β-composite Interval Mapping for robust QTL analysis.

Regression interval mapping and multiple interval mapping are compared with regard to mapping linked quantitative trait loci (QTL) in inbred-line cross experiments. For that purpose, a simulation study was performed using genetic models with two linked QTL. Data were simulated for F(2) populations of different sizes and with all QTL and marker alleles fixed for alternative alleles in the parental lines. The criteria for comparison are power of QTL identification and the accuracy of the QTL position and effect estimates. Further, the estimates of the relative QTL variance are assessed. There are distinct differences in the QTL position estimates between the two methods. Multiple interval mapping tends to be more powerful as compared to regression interval mapping. Multiple interval mapping further leads to more accurate QTL position and QTL effect estimates. The superiority increased with wider marker intervals and larger population sizes. If QTL are in repulsion, the differences between the two methods are very pronounced. For both methods, the reduction of the marker interval size from 10 to 5 cM increases power and greatly improves QTL parameter estimates. This contrasts with findings in the literature for single QTL scenarios, where a marker density of 10 cM is generally considered as sufficient. The use of standard (asymptotic) statistical theory for the computation of the standard errors of the QTL position and effect estimates proves to give much too optimistic standard errors for regression interval mapping as well as for multiple interval mapping.

Composite interval mapping (CIM) is the most widely-used method in linkage analysis. Its main feature is the ability to control genomic background effects via inclusion of co-factors in its genetic model. However, the result often depends on how the co-factors are selected, especially for small-effect and linked quantitative trait loci (QTL). To address this issue, here we proposed a new method under the framework of genome-wide association studies (GWAS). First, a single-locus random-SNP-effect mixed linear model method for GWAS was used to scan each putative QTL on the genome in backcross or doubled haploid populations. Here, controlling background via selecting markers in the CIM was replaced by estimating polygenic variance. Then, all the peaks in the negative logarithm P-value curve were selected as the positions of multiple putative QTL to be included in a multi-locus genetic model, and true QTL were automatically identified by empirical Bayes. This called genome-wide CIM (GCIM). A series of simulated and real datasets was used to validate the new method. As a result, the new method had higher power in QTL detection, greater accuracy in QTL effect estimation, and stronger robustness under various backgrounds as compared with the CIM and empirical Bayes methods.

Quantitative trait loci (QTLs) for grain quality, yield components and other traits were investigated in two Sorghum caudatum×guinea recombinant inbred line (RIL) populations. A total of 16 traits were evaluated (plant height, panicle length, panicle compactness, number of kernels/panicle, thousand-kernel weight, kernel weight/panicle, threshing percentage, dehulling yield, kernel flouriness, kernel friability, kernel hardness, amylose content, protein content, lipid content, germination rate and molds during germination and after harvest) and related to two 113- and 100-point base genetic maps using simple (SIM) and composite (CIM) interval mapping. The number, effects and relative position of QTLs detected in both populations were generally in agreement with the distributions, heritabilities and correlations among traits. Several chromosomal segments markedly affected multiple traits and were suspected of harbouring major genes. The positions of these QTLs are discussed in relation to previously reported studies on sorghum and other grasses. Many QTLs, depending on their relative effects and position, could be used as targets for marker-assisted selection and provide an opportunity for accelerating breeding programmes.

Adequate separation of effects of possible multiple linked quantitative trait loci (QTLs) on mapping QTLs is the key to increasing the precision of QTL mapping. A new method of QTL mapping is proposed and analyzed in this paper by combining interval mapping with multiple regression. The basis of the proposed method is an interval test in which the test statistic on a marker interval is made to be unaffected by QTLs located outside a defined interval. This is achieved by fitting other genetic markers in the statistical model as a control when performing interval mapping. Compared with the current QTL mapping method (i.e., the interval mapping method which uses a pair or two pairs of markers for mapping QTLs), this method has several advantages. (1) By confining the test to one region at a time, it reduces a multiple dimensional search problem (for multiple QTLs) to a one dimensional search problem. (2) By conditioning linked markers in the test, the sensitivity of the test statistic to the position of individual QTLs is increased, and the precision of QTL mapping can be improved. (3) By selectively and simultaneously using other markers in the analysis, the efficiency of QTL mapping can be also improved. The behavior of the test statistic under the null hypothesis and appropriate critical value of the test statistic for an overall test in a genome are discussed and analyzed. A simulation study of QTL mapping is also presented which illustrates the utility, properties, advantages and disadvantages of the method.

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.

Quantitative trait loci (QTL) for growth traits and water-use efficiency have been identified in two water regimes (normal and drought-treated) and for a treatment index. A tetraploid hybrid F2 population originating from a cross between a Salix dasyclados clone (SW901290) and a Salix viminalis clone ('Jorunn') was used in the study. The growth response of each individual including both above and below ground dry-matter production (i.e. shoot length, shoot diameter, aboveground and root dry weight, internode length, root dry weight/total dry weight, relative growth rate and leaf nitrogen content) was analysed in a replicated block experiment with two water treatments. A composite interval mapping approach was used to estimate number of QTL, the magnitude of the QTL and their position on genetic linkage maps. QTL specific for each treatment and for the treatment index were found, but QTL common across the treatments and the treatment index were also detected. Each QTL explained from 8% to 29% of the phenotypic variation, depending on trait and treatment. Clusters of QTL for different traits were mapped close to each other at several linkage groups, indicating either a common genetic base or tightly linked QTL. Common QTL identified between treatments and treatment index in the complex trait dry weight can be useful tools in the breeding and selection for drought stress tolerance in Salix.

Large fruit size is a critical trait for any new sweet cherry (Prunus avium L.) cultivar, as it is directly related to grower profitability. Therefore, determining the genetic control of fruit size in relevant breeding germplasm is a high priority. The objectives of this study were (1) to determine the number and positions of quantitative trait loci (QTL) for sweet cherry fruit size utilizing data simultaneously from multiple families and their pedigreed ancestors, and (2) to estimate fruit size QTL genotype probabilities and genomic breeding values for the plant materials. The sweet cherry material used was a five-generation pedigree consisting of 23 founders and parents and 424 progeny individuals from four full-sib families, which were phenotyped for fruit size and genotyped with 78 RosCOS single nucleotide polymorphism and 86 simple sequence repeat markers. These data were analyzed by a Bayesian approach implemented in FlexQTL™ software. Six QTL were identified: three on linkage group (G) 2 with one each on groups 1, 3, and 6. Of these QTL, the second G2 QTL and the G6 QTL were previously discovered while other QTL were novel. The predicted QTL genotypes show that some QTL were segregating in all families while other QTL were segregating in a subset of the families. The progeny varied for breeding value, with some progeny having higher breeding values than their parents. The results illustrate the use of multiple pedigree-linked families for integrated QTL mapping in an outbred crop to discover novel QTL and predict QTL genotypes and breeding values.

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.

Soybeans [Glycine max (L.) Merr.] are susceptible to many diseases including fungal diseases such as soybean sudden death syndrome (SDS). Several studies reported SDS resistance quantitative trait loci (QTL) on the soybean genome using different recombinant inbred line (RIL) populations and low density genetic linkage maps. High density exclusively single nucleotide polymorphisms-based (SNP-based) maps were not yet reported in soybean. The objectives of this study were (1) to construct a high density SNP-based genetic linkage map of soybean using the ‘PI438489B’ by ‘Hamilton’ (PIxH, n=50) recombinant inbred line population, and (2) to map QTL for SDS resistance using this high-density reliable genetic SNP-based map. The PI438489B by Hamilton high-density SNP-based genetic map was a high density map composed of 31 LGs, 648 SNPs, and covered 1,524.7 cM with an average of 2.37 cM between two adjacent SNP markers. Fourteen significant QTL were identified for SDS resistance using interval mapping (IM) and composite interval mapping (CIM) with LOD scores that ranged between 2.6 and 5.0. Twelve QTL were identified for foliar disease severity (FDS) and three QTL for root rot severity (RRS) of which one QTL underlain both FDS and RRS. The fourteen QTL were mapped onto ten separate chromosomes of the soybean genome. Seven of the intervals encompassing the QTL had been identified previously (on LGs C1, C2, D1b, G, L, N and O) associated with resistance to SDS but seven were novel (LGs A2 (2), B1, C2, D1a, D1b and O). We constructed the first PI438489B by Hamilton exclusively SNP-Based map and identified fourteen QTL that underlie SDS resistance including both resistances to foliar and root rot symptoms caused by Fusarium virguliforme infection. The QTL discovered here for SDS resistance could be useful to include in breeding programs in developing soybean cultivars resistant to SDS.

ABSTRACTIn this study, 147 recombinant inbred soybean lines constructed from the parents Charleston and Dongnong594, were used to map quantitative trait loci (QTLs) for seed weight per plant in multiple years (2006 to 2010 and 2013). QTL mapping was done using a simple sequence repeat (SSR) map combined with specific-locus amplified fragment (SLAF) map and the composite interval mapping (CIM), multiple interval mapping (MIM) and inclusive complete interval mapping method (ICIM) algorithms. By combining QTLs with the QTL physical locations, two QTL intervals located in the D1a and C2 linkage groups were found in different years. KEGG and GO annotations of the 413 genes located within these QTL intervals were used to screen for candidate genes potentially involved in seed production. Based on WeGo online genetic classification, we ultimately selected four genes related to yield traits, Glyma.01G158700, Glyma.01G156800, Glyma.01G125400 and Glyma.01G147800, as candidate genes.

BackgroundIn a previous study, a quantitative trait locus (QTL) exhibiting large effects on both Instron shear force and taste panel tenderness was detected within the Illinois Meat Quality Pedigree (IMQP). This QTL mapped to the q arm of porcine chromosome 2 (SSC2q). Comparative analysis of SSC2q indicates that it is orthologous to a segment of human chromosome 5 (HSA5) containing a strong positional candidate gene, calpastatin (CAST). CAST polymorphisms have recently been shown to be associated with meat quality characteristics; however, the possible involvement of other genes and/or molecular variation in this region cannot be excluded, thus requiring fine-mapping of the QTL.ResultsRecent advances in porcine genome resources, including high-resolution radiation hybrid and bacterial artificial chromosome (BAC) physical maps, were utilized for development of novel informative markers. Marker density in the ~30-Mb region surrounding the most likely QTL position was increased by addition of eighteen new microsatellite markers, including nine publicly-available and nine novel markers. Two newly-developed markers were derived from a porcine BAC clone containing the CAST gene. Refinement of the QTL position was achieved through linkage and haplotype analyses. Within-family linkage analyses revealed at least two families segregating for a highly-significant QTL in strong positional agreement with CAST markers. A combined analysis of these two families yielded QTL intervals of 36 cM and 7 cM for Instron shear force and taste panel tenderness, respectively, while haplotype analyses suggested further refinement to a 1.8 cM interval containing CAST markers. The presence of additional tenderness QTL on SSC2q was also suggested.ConclusionThese results reinforce CAST as a strong positional candidate. Further analysis of CAST molecular variation within the IMQP F1 boars should enhance understanding of the molecular basis of pork tenderness, and thus allow for genetic improvement of pork products. Furthermore, additional resources have been generated for the targeted investigation of other putative QTL on SSC2q, which may lead to further advancements in pork quality.

In addition to locating chromosomal positions of quantitative trait loci (QTL), estimating the sizes of identified QTL is also an important component in QTL mapping. The size of a QTL is usually measured by the proportion of the phenotypic variance contributed by the QTL. However, the genetic variance may be overestimated in a small line crossing experiment. In this study, we investigate this bias and develop a simple method to correct the bias. The bias correction, however, requires the error of the estimated genetic effect, which is not trivial if the genetic effect is estimated using the Expectation and Maximization (EM) algorithm. Therefore, we also develop a simple method to estimate the standard error of the estimated genetic effect, which is subsequently used to correct the bias in the variance estimate.

Using molecular markers in soybean [Glycine max (L.) Merr.] has lead to the identification of major loci controlling quantitative and qualitative traits that include: disease resistance, insect resistance and tolerance to abiotic stresses. Yield has been considered as one of the most important quantitative traits in soybean breeding. Unfortunately, yield is a very complex trait and most yield quantitative trait loci (QTL) that have been identified have had only limited success for marker assisted selection (MAS). The objective of this study was to identify QTL associated with soybean seed yield in preliminary yield trials grown in different environments and to evaluate their effective use for MAS using a yield prediction model (YPM), which included epistasis. To achieve this objective, 875 F5:9 recombinant inbred lines (RIL) from a population developed from a cross between two prominent ancestors of the North American soybean (Essex and Williams 82) were used. The 875 RIL and check cultivars were divided into four groups based on maturity and each group was grown in Knoxville, TN and one other location that had an environment in which the maturity group (MG) was adapted to be grown. Each RIL was genotyped with >50,000 single nucleotide polymorphic markers (SNPs) of which 17,232 were polymorphic across the population. Yield QTL were detected using a single factor (SF) analysis of variance (ANOVA) and composite interval mapping (CIM). Based on CIM, 23 yield QTL were identified. Twenty-one additional QTL were detected using SF ANOVA. Individually, these QTL explained from 4.5% to 11.9% of the phenotypic variation for yield. QTL were identified on all 20 chromosomes and five of the 46 QTL have not been previously reported. This study provides new information concerning yield QTL in soybean and may offer important insights into MAS strategies for soybean.

Identification of salt-tolerance QTL in rice (Oryza sativa L.)

Functional mapping has emerged as a powerful tool for mapping quantitative trait loci (QTL) that control developmental patterns of complex dynamic traits. Original functional mapping has been constructed within the context of simple interval mapping, without consideration of separate multiple linked QTL for a dynamic trait. In this article, we present a statistical framework for mapping QTL that affect dynamic traits by capitalizing on the strengths of functional mapping and composite interval mapping. Within this so-called composite functional-mapping framework, functional mapping models the time-dependent genetic effects of a QTL tested within a marker interval using a biologically meaningful parametric function, whereas composite interval mapping models the time-dependent genetic effects of the markers outside the test interval to control the genome background using a flexible nonparametric approach based on Legendre polynomials. Such a semiparametric framework was formulated by a maximum-likelihood model and implemented with the EM algorithm, allowing for the estimation and the test of the mathematical parameters that define the QTL effects and the regression coefficients of the Legendre polynomials that describe the marker effects. Simulation studies were performed to investigate the statistical behavior of composite functional mapping and compare its advantage in separating multiple linked QTL as compared to functional mapping. We used the new mapping approach to analyze a genetic mapping example in rice, leading to the identification of multiple QTL, some of which are linked on the same chromosome, that control the developmental trajectory of leaf age.