Accuracy Of Quantitative Trait Loci Mapping Research Articles

Mapping QTLs for protein and oil content in soybean by removing the influence of related traits in a four-way recombinant inbred line population

AbstractProtein content (PC) and oil content (OC) are important breeding traits of soybean [Glycine max(L.) Merr.]. Quantitative trait locus (QTL) mapping for PC and OC is important for molecular breeding in soybean; however, the negative correlation between PC and OC influences the accuracy of QTL mapping. In the current study, a four-way recombinant inbred lines (FW-RILs) population comprising 160 lines derived from the cross (Kenfeng14 × Kenfeng15) × (Heinong48 × Kenfeng19) was planted in eight different environments and PC and OC measured. Conditional and unconditional QTL analyses were carried out by interval mapping (IM) and inclusive complete IM based on linkage maps of 275 simple sequences repeat markers in a FW-RILs population. This analysis revealed 59 unconditional QTLs and 52 conditional QTLs among the FW-RILs. An analysis of additive effects indicated that the effects of 13 protein QTLs were not related to OC, whereas OC affected the expression of 13 and eight QTLs either partially or completely, respectively. Eight QTLs affecting OC were not influenced by PC, whereas six and 26 QTLs were partially and fully affected by PC, respectively. Among the QTLs detected in the current study, two protein QTLs and five oil QTLs had not been previously reported. These findings will facilitate marker-assisted selection and molecular breeding of soybean.

Factors influencing QTL mapping accuracy under complicated genetic models by computer simulation.

The accuracy of quantitative trait loci (QTLs) identified using different sample sizes and marker densities was evaluated in different genetic models. Model I assumed one additive QTL; Model II assumed three additive QTLs plus one pair of epistatic QTLs; and Model III assumed two additive QTLs with opposite genetic effects plus two pairs of epistatic QTLs. Recombinant inbred lines (RILs) (50-1500 samples) were simulated according to the Models to study the influence of different sample sizes under different genetic models on QTL mapping accuracy. RILs with 10-100 target chromosome markers were simulated according to Models I and II to evaluate the influence of marker density on QTL mapping accuracy. Different marker densities did not significantly influence accurate estimation of genetic effects with simple additive models, but influenced QTL mapping accuracy in the additive and epistatic models. The optimum marker density was approximately 20 markers when the recombination fraction between two adjacent markers was 0.056 in the additive and epistatic models. A sample size of 150 was sufficient for detecting simple additive QTLs. Thus, a sample size of approximately 450 is needed to detect QTLs with additive and epistatic models. Sample size must be approximately 750 to detect QTLs with additive, epistatic, and combined effects between QTLs. The sample size should be increased to >750 if the genetic models of the data set become more complicated than Model III. Our results provide a theoretical basis for marker-assisted selection breeding and molecular design breeding.

Influence of various quantitative trait loci (QTL) mapping methods on the mapping accuracy under varying heritability levels.

The study of quantitative trait effects is of great significance for molecular marker-assisted breeding. The accuracy of quantitative trait loci (QTL) mapping is the key factor affecting marker-assisted breeding, and is extremely significant. The effect of different heritability rates (10, 30, 50, 70, and 90%) on the accuracy of QTL mapping of five recombinant inbred lines (RILs) were analyzed via computer simulation. RILs display additive and epistatic genetic effects. The QTLs were analyzed using four different mapping procedures: multiple QTL model (MQM), composite interval mapping (CIM), multiple interval mapping (MIMR), and inclusive composite interval mapping (ICIM). The results revealed an increase in the QTL mapping accuracy and QTL detection power, and a decrease in the QTL interval range with the increase in heritability; conversely, an irregular number of false positive QTLs were generated. CIM and MQM only screen the additive and dominant effects; MIMR and ICIM screen the additive, dominant, and epistatic effects. The highest QTL detection power obtained using MQM and CIM was only 75%, while MIMR and ICIM showed a detection power of 100%. At heritability rates of more than 50 and less than 10%, the detection powers of the MIMR and ICIM procedures were >95 and <35%, respectively. QTL mapping has no significance at heritability rates <10%. The results of this study suggest that QTL mapping has significance at a heritability rate >30% (at least >10%) for practical marker-assisted breeding.

Construction of a high-density genetic map based on large-scale markers developed by specific length amplified fragment sequencing (SLAF-seq) and its application to QTL analysis for isoflavone content in Glycine max.

BackgroundQuantitative trait locus (QTL) mapping is an efficient approach to discover the genetic architecture underlying complex quantitative traits. However, the low density of molecular markers in genetic maps has limited the efficiency and accuracy of QTL mapping. In this study, specific length amplified fragment sequencing (SLAF-seq), a new high-throughput strategy for large-scale SNP discovery and genotyping based on next generation sequencing (NGS), was employed to construct a high-density soybean genetic map using recombinant inbred lines (RILs, Luheidou2 × Nanhuizao, F5:8). With this map, the consistent QTLs for isoflavone content across various environments were identified.ResultsIn total, 23 Gb of data containing 87,604,858 pair-end reads were obtained. The average coverage for each SLAF marker was 11.20-fold for the female parent, 12.51-fold for the male parent, and an average of 3.98-fold for individual RILs. Among the 116,216 high-quality SLAFs obtained, 9,948 were polymorphic. The final map consisted of 5,785 SLAFs on 20 linkage groups (LGs) and spanned 2,255.18 cM in genome size with an average distance of 0.43 cM between adjacent markers. Comparative genomic analysis revealed a relatively high collinearity of 20 LGs with the soybean reference genome. Based on this map, 41 QTLs were identified that contributed to the isoflavone content. The high efficiency and accuracy of this map were evidenced by the discovery of genes encoding isoflavone biosynthetic enzymes within these loci. Moreover, 11 of these 41 QTLs (including six novel loci) were associated with isoflavone content across multiple environments. One of them, qIF20-2, contributed to a majority of isoflavone components across various environments and explained a high amount of phenotypic variance (8.7% - 35.3%). This represents a novel major QTL underlying isoflavone content across various environments in soybean.ConclusionsHerein, we reported a high-density genetic map for soybean. This map exhibited high resolution and accuracy. It will facilitate the identification of genes and QTLs underlying essential agronomic traits in soybean. The novel major QTL for isoflavone content is useful not only for further study on the genetic basis of isoflavone accumulation, but also for marker-assisted selection (MAS) in soybean breeding in the future.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-1086) contains supplementary material, which is available to authorized users.

The way to a more precise sheath blight resistance QTL in rice

Rice sheath blight (ShB), caused by Rhizoctonia solani, leads to severe yield losses in many rice production areas worldwide. Resistance to rice ShB is a typical quantitative trait controlled by quantitative trait loci (QTLs). To identify the QTLs responsible for ShB resistance, phenotypes for ShB resistance have been surveyed in different individuals/lines in various mapping populations. The 0–9 rating system, based on the percentage of plant height (PH) above the water line with ShB symptoms, is extensively used in the evaluation of ShB in rice communities. By reviewing previous ShB-QTL-mapping studies, we found that the ShB disease score attained using the 0–9 system was inevitably affected by PH and resulted in the colocalization of QTLs for PH and ShB resistance, i.e., some of the PH-QTLs were mistaken as “ShB-QTLs” and had pleiotropic effects. These colocalizing “ShB-QTLs” are irrelevant to physiological ShB resistance and should not be targeted for utilization. We analyzed the ways in which PH affects ShB resistance through the 0–9 rating system and suggest solutions for improving the accuracy of QTL mapping for rice ShB resistance. We also developed the first physical map of the ShB-QTLs and PH-QTLs detected since 1995. This map will be useful in the marker-assisted selection of ShB-resistant QTLs. Under current circumstances, we believe that conventional breeding remains an effective approach for developing ShB-resistant varieties.

Pinpointing genes underlying the quantitative trait loci for root-knot nematode resistance in palaeopolyploid soybean by whole genome resequencing

The objective of this study was to use next-generation sequencing technologies to dissect quantitative trait loci (QTL) for southern root-knot nematode (RKN) resistance into individual genes in soybean. Two hundred forty-six recombinant inbred lines (RIL) derived from a cross between Magellan (susceptible) and PI 438489B (resistant) were evaluated for RKN resistance in a greenhouse and sequenced at an average of 0.19× depth. A sequence analysis pipeline was developed to identify and validate single-nucleotide polymorphisms (SNPs), infer the parental source of each SNP allele, and genotype the RIL population. Based on 109,273 phased SNPs, recombination events in RILs were identified, and a total of 3,509 bins and 3,489 recombination intervals were defined. About 50.8% of bins contain 1 to 10 genes. A linkage map was subsequently constructed by using bins as molecular markers. Three QTL for RKN resistance were identified. Of these, one major QTL was mapped to bin 10 of chromosome 10, which is 29.7 kb in size and harbors three true genes and two pseudogenes. Based on sequence variations and gene-expression analysis, the candidate genes underlying the major QTL for RKN resistance were pinpointed. They are Glyma10g02150 and Glyma10g02160, encoding a pectin methylesterase inhibitor and a pectin methylesterase inhibitor -pectin methylesterase, respectively. This QTL mapping approach not only combines SNP discovery, SNP validation, and genotyping, but also solves the issues caused by genome duplication and repetitive sequences. Hence, it can be widely used in crops with a reference genome to enhance QTL mapping accuracy.

Fine mapping and single nucleotide polymorphism effects estimation on pig chromosomes 1, 4, 7, 8, 17 and X

Fine mapping of quantitative trait loci (QTL) from previous linkage studies was performed on pig chromosomes 1, 4, 7, 8, 17, and X which were known to harbor QTL. Traits were divided into: growth performance, carcass, internal organs, cut yields, and meat quality. Fifty families were used of a F2 population produced by crossing local Brazilian Piau boars with commercial sows. The linkage map consisted of 237 SNP and 37 microsatellite markers covering 866 centimorgans. QTL were identified by regression interval mapping using GridQTL. Individual marker effects were estimated by Bayesian LASSO regression using R. In total, 32 QTL affecting the evaluated traits were detected along the chromosomes studied. Seven of the QTL were known from previous studies using our F2 population, and 25 novel QTL resulted from the increased marker coverage. Six of the seven QTL that were significant at the 5% genome-wide level had SNPs within their confidence interval whose effects were among the 5% largest effects. The combined use of microsatellites along with SNP markers increased the saturation of the genome map and led to smaller confidence intervals of the QTL. The results showed that the tested models yield similar improvements in QTL mapping accuracy.

QTL linkage analysis of connected populations using ancestral marker and pedigree information

The common assumption in quantitative trait locus (QTL) linkage mapping studies that parents of multiple connected populations are unrelated is unrealistic for many plant breeding programs. We remove this assumption and propose a Bayesian approach that clusters the alleles of the parents of the current mapping populations from locus-specific identity by descent (IBD) matrices that capture ancestral marker and pedigree information. Moreover, we demonstrate how the parental IBD data can be incorporated into a QTL linkage analysis framework by using two approaches: a Threshold IBD model (TIBD) and a Latent Ancestral Allele Model (LAAM). The TIBD and LAAM models are empirically tested via numerical simulation based on the structure of a commercial maize breeding program. The simulations included a pilot dataset with closely linked QTL on a single linkage group and 100 replicated datasets with five linkage groups harboring four unlinked QTL. The simulation results show that including parental IBD data (similarly for TIBD and LAAM) significantly improves the power and particularly accuracy of QTL mapping, e.g., position, effect size and individuals’ genotype probability without significantly increasing computational demand.

Assessment of a new strategy for selective phenotyping applied to complex traits in &amp;lt;i&amp;gt;Brassica napus&amp;lt;/i&amp;gt;

The accurate mapping of quantitative trait loci (QTL) depends notably on the number of recombination events occurring in the segregating population. The cost of phenotyping often limits the sample size used in QTL mapping. To get round this problem, we assessed a selective phenotyping method, called qtlRec sampling. In order to improve the accuracy of QTL mapping, a subset of individuals was selected to maximize the number of recombination events at putative QTL positions; the usefulness of this subset was compared to a selected sample built to maximize the recombination rate over the whole genome. We assessed this method on the quantitative oil content trait in Brassica napus. We showed that the qtlRec strategy could allow increasing accuracy (both support interval and position) of QTL location while it maintained a similar power of detection. We then applied this approach to the B. napus—Leptosphaeria maculans pathosystem for which resistance QTL with minor effect were previously identified. This allowed the validation of the QTL in six genomic regions. The qtlRec method is an attractive strategy for validating QTL in multiple year and/or location trials for a trait which requires costly and time-consuming phenotyping.

Marker-assisted selection for quantitative traits

Although thousands of scientific articles have been published on the subject of marker-assisted selection (MAS) and quantitative trait loci (QTL), the application of MAS for QTL in plant breeding has been restricted. Among the main causes for this limited use are the low accuracy of QTL mapping and the high costs of genotyping thousands of plants with tens or hundreds of molecular markers in routine breeding programs. Recently, new large-scale genotyping technologies have resulted in a cost reduction. Nevertheless, the MAS for QTL has so far been limited to selection programs using several generations per year, where phenotypic selection cannot be performed in all generations, mainly in recurrent selection programs. Methods of MAS for QTL in breeding programs using self-pollination have been developed.

Multiple-Marker Mapping for Selective DNA Pooling Within Large Families

Selective DNA pooling is a very powerful method for quantitative trait loci (QTL) mapping. It considerably reduces genotyping costs while maintaining high statistical power. Applied to a daughter design, milk samples of offspring with extreme phenotypic values for a trait of interest are assigned to high and low groups, respectively, and within each group the pooled DNA is used for densitometric estimation of allele frequencies in the 2 groups. A single-marker test for linkage between marker and QTL considers marker allele frequency differences between high and low groups. Single-marker across-sire test statistics are strongly affected by the number of sires that are heterozygous for a given marker and the QTL status (homozygous or heterozygous) of these sires, which decreases the accuracy of QTL mapping. Here we propose a simple method to deal with this problem by taking information from multiple linked markers into account. In particular, given the single-marker test statistics, a multiple-marker method was developed to predict test statistics for markers for which a sire was homozygous (or at any other location on the chromosome). Power and map resolution of the proposed method were assessed by simulation, and we show that for the same data set, multiple-marker mapping performed better than the commonly used single-marker analyses.

Approximating Identity-by-Descent Matrices Using Multiple Haplotype Configurations on Pedigrees

Identity-by-descent (IBD) matrix calculation is an important step in quantitative trait loci (QTL) analysis using variance component models. To calculate IBD matrices efficiently for large pedigrees with large numbers of loci, an approximation method based on the reconstruction of haplotype configurations for the pedigrees is proposed. The method uses a subset of haplotype configurations with high likelihoods identified by a haplotyping method. The new method is compared with a Markov chain Monte Carlo (MCMC) method (Loki) in terms of QTL mapping performance on simulated pedigrees. Both methods yield almost identical results for the estimation of QTL positions and variance parameters, while the new method is much more computationally efficient than the MCMC approach for large pedigrees and large numbers of loci. The proposed method is also compared with an exact method (Merlin) in small simulated pedigrees, where both methods produce nearly identical estimates of position-specific kinship coefficients. The new method can be used for fine mapping with joint linkage disequilibrium and linkage analysis, which improves the power and accuracy of QTL mapping.