Quantitative Trait Loci Mapping Methods Research Articles

Identification of Clubroot (Plasmodiophora brassicae) Resistance Loci in Chinese Cabbage (Brassica rapa ssp. pekinensis) with Recessive Character.

The soil-borne pathogen Plasmodiophora brassicae is the causal agent of clubroot, a major disease in Chinese cabbage (Brassica rapa ssp. pekinensis). The host's resistance genes often confer immunity to only specific pathotypes and may be rapidly overcome. Identification of novel clubroot resistance (CR) from germplasm sources is necessary. In this study, Bap246 was tested by being crossed with different highly susceptible B. rapa materials and showed recessive resistance to clubroot. An F2 population derived from Bap246 × Bac1344 was used to locate the resistance Quantitative Trait Loci (QTL) by Bulk Segregant Analysis Sequencing (BSA-Seq) and QTL mapping methods. Two QTL on chromosomes A01 (4.67-6.06 Mb) and A08 (10.42-11.43 Mb) were found and named Cr4Ba1.1 and Cr4Ba8.1, respectively. Fifteen and eleven SNP/InDel markers were used to narrow the target regions in the larger F2 population to 4.67-5.17 Mb (A01) and 10.70-10.84 Mb (A08), with 85 and 19 candidate genes, respectively. The phenotypic variation explained (PVE) of the two QTL were 30.97% and 8.65%, respectively. Combined with gene annotation, mutation site analysis, and real-time quantitative polymerase chain reaction (qRT-PCR) analysis, one candidate gene in A08 was identified, namely Bra020861. And an insertion and deletion (InDel) marker (co-segregated) named Crr1-196 was developed based on the gene sequence. Bra013275, Bra013299, Bra013336, Bra013339, Bra013341, and Bra013357 in A01 were the candidate genes that may confer clubroot resistance in Chinese cabbage. The resistance resource and the developed marker will be helpful in Brassica breeding programs.

Quantitative trait loci for sex ratio in monoecious bitter gourd (Momordica charantia)

Sex ratio (male or female flower frequency per plant) is an important trait of monoecious bitter gourds because it directly influences fruit yield. Quantitative trait loci (QTL) for the sex ratio were explored in F2 populations derived from the gynoecious OHB61-5 and monoecious OHB95-1A using both QTL mapping and QTL-seq methods. Female flower frequency data was used as the phenotype data, along with 1,036 bin-marker genotypes, and linkage maps from previous studies to detect sex ratio QTL. For QTL-seq, after the strong effect of the gynoecious locus was masked by marker-assisted selection of its heterozygotes, F2 plants showing higher- or lower-female flower frequency were selected and employed for making bulked samples. Three QTL were found on chromosomes 2 and 5 by QTL-seq, and only the qFR5-2 on chromosome 5 was identified by both QTL mapping and QTL-seq analyses. Focusing on the expressed genes with non-synonymous variations between parental lines in qFR5-2, 16 genes were nominated as candidate causal genes. Four of these encoded L-type lectin receptor kinase (LecRK), and amino acid substitutions in the conserved lectin or kinase domain were observed in three LecRKs between parental lines. Markers or genes for these sex ratio QTL will contribute to the development of bitter gourd inbred lines useful for stable production of fruits.

High-resolution genetic map construction and QTL analysis of important fiber traits in kenaf using RAD-seq

Kenaf fiber extracted from kenaf stem bast is an important natural fiber. The genetic basis of fiber yield and quality is poorly understood. In the present study, Fiber yield and quality phenotypic data showed continuous positive distribution or positively skewed distribution. Fiber yield traits and its related traits including plant height, fresh bast weight, dry bast weight showed a positive correlation. However, there was a negative correlation between fiber weight and fiber count. The results showed that the fiber yield and quality agronomic traits are quantitative traits controlled by multiple genes. Quantitative trait locus (QTL) mapping is a useful method for revealing the mechanism of complex genetic traits and identifying new genomic information to accelerate crop improvement. In this study, a total of 1,997 polymorphic SNP markers were successfully mapped onto 18 different linkage groups. Meanwhile six QTLs were identified based on the genetic map using the multiple QTL mapping (MQM) method, which were assigned to three linkage groups, LG16, LG8, and LG3. QTL-Plant hight, QTL-Fresh bast weight, QTL-Dry bast weight and QTL-fiber weight were related to fiber yield, while QTL-fiber strength and QTL-fiber count were related to fiber quality. This is the first study of its kind to map QTL of fiber yield and fiber quality, which will facilitate further understanding of the molecular genetic basis of these traits. However, there are limitations regarding the utilization of this map because several large gaps remain in some linkage groups. Therefore, additional markers need to be developed to further narrow these regions.

A combinatorial strategy to identify various types of QTLs for quantitative traits using extreme phenotype individuals in an F2 population

Theoretical and applied studies demonstrate the difficulty of detecting extremely over-dominant and small-effect genes for quantitative traits via bulked segregant analysis (BSA) in an F2 population. To address this issue, we proposed an integrated strategy for mapping various types of quantitative trait loci (QTLs) for quantitative traits via a combination of BSA and whole-genome sequencing. In this strategy, the numbers of read counts of marker alleles in two extreme pools were used to predict the numbers of read counts of marker genotypes. These observed and predicted numbers were used to construct a new statistic, Gw, for detecting quantitative trait genes (QTGs), and the method was named dQTG-seq1. This method was significantly better than existing BSA methods. If the goal was to identify extremely over-dominant and small-effect genes, another reserved DNA/RNA sample from each extreme phenotype F2 plant was sequenced, and the observed numbers of marker alleles and genotypes were used to calculate Gw to detect QTGs; this method was named dQTG-seq2. In simulated and real rice dataset analyses, dQTG-seq2 could identify many more extremely over-dominant and small-effect genes than BSA and QTL mapping methods. dQTG-seq2 may be extended to other heterogeneous mapping populations. The significance threshold of Gw in this study was determined by permutation experiments. In addition, a handbook for the R software dQTG.seq, which is available at https://cran.r-project.org/web/packages/dQTG.seq/index.html, has been provided in the supplemental materials for the users’ convenience. This study provides a new strategy for identifying all types of QTLs for quantitative traits in an F2 population.

Genetic Analysis and QTL Mapping of Parthenocarpy in <i>Cucumis sativus</i> L

Breeding of cucumber cultivars with high parthenocarpy rate are the prerequisite that ensuring yield and quality under undesirable environment. The mixed major gene plus polygene inheritance model was used to analyze cucumber parthenocarpy trait in the six generations, including P 1 , P 2 , F 1 , BC 1 , BC 2  and F 2 , derived from a cross between the highly parthenocarpic line DDX and low parthenocarpic line ZK. The major-effect quantitative trait locus (QTL) was identified by genome-wide comparison of single nucleotide polymorphisms (SNPs) between high- and low- parthenocarpy rate F 2  pools, and confirmed by traditional QTL mapping with genetic map. Genetic analysis showed that the genetic model E-2, mixed two additive-dominant-epistatic major genes and additive-dominant polygenes, is the best-fitting genetic model for cucumber parthenocarpy phenotype. A major-effect QTL, namely  par2.1 , were identified by the method of Δ(SNP_index).  par2.1  was confirmed by traditional markers based QTL mapping method with least likelihood (LOD) peak score of 12.6, genetic distance of 11.5cM and accounting for 26.6% of the total phenotypic variance.  par2.1  was narrowed down to a 765kb region through comparison of markers physical locations on chromosome 2. The major-effect QTL,  par2.1  on Chr2, for parthenocarpy identified by this study was not reported previously. Our findings shed light on the genetic architecture underlying cucumber parthenocarpy trait, and lay a solid basis for innovation of cucumber parthenocarpy germplasm and breed of new cucumber varieties with high parthenocarpy.

How to Detect QTLs in the Plant Circadian Clock.

One of the most powerful methods to identify loci controlling complex quantitative traits has been the quantitative trait locus (QTL) mapping. The QTL mapping approach has proven immensely useful to improve our understanding of key pathways such as flowering time, growth, and disease resistance. Since major circadian clock parameters such as period, phase, and amplitude are quantitative in nature, the QTL mapping approach could also be used to study the complex genetic architecture of the circadian clock. Here, we describe a simple QTL mapping method to identify components controlling clock parameters in natural populations of Arabidopsis thaliana.

Development of an Aus-Derived Nested Association Mapping (Aus-NAM) Population in Rice.

A genetic resource for studying genetic architecture of agronomic traits and environmental adaptation is essential for crop improvements. Here, we report the development of a rice nested association mapping population (aus-NAM) using 7 aus varieties as diversity donors and T65 as the common parent. Aus-NAM showed broad phenotypic variations. To test whether aus-NAM was useful for quantitative trait loci (QTL) mapping, known flowering genes (Ehd1, Hd1, and Ghd7) in rice were characterized using single-family QTL mapping, joint QTL mapping, and the methods based on genome-wide association study (GWAS). Ehd1 was detected in all the seven families and all the methods. On the other hand, Hd1 and Ghd7 were detected in some families, and joint QTL mapping and GWAS-based methods resulted in weaker and uncertain peaks. Overall, the high allelic variations in aus-NAM provide a valuable genetic resource for the rice community.

Neighbor QTL: an interval mapping method for quantitative trait loci underlying plant neighborhood effects.

Phenotypes of sessile organisms, such as plants, rely not only on their own genotypes but also on those of neighboring individuals. Previously, we incorporated such neighbor effects into a single-marker regression using the Ising model of ferromagnetism. However, little is known regarding how neighbor effects should be incorporated in quantitative trait locus (QTL) mapping. In this study, we propose a new method for interval QTL mapping of neighbor effects, designated “neighbor QTL,” the algorithm of which includes: (1) obtaining conditional self-genotype probabilities with recombination fraction between flanking markers; (2) calculating conditional neighbor genotypic identity using the self-genotype probabilities; and (3) estimating additive and dominance deviations for neighbor effects. Our simulation using F2 and backcross lines showed that the power to detect neighbor effects increased as the effective range decreased. The neighbor QTL was applied to insect herbivory on Col × Kas recombinant inbred lines of Arabidopsis thaliana. Consistent with previous results, the pilot experiment detected a self-QTL effect on the herbivory at the GLABRA1 locus. Regarding neighbor QTL effects on herbivory, we observed a weak QTL on the top of chromosome 4, at which a weak self-bolting QTL was also identified. The neighbor QTL method is available as an R package (https://cran.r-project.org/package=rNeighborQTL), providing a novel tool to investigate neighbor effects in QTL studies.

Letter to the Editor: Methods for mapping quantitative trait loci in autotetraploid species.

Mapping quantitative trait loci (QTL) in autotetraploid species represents a timely and challenging task. Two papers published by Wu and his colleagues proposed statistical methods for QTL mapping in these evolutionarily and economically important species. In this Letter to the Editor, we present critical comments on the fundamental conceptual errors involved, from both statistical and genetic points of view.

A tetrasomic inheritance model and likelihood-based method for mapping quantitative trait loci in autotetraploid species.

Summary Dissecting the genetic architecture of quantitative traits in autotetraploid species is a methodologically challenging task, but a pivotally important goal for breeding globally important food crops, including potato and blueberry, and ornamental species such as rose. Mapping quantitative trait loci (QTLs) is now a routine practice in diploid species but is far less advanced in autotetraploids, largely due to a lack of analytical methods that account for the complexities of tetrasomic inheritance.We present a novel likelihood‐based method for QTL mapping in outbred segregating populations of autotetraploid species. The method accounts properly for sophisticated features of gene segregation and recombination in an autotetraploid meiosis. It may model and analyse molecular marker data with or without allele dosage information, such as that from microarray or sequencing experiments.The method developed outperforms existing bivalent‐based methods, which may fail to model and analyse the full spectrum of experimental data, in the statistical power of QTL detection, and accuracy of QTL location, as demonstrated by an intensive simulation study and analysis of data sets collected from a segregating population of potato (Solanum tuberosum).The study enables QTL mapping analysis to be conducted in autotetraploid species under a rigorous tetrasomic inheritance model.

Mapping dynamic QTL dissects the genetic architecture of grain size and grain filling rate at different grain-filling stages in barley

Grain filling is an important growth process in formation of yield and quality for barley final yield determination. To explore the grain development behavior during grain filling period in barley, a high-density genetic map with 1962 markers deriving from a doubled haploid (DH) population of 122 lines was used to identify dynamic quantitative trait locus (QTL) for grain filling rate (GFR) and five grain size traits: grain area (GA), grain perimeter (GP), grain length (GL), grain width (GW) and grain diameter (GD). Unconditional QTL mapping is to detect the cumulative effect of genetic factors on a phenotype from development to a certain stage. Conditional QTL mapping is to detect a net effect of genetic factors on the phenotype at adjacent time intervals. Using unconditional, conditional and covariate QTL mapping methods, we successfully detected 34 major consensus QTLs. Moreover, certain candidate genes related to grain size, plant height, yield, and starch synthesis were identified in six QTL clusters, and individual gene was specifically expressed in different grain filling stages. These findings provide useful information for understanding the genetic basis of the grain filling dynamic process and will be useful for molecular marker-assisted selection in barley breeding.

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.

Compensatory trans-regulatory alleles minimizing variation in TDH3 expression are common within Saccharomyces cerevisiae.

Heritable variation in gene expression is common within species. Much of this variation is due to genetic differences outside of the gene with altered expression and is trans‐acting. This trans‐regulatory variation is often polygenic, with individual variants typically having small effects, making the genetic architecture and evolution of trans‐regulatory variation challenging to study. Consequently, key questions about trans‐regulatory variation remain, including the variability of trans‐regulatory variation within a species, how selection affects trans‐regulatory variation, and how trans‐regulatory variants are distributed throughout the genome and within a species. To address these questions, we isolated and measured trans‐regulatory differences affecting TDH3 promoter activity among 56 strains of Saccharomyces cerevisiae, finding that trans‐regulatory backgrounds varied approximately twofold in their effects on TDH3 promoter activity. Comparing this variation to neutral models of trans‐regulatory evolution based on empirical measures of mutational effects revealed that despite this variability in the effects of trans‐regulatory backgrounds, stabilizing selection has constrained trans‐regulatory differences within this species. Using a powerful quantitative trait locus mapping method, we identified ∼100 trans‐acting expression quantitative trait locus in each of three crosses to a common reference strain, indicating that regulatory variation is more polygenic than previous studies have suggested. Loci altering expression were located throughout the genome, and many loci were strain specific. This distribution and prevalence of alleles is consistent with recent theories about the genetic architecture of complex traits. In all mapping experiments, the nonreference strain alleles increased and decreased TDH3 promoter activity with similar frequencies, suggesting that stabilizing selection maintained many trans‐acting variants with opposing effects. This variation may provide the raw material for compensatory evolution and larger scale regulatory rewiring observed in developmental systems drift among species.

Mapping and verification of grain shape QTLs based on high-throughput SNP markers in rice

Grain shape is an important trait that determines appearance quality in rice. In order to dissect the genetic basis of this complex trait, we constructed an F2 and derived F2:3 lines from a cross between a long-grain variety and one with an intermediate grain length. The F2 population was genotyped by the RICE6K array to construct a high-density linkage map. A total of 30 quantitative trait loci (QTL) for grain shape were detected over 2 years. A major QTL cluster on chromosome 7 had a strong effect on grain length and width, consistent with the effect of GL7/GW7. The other QTLs, qGL2, qGW2, and qGL12, had large effects on grain shape and were detected in both years. The effects of four QTL on seed size as well as grain yield were then validated by using BC1F6 populations derived from selected F3 plants with residual heterozygous genotypes on each QTL region. Our study indicated that the SNP array was an efficient genotyping method for QTL mapping, and the novel QTL, qGW2 and qGL12, provided insight into the genetic basis of grain shape as well as additional genetic resources for developing elite rice varieties.

QTL sequencing strategy to map genomic regions associated with resistance to ascochyta blight in chickpea.

SummaryWhole‐genome sequencing‐based bulked segregant analysis (BSA) for mapping quantitative trait loci (QTL) provides an efficient alternative approach to conventional QTL analysis as it significantly reduces the scale and cost of analysis with comparable power to QTL detection using full mapping population. We tested the application of next‐generation sequencing (NGS)‐based BSA approach for mapping QTLs for ascochyta blight resistance in chickpea using two recombinant inbred line populations CPR‐01 and CPR‐02. Eleven QTLs in CPR‐01 and six QTLs in CPR‐02 populations were mapped on chromosomes Ca1, Ca2, Ca4, Ca6 and Ca7. The QTLs identified in CPR‐01 using conventional biparental mapping approach were used to compare the efficiency of NGS‐based BSA in detecting QTLs for ascochyta blight resistance. The QTLs on chromosomes Ca1, Ca4, Ca6 and Ca7 overlapped with the QTLs previously detected in CPR‐01 using conventional QTL mapping method. The QTLs on chromosome Ca4 were detected in both populations and overlapped with the previously reported QTLs indicating conserved region for ascochyta blight resistance across different chickpea genotypes. Six candidate genes in the QTL regions identified using NGS‐based BSA on chromosomes Ca2 and Ca4 were validated for their association with ascochyta blight resistance in the CPR‐02 population. This study demonstrated the efficiency of NGS‐based BSA as a rapid and cost‐effective method to identify QTLs associated with ascochyta blight in chickpea.

QTL-Seq Identified a Major QTL for Grain Length and Weight in Rice Using Near Isogenic F2 Population

Abstract: Mapping and isolation of quantitative trait loci (QTLs) or genes controlling grain size or weight is very important to uncover the molecular mechanisms of seed development and crop breeding. To identify the QTLs controlling grain size and weight, we developed a near isogenic line F2 (NIL-F2) population, which was derived from a residual heterozygous plant in an F7 generation of recombinant inbred line (RIL). With the completion of more than 30y whole genome re-sequencing of the parents, two DNA bulks for large and small grains, a total of 58.94 Gb clean nucleotide data were generated. A total of 455 262 single nucleotide polymorphisms (SNPs) between the parents were identified to perform bulked QTL-seq. A candidate genomic region containing SNPs strongly associated with grain length and weight was identified from 15 to 20 Mb on chromosome 5. We designated the major QTL in the candidate region as qTGW5.3. Then, qTGW5.3 was further validated with PCR-based conventional QTL mapping method through developing simple sequence repeat and Insertion/Deletion markers in the F2 population. Furthermore, recombinants and the progeny tests delimited the candidate region of qTGW5.3 to 1.13 Mb, flanked by HX5009 (15.15 Mb) and HX5003 (16.28 Mb). A set of NILs, selected from the F2 population, was developed to evaluate the genetic effect of qTGW5.3. Significant QTL effects were detected on grain length, grain width and 1000-grain weight of H12-29 allele with 1.14 mm, -0.11 mm and 3.11 g, which explained 99.64%, 95.51% and 97.32% of the phenotypic variations, respectively.

Quantitative trait loci for the diurnal flag leaf starch content during the early grain-filling stage in wheat (Triticum aestivum L.)

Starch is a product of photosynthetic activities in leaves. Wheat yields largely depend on photosynthetic carbon fixation and carbohydrate metabolism in flag leaves. The mapping of quantitative trait loci (QTLs) associated with flag leaf starch content (FLSC) in wheat (Triticum aestivum L.) was completed using unconditional and conditional QTL analyses. The FLSC of this population during the early grain-filling stage was measured at six stages in six environments. Combining unconditional and conditional QTL mapping methods, eight unconditional QTLs and nine conditional QTLs were detected, with five QTLs identified as unconditional and conditional QTLs. Four unconditional QTLs (i.e. qFLS-1B, qFLS-1D-1, qFLS-4A, and qFLS-7D-1) and one conditional QTL (i.e. qFLS-3A-1) were identified in two of six environments. Two QTLs (qFLS-1D-2 and qFLS-7D-1), which significantly affected the FLSC, were identified using the unconditional QTL mapping method, while three QTLs (i.e. qFLS-1A, qFLS-3A-1, and qFLS-7D-1) were detected using the conditional QTL mapping method. Our findings provide new insights into the genetic mechanism and regulatory network underlying the diurnal FLSC in wheat.

Identification of genomic regions associated with low phosphorus tolerance in japonica rice (Oryza sativa L.) by QTL-Seq

ABSTRACTPhosphorus (P) is a major nutrient supporting rice productivity. Improving low-P tolerance of rice is expected to reduce dependence on P fertilizer, thereby reducing rice production costs and environmental impacts. This report describes the mapping of quantitative trait loci (QTL) associated with P deficiency tolerance in japonica rice. An F5 population derived from a cross of the low-P tolerant cultivar Akamai (Yamagata) and the sensitive cultivar Koshihikari was evaluated for shoot growth under low-P conditions. Then single nucleotide polymorphism (SNP) profiles of the low-P tolerant and sensitive bulks were compared on a genome-wide scale by QTL-Seq, a rapid QTL mapping method using next-generation sequencing technology. Results show a major QTL associated with low-P tolerance located on the long arm of chromosome 12. It has been named QTL for low-P tolerance 1 or qLPT1. SNPs were detected in 45 genes of qLPT1 region and the 5 genes were harboring synonymous SNPs, although none of them had been reported as involved in low-P tolerance. This result implies that the novel gene responsible for low-P tolerance exists in qLPT1. This study will contribute to the elucidation of mechanisms underlying low-P tolerance of Akamai and will facilitate the breeding of rice with low-P tolerance.

Genetic dissection of flour whiteness by unconditional and conditional quantitative trait locus mapping in wheat

SUMMARYFlour whiteness (FW) is an important factor in assessing flour quality and determining the end product quality. It is an integrated sensory indicator reflecting flour colour and is negatively correlated with protein content. In order to dissect the genetic relationship between FW and its five related traits at the quantitative trait locus (QTL)/gene level, a recombinant inbred line population was evaluated under three environments. Quantitative trait loci for FW were analysed by unconditional and conditional QTL mapping. Four unconditional additive QTLs and 16 conditional additive QTLs were detected across the three environments. Of these QTLs, only one major additive QTL (Qfw1D1-1) was consistently identified using both unconditional and conditional QTL analysis. This QTL was independent of flour colour a* (a function of red-green with a positive a* for redness and negative for greenness) and b* (a green-blue value with positive value for yellowness and negative for blueness) and was only slightly affected by flour protein content. A minor additive QTL (Qfw4A-4) was also detected using these two QTL mapping methods, being independent of flour colour a* and b*. Five unconditional and ten conditional epistatic minor QTLs were detected, from which only one pair (Qfw3A-10/Qfw6B-6) was identified by both unconditional and conditional QTL mapping, also independent of flour colour a* and b*. The major QTL (Qfw1D1-1) identified in the current study for the first time can be used for improving wheat FW in marker-assisted breeding.

An ultra-high-density bin map facilitates high-throughput QTL mapping of horticultural traits in pepper (Capsicum annuum).

Most agricultural traits are controlled by quantitative trait loci (QTLs); however, there are few studies on QTL mapping of horticultural traits in pepper (Capsicum spp.) due to the lack of high-density molecular maps and the sequence information. In this study, an ultra-high-density map and 120 recombinant inbred lines (RILs) derived from a cross between C. annuum ‘Perennial’ and C. annuum ‘Dempsey’ were used for QTL mapping of horticultural traits. Parental lines and RILs were resequenced at 18× and 1× coverage, respectively. Using a sliding window approach, an ultra-high-density bin map containing 2,578 bins was constructed. The total map length of the map was 1,372 cM, and the average interval between bins was 0.53 cM. A total of 86 significant QTLs controlling 17 horticultural traits were detected. Among these, 32 QTLs controlling 13 traits were major QTLs. Our research shows that the construction of bin maps using low-coverage sequence is a powerful method for QTL mapping, and that the short intervals between bins are helpful for fine-mapping of QTLs. Furthermore, bin maps can be used to improve the quality of reference genomes by elucidating the genetic order of unordered regions and anchoring unassigned scaffolds to linkage groups.