Quantitative Trait Loci Positions Research Articles

QTL and PACE analyses identify candidate genes for anthracnose resistance in tomato.

Anthracnose, caused by the fungal pathogen Colletotrichum spp., is one of the most significant tomato diseases in the United States and worldwide. No commercial cultivars with anthracnose resistance are available, limiting resistant breeding. Cultivars with genetic resistance would significantly reduce crop losses, reduce the use of fungicides, and lessen the risks associated with chemical application. A recombinant inbred line (RIL) mapping population (N=243) has been made from a cross between the susceptible US28 cultivar and the resistant but semiwild and small-fruited 95L368 to identify quantitative trait loci (QTLs) associated with anthracnose resistance. The RIL population was phenotyped for resistance by inoculating ripe field-harvested tomato fruits with Colletotrichum coccodes for two seasons. In this study, we identified twenty QTLs underlying resistance, with a range of phenotypic variance of 4.5 to 17.2% using a skeletal linkage map and a GWAS. In addition, a QTLseq analysis was performed using deep sequencing of extreme bulks that validated QTL positions identified using traditional mapping and resolved candidate genes underlying various QTLs. We further validated AP2-like ethylene-responsive transcription factor, N-alpha-acetyltransferase (NatA), cytochrome P450, amidase family protein, tetratricopeptide repeat, bHLH transcription factor, and disease resistance protein RGA2-like using PCR allelic competitive extension (PACE) genotyping. PACE assays developed in this study will enable high-throughput screening for use in anthracnose resistance breeding in tomato.

A physical map of traits of agronomic importance based on potato and tomato genome sequences.

Potato, tomato, pepper, and eggplant are worldwide important crop and vegetable species of the Solanaceae family. Molecular linkage maps of these plants have been constructed and used to map qualitative and quantitative traits of agronomic importance. This research has been undertaken with the vision to identify the molecular basis of agronomic characters on the one hand, and on the other hand, to assist the selection of improved varieties in breeding programs by providing DNA-based markers that are diagnostic for specific agronomic characters. Since 2011, whole genome sequences of tomato and potato became available in public databases. They were used to combine the results of several hundred mapping and map-based cloning studies of phenotypic characters between 1988 and 2022 in physical maps of the twelve tomato and potato chromosomes. The traits evaluated were qualitative and quantitative resistance to pathogenic oomycetes, fungi, bacteria, viruses, nematodes, and insects. Furthermore, quantitative trait loci for yield and sugar content of tomato fruits and potato tubers and maturity or earliness were physically mapped. Cloned genes for pathogen resistance, a few genes underlying quantitative trait loci for yield, sugar content, and maturity, and several hundred candidate genes for these traits were included in the physical maps. The comparison between the physical chromosome maps revealed, in addition to known intrachromosomal inversions, several additional inversions and translocations between the otherwise highly collinear tomato and potato genomes. The integration of the positional information from independent mapping studies revealed the colocalization of qualitative and quantitative loci for resistance to different types of pathogens, called resistance hotspots, suggesting a similar molecular basis. Synteny between potato and tomato with respect to genomic positions of quantitative trait loci was frequently observed, indicating eventual similarity between the underlying genes.

Combined QTL mapping on bi-parental immortalized heterozygous populations to detect the genetic architecture on heterosis.

From bi-parental pure-inbred lines (PIL), immortalized backcross (i.e., IB1 and IB2, representing the two directions of backcrossing) and F2 (i.e., IF2) populations can be developed. These populations are suitable for genetic studies on heterosis, due to the present of both homozygous and heterozygous genotypes, and in the meantime allow repeated phenotyping trials across multiple locations and years. In this study, we developed a combined approach of quantitative trait locus (QTL) mapping, when some or all of the four immortalized populations (i.e., PIL, IB1, IB2, and IF2) are available. To estimate the additive and dominant effects simultaneously and accurately, suitable transformations are made on phenotypic values from different populations. When IB1 and IB2 are present, summation and subtraction are used. When IF2 and PIL are available, mid-parental values and mid-parental heterosis are used. One-dimensional genomic scanning is performed to detect the additive and dominant QTLs, based on the algorithm of inclusive composite interval mapping (ICIM). The proposed approach was applied to one IF2 population together with PIL in maize, and identified ten QTLs on ear length, showing varied degrees of dominance. Simulation studies indicated the proposed approach is similar to or better than individual population mapping by QTL detection power, false discovery rate (FDR), and estimated QTL position and effects.

A Compendium for Novel Marker-Based Breeding Strategies in Eggplant.

The worldwide production of eggplant is estimated at about 58 Mt, with China, India and Egypt being the major producing countries. Breeding efforts in the species have mainly focused on increasing productivity, abiotic and biotic tolerance/resistance, shelf-life, the content of health-promoting metabolites in the fruit rather than decreasing the content of anti-nutritional compounds in the fruit. From the literature, we collected information on mapping quantitative trait loci (QTLs) affecting eggplant's traits following a biparental or multi-parent approach as well as genome-wide association (GWA) studies. The positions of QTLs were lifted according to the eggplant reference line (v4.1) and more than 700 QTLs were identified, here organized into 180 quantitative genomic regions (QGRs). Our findings thus provide a tool to: (i) determine the best donor genotypes for specific traits; (ii) narrow down QTL regions affecting a trait by combining information from different populations; (iii) pinpoint potential candidate genes.

A multi-locus linear mixed model methodology for detecting small-effect QTLs for quantitative traits in MAGIC, NAM, and ROAM populations

Although multi-parent populations (MPPs) integrate the advantages of linkage and association mapping populations in the genetic dissection of complex traits and especially combine genetic analysis with crop breeding, it is difficult to detect small-effect quantitative trait loci (QTL) for complex traits in multiparent advanced generation intercross (MAGIC), nested association mapping (NAM), and random-open-parent association mapping (ROAM) populations. To address this issue, here we proposed a multi-locus linear mixed model method, namely mppQTL, to detect QTLs, especially small-effect QTLs, in these MPPs. The new method includes two steps. The first is genome-wide scanning based on a single-locus linear mixed model; the P-values are obtained from likelihood-ratio test, the peaks of negative logarithm P-value curve are selected by group-lasso, and all the selected peaks are regarded as potential QTLs. In the second step, all the potential QTLs are placed on a multi-locus linear mixed model, all the effects are estimated using expectation-maximization empirical Bayes algorithm, and all the non-zero effect vectors are further evaluated via likelihood-ratio test for significant QTLs. In Monte Carlo simulation studies, the new method has higher power in QTL detection, lower false positive rate, lower mean absolute deviation for QTL position estimate, and lower mean squared error for the estimate of QTL size (r2) than existing methods because the new method increases the power of detecting small-effect QTLs. In real dataset analysis, the new method (19) identified five more known genes than the existing three methods (14). This study provides an effective method for detecting small-effect QTLs in any MPPs.

The 4R QTLs pollen fertility restoration in triticale with CMS Triticum timopheevii

AbstractThe Triticum timopheevii Zhuk. (Tt) cytoplasm is the most exploited cytoplasmic male sterility (CMS) type used in triticale in commercial seed production of hybrid varieties. The present study established a recombinant inbred line (RIL) mapping population to identify genetic loci controlling fertility restoration in triticale with CMS Tt. The trait was evaluated via phenotyping the BC1F1 materials originating from a backcross of maternal line MS 112(15)‐2‐1 (on CMS Tt) and RILs. A genetic map spanning 2293 cM over 21 linkage groups was constructed using 1,170 single nucleotide polymorphisms and silico diversity array technology (DArT) markers generated through DArTseq genotyping. Two quantitative trait loci (QTL) controlling fertility restoration were localized at the 4RL chromosome. They were stable during two vegetative seasons and, depending on the year, explained 17.8 to 37.5 (QRft‐4R.1) and 17.8 to 27.1% (QRft‐4R.2) of phenotypic variance. Bioinformatic analysis was performed to predict genes potentially responsible for the trait. Four markers linked to the trait were successfully converted into a polymerase chain reaction‐based assay to select the pollen fertility QTLs easily. The exact position of QTLs detected previously in the rye and here in the triticale suggests that the same loci might control the trait.

Integrating multi-locus genome-wide association studies with transcriptomic data to identify genetic loci underlying adult root trait responses to drought stress in flax (Linum usitatissimum L.)

Water stress is a major constraint on crop production and, root systems are one of the key features of plants determining drought tolerance and yield. In this study, five root morphological traits and their stability indices were evaluated at the reproductive stage on an association panel consisting of 120 diverse Linum usitatissimum accessions grown under well-watered and water-limiting conditions. To identify quantitative trait loci (QTL) and candidate genes positively influencing root traits under drought, combined multi-locus genome-wide association studies (ML-GWAS) and transcriptomic data analysis were carried out. A total of 112 QTL were identified accounting for 0.45–24.28 % of the phenotypic variation for root-related traits. Fifty QTL were detected by at least two ML-GWAS models, and 26 were co-located with previously reported QTL associated with yield-related traits under drought and early root and shoot development. Phenotypic differences between two contrasting subsets of accessions for the traits assessed were attributable to the accumulation of positive QTL alleles (PQTL), where the subset with the largest number of PQTL registered 154 % and 69 % greater total root length and yield under drought stress, respectively, than the subset with the fewest PQTL. Transcriptomic data analysis identified 766 differentially expressed genes (DEGs) responsive to drought stress in 108 QTL. Annotation of the 766 DEGs yielded functional genes involved in root development, stomatal closure, ROS (reactive oxygen species) scavenging, and hormone responses, among others. Taken together, the combined use of GWAS and transcriptomic data analyses provide the foundation for our enhanced understanding of root trait morphological changes in response to drought stress, and to achieve superior yield resilience/stability under water shortages in flax through enhanced molecular breeding. All data supporting the findings of this study are available within the paper and within its Supplementary data • Large genetic variation observed for adult root traits under drought and irrigated conditions. • Root traits explained 25–32 % of yield variation under drought stress. • A total of 112 QTL were identified for root traits and their stability indices in flax. • Twenty-six QTL co-located with previously reported QTL for yield traits under drought and root and shoot development. • Transcriptomic data analysis identified 766 drought responsive differentially expressed genes within the LD blocks of 108 significant QTL.

Genome analysis in Avena sativa reveals hidden breeding barriers and opportunities for oat improvement

Oat (Avena sativa L.) is an important and nutritious cereal crop, and there is a growing need to identify genes that contribute to improved oat varieties. Here we utilize a newly sequenced and annotated oat reference genome to locate and characterize quantitative trait loci (QTLs) affecting agronomic and grain-quality traits in five oat populations. We find strong and significant associations between the positions of candidate genes and QTL that affect heading date, as well as those that influence the concentrations of oil and β-glucan in the grain. We examine genome-wide recombination profiles to confirm the presence of a large, unbalanced translocation from chromosome 1 C to 1 A, and a possible inversion on chromosome 7D. Such chromosome rearrangements appear to be common in oat, where they cause pseudo-linkage and recombination suppression, affecting the segregation, localization, and deployment of QTLs in breeding programs.

Allelically and Differentially Expressed Genes After Infection of Edwardsiella ictaluri in Channel Catfish as Determined by Bulk Segregant RNA-Seq.

Identification of genetic markers associated with resistance against enteric septicemia of catfish (ESC) is of great interest for genetic enhancement programs of catfish. In the present study, bulk segregant RNA-Seq analysis was applied to determine differentially expressed genes and alleles after ESC infection. Here we report three genomic regions on LG1, LG12, and LG26, containing significant single-nucleotide polymorphisms (SNPs). These genomic regions aligned well with quantitative trait loci (QTL) previously identified. Within the QTL regions, eleven genes were found to be differentially regulated between phenotypic bulks. Importantly, the QTL on linkage group 1 (LG1) were found to be expressed in the liver, whereas the QTL on LG12 and LG26 were expressed in the intestine, suggesting multiple mechanisms of ESC resistance. It is apparent that apolipoproteins may be important for ESC resistance as the QTL on LG1 included the 14-kDa apolipoprotein genes that are both allelically expressed and differentially expressed between the resistant and susceptible bulks. Traf2 and NCK-interacting protein kinase (TNIK) were found in the QTL on LG12, and it was downregulated in resistant fish, suggesting the importance of NCK downregulation in ESC resistance, as previously reported. In addition, we observed divergent gene expression patterns between the liver and intestine after infection. Immune/inflammatory-related processes were overrepresented from liver DEGs, while those DEGs identified from intestine were enriched for proteolysis and wounding processes. Taken together, the BSR-Seq analysis presented here advanced the knowledge of ESC resistance, providing information of not only positions of QTL but also genes and their differential expression between resistant and susceptible fish, making it one step closer to the identification of the causal genes for ESC resistance.

Identification of Two Major QTLs in Brassica napus Lines With Introgressed Clubroot Resistance From Turnip Cultivar ECD01.

Plasmodiophora brassicae causes clubroot disease in brassica crops worldwide. Brassica rapa, a progenitor of Brassica napus (canola), possesses important sources for resistance to clubroot. A doubled haploid (DH) population consisting of 84 DH lines were developed from a Backcross2 (BC2) plant through an interspecific cross of B. rapa turnip cv. ECD01 (resistant, R) with canola line DH16516 (susceptible, S) and then backcrossed with DH16516 as the recurrent parent. The DH lines and their parental lines were tested for resistance to four major pathotypes (3A, 3D, 3H, and 5X) of P. brassicae identified from canola. The R:S segregation ratio for pathotype 3A was 1:3, and 3:1 for pathotypes 3D, 3H, and 5X. From genotyping by sequencing (GBS), a total of 355.3 M short reads were obtained from the 84 DH lines, ranging from 0.81 to 11.67 M sequences per line. The short reads were aligned into the A-genome of B. napus “Darmor-bzh” version 4.1 with a total of 260 non-redundant single-nucleotide polymorphism (SNP) sites. Two quantitative trait loci (QTLs), Rcr10ECD01 and Rcr9ECD01, were detected for the pathotypes in chromosomes A03 and A08, respectively. Rcr10ECD01 and Rcr9ECD01 were responsible for resistance to 3A, 3D, and 3H, while only one QTL, Rcr9ECD01, was responsible for resistance to pathotype 5X. The logarithm of the odds (LOD) values, phenotypic variation explained (PVE), additive (Add) values, and confidence interval (CI) from the estimated QTL position varied with QTL, with a range of 5.2–12.2 for LOD, 16.2–43.3% for PVE, 14.3–25.4 for Add, and 1.5–12.0 cM for CI. The presence of the QTLs on the chromosomes was confirmed through the identification of the percentage of polymorphic variants using bulked-segregant analysis. There was one gene encoding a disease resistance protein and 24 genes encoding proteins with function related to plant defense response in the Rcr10ECD01 target region. In the Rcr9ECD01 region, two genes encoded disease resistance proteins and 10 genes encoded with defense-related function. The target regions for Rcr10ECD01 and Rcr9ECD01 in B. napus were homologous to the 11.0–16.0 Mb interval of chromosome A03 and the 12.0–14.5 Mb interval of A08 in B. rapa “Chiifu” reference genome, respectively.

Prioritization of vigor QTL-associated genes for future genome-directed Vitis breeding

Vigor control in grapevine may become especially important under climate change. A better understanding of gene-phenotype relationships is required in order to exploit plant genomics for breeding purposes. This research aims to use quantitative trait loci (QTLs) for vigor identified in the progeny from a cross of Ramsey (Vitis champinii) × Riparia Gloire (V. riparia). Genes located 700 kb up and downstream from each QTL position were interrogated for functional enrichment through ShinyGO online tool, based on the gene ontology annotation of Vitis vinifera PN40024. Key biological processes like phloem and xylem development, cell cycle, response to hormones, amino acid transport, tissue development, sugar metabolism, nitrogen transport, and stress/immune responses, showed functional enrichment. Integral response to light and auxin might be required for fine molecular tuning of vegetative growth in Vitis. Fifty out of 1318 candidate genes were prioritized, reducing their amount to a manageable number of candidates for further directed breeding strategies. Highlights Plant vigor control may become especially important under climate change. Genes from various vigor-related QTLs were interrogated for functional enrichment. The analysis reduced candidate gene number based on marker proximity and functional enrichment, constituting a suitable shortcut for target-directed genome-guided breeding strategies. Three TFs are strong candidates for targeted breeding: TIF - HY5, TIF - SUS1, TIF - VOZ1 potentially enhance growth by relating light response to hormone activation, and then to photosynthesis and morphogenesis.

Mapping pre-harvest sprouting resistance loci in AAC Innova \xd7 AAC Tenacious spring wheat population

BackgroundPre-harvest sprouting (PHS) is a major problem for wheat production due to its direct detrimental effects on wheat yield, end-use quality and seed viability. Annually, PHS is estimated to cause > 1.0 billion USD in losses worldwide. Therefore, identifying PHS resistance quantitative trait loci (QTLs) is crucial to aid molecular breeding efforts to minimize losses. Thus, a doubled haploid mapping population derived from a cross between white-grained PHS susceptible cv AAC Innova and red-grained resistant cv AAC Tenacious was screened for PHS resistance in four environments and utilized for QTL mapping.ResultsTwenty-one PHS resistance QTLs, including seven major loci (on chromosomes 1A, 2B, 3A, 3B, 3D, and 7D), each explaining ≥10% phenotypic variation for PHS resistance, were identified. In every environment, at least one major QTL was identified. PHS resistance at most of these loci was contributed by AAC Tenacious except at two loci on chromosomes 3D and 7D where it was contributed by AAC Innova. Thirteen of the total twenty-one identified loci were located to chromosome positions where at least one QTL have been previously identified in other wheat genotype(s). The remaining eight QTLs are new which have been identified for the first time in this study. Pedigree analysis traced several known donors of PHS resistance in AAC Tenacious genealogy. Comparative analyses of the genetic intervals of identified QTLs with that of already identified and cloned PHS resistance gene intervals using IWGSC RefSeq v2.0 identified MFT-A1b (in QTL interval QPhs.lrdc-3A.1) and AGO802A (in QTL interval QPhs.lrdc-3A.2) on chromosome 3A, MFT-3B-1 (in QTL interval QPhs.lrdc-3B.1) on chromosome 3B, and AGO802D, HUB1, TaVp1-D1 (in QTL interval QPhs.lrdc-3D.1) and TaMyb10-D1 (in QTL interval QPhs.lrdc-3D.2) on chromosome 3D. These candidate genes are involved in embryo- and seed coat-imposed dormancy as well as in epigenetic control of dormancy.ConclusionsOur results revealed the complex PHS resistance genetics of AAC Tenacious and AAC Innova. AAC Tenacious possesses a great reservoir of important PHS resistance QTLs/genes supposed to be derived from different resources. The tracing of pedigrees of AAC Tenacious and other sources complements the validation of QTL analysis results. Finally, comparing our results with previous PHS studies in wheat, we have confirmed the position of several major PHS resistance QTLs and candidate genes.

Mapping freezing tolerance QTL in alfalfa: based on indoor phenotyping

BackgroundWinter freezing temperature impacts alfalfa (Medicago sativa L.) persistence and seasonal yield and can lead to the death of the plant. Understanding the genetic mechanisms of alfalfa freezing tolerance (FT) using high-throughput phenotyping and genotyping is crucial to select suitable germplasm and develop winter-hardy cultivars. Several clones of an alfalfa F1 mapping population (3010 x CW 1010) were tested for FT using a cold chamber. The population was genotyped with SNP markers identified using genotyping-by-sequencing (GBS) and the quantitative trait loci (QTL) associated with FT were mapped on the parent-specific linkage maps. The ultimate goal is to develop non-dormant and winter-hardy alfalfa cultivars that can produce extended growth in the areas where winters are often mild.ResultsAlfalfa FT screening method optimized in this experiment comprises three major steps: clone preparation, acclimation, and freezing test. Twenty clones of each genotype were tested, where 10 samples were treated with freezing temperature, and 10 were used as controls. A moderate positive correlation (r ~ 0.36, P < 0.01) was observed between indoor FT and field-based winter hardiness (WH), suggesting that the indoor FT test is a useful indirect selection method for winter hardiness of alfalfa germplasm. We detected a total of 20 QTL associated with four traits; nine for visual rating-based FT, five for percentage survival (PS), four for treated to control regrowth ratio (RR), and two for treated to control biomass ratio (BR). Some QTL positions overlapped with WH QTL reported previously, suggesting a genetic relationship between FT and WH. Some favorable QTL from the winter-hardy parent (3010) were from the potential genic region for a cold tolerance gene CBF. The BLAST alignment of a CBF sequence of M. truncatula, a close relative of alfalfa, against the alfalfa reference showed that the gene’s ortholog resides around 75 Mb on chromosome 6.ConclusionsThe indoor freezing tolerance selection method reported is useful for alfalfa breeders to accelerate breeding cycles through indirect selection. The QTL and associated markers add to the genomic resources for the research community and can be used in marker-assisted selection (MAS) for alfalfa cold tolerance improvement.

Robust regressionbased genome-wide multi-trait QTL analysis.

In genome-wide quantitative trait locus (QTL) mapping studies, multiple quantitative traits are often measured along with the marker genotypes. Multi-trait QTL (MtQTL) analysis, which includes multiple quantitative traits together in a single model, is an efficient technique to increase the power of QTL identification. The two most widely used classical approaches for MtQTL mapping are Gaussian Mixture Model-based MtQTL (GMM-MtQTL) and Linear Regression Model-based MtQTL (LRM-MtQTL) analyses. There are two types of LRM-MtQTL approach known as least squares-based LRM-MtQTL (LS-LRM-MtQTL) and maximum likelihood-based LRM-MtQTL (ML-LRM-MtQTL). These three classical approaches are equivalent alternatives for QTL detection, but ML-LRM-MtQTL is computationally faster than GMM-MtQTL and LS-LRM-MtQTL. However, one major limitation common to all the above classical approaches is that they are very sensitive to outliers, which leads to misleading results. Therefore, in this study, we developed an LRM-based robust MtQTL approach, called LRM-RobMtQTL, for the backcross population based on the robust estimation of regression parameters by maximizing the β-likelihood function induced from the β-divergence with multivariate normal distribution. When β = 0, the proposed LRM-RobMtQTL method reduces to the classical ML-LRM-MtQTL approach. Simulation studies showed that both ML-LRM-MtQTL and LRM-RobMtQTL methods identified the same QTL positions in the absence of outliers. However, in the presence of outliers, only the proposed method was able to identify all the true QTL positions. Real data analysis results revealed that in the presence of outliers only our LRM-RobMtQTL approach can identify all the QTL positions as those identified in the absence of outliers by both methods. We conclude that our proposed LRM-RobMtQTL analysis approach outperforms the classical MtQTL analysis methods.

Mapping QTL for leaf pigment content at dynamic development stage and analyzing Meta-QTL in rice

Leaf pigment content is a complicated quantitative trait which results from the cooperation of multiple related genes or quantitative trait loci (QTL) in rice. So far, more than 600 QTL for leaf pigment content has been reported. However, most QTL positions could not be directly compared with each other. In this study, our goal was to analyze consistent QTL regions affecting leaf pigment content by applying a genome wide QTL meta-analysis approach. 20 QTL related to leaf pigment content were detected at 5 different growth periods in 134 recombinant inbred lines (RILs) from a typical Indica-Japonica cross derived from PA64s and 9311 (two parents of super hybrid rice Liangyoupei 9). There was a candidate gene GLTP in the region of 3 QTL (qFS_Chla4, qFS_Chla+b4, and qFS_Car4), which related to component of chloroplast thylakoid membranes. After sequencing and qRT-PCR analysis, the gene showed significant difference in parents of the RILs. Meta-analysis was performed on 618 QTL collected from 38 previous studies. Consequently, a total of 64 MQTL (Meta-QTL) were obtained, of which 58.95% confidence intervals (CI) were smaller than the average Cl of the original QTL. In addition, 64 MQTL covered 6 of 20 QTL detected. Among them, four main QTL were included in 3 MQTL, and phenotypic variation of 21 MQTL was greater than 20%. The traits with higher number of QTL had higher number of MQTL, and candidate genes in overlapping MQTL include multiple effects. 7 candidate genes related to leaf pigment content were found in 4 MQTL spanning physical intervals < 0.5 Mb. These findings are not only anticipated to help molecular marker-assisted selection and pyramiding, but also contribute to determine candidate genes that regulate pigment content.

Regression based fast multi-trait genome-wide QTL analysis.

Multivariate simple interval mapping (SIM) is one of the most popular approaches for multiple quantitative trait locus (QTL) analysis. Both maximum likelihood (ML) and least squares (LS) multivariate regression (MVR) are widely used methods for multi-trait SIM. ML-based MVR (MVR-ML) is an expectation maximization (EM) algorithm based iterative and complex time-consuming approach. Although the LS-based MVR (MVR-LS) approach is not an iterative process, the calculation of likelihood ratio (LR) statistic in MVR-LS is also a time-consuming complex process. We have introduced a new approach (called FastMtQTL) for multi-trait QTL analysis based on the assumption of multivariate normal distribution of phenotypic observations. Our proposed method can identify almost the same QTL positions as those identified by the existing methods. Moreover, the proposed method takes comparatively less computation time because of the simplicity in the calculation of LR statistic by this method. In the proposed method, LR statistic is calculated only using the sample variance-covariance matrix of phenotypes and the conditional probability of QTL genotype given the marker genotypes. This improvement in computation time is advantageous when the numbers of phenotypes and individuals are larger, and the markers are very dense resulting in a QTL mapping with a bigger dataset.

Identification of candidate genes controlling fiber quality traits in upland cotton through integration of meta-QTL, significant SNP and transcriptomic data

BackgroundMeta-analysis of quantitative trait locus (QTL) is a computational technique to identify consensus QTL and refine QTL positions on the consensus map from multiple mapping studies. The combination of meta-QTL intervals, significant SNPs and transcriptome analysis has been widely used to identify candidate genes in various plants.ResultsIn our study, 884 QTLs associated with cotton fiber quality traits from 12 studies were used for meta-QTL analysis based on reference genome TM-1, as a result, 74 meta-QTLs were identified, including 19 meta-QTLs for fiber length; 18 meta-QTLs for fiber strength; 11 meta-QTLs for fiber uniformity; 11 meta-QTLs for fiber elongation; and 15 meta-QTLs for micronaire. Combined with 8 589 significant single nucleotide polymorphisms associated with fiber quality traits collected from 15 studies, 297 candidate genes were identified in the meta-QTL intervals, 20 of which showed high expression levels specifically in the developing fibers. According to the function annotations, some of the 20 key candidate genes are associated with the fiber development.ConclusionsThis study provides not only stable QTLs used for marker-assisted selection, but also candidate genes to uncover the molecular mechanisms for cotton fiber development.

Genetic architecture of polyphenol oxidase activity in wheat flour by genome‐wide association study

AbstractThe primary cause of time‐dependent discoloration in wheat (Triticum aestivum L.)‐based products is polyphenol oxidase (PPO) activity. A comprehensive genetic characterization of PPO activity in wheat could expedite the development of wheat varieties with low PPO activity. To dissect the genetic architecture of PPO activity in wheat flour, a panel of 166 diverse bread wheat cultivars was phenotyped in four different environments and genotyped by high‐density wheat 90K and 660K arrays. A genome‐wide association study (GWAS) was performed by a mixed linear model that incorporated population structure and relative kinship. Four hundred and sixty‐five stable marker–trait associations representative of 43 quantitative trait loci (QTL) for PPO activity identified in at least two environments were located on all wheat chromosomes except 5A, with each explaining 6.6–32.4% of the phenotypic variance. Based on IWGSC RefSeq v1.0, we found that QPPO2A.3, QPPO2B.1, QPPO2B.2, and QPPO2D.2 were consistent with the previously reported QTL, and 12 QTL located on homoeologous group 1 chromosomes (6 QTL), chromosomes 4B, 4D, and 7A (2 QTL), and chromosome 7B (2 QTL) are likely to be new PPO loci. Based on physical positions of QTL and previous studies, Ppo‐A1, Ppo‐A2, Ppo‐B2, Ppo‐D2, Pinb‐D1, and genes encoding a Cu ion‐binding protein and a Cu‐transporting ATPase RAN1‐like protein were considered as candidates for QPPO2A.3, QPPO2B.1, QPPO2D.2, QPPO5D.1, QPPO4A.2, and QPPO7D.2, respectively. This study provided insights into the molecular basis of wheat PPO activity, and associated markers can be used for selection of low PPO breeding lines as a means of improving the marketing quality of wheat products.

Assessing by Modeling the Consequences of Increased Recombination in Recurrent Selection of Oryza sativa and Brassica rapa.

Meiotic recombination generates genetic diversity but in most species the number of crossovers per meiosis is limited. Previous modeling studies showed that increasing recombination can enhance response to selection. However, such studies did not assume a specific method of modifying recombination. Our objective was to test whether two methods used to increase recombination in plants could increase genetic gain in a population undergoing recurrent selection such as in genomic selection programs. The first method, in Oryza sativa, used a mutant of anti-crossover genes, increasing global recombination without affecting the recombination landscape shape. The second one used the ploidy level of a cross between Brassica rapa and Brassica napus, increasing recombination especially in pericentromeric regions. Our modeling framework used these recombination landscapes and sampled quantitative trait loci positions from the actual gene distributions. We simulated selection programs with initially a cross between two inbred lines, for two species. Increased recombination enhanced the response to selection. The amount of enhancement in the cumulative gain largely depended on the species and the number of quantitative trait loci (2, 10, 20, 50, 200 or 1000 per chromosome). Genetic gains were increased up to 30% after 20 generations. Furthermore, increasing recombination in cold regions was the most effective: the gain was larger by 25% with the first method and 34% with the second one in B. rapa, and 12% compared to 16% in O. sativa. In summary, increased recombination enhances the genetic gain in long-term selection programs, with visible effects after four to five generations.

BRM: a statistical method for QTL mapping based on bulked segregant analysis by deep sequencing.

Bulked segregant analysis by deep sequencing (BSA-seq) has been widely used for quantitative trait locus (QTL) mapping in recent years. A number of different statistical methods for BSA-seq have been proposed. However, determination of significance threshold, the key point for QTL identification, remains to be a problem that has not been well solved due to the difficulty of multiple testing correction. In addition, estimation of the confidence interval is also a problem to be solved. In this paper, we propose a new statistical method for BSA-seq, named Block Regression Mapping (BRM). BRM is robust to sequencing noise and is applicable to the case of low sequencing depth. Significance threshold can be reasonably determined by taking multiple testing correction into account. Meanwhile, the confidence interval of QTL position can also be estimated. The R scripts of our method are open source under GPLv3 license at https://github.com/huanglikun/BRM. Supplementary data are available at Bioinformatics online.