The effect of an imprecise map on interval mapping QTLs

The statistical analysis of quantitative trait locus (QTL) experiments relies on the use of a linkage map of the markers genotyped. Such a map is, at best, a good estimate of the true map. Resources might be diverted into developing better marker maps or improved maps become available after the analysis, raising concerns over the original analysis. It is therefore important to understand the sensitivity of QTL analysis to map inaccuracy. We have used simulation methods to investigate the consequences of an incorrect map on the results of a QTL analysis using interval mapping. Backcross data sets were generated with a particular map and then analysed with both the correct map and incorrect maps. If the incorrect maps maintained the true linkage groups (i.e. no markers were incorrectly assigned to another linkage group), the accuracy of the map had little or no impact on the ability to detect QTLs, the true significance levels of the tests or the relative placement of QTLs. When a marker was incorrectly placed on another linkage group, there was a small increase in the level of the test. After adjusting for this increase, there was a decrease in power to detect a QTL near the misplaced marker. This decrease was of a similar magnitude to that found when using a single-marker analysis compared with interval mapping. These results mean that QTL analyses can proceed without the need for very accurate marker maps, and that estimated QTL positions can be translated onto updated maps without the need for reanalysis.     

A modified algorithm for the improvement of composite interval mapping

Composite interval mapping (CIM) is the most commonly used method for mapping quantitative trait loci (QTL) with populations derived from biparental crosses. However, the algorithm implemented in the popular QTL Cartographer software may not completely ensure all its advantageous properties. In addition, different background marker selection methods may give very different mapping results, and the nature of the preferred method is not clear. A modified algorithm called inclusive composite interval mapping (ICIM) is proposed in this article. In ICIM, marker selection is conducted only once through stepwise regression by considering all marker information simultaneously, and the phenotypic values are then adjusted by all markers retained in the regression equation except the two markers flanking the current mapping interval. The adjusted phenotypic values are finally used in interval mapping (IM). The modified algorithm has a simpler form than that used in CIM, but a faster convergence speed. ICIM retains all advantages of CIM over IM and avoids the possible increase of sampling variance and the complicated background marker selection process in CIM. Extensive simulations using two genomes and various genetic models indicated that ICIM has increased detection power, a reduced false detection rate, and less biased estimates of QTL effects.     

On the efficiency of composite interval mapping in an outbred population

Composite interval mapping (CIM) has been successfully applied to the detection of QTL in experimental animals and plants. However, practical analyses based on CIM have been reported mainly for populations derived from cross between inbred lines. There are few studies on QTL analyses with CIM in outbred populations. To evaluate the applicability of CIM to outbred populations is prerequisite for the fine mapping of QTL in industrial animals such as pig and chicken. Some markers are usually not fully informative in outbred populations. In application of CIM to outbred populations, the influence of inclusion of such uninformative markers used as covariates on the efficiency of CIM should be investigated. In this paper a least-squares method for CIM was formalized in an F(2) population derived by crossing two outbred lines. The efficiencies of CIM were evaluated for outbred populations in comparison with simple interval mapping (SIM) for several cases of marker informativeness using simulations. By incorporating markers linked to a tested position as well as those unlinked, CIM showed a higher efficiency to separate two linked QTL over SIM. The efficiency of dissection was enhanced as the marker informativeness was increased. The power of CIM to detect an isolated QTL was improved by excluding markers linked to a tested position from covariates and higher than SIM regardless of marker informativeness. In conclusion, CIM is a useful procedure for the analysis of QTL in outbred populations even under low marker informativeness.     

Detection of additive and dominance effects of QTLs in interval mapping of F2 RFLP data

A model for interval mapping of quantitative trait loci (QTLs) in an F₂ population using genetic markers such as restriction fragment length polymorphisms (RFLPs) is proposed. Based on this model the log-likelihood ratio statistic is divided into two useful statistics which can be used to detect additive and dominance effects of QTLs, separately. The properties of the two statistics were investigated for the theoretical construction of critical regions to test the gene action of QTLs. The model was applied to 1000 simulated-sets of 200 F₂ individuals.     

Fine mapping of mouse QTLs for fatness using SNP data

Quantitative trait loci (QTLs), as determined in crossbred studies, are a valuable resource to identify genes responsible for the corresponding phenotypic variances. Due to their broad chromosomal extension of some dozens of megabases, further steps are necessary to bring the number of candidate genes that underlie the detected effects to a reasonable order of magnitude. We use a set of 13,370 SNPs to identify informative haplotype blocks in 22 mouse QTLs for fatness. About half of the genes in a typical QTL overlap with haplotype blocks, which are different for the two base mouse lines, and which, thus, qualify for further analysis. For these genes we collect four more pieces of evidence for association with fat accumulation, namely (1) homology to genes identified in a Caenorhabditis elegans knock-out experiment as fat decreasing or fat increasing, (2) the overexpression of the genes in mouse fat, liver, muscle, or hypothalamus tissues, (3) the occurrence of a gene in several independently found QTLs, and (4) the information provided by gene ontology, to achieve a ranked list of 131 candidate genes. Ten genes fulfill three or four of the above sketched criteria and are discussed briefly, 121 further genes fulfilling two criteria are provided as on-line material. Viewing the genomic region of fatness-related QTLs under several different aspects is appropriate to assess the many thousands of genes that reside in such QTLs and to produce lists of more robust candidate genes.     

Molecular mapping of QTLs for wheat flag leaf senescence under water-stress

A segregating population from the cross between drought sensitive (Variant-2) and drought tolerant (Cham-6) genotypes was made to identify molecular markers linked to wheat (Triticum aestivum L.) flag leaf senescence under water-stress. From 38 random amplified polymorphic DNA (RAPD) primers, 25 inter-simple sequence repeat (ISSR) primers and 46 simple sequence repeat (SRR) primers, tested for polymorphism among parental genotypes and F2 population. Quantitative trait locus (QTL) for flag leaf senescence was associated with 1 RAPD marker (Pr9), 4 ISSR markers (Pr8, AD5, AD2 and AD3), and 1 SSR marker (Xgwm382) and explained 44, 50, 35, 31, 22 and 73 % phenotypic variation, respectively. The genetic distance between flag leaf senescence gene and Pr9 was 10.0 cM (LOD score 22.9). The markers Pr8, AD5, AD2 and AD3 had genetic distances of 10.5, 14.6, 15.6 and 18.1 cM, respectively (LOD scores 22.6, 17.8, 17.5 and 14.6). The genetic distance between Xgwm382 was 3.9 cM (LOD score 33.8). Therefore, the RAPD, ISSR and SSR markers linked to the QTL for the drought-induced flag leaf senescence can be further used in breeding for drought tolerance in wheat.     

Genetic mapping of folate QTLs using a segregated population in maizeFA

As essential B vitamin for humans, folates accumulation in edible parts of crops, such as maize kernels, is of great importance for human health. But its breeding is always limited by the prohibitive cost of folate profiling. The molecular breeding is a more executable and efficient way for folate fortification, but is limited by the molecular knowledge of folate regulation. Here we report the genetic mapping of folate quantitative trait loci(QTLs) using a segregated population crossed by two maize lines, one high in folate(GEMS31) and the other low in folate(DAN3130). Two folate QTLs on chromosome 5 were obtained by the combination of F_2 whole-exome sequencing and F_3 kernel-folate profiling. These two QTLs had been confirmed by bulk segregant analysis using F_6 pooled DNA and F_7 kernel-folate profiling, and were overlapped with QTLs identified by another segregated population. These two QTLs contributed 41.6% of phenotypic variation of 5-formyltetrahydrofolate, the most abundant storage form among folate derivatives in dry maize grains, in the GEMS31 DAN3130 population.Their fine mapping and functional analysis will reveal details of folate metabolism, and provide a basis for marker-assisted breeding aimed at the enrichment of folates in maize kernels.     

A comparison of regression interval mapping and multiple interval mapping for linked QTL

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.     

A comparison of interval mapping procedures including the QTL-cluster mapping

Using the deterministic sampling, patterns of the log-likelihood surfaces expected in some interval mapping procedures for estimating the position of, and the effect for, QTL(s) were investigated for the situations where a single QTL or closely linked QTLs are contained in a chromosome segment bracketed with two markers. The mapping procedures compared were the conventional, likelihood-based interval mapping (IM), the regression interval mapping (RIM), and the QTL-cluster mapping (CM) in which the conditional probabilities of transmission of the whole segment marked by the flanking markers were taken into consideration. The half-sib design was used here, and several cases of the true genetic model were considered, differing in the number of QTLs contained in the marker interval, the linkage phase for the sire, and the magnitude of the QTL(s) effect. For the true genetic models where a single QTL or closely linked QTLs being in coupling phase are contained in the interval, with (R)IM, clear global maxima of the log-likelihood were observed within the range of the marker interval. It was shown that the estimates of the QTL(s) effect at the marked segment level are expected to be unbiased. On the other hand, in a setting where the linkage phase for the linked QTLs located in the interval was different from coupling and repulsion, there was found a ridge along the interval for the log-likelihood surface with (R)IM, indicating the dependency between the estimates of the position of, and the effect for, the putative QTL. For this case, it was found that the position of the putative QTL could be estimated as that of one of the flanking markers, and the estimate of the QTL effect be biased. In contrast, it was revealed that CM is expected to provide the unbiased estimate of the QTL(s)-effect at the segment level for any case of the true genetic models considered here. If the aim is for marker-assisted selection rather than mapping closely linked QTLs individually, then the CM approach is considered to be useful.     

Light-response quantitative trait loci identified with composite interval and eXtreme array mapping in Arabidopsis thaliana

Genetic analysis of natural variation in ecotypes of Arabidopsis thaliana can facilitate the discovery of new genes or of allelic variants of previously identified genes controlling physiological processes in plants. We mapped quantitative trait loci (QTL) for light response in recombinant inbred lines (RILs) derived from the Columbia and Kashmir accessions via two methods: composite interval mapping and eXtreme array mapping (XAM). After measuring seedling hypocotyl lengths in blue, red, far-red, and white light, and in darkness, eight QTL were identified by composite interval mapping and five localized near photoreceptor loci. Two QTL in blue light were associated with CRY1 and CRY2, two in red light were near PHYB and PHYC, and one in far-red light localized near PHYA. The RED2 and RED5 QTL were verified in segregating lines. XAM was tested for the identification of QTL in red light with pools of RILs selected for extreme phenotypes. Thousands of single feature polymorphisms detected by differential DNA hybridized to high-density oligo-nucleotide arrays were used to estimate allele frequency differences between the pools. The RED2 QTL was identified clearly; differences exceeded a threshold of significance determined by simulations. The sensitivities of XAM to population type and size and genetic models were also determined by simulation analysis.     

Genetic mapping of QTLs conditioning soybean sprout yield and quality

Soybean sprouts have been used as a food in the Orient since ancient times. In this study, 92 restriction fragment length polymorphism (RFLP) loci and two morphological markers (W1 and T) were used to identify quantitative trait loci (QTLs) associated with soybean sprout-related traits in 100 F₂-derived lines from the cross of ’Pureunkong’×’Jinpumkong 2’. The genetic map consisted of 76 loci which covered about 756 cM and converged into 20 linkage groups. Eighteen markers remained unlinked. Phenotypic data were collected in 1996 and 1997 for hypocotyl length, percentage of abnormal seedlings, and sprout yield 6 days after germination at 20°C. Hypocotyl length was determined as the average length from the point of initiation of the first secondary root to the point of attachment of the cotyledons. The number of decayed seeds and seedlings, plus the number of stunted seedlings (less than 2-cm growth), was recorded a s abnormal seedlings. Seed weight was determined based on the 50-seed sample. Sprout yield was recorded as the total fresh weight of soybean sprouts produced from the 50-seed sample divided by the dry weight of the 50-seed sample. Four QTLs were associated with sprout yield in the combined analysis across 2 years. For the QTL linked to L154 on the Linkage Group (LG) G the positive allele was derived from Pureunkong (R ² = 0.19), whereas at the other three QTLs (A089 on LG B1, A668n on LG K and B046 on LG L) the positive alleles were from Jinpumkong 2. QTLs conditioning seed weight were linked to markers A802n (LG B1), A069 (LG E), Cr321 (LG F) and A235 (LG G). At these four markers, the Jinpumkong allele increased seed weight. Markers K011n on LG B1, W1 on LG F and A757 on LG L were linked to QTLs conditioning hypocotyl length; and Bng119, K455n and K418n to QTLs conditioning the abnormal seedlings. The QTLs conditioning sprout yield were in the same genomic locations as the QTLs for seed weight identified in this population or from previously published research, indicating that QTLs for sprout yield are genetically linked to seed-weight QTLs or else that seed-weight QTLs pleiotropically condition sprout yield. These data demonstrate that effective marker-assisted selection may be feasible for enhancing sprout yield in a soybean. The transgressive segregation of sprout yield, as well as the existence of two QTLs conditioning greater than 10% of the phenotypic variation in sprout yields provides an opportunity to select for progeny lines with a greater sprout yield than currently preferred cultivars such as Pureunkong. Received: 23 August 2000 / Accepted: 23 January 2001     

Multipoint interval mapping of quantitative trait loci, using sib pairs.

The sib-pair interval-mapping procedure of Fulker and Cardon is extended to take account of all available marker information on a chromosome simultaneously. The method provides a computationally fast multipoint analysis of sib-pair data, using a modified Haseman-Elston approach. It gives results very similar to those of the earlier interval-mapping procedure when marker information is relatively uniform and a coarse map is used. However, there is a substantial improvement over the original method when markers differ in information content and/or when a dense map is employed. The method is illustrated by using simulated sib-pair data.     

Comparative mapping of QTLs determining the plant size of Brassica oleracea

Quantitative trait loci (QTLs) influencing the size of leaves and stems were detected by restriction fragment length polymorphisms (RFLP) in three Brassica oleracea F₂ populations derived from crosses of rapid-cycling Brassica to three B. oleracea varieties, Cantanese, Pusa Katki and Bugh Kana. Morphological traits, including lamina length, lamina width, petiole length, stem length, stem width and node number were evaluated. A total of 47 QTLs were detected based on a LOD threshold of 2.5. Through comparative mapping we inferred that the 47 QTLs might reflect variation in as few as 35 different genetic loci, and 28 ancestral genes. For the trait of lamina length, we identified QTLs corresponding to five ancestral genes, which mapped near the locations corresponding to five known Arabidopsis mutations, rev, axr1, axr3, axr4 and as2. For the trait of stem length, we identified QTLs corresponding to five ancestral genes, which mapped near the locations corresponding to nine known Arabidopsis mutations, dw3, dw6, acl5, dw7, ga4, ga1, dw1, axr1 and axr3. The possibility of using Arabidopsis/Brassica as a model to extrapolate genetic information into other crops was examined. Received: 30 October 2000 / Accepted: 24 November 2000     

Comparative mapping of QTLs for agronomic traits of rice across environments by using a doubled-haploid population

We report here the RFLP mapping of quantitative trait loci (QTLs) which affect some important agronomic traits in cultivated rice. An anther culture-derived doubled-haploid (DH) population was established from a cross between indica and japonica rice varieties. A molecular linkage map comprising 137 markers was constructed based on this population which covered the rice genome at intervals of 14.8 cM on average. The linkage map was used to locate QTLs for such important agronomic traits as heading date, plant height, number of spikelets per panicle, number of grains per panicle, 1 000-grain weight and the percentage of seed set, by interval mapping. Evidence of genotype-by-environment interaction was found by comparing QTL maps of the same population grown in three diverse environments. A total of 22 QTLs for six agronomic traits was detected which were significant in at least one environment, but only seven were significant in all three environments; seven were significant in two environments and eight could only be detected in a single environment. However, QTLs-by-environment interaction was trait dependent. QTLs for spikelets and grains per panicle were common across environments while traits like heading date and plant height were more sensitive to environment. Received: 22 February 1996 / Accepted: 10 May 1996     

Inclusive composite interval mapping (ICIM) for digenic epistasis of quantitative traits in biparental populations

It has long been recognized that epistasis or interactions between non-allelic genes plays an important role in the genetic control and evolution of quantitative traits. However, the detection of epistasis and estimation of epistatic effects are difficult due to the complexity of epistatic patterns, insufficient sample size of mapping populations and lack of efficient statistical methods. Under the assumption of additivity of QTL effects on the phenotype of a trait in interest, the additive effect of a QTL can be completely absorbed by the flanking marker variables, and the epistatic effect between two QTL can be completely absorbed by the four marker-pair multiplication variables between the two pairs of flanking markers. Based on this property, we proposed an inclusive composite interval mapping (ICIM) by simultaneously considering marker variables and marker-pair multiplications in a linear model. Stepwise regression was applied to identify the most significant markers and marker-pair multiplications. Then a two-dimensional scanning (or interval mapping) was conducted to identify QTL with significant digenic epistasis using adjusted phenotypic values based on the best multiple regression model. The adjusted values retain the information of QTL on the two current mapping intervals but exclude the influence of QTL on other intervals and chromosomes. Epistatic QTL can be identified by ICIM, no matter whether the two interacting QTL have any additive effects. Simulated populations and one barley doubled haploids (DH) population were used to demonstrate the efficiency of ICIM in mapping both additive QTL and digenic interactions.     

On the mapping of quantitative trait loci at marker and non-marker locations

Previous studies have noted that the estimated positions of a large proportion of mapped quantitative trait loci (QTLs) coincide with marker locations and have suggested that this indicates a bias in the mapping methodology. In this study we predict the expected proportion of QTLs with positions estimated to be at the location of a marker and further examine the problem using simulated data. The results show that the higher proportion of putative QTLs estimated to be at marker positions compared with non-marker positions is an expected consequence of the estimation methods. The study initially focused on a single interval with no QTLs and was extended to include multiple intervals and QTLs of large effect. Further, the study demonstrated that the larger proportion of estimated QTL positions at the location of markers was not unique to linear regression mapping. Maximum likelihood produced similar results, although the accumulation of positional estimates at outermost markers was reduced when regions outside the linkage group were also considered. The bias towards marker positions is greatest under the null hypothesis of no QTLs or when QTL effects are small. This study discusses the impact the findings could have on the calculation of thresholds and confidence intervals produced by bootstrap methods.     

Power of quantitative trait locus mapping for polygenic binary traits using generalized and regression interval mapping in multi-family half-sib designs

A generalized interval mapping (GIM) method to map quantitative trait loci (QTL) for binary polygenic traits in a multi-family half-sib design is developed based on threshold theory and implemented using a Newton-Raphson algorithm. Statistical power and bias of QTL mapping for binary traits by GIM is compared with linear regression interval mapping (RIM) using simulation. Data on 20 paternal half-sib families were simulated with two genetic markers that bracketed an additive QTL. Data simulated and analysed were: (1) data on the underlying normally distributed liability (NDL) scale, (2) binary data created by truncating NDL data based on three thresholds yielding data sets with three different incidences, and (3) NDL data with polygenic and QTL effects reduced by a proportion equal to the ratio of the heritabilities on the binary versus NDL scale (reduced-NDL). Binary data were simulated with and without systematic environmental (herd) effects in an unbalanced design. GIM and RIM gave similar power to detect the QTL and similar estimates of QTL location, effects and variances. Presence of fixed effects caused differences in bias between RIM and GIM, where GIM showed smaller bias which was affected less by incidence. The original NDL data had higher power and lower bias in QTL parameter estimates than binary and reduced-NDL data. RIM for reduced-NDL and binary data gave similar power and estimates of QTL parameters, indicating that the impact of the binary nature of data on QTL analysis is equivalent to its impact on heritability.     

Discovery of QTLs for water mining and water use efficiency traits in rice under water-limited condition through association mapping

Developing trait introgressed rice cultivars is essential to sustain yield under aerobic conditions. Here, we report DNA markers governing variability in root traits, water use efficiency (WUE) and other biometric traits like total leaf area by association mapping. A set of 173 diverse rice germplasm accessions were phenotyped for root traits in specially designed root structures and WUE using carbon isotope discrimination (Δ¹³C) during the monsoon season (July to October) of two consecutive years (2007 and 2008). The panel was genotyped using 291 SSR markers spanning the entire genome of rice. Root biomass varied between 1.8 and 16.3 g plant^?1 while root length between 22 and 78 cm representing significant genetic variability. Similarly, Δ¹³C varied from 18 to 23 ‰. The SSR markers showed extensive polymorphism with around 73 % of all the markers revealing polymorphism information content values more than 0.5. Model-based structure analysis using the squared-allele frequency correlations revealed six subgroups among the panel with an average LD decay of about 10–20 cM. The Benjamini–Hochberg analysis was carried out to compute the false discovery rate combined with the analysis of effective LD. A total of 82 markers were involved in 175 significant (corrected P values and Q values <0.05) marker–trait associations (MTAs) across experiment 1 and experiment 2 and for the pooled data. Out of these, 22 markers were found to be associated with more than one trait. Common markers with significant associations were discovered for root biomass, total leaf area and total biomass suggesting the interdependency of these traits. Finally, 12 markers showed significant and stable MTAs across the experiments for different traits. An in silico analysis indicated that 45 % of the MTAs overlapped with previously reported QTLs and can be used for QTL introgression through breeding.     

Molecular mapping of QTLs for fiber qualities in three diverse lines in Upland cotton using SSR markers

The improvement of cotton fiber quality is extremely important because of changes in spinning technology. The identification of the stable QTLs affecting fiber traits across different generations will be greatly helpful to be used effectively in molecular marker-assisted selection to improve fiber quality of cotton cultivars in the future. Using three elite fiber lines of Upland cotton (Gossypium hirsutum L.) as parents, three linkage maps were constructed to tag QTLs for fiber qualities using SSR markers. There were 39 QTLs, 17 significant QTLs, LOD 3.0 and 22 suggestive QTLs, 3.0 > LOD 2.0, detected by composite interval mapping for fiber traits, in which 11 QTLs were for fiber length, 10 for fiber strength, 9 for micronaire and 9 for fiber elongation. Out of 17 significant QTLs, 5 QTLs with high logarithm of odds (LOD) score value and stable effect could be found in both F₂ and F_2:3 segregating populations, showing a great potential for molecular-assisted selection in improving fiber quality. At least three common QTLs could be identified in two populations. These common QTLs detected in different populations suggested that there existed elite fiber genes and possibly of the same origin. In addition, we found three pairs of putative homoeologous QTLs, qFL-7-1c and qFL-16-1c, qFS-D03-1a, qFS-A02-1b and qFS-A02-1c, and qFE–D03-1a and qFE-A02-1c. Our results provided a better understanding of the genetic factors of fiber traits in AD tetraploid cottons.