted and putative orthologies with other Solanaceae crops are discussed. The present results supply valuable information to eggplant breeders on the inheritance of key fruit quality traits, thus providing potential tools to assist future breeding programs.

Confirmation of delayed canopy wilting QTLs from multiple soybean mapping populations

KEY MESSAGE

QTLs for delayed canopy wilting from five soybean populations were projected onto the consensus map to identify eight QTL clusters that had QTLs from at least two independent populations. Quantitative trait loci (QTLs) for canopy wilting were identified in five recombinant inbred line (RIL) populations, 93705 KS4895 × Jackson, 08705 KS4895 × Jackson, KS4895 × PI 424140, A5959 × PI 416937, and Benning × PI 416937 in a total of 15 site-years. For most environments, heritability of canopy wilting ranged from 0.65 to 0.85 but was somewhat lower when averaged over environments. Putative QTLs were identified with composite interval mapping and/or multiple interval mapping methods in each population and positioned on the consensus map along with their 95% confidence intervals (CIs). We initially found nine QTL clusters with overlapping CIs on Gm02, Gm05, Gm11, Gm14, Gm17, and Gm19 identified from at least two different populations, but a simulation study indicated that the QTLs on Gm14 could be false positives. A QTL on Gm08 in the 93705 KS4895 × Jackson population co-segregated with a QTL for wilting published previously in a Kefeng1 × Nannong 1138-2 population, indicating that this may be an additional QTL cluster. Excluding the QTL cluster on Gm14, results of the simulation study indicated that the eight remaining QTL clusters and the QTL on Gm08 appeared to be authentic QTLs. QTL × year interactions indicated that QTLs were stable over years except for major QTLs on Gm11 and Gm19. The stability of QTLs located on seven clusters indicates that they are possible candidates for use in marker-assisted selection.

Genetic analysis and chromosome mapping of resistance to Fusarium oxysporum f. sp. niveum (FON) race 1 and race 2 in watermelon (Citrullus lanatus L.)

Fusarium wilt (FW) caused by Fusarium oxysporum f. sp. niveum (FON) is the major soilborne disease of watermelon (Citrullus lanatus L.). The development and deployment of resistant cultivars is generally considered to be an effective approach to control FW. In this study, an F8 population consisting of 103 recombinant inbred lines derived from a cross between the cultivar 97103 and a wild accession PI 296341-FR was used for FON race 1 and race 2 fungal inoculations. One major QTL on chromosome 1 for FON race 1 resistance was detected with a logarithm of odds of 13.2 and explained phenotypic variation R2 = 48.1 %; two QTLs of FON race 2 resistance on chromosomes 9 and 10 were discovered based on the high-density integrated genetic map we constructed. The nearest molecular marker should be useful for marker-assisted selection of FON race 1 and race 2 resistance. One receptor kinase, one glucan endo-1,3-β-glucosidase precursors and three acidic chitinase located in the FON-1 QTL genomic region. In Qfon2.1 QTL region, one lipoxygenase gene, five receptor-like kinases and four glutathione S-transferase genes are discovered. One arginine biosynthesis bifunctional protein, two receptor kinase proteins and one lipid-transfer protein located in Qfon2.2 QTL region. Based on SNP analysis by using 20 re-sequenced accessions of watermelon and 231-plant F2 population generated from Black Diamond × Calhoun Grey, we developed a SNP marker Chr1SNP_502124 for FON-1 detection.

Understanding rice adaptation to varying agro-ecosystems: trait interactions and quantitative trait loci

Background

Interaction and genetic control for traits influencing the adaptation of the rice crop to varying environments was studied in a mapping population derived from parents (Moroberekan and Swarna) contrasting for drought tolerance, yield potential, lodging resistance, and adaptation to dry direct seeding. A BC₂F₃-derived mapping population for traits related to these four trait groups was phenotyped to understand the interactions among traits and to map and align QTLs using composite interval mapping (CIM). The study also aimed to identify QTLs for the four trait groups as composite traits using multivariate least square interval mapping (MLSIM) to further understand the genetic control of these traits.

Results

Significant correlations between drought- and yield-related traits at seedling and reproductive stages respectively with traits for adaptation to dry direct-seeded conditions were observed. CIM and MLSIM methods were applied to identify QTLs for univariate and composite traits. QTL clusters showing alignment of QTLs for several traits within and across trait groups were detected at chromosomes 3, 4, and 7 through CIM. The largest number of QTLs related to traits belonging to all four trait groups were identified on chromosome 3 close to the qDTY_3.2 locus. These included QTLs for traits such as bleeding rate, shoot biomass, stem strength, and spikelet fertility. Multivariate QTLs were identified at loci supported by univariate QTLs such as on chromosomes 3 and 4 as well as at distinctly different loci on chromosome 8 which were undetected through CIM.

Conclusion

Rice requires better adaptation across a wide range of environments and cultivation practices to adjust to climate change. Understanding the genetics and trade-offs related to each of these environments and cultivation practices thus becomes highly important to develop varieties with stability of yield across them. This study provides a wider picture of the genetics and physiology of adaptation of rice to wide range of environments. With a complete understanding of the processes and relationships between traits and trait groups, marker-assisted breeding can be used more efficiently to develop plant types that can combine all or most of the beneficial traits and show high stability across environments, ecosystems, and cultivation practices.

Electronic supplementary material

The online version of this article (doi:10.1186/s12863-015-0249-1) contains supplementary material, which is available to authorized users.

Detection of Epistatic and Gene-Environment Interactions Underlying Three Quality Traits in Rice Using High-Throughput Genome-Wide Data

With development of sequencing technology, dense single nucleotide polymorphisms (SNPs) have been available, enabling uncovering genetic architecture of complex traits by genome-wide association study (GWAS). However, the current GWAS strategy usually ignores epistatic and gene-environment interactions due to absence of appropriate methodology and heavy computational burden. This study proposed a new GWAS strategy by combining the graphics processing unit- (GPU-) based generalized multifactor dimensionality reduction (GMDR) algorithm with mixed linear model approach. The reliability and efficiency of the analytical methods were verified through Monte Carlo simulations, suggesting that a population size of nearly 150 recombinant inbred lines (RILs) had a reasonable resolution for the scenarios considered. Further, a GWAS was conducted with the above two-step strategy to investigate the additive, epistatic, and gene-environment associations between 701,867 SNPs and three important quality traits, gelatinization temperature, amylose content, and gel consistency, in a RIL population with 138 individuals derived from super-hybrid rice Xieyou9308 in two environments. Four significant SNPs were identified with additive, epistatic, and gene-environment interaction effects. Our study showed that the mixed linear model approach combining with the GPU-based GMDR algorithm is a feasible strategy for implementing GWAS to uncover genetic architecture of crop complex traits.

Quantitative trait locus mapping of deep rooting by linkage and association analysis in rice

Deep rooting is a very important trait for plants' drought avoidance, and it is usually represented by the ratio of deep rooting (RDR). Three sets of rice populations were used to determine the genetic base for RDR. A linkage mapping population with 180 recombinant inbred lines and an association mapping population containing 237 rice varieties were used to identify genes linked to RDR. Six quantitative trait loci (QTLs) of RDR were identified as being located on chromosomes 1, 2, 4, 7, and 10. Using 1 019 883 single-nucleotide polymorphisms (SNPs), a genome-wide association study of the RDR was performed. Forty-eight significant SNPs of the RDR were identified and formed a clear peak on the short arm of chromosome 1 in a Manhattan plot. Compared with the shallow-rooting group and the whole collection, the deep-rooting group had selective sweep regions on chromosomes 1 and 2, especially in the major QTL region on chromosome 2. Seven of the nine candidate SNPs identified by association mapping were verified in two RDR extreme groups. The findings from this study will be beneficial to rice drought-resistance research and breeding.

Mixed Linear Model Approaches of Association Mapping for Complex Traits Based on Omics Variants

Precise prediction for genetic architecture of complex traits is impeded by the limited understanding on genetic effects of complex traits, especially on gene-by-gene (GxG) and gene-by-environment (GxE) interaction. In the past decades, an explosion of high throughput technologies enables omics studies at multiple levels (such as genomics, transcriptomics, proteomics, and metabolomics). The analyses of large omics data, especially two-loci interaction analysis, are very time intensive. Integrating the diverse omics data and environmental effects in the analyses also remain challenges. We proposed mixed linear model approaches using GPU (Graphic Processing Unit) computation to simultaneously dissect various genetic effects. Analyses can be performed for estimating genetic main effects, GxG epistasis effects, and GxE environment interaction effects on large-scale omics data for complex traits, and for estimating heritability of specific genetic effects. Both mouse data analyses and Monte Carlo simulations demonstrated that genetic effects and environment interaction effects could be unbiasedly estimated with high statistical power by using the proposed approaches.

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.

Genetic mapping and legume synteny of aphid resistance in African cowpea (Vigna unguiculata L. Walp.) grown in California

The cowpea aphid Aphis craccivora Koch (CPA) is a destructive insect pest of cowpea, a staple legume crop in Sub-Saharan Africa and other semiarid warm tropics and subtropics. In California, CPA causes damage on all local cultivars from early vegetative to pod development growth stages. Sources of CPA resistance are available in African cowpea germplasm. However, their utilization in breeding is limited by the lack of information on inheritance, genomic location and marker linkage associations of the resistance determinants. In the research reported here, a recombinant inbred line (RIL) population derived from a cross between a susceptible California blackeye cultivar (CB27) and a resistant African breeding line (IT97K-556-6) was genotyped with 1,536 SNP markers. The RILs and parents were phenotyped for CPA resistance using field-based screenings during two main crop seasons in a 'hotspot' location for this pest within the primary growing region of the Central Valley of California. One minor and one major quantitative trait locus (QTL) were consistently mapped on linkage groups 1 and 7, respectively, both with favorable alleles contributed from IT97K-556-6. The major QTL appeared dominant based on a validation test in a related F2 population. SNP markers flanking each QTL were positioned in physical contigs carrying genes involved in plant defense based on synteny with related legumes. These markers could be used to introgress resistance alleles from IT97K-556-6 into susceptible local blackeye varieties by backcrossing.

Traits and QTLs for development of dry direct-seeded rainfed rice varieties

The development of rice varieties for dry direct-seeded conditions can be accelerated by selecting suitable traits. In the present investigation, traits hypothesized to be important for direct-seeded conditions in rainfed systems, including seedling emergence, early vegetative vigour, nutrient uptake, nodal root number, and root hair length and density, were characterized to study the genetic control of these traits and their relationship with grain yield under seedling- and reproductive-stage drought stress. Two BC₂F₄ mapping populations derived from crosses of Aus276, a drought-tolerant aus variety, with MTU1010 and IR64, high-yielding indica mega-varieties, were developed and studied to identify quantitative trait loci (QTLs) that showed large and consistent effects. A total of 26 QTLs associated with 23 traits and 20 QTLs associated with 13 traits were mapped in the Aus276/3*IR64 and Aus276/3*MTU1010 populations, respectively. qGY₆.₁, qGY₁₀.₁, qGY₁.₁, and qEVV₉.₁ were found to be effective in both populations under a wide range of conditions. QTLs for several seedling-stage traits co-located with QTLs for grain yield, including early vegetative vigour and root hair length. On chromosome 5, several QTLs for nutrient uptake co-located with QTLs for root hair density and nematode gall rating. Six lines were selected from both populations based on grain yield and the presence of QTLs, and these lines typically showed improved seedling-stage traits (nodal root number, dry shoot weight, and root hair length and density). The co-located QTLs identified here can be used in research aimed at increasing the yield and adaptability of rainfed rice to direct-seeded conditions.

Identification and evaluation of quantitative trait loci underlying resistance to multiple HG types of soybean cyst nematode in soybean PI 437655

KEY MESSAGE

We performed QTL analysis for SCN resistance in PI 437655 in two mapping populations, characterized CNV of Rhg1 through whole-genome resequencing and evaluated the effects of QTL pyramiding to enhance resistance. Soybean cyst nematode (SCN, Heterodera glycines Ichinohe) is one of the most serious pests of soybean worldwide. PI 437655 has broader resistance to SCN HG types than PI 88788. The objectives of this study were to identify quantitative trait loci (QTL) underlying SCN resistance in PI 437655, and to evaluate the QTL for their contribution to SCN resistance. Two F6:7 recombinant inbred line populations, derived from cv. Williams 82 × PI 437655 and cv. Hutcheson × PI 437655 crosses, were evaluated for resistance to SCN HG types 1.2.5.7 (PA2), 0 (PA3), 1.3.5.6.7 (PA14), and 1.2.3.4.5.6.7 (LY2). The 1,536 SNP array was used to genotype the mapping populations and construct genetic linkage maps. Two significant QTL were consistently mapped on chromosomes (Chr.) 18 and 20 in these two populations. One QTL on Chr. 18, which corresponds to the known Rhg1 locus, contributed resistance to SCN HG types 1.2.5.7, 0, 1.3.5.6.7, and 1.2.3.4.5.6.7 (PA2, PA3, PA14, and LY2, respectively). Copy number variation (CNV) analysis by whole-genome resequencing showed that PI 437655 and PI 88788 had similar CNV at the Rhg1 locus. The QTL on Chr. 20 contributed resistance to SCN HG types 1.3.5.6.7 (PA14) and 1.2.3.4.5.6.7 (LY2). Evaluation of both QTL showed that pyramiding of Rhg1 and the QTL on Chr. 20 significantly improved the resistance to SCN HG types 1.3.5.6.7 (PA14) and 1.2.3.4.5.6.7 (LY2) in both populations. Our studies provided useful information for deploying PI 437655 as a donor for SCN resistance in soybean breeding through marker-assisted selection.

Dissecting genetic architecture underlying seed traits in multiple environments

The seeds of flowering plants develop from double fertilization and play a vital role in reproduction and supplying human and animal food. The genetic variation of seed traits is influenced by multiple genetic systems, e.g., maternal, embryo, and/or endosperm genomes. Understanding the genetic architecture of seed traits is a major challenge because of this complex mechanism of multiple genetic systems, especially the epistasis within or between different genomes and their interactions with the environment. In this study, a statistical model was proposed for mapping QTL with epistasis and QTL-by-environment (QE) interactions underlying endosperm and embryo traits. Our model integrates the maternal and the offspring genomes into one mapping framework and can accurately analyze maternal additive and dominant effects, endosperm/embryo additive and dominant effects, and epistatic effects of two loci in the same or two different genomes, as well as interaction effects of each genetic component of QTL with environment. Intensive simulations under different sampling strategies, heritabilities, and model parameters were performed to investigate the statistical properties of the model. A set of real cottonseed data was analyzed to demonstrate our methods. A software package, QTLNetwork-Seed-1.0.exe, was developed for QTL analysis of seed traits.

High-density genetic map construction and QTLs analysis of grain yield-related traits in sesame (Sesamum indicum L.) based on RAD-Seq techonology

BACKGROUND

Sesame (Sesamum indicum L., 2n = 26) is an important oilseed crop with an estimated genome size of 369 Mb. The genetic basis, including the number and locations of quantitative trait loci (QTLs) of sesame grain yield and quality remain poorly understood, due in part to the lack of reliable markers and genetic maps. Here we report on the construction of a hitherto most high-density genetic map of sesame using the restriction-site associated DNA sequencing (RAD-seq) combined with 89 PCR markers, and the identification of grain yield-related QTLs using a recombinant inbred line (RIL) population.

RESULT

In total, 3,769 single-nucleotide polymorphism (SNP) markers were identified from RAD-seq, and 89 polymorphic PCR markers were identified including 44 expressed sequence tag-simple sequence repeats (EST-SSRs), 10 genomic-SSRs and 35 Insertion-Deletion markers (InDels). The final map included 1,230 markers distributed on 14 linkage groups (LGs) and was 844.46 cM in length with an average of 0.69 cM between adjacent markers. Using this map and RIL population, we detected 13 QTLs on 7 LGs and 17 QTLs on 10 LGs for seven grain yield-related traits by the multiple interval mapping (MIM) and the mixed linear composite interval mapping (MCIM), respectively. Three major QTLs had been identified using MIM with R2 > 10.0% or MCIM with ha 2 > 5.0%. Two co-localized QTL groups were identified that partially explained the correlations among five yield-related traits.

CONCLUSION

Three thousand eight hundred and four pairs of new DNA markers including SNPs and InDels were developed by RAD-seq, and a so far most high-density genetic map was constructed based on these markers in combination with SSR markers. Several grain yield-related QTLs had been identified using this population and genetic map. We report here the first QTL mapping of yield-related traits with a high-density genetic map using a RIL population in sesame. Results of this study solidified the basis for studying important agricultural traits and implementing marker-assisted selection (MAS) toward genetic improvement in sesame.

Identification and mapping of stable QTL with main and epistasis effect on rice grain yield under upland drought stress

BACKGROUND

Drought is one of the most important abiotic stresses that cause drastic reduction in rice grain yield (GY) in rainfed environments. The identification and introgression of QTL leading to high GY under drought have been advocated to be the preferred breeding strategy to improve drought tolerance of popular rice varieties. Genetic control of GY under reproductive-stage drought stress (RS) was studied in two BC1F4 mapping populations derived from crosses of Kali Aus, a drought-tolerant aus cultivar, with high-yielding popular varieties MTU1010 and IR64. The aim was to identify QTL for GY under RS that show a large and consistent effect for the trait. Bulk segregant analysis (BSA) was used to identify significant markers putatively linked with high GY under drought.

RESULTS

QTL analysis revealed major-effect GY QTL: qDTY1.2, qDTY2.2 and qDTY1.3, qDTY2.3 (DTY; Drought grain yield) under drought consistently over two seasons in Kali Aus/2*MTU1010 and Kali Aus/2*IR64 populations, respectively. qDTY1.2 and qDTY2.2 explained an additive effect of 288 kg ha-1 and 567 kg ha-1 in Kali Aus/2*MTU1010, whereas qDTY1.3 and qDTY2.3 explained an additive effect of 198 kg ha-1 and 147 kg ha-1 in Kali Aus/2*IR64 populations, respectively.Epistatic interaction was observed for DTF (days to flowering) between regions on chromosome 2 flanked by markers RM154-RM324 and RM263-RM573 and major epistatic QTL for GY showing interaction between genomic locations on chromosome 1 at marker interval RM488-RM315 and chromosome 2 at RM324-RM263 in 2012 DS and 2013 DS RS in Kali Aus/2*IR64 mapping populations.

CONCLUSION

The QTL, qDTY1.2, qDTY1.3, qDTY2.2, and qDTY2.3, identified in this study can be used to improve GY of mega varieties MTU1010 and IR64 under different degrees of severity of drought stress through marker-aided backcrossing and provide farmers with improved varieties that effectively combine high yield potential with good yield under drought. The observed epistatic interaction for GY and DTF will contribute to our understanding of the genetic basis of agronomically important traits and enhance predictive ability at an individualized level in agriculture.

Genetic analysis and major QTL detection for maize kernel size and weight in multi-environments

Twelve major QTL in five optimal clusters and several epistatic QTL are identified for maize kernel size and weight, some with pleiotropic will be promising for fine-mapping and yield improvement. Kernel size and weight are important target traits in maize (Zea mays L.) breeding programs. Here, we report a set of quantitative trait loci (QTL) scattered through the genome and significantly controlled the performance of four kernel traits including length, width, thickness and weight. From the cross V671 (large kernel) × Mc (small kernel), 270 derived F2:3 families were used to identify QTL of maize kernel-size traits and kernel weight in five environments, using composite interval mapping (CIM) for single-environment analysis along with mixed linear model-based CIM for joint analysis. These two mapping strategies identified 55 and 28 QTL, respectively. Among them, 6 of 23 coincident were detected as interacting with environment. Single-environment analysis showed that 8 genetic regions on chromosomes 1, 2, 4, 5 and 9 clustered more than 60 % of the identified QTL. Twelve stable major QTLs accounting for over 10 % of phenotypic variation were included in five optimal clusters on the genetic region of bins 1.02-1.03, 1.04-1.06, 2.05-2.07, 4.07-4.08 and 9.03-9.04; the addition and partial dominance effects of significant QTL play an important role in controlling the development of maize kernel. These putative QTL may have great promising for further fine-mapping with more markers, and genetic improvement of maize kernel size and weight through marker-assisted breeding.

Mixed linear model approach for mapping quantitative trait loci underlying crop seed traits

The crop seed is a complex organ that may be composed of the diploid embryo, the triploid endosperm and the diploid maternal tissues. According to the genetic features of seed characters, two genetic models for mapping quantitative trait loci (QTLs) of crop seed traits are proposed, with inclusion of maternal effects, embryo or endosperm effects of QTL, environmental effects and QTL-by-environment (QE) interactions. The mapping population can be generated either from double back-cross of immortalized F2 (IF2) to the two parents, from random-cross of IF2 or from selfing of IF2 population. Candidate marker intervals potentially harboring QTLs are first selected through one-dimensional scanning across the whole genome. The selected candidate marker intervals are then included in the model as cofactors to control background genetic effects on the putative QTL(s). Finally, a QTL full model is constructed and model selection is conducted to eliminate false positive QTLs. The genetic main effects of QTLs, QE interaction effects and the corresponding P-values are computed by Markov chain Monte Carlo algorithm for Gaussian mixed linear model via Gibbs sampling. Monte Carlo simulations were performed to investigate the reliability and efficiency of the proposed method. The simulation results showed that the proposed method had higher power to accurately detect simulated QTLs and properly estimated effect of these QTLs. To demonstrate the usefulness, the proposed method was used to identify the QTLs underlying fiber percentage in an upland cotton IF2 population. A computer software, QTLNetwork-Seed, was developed for QTL analysis of seed traits.

QTL mapping in eggplant reveals clusters of yield-related loci and orthology with the tomato genome

In spite of its widespread cultivation and nutritional and economic importance, the eggplant (Solanum melongena L.) genome has not been extensively explored. A lack of knowledge of the patterns of inheritance of key agronomic traits has hindered the exploitation of marker technologies to accelerate its genetic improvement. An already established F₂ intraspecific population of eggplant bred from the cross ‘305E40’ x ‘67/3’ was phenotyped for 20 agronomically relevant traits at two sites. Up to seven quantitative trait loci (QTL) per trait were identified and the percentage of the phenotypic variance (PV) explained per QTL ranged from 4 to 93%. Not all the QTL were detectable at both sites, but for each trait at least one major QTL (PV explained ≥10%) was identified. Although no detectable QTL x environment interaction was found, some QTL identified were location-specific. Many of the fruit-related QTL clustered within specific chromosomal regions, reflecting either linkage and/or pleiotropy. Evidence for putative tomato orthologous QTL/genes was obtained for several of the eggplant QTL. Information regarding the inheritance of key agronomic traits was obtained. Some of the QTL, along with their respective linked markers, may be useful in the context of marker-assisted breeding.

An integrated genetic map based on four mapping populations and quantitative trait loci associated with economically important traits in watermelon (Citrullus lanatus)

BACKGROUND

Modern watermelon (Citrullus lanatus L.) cultivars share a narrow genetic base due to many years of selection for desirable horticultural qualities. Wild subspecies within C. lanatus are important potential sources of novel alleles for watermelon breeding, but successful trait introgression into elite cultivars has had limited success. The application of marker assisted selection (MAS) in watermelon is yet to be realized, mainly due to the past lack of high quality genetic maps. Recently, a number of useful maps have become available, however these maps have few common markers, and were constructed using different marker sets, thus, making integration and comparative analysis among maps difficult. The objective of this research was to use single-nucleotide polymorphism (SNP) anchor markers to construct an integrated genetic map for C. lanatus.

RESULTS

Under the framework of the high density genetic map, an integrated genetic map was constructed by merging data from four independent mapping experiments using a genetically diverse array of parental lines, which included three subspecies of watermelon. The 698 simple sequence repeat (SSR), 219 insertion-deletion (InDel), 36 structure variation (SV) and 386 SNP markers from the four maps were used to construct an integrated map. This integrated map contained 1339 markers, spanning 798 cM with an average marker interval of 0.6 cM. Fifty-eight previously reported quantitative trait loci (QTL) for 12 traits in these populations were also integrated into the map. In addition, new QTL identified for brix, fructose, glucose and sucrose were added. Some QTL associated with economically important traits detected in different genetic backgrounds mapped to similar genomic regions of the integrated map, suggesting that such QTL are responsible for the phenotypic variability observed in a broad array of watermelon germplasm.

CONCLUSIONS

The integrated map described herein enhances the utility of genomic tools over previous watermelon genetic maps. A large proportion of the markers in the integrated map are SSRs, InDels and SNPs, which are easily transferable across laboratories. Moreover, the populations used to construct the integrated map include all three watermelon subspecies, making this integrated map useful for the selection of breeding traits, identification of QTL, MAS, analysis of germplasm and commercial hybrid seed detection.

Identification of QTLs for resistance to sclerotinia stem rot and BnaC.IGMT5.a as a candidate gene of the major resistant QTL SRC6 in Brassica napus

Stem rot caused by Sclerotinia sclerotiorum in many important dicotyledonous crops, including oilseed rape (Brassica napus), is one of the most devastating fungal diseases and imposes huge yield loss each year worldwide. Currently, breeding for Sclerotinia resistance in B. napus, as in other crops, can only rely on germplasms with quantitative resistance genes. Thus, the identification of quantitative trait locus (QTL) for S. sclerotiorum resistance/tolerance in this crop holds immediate promise for the genetic improvement of the disease resistance. In this study, ten QTLs for stem resistance (SR) at the mature plant stage and three QTLs for leaf resistance (LR) at the seedling stage in multiple environments were mapped on nine linkage groups (LGs) of a whole genome map for B. napus constructed with SSR markers. Two major QTLs, LRA9 on LG A9 and SRC6 on LG C6, were repeatedly detected across all environments and explained 8.54-15.86% and 29.01%-32.61% of the phenotypic variations, respectively. Genotypes containing resistant SRC6 or LRA9 allele showed a significant reduction in disease lesion after pathogen infection. Comparative mapping with Arabidopsis and data mining from previous gene profiling experiments identified that the Arabidopsis homologous gene of IGMT5 (At1g76790) was related to the SRC6 locus. Four copies of the IGMT5 gene in B. napus were isolated through homologous cloning, among which, only BnaC.IGMT5.a showed a polymorphism between parental lines and can be associated with the SRC6. Furthermore, two parental lines exhibited a differential expression pattern of the BnaC.IGMT5.a gene in responding to pathogen inoculation. Thus, our data suggested that BnaC.IGMT5.a was very likely a candidate gene of this major resistance QTL.

Association mapping combined with linkage analysis for aluminum tolerance among soybean cultivars released in Yellow and Changjiang River Valleys in China

Association mapping (AM) combined with linkage mapping (LM) was executed to identify molecular markers and QTL regions associated with aluminum (Al) tolerance using relative root elongation (RRE) in hydroponics as an indicator. A set of 188 soybean cultivars released in Yellow and Changjiang River Valleys and 184 recombinant inbred lines (RIL) derived from a cross KF No. 1 (tolerant) × NN1138-2 (susceptible) was used in the study. Inheritance analysis of the RIL population suggested four major genes and polygenes controlled Al-tolerance. Further, LM indicated four additive and four epistatic QTL pairs plus a collective unmapped minor QTL were responsible for Al-tolerance and explained 29.39, 18.75 and 43.07 % of the phenotypic variation (PV), respectively. In the set of released cultivars, AM identified 11 markers significant at P < 0.03 that explained 85.2 % of PV with six of which at P < 0.01 accounted for 57.9 % of PV. Ten of these eleven AM marker-QTL were mapped within range of ~2.0 cM to ~43.0 cM outside confidence interval of respective Al-tolerance QTL in previous studies. Five markers, Satt209, Sat_364, Sat_240, Sct_190 and Satt284, were located near Al-tolerance QTL regions in this and previous LM studies. Thus, the two methods confirmed these markers as being the most likely candidate regions for Al-tolerance. Allele effects relative to the population mean for the 11 QTL were estimated, and the allele A210 of Satt209 showed greatest phenotypic effect on Al-tolerance. The two most favorable alleles from each of the 11 marker loci and their carriers were identified, and accordingly the genetic constitution of Al-tolerance for the 188 cultivars was dissected as a QTL-allele matrix. Therefore, marker-assisted pairing of crosses and marker-assisted selection of progenies can be carried out to pyramid favorable alleles of all the 11 loci. This marker-assisted breeding procedure was designated as breeding by design using a QTL-allele matrix.

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.

QTL mapping and confirmation for tolerance of anaerobic conditions during germination derived from the rice landrace Ma-Zhan Red

Wide adoption of direct-seeded rice practices has been hindered by poorly leveled fields, heavy rainfall and poor drainage, which cause accumulation of water in the fields shortly after sowing, leading to poor crop establishment. This is due to the inability of most rice varieties to germinate and reach the water surface under complete submergence. Hence, tolerance of anaerobic conditions during germination is an essential trait for direct-seeded rice cultivation in both rainfed and irrigated ecosystems. A QTL study was conducted to unravel the genetic basis of tolerance of anaerobic conditions during germination using a population derived from a cross between IR42, a susceptible variety, and Ma-Zhan Red, a tolerant landrace from China. Phenotypic data was collected based on the survival rates of the seedlings at 21 days after sowing of dry seeds under 10 cm of water. QTL analysis of the mapping population consisting of 175 F2:3 families genotyped with 118 SSR markers identified six significant QTLs on chromosomes 2, 5, 6, and 7, and in all cases the tolerant alleles were contributed by Ma-Zhan Red. The largest QTL on chromosome 7, having a LOD score of 14.5 and an R (2) of 31.7 %, was confirmed using a BC2F3 population. The QTLs detected in this study provide promising targets for further genetic characterization and for use in marker-assisted selection to rapidly develop varieties with improved tolerance to anaerobic condition during germination. Ultimately, this trait can be combined with other abiotic stress tolerance QTLs to provide resilient varieties for direct-seeded systems.

Identification of functional genic components of major fusarium head blight resistance quantitative trait loci in wheat cultivar Sumai 3

Fusarium head blight (FHB) is a devastating disease worldwide, affecting wheat and other small grains. To identify key wheat genes involved in FHB pathogenesis, 406 FHB-related wheat expressed sequence tags functionally identified in Sumai 3 were investigated for their association with FHB-resistance quantitative trait loci (QTL) Fhb1 and Fhb_6BL in 2010 and 2011. A total of 47 candidate genes were identified by bulk analysis, near-isogenic screening and expression QTL mapping, and were finally mapped to their carrier chromosomes with Chinese Spring nulli-tetra deficiency lines. One gene, designated WFhb1_c1 (wheat Fhb1 candidate gene 1), was both functionally associated with and physically located within Fhb1 and was found to be weakly similar (E = 5e+0) to an Arabidopsis gene encoding pectin methyl esterase inhibitor. Two other genes, designated WFI_6BL1 and WFI_6BL2 (wheat-Fusarium interaction genes 6BL1 and 6BL2), were functionally associated with Fhb_6BL but physically mapped on chromosomes 7D and 5A, respectively. WFI_6BL1 was annotated as a 13- lipoxygenase gene and WFI_6BL2 might encode a PR-4-like protein. Our data suggested that i) Fhb1 seems to contribute to FHB resistance by reducing susceptibility in the first 60 h, ii) Fhb_6BL makes its contribution via the jasmonate-mediated pathways, and iii) wheat seems to activate its defense mechanism in the biotrophic phase of FHB pathogenesis.

QTL mapping based on different genetic systems for essential amino acid contents in cottonseeds in different environments

Cottonseeds are rich in various essential amino acids. However, the inheritance of them at molecular level are still not defined across various genetic systems. In the present study, using a newly developed mapping model that can analyze the embryo and maternal main effects as well as QTL × environment interaction effects on quantitative quality trait loci (QTLs) in cottonseeds, a study on QTL located in the tetraploid embryo and tetraploid maternal plant genomes for essential amino acid contents in cottonseeds under different environments was carried out, using the immortal F₂ (IF₂) populations from a set of 188 recombinant inbred lines derived from an intraspecific hybrid cross of two upland cotton germplasms HS46 and MARKCBUCAG8US-1-88 as experimental materials. The results showed a total of 35 QTLs associated with these quality traits in cottonseeds. Nineteen QTLs were subsequently mapped on chromosome 5, 6 and 8 in sub-A genome and chromosome 15, 18, 22 and 23 in sub-D genome. Eighteen QTLs were also found having QTL × environment (QE) interaction effects. The genetic main effects from QTLs located on chromosomes in the embryo and maternal plant genomes and their QE effects in different environments were all important for these essential amino acids in cottonseeds. The results suggested that the influence of environmental factors on the expression of some QTLs located in different genetic systems should be considered when improving for these amino acids. This study can serve as the foundation for the improvement of these essential amino acids in cottonseeds.

qDTY12.1: a locus with a consistent effect on grain yield under drought in rice

Background

Selection for grain yield under drought is an efficient criterion for improving the drought tolerance of rice. Recently, some drought-tolerant rice varieties have been developed using this selection criterion and successfully released for cultivation in drought-prone target environments. The process can be made more efficient and rapid through marker-assisted breeding, a well-known fast-track approach in crop improvement. QTLs have been identified for grain yield under drought with large effects against drought-susceptible varieties. Most of the identified QTLs show large QTL × environment or QTL × genetic background interactions. The development of mapping populations in the background of popular high-yielding varieties, screening across environments, including the target environments, and the identification of QTLs with a consistent effect across environments can be a suitable alternative marker-assisted breeding strategy. An IR74371-46-1-1 × Sabitri backcross inbred line population was screened for reproductive-stage drought stress at the International Rice Research Institute, Philippines, and Regional Agricultural Research Station, Nepalgunj, Nepal, in the dry and wet seasons of 2011, respectively. A bulk segregant analysis approach was used to identify markers associated with high grain yield under drought.

Results

A QTL, qDTY_12.1, significantly associated with grain yield under reproductive-stage drought stress was identified on chromosome 12 with a consistent effect in two environments: IRRI, Philippines, and RARS, Nepalgunj, Nepal. This QTL explained phenotypic variance of 23.8% and contributed an additive effect of 45.3% for grain yield under drought. The positive QTL allele for qDTY_12.1 was contributed by tolerant parent IR74371-46-1-1.

Conclusions

In this study, qDTY_12.1 showed a consistent effect across environments for high grain yield under lowland reproductive-stage drought stress in the background of popular high-yielding but drought-susceptible recipient variety Sabitri. qDTY_12.1 was also reported previously [Crop Sci 47:507–516, 2007] to increase grain yield under upland reproductive-stage drought stress situations. qDTY_12.1 is the only QTL reported so far in rice to have shown a large effect against multiple recipient genetic backgrounds as well as under highly diverse upland and lowland rice ecosystems. qDTY_12.1 can be successfully introgressed to improve grain yield under drought of popular high-yielding but drought-susceptible lowland as well as upland adapted varieties following marker-assisted breeding.

Identification of the quantitative trait loci (QTL) underlying water soluble protein content in soybean

Water soluble protein content (SPC) plays an important role in the functional efficacy of protein in food products. Therefore, for the identification of quantitative trait loci (QTL) associated with SPC, 212 F(2:9) lines of the recombinant inbred line (RIL) population derived from the cross of ZDD09454 × Yudou12 were grown along with the parents, in six different environments (location × year) to determine inheritance and map solubility-related genes. A linkage map comprising of 301 SSR markers covering 3,576.81 cM was constructed in the RIL population. Seed SPC was quantified with a macro-Kjeldahl procedure in samples collected over multiple years from three locations (Nantong in 2007 and 2008, Zhengzhou in 2007 and 2008, and Xinxiang in 2008 and 2009). SPC demonstrated transgressive segregation, indicating a complementary genetic structure between the parents. Eleven putative QTL were associated with SPC explaining 4.5-18.2 % of the observed phenotypic variation across the 6 year/location environments. Among these, two QTL (qsp8-4, qsp8-5) near GMENOD2B and Sat_215 showed an association with SPC in multiple environments, suggesting that they were key QTL related to protein solubility. The QTL × environment interaction demonstrated the complex genetic mechanism of SPC. These SPC-associated QTL and linked markers in soybean will provide important information that can be utilized by breeders to improve the functional quality of soybean varieties.

Mapping of quantitative trait loci for oil content in cottonseed kernel

Oil content in cottonseed is a major quality trait which when improved through breeding could enhance the competitiveness of cottonseed oil among other vegetable oils. Cottonseed oil content is a quantitative trait controlled by genes in the tetraploid embryo and tetraploid maternal plant genomes, and the knowledge of quantitative trait loci (QTLs) and the genetic effects related to oil content in both genomes could facilitate the improvement in its quality and quantity. However, till date, QTL mapping and genetic analysis related to this trait in cotton have only been conducted in the tetraploid embryo genome. In the current experiment, an IF(2) population of cottonseed kernels from the random crossing of 188 intraspecific recombinant inbred lines which were derived from the hybrid of two parents, HS46 and MARCABUCAG8US-1-88, were used to simultaneously locate QTLs for oil content in the embryo and maternal plant genomes. The four QTLs found to be associated with oil content in cottonseed were: qOC-18-1 on chromosome 18; qOC-LG-11 on linkage group 11; qOC-18-2 on chromosome 18; and qOC-22 on chromosome 22. At a high selection threshold of 0.05, there was strong evidence linking the QTLs above the oil content in cottonseed. Embryo additive and dominant effects from the tetraploid embryo genome, as well as maternal additive effects from the tetraploid maternal plant genome were found to be significant contributors to genetic variation in cottonseed oil content.