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PubMed2002

Substitution mapping of Pup1: a major QTL increasing phosphorus uptake of rice from a phosphorus-deficient soil.

A major QTL for P uptake had previously been mapped to a 13-cM marker interval on the long arm of chromosome 12. To map that major QTL with higher precision and certainty, a secondary mapping population was developed by backcrossing a near-isogenic line containing the QTL from the donor parent to the recurrent parent of low P uptake. Two different mapping strategies have been followed in this study. A conventional QTL mapping approach was based on individual F(2) RFLP data and the phenotypic evaluation of family means in the F(3). The second strategy employed a substitution-mapping approach. Phenotypic and marker data were obtained for 160 F(3) individuals of six highly informative families that differed in the size of donor chromosomal segments in the region of the putative QTL. QTL mapping showed that close to 80% of the variation between families was due to a single QTL, hereafter referred to as Pup1 (Phosphorus uptake 1). Pup1 was placed in a 3-cM interval flanked by markers S14025 and S13126, which is within 1 cM of the position identified in the original QTL mapping experiment. Other chromosomal regions and epistatic effects were not significant. Substitution mapping revealed that Pup1 co-segregated with marker S13126 and that the flanking markers, S14025 and S13752, were outside the interval containing Pup1. The two mapping strategies therefore yielded almost identical results and, in combining the advantages of both, Pup1 could be mapped with high certainty. The QTL mapping appoach showed that the phenotypic variation between families was due to only one QTL without any additional epistacic interactions, whereas the advantage of substitution mapping was to place clearly defined borders around the QTL.

Journal Article↗

PubMed2002

High resolution mapping of quantitative trait loci by linkage disequilibrium analysis.

Two methods, linkage analysis and linkage disequilibrium (LD) mapping or association study, are usually utilised for mapping quantitative trait loci (QTL). Linkage mapping is appropriate for low resolution mapping to localise trait loci to broad chromosome regions within a few cM (<10 cM), and is based on family data. Linkage disequilibrium mapping, on the other hand, is useful in high resolution or fine mapping, and is based on both population and family data. Using only one marker, one may carry out single-point linkage analysis and linkage disequilibrium mapping. Using two or more markers, it is possible to flank the QTL by multipoint analysis. The development and thus availability of dense marker maps, such as single nucleotide polymorphisms (SNP) in human genome, presents a tremendous opportunity for multipoint fine mapping. In this article, we propose a regression approach of mapping QTL by linkage disequilibrium mapping based on population data. Assuming that two marker loci flank one quantitative trait locus, a two-point linear regression is proposed to analyse population data. We derive analytical formulas of parameter estimations, and non-centrality parameters of appropriate tests of genetic effects and linkage disequilibrium coefficients. The merit of the method is shown by the power calculation and comparison. The two-point regression model can capture much more linkage and linkage disequilibrium information than that derived when only one marker is used. For a complex disease with heritability h(2)> or =0.15, a study with sample size of 250 can provide high power for QTL detection under moderate linkage disequilibria.

Chromosome Mapping↗

PubMed2005

A QTL affecting daily feed intake maps to Chromosome 2 in pigs.

Our understanding of the molecular genetic basis of several key performance traits in pigs has been significantly advanced through the quantitative trait loci (QTL) mapping approach. However, in contrast to growth and fatness traits, the genetic basis of feed intake traits has rarely been investigated through QTL mapping. Since feed intake is an important component of efficient pig production, the identification of QTL affecting feed intake may lead to the identification of genetic markers that can be used in selection programs. In this study a QTL analysis for feed intake, feeding behavior, and growth traits was performed in an F2 population derived from a cross between Chinese Meishan and European Large White pigs. A QTL with a significant effect on daily feed intake (DFI) was identified on Sus scrofa Chromosome 2 (SSC2). A number of suggestive QTL with effects on daily gain, feed conversion, and feeding behavior traits were also located. The significant QTL lies close to a previously identified mutation in the insulin-like growth factor 2 gene (IGF2) that affects carcass composition traits, although the IGF2 mutation is not segregating in the populations analyzed in the current study. Therefore, a distinct causal variant may exist on the P arm of SSC2 with an effect on feed intake.

Animals↗

PubMed2002

An update of the Courtot x Chinese Spring intervarietal molecular marker linkage map for the QTL detection of agronomic traits in wheat.

We made an update of the intervarietal molecular marker linkage map of the wheat genome developed using a doubled-haploid (DH) population derived from the cross between the cultivars "Courtot" and "Chinese Spring". This map was constructed using 187 DH lines and 659 markers. The genome was well covered (more than 95%) except for chromosomes from homoeologous group 4 and chromosomes 5D and 7D, which had gaps slightly larger than 50 cM. A core-map based on a set of 200 anchor loci (one marker each 18.4 cM) was developed. The total length of this map was 3,685 cM which is similar to the size of the international reference map of the ITMI population (3,551 cM). Map coverage was identical for the three genomes (A, B and D) and for the number of anchor loci, as well as for the size of the map. Using this map, QTLs for several agronomic traits were detected on phenotypic data from the population grown in Clermont-Ferrand (France) under natural field conditions over 6 years, and in Norwich (UK) in controlled conditions and under natural field conditions in 1 year. Almost all of the 21 chromosomes were involved in at least one trait. However, several regions seemed to contain gene clusters either for grain traits (and thus bread-making quality) or plant development traits.

Chromosome Mapping↗

PubMed2006

Elucidating genomic regions determining enhanced leaf growth and delayed senescence in elevated CO2.

Limited information is available on the genetic variation and control for plant growth response to elevated CO(2) (e[CO(2)]). Such information is necessary to understand plant adaptation and evolution in future rising CO(2). Here, quantitative trait loci (QTL) for leaf growth, development, quality and leaf senescence were determined in a tree pedigree - an F(2) hybrid of Populus trichocarpa T. & G and Populus deltoides Marsh, following season-long exposure to either current day ambient carbon dioxide (a[CO(2)]) or e[CO(2)] at 600 microL L(-1). Leaf growth and development differed between the grandparents such that P. trichocarpa showed greater response to e[CO(2)]. In the F(2) generation, leaf development and quality traits including leaf area, leaf shape, epidermal cell area, and stomatal number, specific leaf area (SLA), and the phenology trait, canopy senescence index, were sensitive to e[CO(2)]. Sixty-nine QTL were mapped for the 19 traits of plants in a[CO(2)] while 60 QTL were mapped for plants in e[CO(2)]. The results suggest that although many QTL mapped to common positions in a[CO(2)] and e[CO(2)], confirming their importance in determining growth, there was also differential genetic control for a number of traits including leaf senescence. Candidate genes were shown to collocate to regions where response QTL mapped. This study is the first to identify candidate genes that may be important in determining plant adaptation to future high-CO(2) world.

Carbon Dioxide↗

PubMedDate not supplied

Mapping of a QTL for serum HDL cholesterol in the rabbit using AFLP technology.

The amplified fragment length polymorphism (AFLP) technique is a DNA technology that generates the so-called AFLP markers. These markers are genomic restriction fragments detected after two rounds of polymerase chain reaction (PCR) without prior knowledge of nucleotide sequence. Here we describe the first application of the AFLP technique in the rabbit. We have tested two primer combinations. The results obtained with the DNA from rabbits of different breeds justify the conclusion that AFLP analysis is an effective tool for genetic studies in the rabbit. In addition, we contribute to the linkage map of the rabbit by localizing two AFLP markers on rabbit linkage group VI (LG VI). For this purpose the progeny of a IIIVO/JU x [IIIVO/JU x AX/JU]F(1) backcross were genotyped for 12 AFLP markers and 3 LG VI classical markers [one coat color marker (e) and two biochemical markers (Es-1 and Est-2)]. AX/JU is a dietary cholesterol-susceptible (hyperresponding) inbred strain and IIIVO/JU is a dietary cholesterol resistant (hyporesponding) inbred strain. Moreover, it is possible to evoke dietary cholesterol-induced aorta atherosclerosis in a relatively short time period in AX/JU rabbits, in contrast to IIIVO/JU rabbits. A significant cosegregation was found between basal serum HDL cholesterol level (i.e., the level on a low-cholesterol, control diet) and an AFLP marker on LG VI. It is concluded that one or more genes of LG VI are regulating the basal serum HDL cholesterol level in rabbits. Thus the present study with rabbits clearly illustrates the value of AFLP markers for the construction of linkage maps and mapping of quantitative trait loci (QTL).

Animals↗

PubMedDate not supplied

Power of QTL detection by either fixed or random models in half-sib designs.

The aim of this study was to compare the variance component approach for QTL linkage mapping in half-sib designs to the simple regression method. Empirical power was determined by Monte Carlo simulation in granddaughter designs. The factors studied (base values in parentheses) included the number of sires (5) and sons per sire (80), ratio of QTL variance to total genetic variance (lambda= 0.1), marker spacing (10 cM), and QTL allele frequency (0.5). A single bi-allelic QTL and six equally spaced markers with six alleles each were simulated. Empirical power using the regression method was 0.80, 0.92 and 0.98 for 5, 10, and 20 sires, respectively, versus 0.88, 0.98 and 0.99 using the variance component method. Power was 0.74, 0.80, 0.93, and 0.95 using regression versus 0.77, 0.88, 0.94, and 0.97 using the variance component method for QTL variance ratios (lambda) of 0.05, 0.1, 0.2, and 0.3, respectively. Power was 0.79, 0.85, 0.80 and 0.87 using regression versus 0.80, 0.86, 0.88, and 0.85 using the variance component method for QTL allele frequencies of 0.1, 0.3, 0.5, and 0.8, respectively. The log10 of type I error profiles were quite flat at close marker spacing (1 cM), confirming the inability to fine-map QTL by linkage analysis in half-sib designs. The variance component method showed slightly more potential than the regression method in QTL mapping.

Alleles↗

PubMed1997

Mapping quantitative trait loci with dominant and missing markers in various crosses from two inbred lines.

Dominant phenotype of a genetic marker provides incomplete information about the marker genotype of an individual. A consequence of using this incomplete information for mapping quantitative trait loci (QTL) is that the inference of the genotype of a putative QTL flanked by a marker with dominant phenotype will depend on the genotype or phenotype of the next marker. This dependence can be extended further until a marker genotype is fully observed. A general algorithm is derived to calculate the probability distribution of the genotype of a putative QTL at a given genomic position, conditional on all observed marker phenotypes in the region with dominant and missing marker information for an individual. The algorithm is implemented for various populations stemming from two inbred lines in the context of mapping QTL. Simulation results show that if only a proportion of markers contain missing or dominant phenotypes, QTL mapping can be almost as efficient as if there were no missing information in the data. The efficiency of the analysis, however, may decrease substantially when a very large proportion of markers contain missing or dominant phenotypes and a genetic map has to be reconstructed first on the same data as well. So it is important to combine dominant markers with codominant markers in a QTL mapping study.

Algorithms↗

PubMed1993

Detecting marker-QTL linkage and estimating QTL gene effect and map location using a saturated genetic map.

A simulation study was carried out on a backcross population in order to determine the effect of marker spacing, gene effect and population size on the power of marker-quantitative trait loci (QTL) linkage experiments and on the standard error of maximum likelihood estimates (MLE) of QTL gene effect and map location. Power of detecting a QTL was virtually the same for a marker spacing of 10 cM as for an infinite number of markers and was only slightly decreased for marker spacing of 20 or even 50 cM. The advantage of using interval mapping as compared to single-marker analysis was slight. "Resolving power" of a marker-QTL linkage experiment was defined as the 95% confidence interval for the QTL map location that would be obtained when scoring an infinite number of markers. It was found that reducing marker spacing below the resolving power did not add appreciably to narrowing the confidence interval. Thus, the 95% confidence interval with infinite markers sets the useful marker spacing for estimating QTL map location for a given population size and estimated gene effect.

Chromosome Mapping↗

PubMed2004

Comparative map and trait viewer (CMTV): an integrated bioinformatic tool to construct consensus maps and compare QTL and functional genomics data across genomes and experiments.

In the past few decades, a wealth of genomic data has been produced in a wide variety of species using a diverse array of functional and molecular marker approaches. In order to unlock the full potential of the information contained in these independent experiments, researchers need efficient and intuitive means to identify common genomic regions and genes involved in the expression of target phenotypic traits across diverse conditions. To address this need, we have developed a Comparative Map and Trait Viewer (CMTV) tool that can be used to construct dynamic aggregations of a variety of types of genomic datasets. By algorithmically determining correspondences between sets of objects on multiple genomic maps, the CMTV can display syntenic regions across taxa, combine maps from separate experiments into a consensus map, or project data from different maps into a common coordinate framework using dynamic coordinate translations between source and target maps. We present a case study that illustrates the utility of the tool for managing large and varied datasets by integrating data collected by CIMMYT in maize drought tolerance research with data from public sources. This example will focus on one of the visualization features for Quantitative Trait Locus (QTL) data, using likelihood ratio (LR) files produced by generic QTL analysis software and displaying the data in a unique visual manner across different combinations of traits, environments and crosses. Once a genomic region of interest has been identified, the CMTV can search and display additional QTLs meeting a particular threshold for that region, or other functional data such as sets of differentially expressed genes located in the region; it thus provides an easily used means for organizing and manipulating data sets that have been dynamically integrated under the focus of the researcher's specific hypothesis.

Adaptation, Physiological↗

PubMed2001

Mapping quantitative trait Loci using generalized estimating equations.

A number of statistical methods are now available to map quantitative trait loci (QTL) relative to markers. However, no existing methodology can simultaneously map QTL for multiple nonnormal traits. In this article we rectify this deficiency by developing a QTL-mapping approach based on generalized estimating equations (GEE). Simulation experiments are used to illustrate the application of the GEE-based approach.

Algorithms↗

PubMed2006

Assessing the significance of quantitative trait loci in replicable mapping populations.

Replicable populations, such as panels of recombinant inbred or doubled haploid lines, are convenient resources for the mapping of QTL. To increase mapping power, replications are often collected within each RI line and a common way to analyze such data is to include in the QTL model only a single measurement from each line that represents the average among the replicates (a line means model). An obvious, but seldom explored, alternative, is to include every replicate in the model (a full data model). Here, we use simulations to compare these two approaches. Further, we propose an extension of the standard permutation procedure that is required to correctly control the type I error in mapping populations with nested structure.

Animals↗

PubMed2001

Genetic mapping of the QTL affecting body weight in chickens using a F2 family.

1. To identify the quantitative trait loci (QTL) affecting growth in chickens, we carried out QTL analysis on chicken growth traits using a population of 227 F2 crosses between a Satsumadori (slow-growing, light-weight Japanese native breed used as a meat chicken) male and a White Plymouth Rock (early-maturing, heavy weight broiler). 2. We chose 78 microsatellite loci from 331 publicly available on 14 linkage groups, with respect to their utility and location. 3. Two QTLs affecting body weight at 13 and 16 weeks were mapped at 220 cM on chromosome 1 (LOD scores, 2.8 and 4.5, respectively, at 13 and 16 weeks), and at 60 cM on chromosome 2 (LOD scores, 6.2 and 8.1, respectively, at 13 and 16 weeks). 4. The closest loci to the QTLs were LEI71 on chromosome 1 and LMU13 and MCW184 on chromosome 2. 5. The sites of the QTLs agreed closely with those already reported. Therefore, it seems likely that QTLs affecting growth of chickens are located at these sites.

Age Factors↗

PubMed2006

Development of a near-isogenic line population of Arabidopsis thaliana and comparison of mapping power with a recombinant inbred line population.

In Arabidopsis recombinant inbred line (RIL) populations are widely used for quantitative trait locus (QTL) analyses. However, mapping analyses with this type of population can be limited because of the masking effects of major QTL and epistatic interactions of multiple QTL. An alternative type of immortal experimental population commonly used in plant species are sets of introgression lines. Here we introduce the development of a genomewide coverage near-isogenic line (NIL) population of Arabidopsis thaliana, by introgressing genomic regions from the Cape Verde Islands (Cvi) accession into the Landsberg erecta (Ler) genetic background. We have empirically compared the QTL mapping power of this new population with an already existing RIL population derived from the same parents. For that, we analyzed and mapped QTL affecting six developmental traits with different heritability. Overall, in the NIL population smaller-effect QTL than in the RIL population could be detected although the localization resolution was lower. Furthermore, we estimated the effect of population size and of the number of replicates on the detection power of QTL affecting the developmental traits. In general, population size is more important than the number of replicates to increase the mapping power of RILs, whereas for NILs several replicates are absolutely required. These analyses are expected to facilitate experimental design for QTL mapping using these two common types of segregating populations.

Arabidopsis↗

PubMedDate not supplied

A simulation study on the accuracy of position and effect estimates of linked QTL and their asymptotic standard deviations using multiple interval mapping in an F(2) scheme.

Approaches like multiple interval mapping using a multiple-QTL model for simultaneously mapping QTL can aid the identification of multiple QTL, improve the precision of estimating QTL positions and effects, and are able to identify patterns and individual elements of QTL epistasis. Because of the statistical problems in analytically deriving the standard errors and the distributional form of the estimates and because the use of resampling techniques is not feasible for several linked QTL, there is the need to perform large-scale simulation studies in order to evaluate the accuracy of multiple interval mapping for linked QTL and to assess confidence intervals based on the standard statistical theory. From our simulation study it can be concluded that in comparison with a monogenetic background a reliable and accurate estimation of QTL positions and QTL effects of multiple QTL in a linkage group requires much more information from the data. The reduction of the marker interval size from 10 cM to 5 cM led to a higher power in QTL detection and to a remarkable improvement of the QTL position as well as the QTL effect estimates. This is different from the findings for (single) interval mapping. The empirical standard deviations of the genetic effect estimates were generally large and they were the largest for the epistatic effects. These of the dominance effects were larger than those of the additive effects. The asymptotic standard deviation of the position estimates was not a good criterion for the accuracy of the position estimates and confidence intervals based on the standard statistical theory had a clearly smaller empirical coverage probability as compared to the nominal probability. Furthermore the asymptotic standard deviation of the additive, dominance and epistatic effects did not reflect the empirical standard deviations of the estimates very well, when the relative QTL variance was smaller/equal to 0.5. The implications of the above findings are discussed.

Animals↗

PubMed2003

New quantitative trait loci that contribute to cholesterol gallstone formation detected in an intercross of CAST/Ei and 129S1/SvImJ inbred mice.

Cholesterol gallstone formation is a response to interactions between multiple genes and environmental stimuli. To determine the subset of cholesterol gallstone susceptibility (Lith) genes possessed by strains CAST/Ei (susceptible) and 129S1/SvImJ (resistant), we conducted quantitative trait locus (QTL) analyses of an intercross between these strains. Parental strains and F(1) mice of both genders were evaluated for gallstone formation after consumption of a lithogenic diet for 8 wk. Gallstone susceptibility of strain CAST was predominantly due to cholesterol hypersecretion. Male intercross offspring were genotyped and phenotyped for cholesterol gallstone formation after consumption of the lithogenic diet for 10 wk. Linkage analysis was performed using PSEUDOMARKER software. One significant, new QTL was detected and named Lith13 [chromosome (Chr) 5, 30 cM]. Statistical analyses and QTL fine mapping suggest this QTL may comprise two closely linked loci. We confirmed the presence of Lith6 (Chr 6). Suggestive QTL were detected on Chrs 1, 2, 5, 14, and 16. The QTL on Chrs 2 and 16 confirmed previously identified, suggestive QTL. Therefore, they were named Lith12 (101 cM) and Lith14 (42 cM), respectively. We identified candidate genes based on known function and location and performed mRNA expression analyses using both parental strains and intercross progeny for preliminary evaluation of their contributions to gallstone formation. Cebpb (Lith12), Pparg (Lith6), and Slc21a1 (Lith6) displayed expression differences. Our work continues to demonstrate the genetic complexity and to elucidate the pathophysiology of cholesterol gallstone formation. It should facilitate the development of new approaches for treating this common human disorder.

Alleles↗

PubMed2004

Comparing linkage disequilibrium-based methods for fine mapping quantitative trait loci.

Recently, a method for fine mapping quantitative trait loci (QTL) using linkage disequilibrium was proposed to map QTL by modeling covariance between individuals, due to identical-by-descent (IBD) QTL alleles, on the basis of the similarity of their marker haplotypes under an assumed population history. In the work presented here, the advantage of using marker haplotype information for fine mapping QTL was studied by comparing the IBD-based method with 10 markers to regression on a single marker, a pair of markers, or a two-locus haplotype under alternative population histories. When 10 markers were genotyped, the IBD-based method estimated the position of the QTL more accurately than did single-marker regression in all populations. When 20 markers were genotyped for regression, as single-marker methods do not require knowledge of haplotypes, the mapping accuracy of regression in all populations was similar to or greater than that of the IBD-based method using 10 markers. Thus for populations similar to those simulated here, the IBD-based method is comparable to single-marker regression analysis for fine mapping QTL.

Alleles↗

PubMed2002

Mapping and characterisation of QTL x E interactions for traits determining grain and stover yield in pearl millet.

A mapping population of 104 F(3) lines of pearl millet, derived from a cross between two inbred lines H 77/833-2 x PRLT 2/89-33, was evaluated, as testcrosses on a common tester, for traits determining grain and stover yield in seven different field trials, distributed over 3 years and two seasons. The total genetic variation was partitioned into effects due to season (S), genotype (G), genotype x season interaction (G x S), and genotype x environment-within-season interaction [G x E(S)]. QTLs were determined for traits for their G, G x S, and G x E(S) effects, to assess the magnitude and the nature (cross over/non-crossover) of environmental interaction effects on individual QTLs. QTLs for some traits were associated with G effects only, while others were associated with the effects of both G and G x S and/or G, G x S and G x E(S) effects. The major G x S QTLs detected were for flowering time (on LG 4 and LG 6), and mapped to the same intervals as G x S QTLs for several other traits (including stover yield, harvest index, biomass yield and panicle number m(-2)). All three QTLs detected for grain yield were unaffected by G x S interaction however. All three QTLs for stover yield (mapping on LG 2, LG 4 and LG 6) and one of the three QTLs for grain yield (mapping on LG 4) were also free of QTL x E(S) interactions. The grain yield QTLs that were affected by QTL x E(S) interactions (mapping on LG 2 and LG 6), appeared to be linked to parallel QTL x E(S) interactions of the QTLs for panicle number m(-2) on (LG 2) and of QTLs for both panicle number m(-2) and harvest index (LG 6). In general, QTL x E(S) interactions were more frequently observed for component traits of grain and stover yield, than for grain or stover yield per se.

Chromosome Mapping↗