Multiple Quantitative Trait Research Articles

Bivariate combined linkage and association mapping of quantitative trait loci

In this paper, bivariate/multivariate variance component models are proposed for high-resolution combined linkage and association mapping of quantitative trait loci (QTL), based on combinations of pedigree and population data. Suppose that a quantitative trait locus is located in a chromosome region that exerts pleiotropic effects on multiple quantitative traits. In the region, multiple markers such as single nucleotide polymorphisms are typed. Two regression models, "genotype effect model" and "additive effect model", are proposed to model the association between the markers and the trait locus. The linkage information, i.e., recombination fractions between the QTL and the markers, is modeled in the variance and covariance matrix. By analytical formulae, we show that the "genotype effect model" can be used to model the additive and dominant effects simultaneously; the "additive effect model" only takes care of additive effect. Based on the two models, F-test statistics are proposed to test association between the QTL and markers. By analytical power analysis, we show that bivariate models can be more powerful than univariate models. For moderate-sized samples, the proposed models lead to correct type I error rates; and so the models are reasonably robust. As a practical example, the method is applied to analyze the genetic inheritance of rheumatoid arthritis for the data of The North American Rheumatoid Arthritis Consortium, Problem 2, Genetic Analysis Workshop 15, which confirms the advantage of the proposed bivariate models.

Deviance information criterion (DIC) in Bayesian multiple QTL mapping

Mapping multiple quantitative trait loci (QTL) is commonly viewed as a problem of model selection. Various model selection criteria have been proposed, primarily in the non-Bayesian framework. The deviance information criterion (DIC) is the most popular criterion for Bayesian model selection and model comparison but has not been applied to Bayesian multiple QTL mapping. A derivation of the DIC is presented for multiple interacting QTL models and calculation of the DIC is demonstrated using posterior samples generated by Markov chain Monte Carlo (MCMC) algorithms. The DIC measures posterior predictive error by penalizing the fit of a model (deviance) by its complexity, determined by the effective number of parameters. The effective number of parameters simultaneously accounts for the sample size, the cross design, the number and lengths of chromosomes, covariates, the number of QTL, the type of QTL effects, and QTL effect sizes. The DIC provides a computationally efficient way to perform sensitivity analysis and can be used to quantitatively evaluate if including environmental effects, gene–gene interactions, and/or gene–environment interactions in the prior specification is worth the extra parameterization. The DIC has been implemented in the freely available package R/qtlbim, which greatly facilitates the general usage of Bayesian methodology for genome-wide interacting QTL analysis.

Introgressing multiple QTL in breeding programmes of limited size

The ability to enrich a breed with favourable alleles from multiple unlinked quantitative trait loci (QTL) of a donor breed through marker-assisted introgression (MAI) in a population of limited size was evaluated by considering the effects of the proportion selected, the size of the marker intervals, the number of introgressed QTL and the uncertainty of QTL position. Informative flanking markers were used to select progeny with the largest expected number of donor QTL alleles over five generations of backcrossing and five generations of intercrossing. In the backcrossing phase, with 5% selected and 20 cM marker intervals for three QTL, there were sufficient backcross progeny that were heterozygous for all markers, and QTL frequencies dropped below 0.5 only because of double recombinants. For higher fractions selected, longer marker intervals, and more QTL, frequency reductions from 0.5 were greater and increased with additional generations of backcrossing. However, even with 20% selected, three QTL, and marker intervals of 5 or 20 cM, mean QTL frequencies in generation 5 were 0.35 and 0.30, sufficient to allow subsequent selection of QTL in the intercrossing phase. After five generations of intercrossing, over 90% of individuals were homozygous for all QTL, and 85% when five QTL were introgressed. The higher the proportions selected, the longer the marker intervals, and larger numbers of introgressed QTL increased the number of intercrossing generations required to achieve fixation of QTL. Location of the QTL in the marked intervals did not affect QTL frequencies or the proportion of QTL lost at the end of the introgression programme. In conclusion, introgressing multiple QTL can be accomplished in a MAI programme of limited size without requiring that all individuals selected during the backcrossing phase to be carriers of favourable alleles at all QTL.

Advances in Bayesian multiple quantitative trait loci mapping in experimental crosses.

Many complex human diseases and traits of biological and/or economic importance are determined by interacting networks of multiple quantitative trait loci (QTL) and environmental factors. Mapping QTL is critical for understanding the genetic basis of complex traits, and for ultimate identification of genes responsible. A variety of sophisticated statistical methods for QTL mapping have been developed. Among these developments, the evolution of Bayesian approaches for multiple QTL mapping over the past decade has been remarkable. Bayesian methods can jointly infer the number of QTL, their genomic positions and their genetic effects. Here, we review recently developed and still developing Bayesian methods and associated computer software for mapping multiple QTL in experimental crosses. We compare and contrast these methods to clearly describe the relationships among different Bayesian methods. We conclude this review by highlighting some areas of future research.

Breeding Value Estimation for Fat Percentage Using Dense Markers on Bos taurus Autosome 14

Prediction of breeding values using whole-genome dense marker maps for genomic selection has become feasible with the advances in DNA chip technology and the discovery of thousands of single nucleotide polymorphisms in genome-sequencing projects. The objective of this study was to compare the accuracy of predicted breeding values from genomic selection (GS), selection without genetic marker information (BLUP), and gene-assisted selection (GEN) on real dairy cattle data for 1 chromosome. Estimated breeding values of 1,300 bulls for fat percentage, based on daughter performance records, were obtained from the national genetic evaluation and used as phenotypic data. All bulls were genotyped for 32 genetic markers on chromosome 14, of which 1 marker was the causative mutation in a gene with a large effect on fat percentage. In GS, the data were analyzed with a multiple quantitative trait loci (QTL) model with haplotype effects for each marker bracket and a polygenic effect. Identical-by-descent probabilities based on linkage and linkage disequilibrium information were used to model the covariances between haplotypes. A Bayesian method using Gibbs sampling was used to predict the presence of a putative QTL and the effects of the haplotypes in each marker bracket. In BLUP, the haplotype effects were removed from the model, whereas in GEN, the haplotype effects were replaced by the effect of the genotype at the known causative mutation. The breeding values from the national genetic evaluation were treated as true breeding values because of their high accuracy and were used to compute the accuracy of prediction for GS, BLUP, and GEN. The allele substitution effect for the causative mutation, obtained from GEN, was 0.35% fat. The accuracy of the predicted breeding values for GS (0.75) was as high as for GEN (0.75) and higher than for BLUP (0.51). When some markers close to the QTL were omitted from the model, the accuracy of prediction was only slightly lower, around 0.72. The removal of all markers within 8 cM from the QTL reduced the accuracy to 0.64, which was still much higher than BLUP. It is concluded that, when applied to 1 chromosome and if genetic markers close to the QTL are available, the presented model for GS is as accurate as GEN.

Quantitative trait locus for seizure susceptibility on mouse chromosome 5 confirmed with reciprocal congenic strains

Multiple quantitative trait locus (QTL) mapping studies designed to localize seizure susceptibility genes in C57BL/6 (B6, seizure resistant) and DBA/2 (D2, seizure susceptible) mice have detected a significant effect originating from midchromosome 5. To confirm the presence and refine the position of the chromosome 5 QTL for maximal electroshock seizure threshold (MEST), reciprocal congenic strains between B6 and D2 mice were created by a DNA marker-assisted backcross breeding strategy and studied with respect to changes in MEST. A genomic interval delimited by marker D5Mit75 (proximal to the acromere) and D5Mit403 (distal to the acromere) was introgressed for 10 generations. A set of chromosome 5 congenic strains produced by an independent laboratory was also studied. Comparison of MEST between congenic and control (parental genetic background) mice indicates that genes influencing this trait were captured in all strains. Thus, mice from strains having D2 alleles from chromosome 5 on a B6 genetic background exhibit significantly lower MEST compared with control littermates, whereas congenic mice harboring B6 chromosome 5 alleles on a D2 genetic background exhibit significantly higher MEST compared with control littermates. Combining data from all congenic strains, we conclude that the gene(s) underlying the chromosome 5 QTL for MEST resides in the interval between D5Mit108 (26 cM) and D5Mit278 (61 cM). Generation of interval-specific congenic strains from the primary congenic strains described here may be used to achieve high-resolution mapping of the chromosome 5 gene(s) that contributes to the large difference in seizure susceptibility between B6 and D2 mice.

Bayesian Multiple Quantitative Trait Loci Mapping for Complex Traits Using Markers of the Entire Genome

A Bayesian methodology has been developed for multiple quantitative trait loci (QTL) mapping of complex binary traits that follow liability threshold models. Unlike most QTL mapping methods where only one or a few markers are used at a time, the proposed method utilizes all markers across the genome simultaneously. The outperformance of our Bayesian method over the traditional single-marker analysis and interval mapping has been illustrated via simulations and real data analysis to identify candidate loci associated with colorectal cancer.

Multivariate segregation analysis for quantitative traits in line crosses

Segregation analysis is a method of detecting major genes for quantitative traits without using marker information. It serves as an important tool in helping investigators to plan further studies such as quantitative trait loci mapping or more sophisticated genomic analyses. However, current methods of segregation analysis for a single trait typically have low statistical power. We propose a multivariate segregation analysis (MSA) that takes advantage of the correlation structure of multiple quantitative traits to detect major genes. This method not only increases the statistical power, but allows dissection of the genetic architecture underlying the trait complex. In MSA the observed phenotypes of multiple correlated traits are fitted to a multivariate Gaussian mixture model. Model parameters are estimated under the maximum likelihood framework via the expectation-maximization algorithm. The presence of major genes is tested using likelihood ratio test statistics. Pleiotropy is distinguished from close linkage by comparing three possible models using the Bayesian information criterion. Two simulation experiments were performed based on the F(2) mating design. In the first, the statistical properties of MSA under varying heritabilities and sample sizes were investigated and the results compared with those obtained from single-trait analysis. In the second simulation the efficacy of MSA in separating pleiotropy from close linkage was demonstrated. Finally, the new method was applied to real data and detected a major gene responsible for both plant height and tiller number in rice.

Bayesian association mapping of multiple quantitative trait loci and its application to the analysis of genetic variation among Oryza sativa L. germplasms

One way to use a crop germplasm collection directly to map QTLs without using line-crossing experiments is the whole genome association mapping. A major problem with association mapping is the presence of population structure, which can lead to both false positives and failure to detect genuine associations (i.e., false negatives). Particularly in highly selfing species such as Asian cultivated rice, high levels of population structure are expected and therefore the efficiency of association mapping remains almost unknown. Here, we propose an approach that combines a Bayesian method for mapping multiple QTLs with a regression method that directly incorporates estimates of population structure. That is, the effects due to both multiple QTLs and population structure were included in our statistical model. We evaluated the efficiency of our approach in simulated- and real-trait analyses of a rice germplasm collection. Simulation analyses based on real marker data showed that our model could suppress both false-positive and false-negative rates and the error of estimation of genetic effects over single QTL models, indicating that our model has statistically desirable attributes over single QTL models. As real traits, we analyzed the size and shape of milled rice grains and found significant markers that may be linked to QTLs reported previously. Association mapping should have good prospects in highly selfing species such as rice if proper methods are adopted. Our approach will be useful for the whole genome association mapping of various selfing crop species.

Phenotype selection for detecting variable genes: a survey of cardiovascular quantitative traits and TNF locus polymorphism

The practice of using discrete clinical diagnoses in genetic association studies has seldom led to a replicable genetic model. If, as the literature suggests, weak genotype-phenotype relationships are detected when clinical diagnoses are used, power might be increased by exploring more fundamental biological traits. Emerging solutions to this include directly modeling levels of the protein product of a gene (usually in plasma) and sequence variation specifically in/around that gene, as well as exploring multiple quantitative traits related to a disease of interest. Here, we attempt a strategy based upon these premises examining sequence variants near the TNF locus, a region widely studied in cardiovascular disease. Multilocus genotype models were used to perform a systematic screen of 18 metabolic and anthropometric traits for genetic association. While there was no evidence for an effect of TNF polymorphism on plasma TNF levels, a relatively strong effect on plasma PAI-1 levels did emerge (P=0.000019), but this was only evident in post-myocardial infarction patients. Modeled jointly with the common 4G/5G insertion/deletion polymorphism of SERPINE1 (formerly PAI), this effect appears large (10% of variance explained versus 2% for SERPINE1 4G/5G). We exhibit this finding cautiously, and use it to illustrate how transitioning the study of disease risk to quantitative traits might empower the identification of functionally variable genes. Further, a case is highlighted where association between sequence variation in a gene and its product is not readily apparent even in large samples, but where association with a down-stream pathway may be.

A Unified Semiparametric Framework for Quantitative Trait Loci Analyses, With Application to Spike Phenotypes

This article proposes a general semiparametric model for multiple quantitative trait loci (QTL) analyses of complex phenotypes in backcross and intercross designs. The model provides tests about genetic hypotheses, such as additivity, dominance, and epistasis, that do not require specifying the form of the phenotypic distribution. This contrasts with previous approaches based on transformations to normality and generalized linear models, which require careful consideration of the phenotypic distribution. Inferences involve extensions of partial and conditional likelihoods developed for single-QTL backcross models. We demonstrate that conditional likelihood is robust to unobserved selective genotyping, whereas partial likelihood and other standard methods are not. To facilitate genome screens, a novel resampling method is proposed that is similar in spirit to the popular permutation tests. Its main advantages are that it is broadly applicable to multiple QTLs with nonnormal phenotypes and achieves a substantial reduction in computational burden. A thorough case study of spike data on the genetic influences to recovery from Listeria infection in a mouse intercross experiment is presented. The application reveals that the proposed methods may give substantively different conclusions than those obtained with existing interval mapping methods from parametric models in the presence of unobserved selection.

Bayesian Multiple Quantitative Trait Loci Mapping for Complex Traits Using Markers of the Entire Genome

Bayesian Multiple Quantitative Trait Loci Mapping for Complex Traits Using Markers of the Entire Genome

Applicability of DNA pools on 500 K SNP microarrays for cost-effective initial screens in genomewide association studies.

BackgroundGenetic influences underpinning complex traits are thought to involve multiple quantitative trait loci (QTLs) of small effect size. Detection of such QTL associations requires systematic screening of large numbers of DNA markers within large sample populations. Using pooled DNA on SNP microarrays to screen for allelic frequency differences between groups such as cases and controls (called SNP Microarray and Pooling, or SNP-MaP) has been validated as an efficient solution on both 10 k and 100 k platforms. We demonstrate that this approach can be effectively applied to the truly genomewide Affymetrix GeneChip® Mapping 500 K Array.ResultsIn comparisons between five independent DNA pools (N ~200 per pool) on separate Affymetrix GeneChip® Mapping 500 K Array sets, we show that, for SNPs with minor allele frequencies > 0.05, the reliability of the rank order of estimated allele frequencies, assessed as the average correlation between allele frequency estimates across the DNA pools, was 0.948 (average mean difference across the five pools = 0.069). Similarly, validity of the SNP-MaP approach was demonstrated by a rank-order correlation of 0.937 (average mean difference = 0.095) between the average DNA pool allele frequency estimates and the allele frequencies of an independent (CEPH) sample of 60 unrelated individually genotyped subjects.ConclusionWe conclude that SNP-MaP can be extended for use on the Affymetrix GeneChip® Mapping 500 K Array, providing a cost-effective, reliable and valid initial screen of 500 K SNP microarrays in genomewide association scans.

An integrative genomics strategy for the identification of collagen-induced arthritis susceptibility genes

Murine collagen-induced arthritis (CIA) is a chronic inflammatory disease bearing all the hallmarks of rheumatoid arthritis. CIA has been widely used to study the etiology, pathogenesis and new therapeutic approaches of rheumatoid arthritis. Previous studies have identified multiple quantitative trait loci (QTL) controlling different aspects of disease pathogenesis. However, progress in identifying the new susceptibility genes outside the MHC locus has been slow. With the advent of new global methods for genetic analysis such as large-scale sequencing, gene expression profiling, combined with classical linkage analysis, congenic and physical and in silico fine mapping, progress is considerably accelerating. Here we present preliminary data using an integrative genomics strategy to identify new putative susceptibility genes contributing to the pathogenesis of CIA. The strategy is based on integrating a genome scan to identify QTLs linked to clinical disease phenotypes and subtraits in a cross between the CIA-susceptible DBA1/J and resistant FVB/NJ mouse strains. Additionally, gene expression profiling in target tissues and immune cells in parental strains and their F2 progeny is performed. Numerous classical QTL and expression QTL were identified. Master QTLs/expression QTLs controlling basic disease traits and subtraits were selected for further analysis using in silico tools; for example, haplotype sharing analysis, interspecies synteny analysis, congenic mapping, and pathway construction. So far we have confirmed C5 as a putative susceptibility gene for the Cia2 locus on chromosome 2. We also identified the mitochondrial ATP8 as a novel susceptibility gene. We are also pursuing three additional QTLs on chromosomes 1, 5, 18 controlling leukocyte–endothelial cell adhesion, anticollagen antibody production and arthritis severity. Functional analysis of candidate genes for those loci is underway.

Shrinkage Estimation Method for Mapping Multiple Quantitative Trait Loci

In this article, shrinkage estimation method for multiple-marker analysis and for mapping multiple quantitative trait loci (QTL) was reviewed. For multiple-marker analysis, Xu (Genetics, 2003, 163:789-801) developed a Bayesian shrinkage estimation (BSE) method. The key to the success of this method is to allow each marker effect have its own variance parameter, which in turn has its own prior distribution so that the variance can be estimated from the data. Under this hierarchical model, a large number of markers can be handled although most of them may have negligible effects. Under epistatic genetic model, however, the running time is very long. To overcome this problem, a novel method of incorporating the idea described above into maximum likelihood, known as penalized likelihood method, was proposed. A simulated study showed that this method can handle a model with multiple effects, which are ten times larger than the sample size. For multiple QTL analysis, two modified versions for the BSE method were introduced: one is the fixed-interval method and another is the variable-interval method. The former deals with markers with intermediate density, and the latter can handle markers with extremely high density as well as model with epistatic effects. For the detection of epistatic effects, penalized likelihood method and the variable-interval approach of the BSE method are available.

Simultaneous fine mapping of multiple closely linked quantitative trait Loci using combined linkage disequilibrium and linkage with a general pedigree.

Within a small region (e.g., <10 cM), there can be multiple quantitative trait loci (QTL) underlying phenotypes of a trait. Simultaneous fine mapping of closely linked QTL needs an efficient tool to remove confounded shade effects among QTL within such a small region. We propose a variance component method using combined linkage disequilibrium (LD) and linkage information and a reversible jump Markov chain Monte Carlo (MCMC) sampling for model selection. QTL identity-by-descent (IBD) coefficients between individuals are estimated by a hybrid MCMC combining the random walk and the meiosis Gibbs sampler. These coefficients are used in a mixed linear model and an empirical Bayesian procedure combines residual maximum likelihood (REML) to estimate QTL effects and a reversible jump MCMC that samples the number of QTL and the posterior QTL intensities across the tested region. Note that two MCMC processes are used, i.e., an (internal) MCMC for IBD estimation and an (external) MCMC for model selection. In a simulation study, the use of the multiple-QTL model clearly removes the shade effects between three closely linked QTL located at 1.125, 3.875, and 7.875 cM across the region of 10 cM, using 40 markers at 0.25-cM intervals. It is shown that the use of combined LD and linkage information gives much more useful information compared to using linkage information alone for both single- and multiple-QTL analyses. When using a lower marker density (11 markers at 1-cM intervals), the signal of the second QTL can disappear. Extreme values of past effective size (resulting in extreme levels of LD) decrease the mapping accuracy.

A Meeting of the Minds on Mice

If genetics research is ever to fulfill its promise of revolutionizing medicine, genotypes must be linked to phenotypes—that is, individual genomic characteristics must be identified and associated with outcomes in the forms of disease susceptibility and/or development; individual responses to drugs, infectious agents, or environmental exposures; or other individual characteristics such as behavioral tendencies. Why does the person who never smoked develop lung cancer, while the three-pack-a-day smoker remains healthy? Why do some people become addicted to drugs, while other users are never hooked? Why does a particular medication work wonders in some people, but not work at all in others? These and countless similar questions represent the enormous challenge still facing researchers as they strive to make personalized medicine a clinical reality. The answers to many of these questions may yet be discovered in the genomes of mice, our diminutive mammalian relatives. That’s certainly the hope and belief of the members of the mouse genetics community, 180 of whom gathered 6–9 May 2006 in Chapel Hill, North Carolina, for the fifth annual meeting of the Complex Trait Consortium (CTC), a loosely woven international organization tightly knit in its dedication to elucidating human characteristics by identifying their genetic counterparts in mice. The “complexity” of complex traits derives from the fact that they are polygenic—multiple genes interact to cause these conditions, and the genes involved may not interact additively. Ninety-three reports presented at the CTC meeting updated progress in the hunt for the multiple genes and quantitative trait loci (or chromosomal “hot spots”) associated with a wide variety of complex traits such as heart failure, tumor resistance, obesity, drug and alcohol addiction, and schizophrenia. Sponsored by the NIEHS, the UNC–Chapel Hill, and Agilent Technologies, the conference brought together a diverse group of mouse geneticists, molecular biologists, statisticians, and bioinformaticists from 10 countries. The CTC is all about collaboration and interaction. “It’s unquestionably the best meeting that I go to every year,” says Abraham Palmer, an assistant professor of human genetics at the University of Chicago. “The opportunities to communicate with other geneticists working in other fields and with the people who develop our methodology are critically important, and accelerate by months or even years the rate at which the field can move forward,” he says. Karlyne Reilly, a principal investigator in the Mouse Cancer Genetics Program at the National Cancer Institute, agrees. “It brings together a wide variety of science around techniques and how you solve the problems that are common to these different areas,” she says. “I always come away with new tools to play with, that I can apply to my own research.”

Definition of a 1.06-Mb Region Linked to Neuroinflammation in Humans, Rats and Mice

Unbiased identification of susceptibility genes might provide new insights into pathogenic mechanisms that govern complex inflammatory diseases such as multiple sclerosis. In this study we fine mapped Eae18a, a region on rat chromosome 10 that regulates experimental autoimmune encephalomyelitis (EAE), an animal model for multiple sclerosis. We utilized two independent approaches: (1) in silico mapping based on sequence similarity between human multiple sclerosis susceptibility regions and rodent EAE quantitative trait loci and (2) linkage mapping in an F10 (DA x PVG.AV1) rat advanced intercrossed line. The linkage mapping defines Eae18a to a 5-Mb region, which overlaps one intergenomic consensus region identified in silico. The combined approach confirms experimentally, for the first time, the accuracy of the in silico method. Moreover, the shared intersection between the results of both mapping techniques defines a 1.06-Mb region containing 13 candidate genes for the regulation of neuroinflammation in humans, rats, and mice.

Phenotypic Variation and Natural Selection at Catsup, a Pleiotropic Quantitative Trait Gene in Drosophila

Quantitative traits are shaped by networks of pleiotropic genes . To understand the mechanisms that maintain genetic variation for quantitative traits in natural populations and to predict responses to artificial and natural selection, we must evaluate pleiotropic effects of underlying quantitative trait genes and define functional allelic variation at the level of quantitative trait nucleotides (QTNs). Catecholamines up (Catsup), which encodes a negative regulator of tyrosine hydroxylase , the rate-limiting step in the synthesis of the neurotransmitter dopamine, is a pleiotropic quantitative trait gene in Drosophila melanogaster. We used association mapping to determine whether the same or different QTNs at Catsup are associated with naturally occurring variation in multiple quantitative traits. We sequenced 169 Catsup alleles from a single population and detected 33 polymorphisms with little linkage disequilibrium (LD). Different molecular polymorphisms in Catsup are independently associated with variation in longevity, locomotor behavior, and sensory bristle number. Most of these polymorphisms are potentially functional variants in protein coding regions, have large effects, and are not common. Thus, Catsup is a pleiotropic quantitative trait gene, but individual QTNs do not have pleiotropic effects. Molecular population genetic analyses of Catsup sequences are consistent with balancing selection maintaining multiple functional polymorphisms.

Pooled analysis of data from multiple quantitative trait locus mapping populations

Quantitative trait locus (QTL) analysis on pooled data from multiple populations (pooled analysis) provides a means for evaluating, as a whole, evidence for existence of a QTL from different studies and examining differences in gene effect of a QTL among different populations. Objectives of this study were to: (1) develop a method for pooled analysis and (2) conduct pooled analysis on data from two soybean mapping populations. Least square interval mapping was extended for pooled analysis by inclusion of populations and cofactor markers as indicator variables and covariate variables separately in the multiple linear models. The general linear test approach was applied for detecting a QTL. Single population-based and pooled analyses were conducted on data from two F(2:3) mapping populations, Hamilton (susceptible) x PI 90763 (resistant) and Magellan (susceptible) x PI 404198A (resistant), for resistance to soybean cyst nematode (SCN) in soybean. It was demonstrated that where a QTL was shared among populations, pooled analysis showed increased LOD values on the QTL candidate region over single population analyses. Where a QTL was not shared among populations, however, the pooled analysis showed decreased LOD values on the QTL candidate region over single population analyses. Pooled analysis on data from genetically similar populations may have higher power of QTL detection than single population-based analyses. QTLs were identified by pooled analysis on linkage groups (LGs) G, B1 and J for resistance to SCN race 2 whereas QTLs on LGs G, B1 and E for resistance to SCN race 5 in soybean PI 90763 and PI 404198A. QTLs on LG G and B1 were identified in both PI 90763 and PI 404198A whereas QTLs on LG E and J were identified in PI 90763 only. QTLs on LGs G and B1 for resistance to race 2 may be the same or closely linked with QTLs on LG G and B1 for resistance to race 5, respectively. It was further demonstrated that QTLs on G and B1 carried by PI 90763 were not significantly different in gene effect from QTLs on LGs G and B1 in PI 404198A, respectively.