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At least 127 records · Page 7Linked to original sources
PubMed2005

Complex trait mapping in isolated populations: Are specific statistical methods required?

In this paper, we review the statistical methods that can be used in isolated populations to map genes involved in complex diseases. Our intention is to highlight the fact that if the features of population isolates may help in the identification of susceptibility factors for complex traits, the choice and design of methods for statistical analysis in these populations deserve particular care. We show that methods designed for outbred samples are generally not appropriate for isolated populations and could lead to false conclusions.

Chromosome Mapping↗
PubMed2003

Approaches to mapping genetically correlated complex traits.

Our Markov chain Monte Carlo (MCMC) methods were used in linkage analyses of the Framingham Heart Study data using all available pedigrees. Our goal was to detect and map loci associated with covariate-adjusted traits log triglyceride (lnTG) and high-density lipoprotein cholesterol (HDL) using multipoint LOD score analysis, Bayesian oligogenic linkage analysis and identity-by-descent (IBD) scoring methods. Each method used all marker data for all markers on a chromosome. Bayesian linkage analysis detected a linkage signal on chromosome 7 for lnTG and HDL, corroborating previously published results. However, these results were not replicated in a classical linkage analysis of the data or by using IBD scoring methods.We conclude that Bayesian linkage analysis provides a powerful paradigm for mapping trait loci but interpretation of the Bayesian linkage signals is subjective. In the absence of a LOD score method accommodating genetically complex traits and linkage heterogeneity, validation of these signals remains elusive.

Cholesterol, HDL↗
PubMedDate not supplied

QTL analysis of proteome and transcriptome variations for dissecting the genetic architecture of complex traits in maize.

In this review, we present some studies on genetic analysis of proteome and transcriptome variations, which exemplify new strategies for a better understanding of the molecular and genetic bases of complex traits. A large genetic variability was revealed at the proteome expression level, which raised the possibility to predict phenotypical performance on the basis of gene product variability. This approach yielded limited results, but could be re-newed by extensive identification of proteins now allowed by mass spectrometry. The dissection of the genetic basis of the variation of individual protein amounts proves very powerful to select 'candidate' proteins, physiologically relevant for a given phenotypical trait, as shown by a study on the effect of water stress in maize. In order to investigate factors of grain quality in maize, we selected a regulatory locus known to control the expression of several storage protein genes, Opaque-2, and investigated the relationships between variability in zein amount and composition and the molecular polymorphism at this locus. Moreover, a QTL analysis revealed that the variability in Opaque-2 transcript abundance was controlled by several polymorphic trans-acting regulators unlinked to the Opaque-2 structural gene. Such genetic approaches should represent additional tools for physiological analysis of the huge amounts of data generated by transcritome and proteome projects.

DNA-Binding Proteins↗
PubMed1999

Assessing the feasibility of linkage disequilibrium methods for mapping complex traits: an initial screen for bipolar disorder loci on chromosome 18.

Linkage disequilibrium (LD) analysis has been promoted as a method of mapping disease genes, particularly in isolated populations, but has not yet been used for genome-screening studies of complex disorders. We present results of a study to investigate the feasibility of LD methods for genome screening using a sample of individuals affected with severe bipolar mood disorder (BP-I), from an isolated population of the Costa Rican central valley. Forty-eight patients with BP-I were genotyped for markers spaced at approximately 6-cM intervals across chromosome 18. Chromosome 18 was chosen because a previous genome-screening linkage study of two Costa Rican families had suggested a BP-I locus on this chromosome. Results of the current study suggest that LD methods will be useful for mapping BP-I in a larger sample. The results also support previously reported possible localizations (obtained from a separate collection of patients) of BP-I-susceptibility genes at two distinct sites on this chromosome. Current limitations of LD screening for identifying loci for complex traits are discussed, and recommendations are made for future research with these methods.

Bipolar Disorder↗
PubMed2001

Complex trait analysis of the mouse striatum: independent QTLs modulate volume and neuron number.

BACKGROUND: The striatum plays a pivotal role in modulating motor activity and higher cognitive function. We analyzed variation in striatal volume and neuron number in mice and initiated a complex trait analysis to discover polymorphic genes that modulate the structure of the basal ganglia. RESULTS: Brain weight, brain and striatal volume, neuron-packing density and number were estimated bilaterally using unbiased stereological procedures in five inbred strains (A/J, C57BL/6J, DBA/2J, BALB/cJ, and BXD5) and an F2 intercross between A/J and BXD5. Striatal volume ranged from 20 to 37 mm3. Neuron-packing density ranged from approximately 50,000 to 100,000 neurons/mm3, and the striatal neuron population ranged from 1.4 to 2.5 million. Inbred animals with larger brains had larger striata but lower neuron-packing density resulting in a narrow range of average neuron populations. In contrast, there was a strong positive correlation between volume and neuron number among intercross progeny. We mapped two quantitative trait loci (QTLs) with selective effects on striatal architecture. Bsc10a maps to the central region of Chr 10 (LRS of 17.5 near D10Mit186) and has intense effects on striatal volume and moderate effects on brain volume. Stnn19a maps to distal Chr 19 (LRS of 15 at D19Mit123) and is associated with differences of up to 400,000 neurons among animals. CONCLUSION: We have discovered remarkable numerical and volumetric variation in the mouse striatum, and we have been able to map two QTLs that modulate independent anatomic parameters.

Animals↗
PubMed2007

MCMC-based linkage analysis for complex traits on general pedigrees: multipoint analysis with a two-locus model and a polygenic component.

We describe a new program lm_twoqtl, part of the MORGAN package, for parametric linkage analysis with a quantitative trait locus (QTL) model having one or two QTLs and a polygenic component, which models additional familial correlation from other unlinked QTLs. The program has no restriction on number of markers or complexity of pedigrees, facilitating use of more complex models with general pedigrees. This is the first available program that can handle a model with both two QTLs and a polygenic component. Competing programs use only simpler models: one QTL, one QTL plus a polygenic component, or variance components (VC). Use of simple models when they are incorrect, as for complex traits that are influenced by multiple genes, can bias estimates of QTL location or reduce power to detect linkage. We compute the likelihood with Markov Chain Monte Carlo (MCMC) realization of segregation indicators at the hypothesized QTL locations conditional on marker data, summation over phased multilocus genotypes of founders, and peeling of the polygenic component. Simulated examples, with various sized pedigrees, show that two-QTL analysis correctly identifies the location of both QTLs, even when they are closely linked, whereas other analyses, including the VC approach, fail to identify the location of QTLs with modest contribution. Our examples illustrate the advantage of parametric linkage analysis with two QTLs, which provides higher power for linkage detection and better localization than use of simpler models.

Chromosome Mapping↗
PubMed2000

Etiology and pathophysiology of stroke as a complex trait.

Stroke (brain attack) is currently the third leading cause of death in Western societies. Recent advances in molecular genetics have finally demonstrated what has long been suggested by the clinical observation, that is, stroke is not only the complication of major pathologic conditions such as atherosclerosis, hypertension, or cardiac diseases, but rather it represents a complex trait itself. Thus, the pathogenesis of stroke is often the result of the combined effects of genes exerting a direct contributory role and of their interactions with several environmental determinants. A genetic dissection of stroke has been attempted in suitable animal models and in humans. With this approach, the genetic defects underlying monogenic disorders associated with stroke were identified. Moreover, important findings have recently highlighted the contribution of genes encoding cardiovascular hormones, such as the atrial natriuretic peptide, for the pathogenesis of multifactorial, polygenic forms of stroke. A more thorough understanding of the fine mechanisms, dependent from mutations within stroke susceptibility genes and underlying the disease pathogenesis, may help to introduce new specific tools to achieve better prevention and treatment of stroke.

Animals↗
PubMed1991

RFLP analysis of complex traits in crop plants.

Detailed genetic maps, based upon molecular markers (in particular, on restriction fragment length polymorphisms-RFLPs) have now been constructed for a number of crop plant species, and permit a range of genetic analyses hitherto considered impossible. The availability of such maps has made it possible to approach the dissection and manipulation of both simply-inherited and complex characteristics. Even those characters that show apparently 'quantitative' inheritance (displaying essentially continuous variation within a segregating family) can frequently be resolved into a handful of major gene effects. Once tagged with molecular markers, the genes can be assembled in any desired combination, permitting the testing of hypotheses on gene action/interaction, or the construction of varieties of plant species with improved agronomic performance. The next technological challenge is to 'walk' from RFLP markers to isolate the actual genes responsible for the complex trait, by a combination of genetical and physical mapping techniques. Such analyses will begin to clarify our picture of the relationship between genetic and physical maps, of recombination, and of the arrangement of diverse families of DNA sequences in plant genomes.

Genes, Plant↗
PubMed2005

Application of family-based association testing to assess the genotype-phenotype association involved in complex traits using single-nucleotide polymorphisms.

BACKGROUND: We used the FBAT (family-based association test) software to test for association between 300 individual single-nucleotide polymorphisms and P1 (a latent trait of Kofendred Personality Disorder) in 100 simulated replicates of the Aipotu population. Using the Genetic Analysis Workshop 14 dataset, we calculated the power of FBAT to detect linkage disequilibrium on chromosome 3 (D2). Also, we calculated the false-positive rate on chromosome 1, which contains a true locus (D1) but no linkage disequilibrium was simulated between the trait and all the surrounding single-nucleotide polymorphisms. RESULTS: We were able to detect the associations between phenotype P1 and three adjacent markers B03T3056 (average p-value = 0.0002), B03T3057 (average p-value = 0.00072), and B03T3058 (average p-value = 0.0038) with power of 98%, 87%, 71% on chromosome 3, respectively. The overall false positive rate to detect association was 0.06 on chromosome 1. CONCLUSION: The power to detect a significant association in 100 nuclear families affected with the latent trait of Kofendred Personality Disorder by using FBAT was reasonable (based on 100 replicates). In the future, we will compare the performance of FBAT with alternative approaches, such as using FBAT-generalized estimating equations methods to test for association in families affected with complex traits.

Chromosomes, Human, Pair 1↗
PubMed1994

Monte Carlo likelihood in the genetic mapping of complex traits.

Many of the likelihoods arising in the analysis of complex genetic traits, particularly in linkage analysis, are computationally infeasible. Where exact likelihoods cannot be computed, Monte Carlo estimates of likelihoods may provide a satisfactory alternative. Although simulation on pedigrees is straightforward, simulation conditional upon observed phenotypic data is not. However, recent advances in Markov chain Monte Carlo methods have provided a method well suited to this problem. From realizations of underlying genes, simulated under a genetic model, conditional upon observed data, a Monte Carlo estimate of this likelihood surface can be formed. Various sampler and model modifications are needed to enhance the statistical efficiency of the Monte Carlo estimator; as these methods become increasingly developed, this approach becomes a useful tool in resolving the genes contributing to the phenotypes associated with genetically complex diseases.

Algorithms↗
PubMed2005

Dissecting complex traits: the geneticists' "Around the world in 80 days".

The identification of genes mildly affecting quantitative phenotypes constitutes a difficult task that has almost always eluded application, particularly in behavioral phenotypes. Recently, the first study that identified a gene underlying a QTL affecting anxiety was published. In the course of that study, novel approaches were developed that can significantly reduce the time required to identify such genes. The identification of genes affecting complex traits is expected to provide significant insights into the biochemical mechanisms underlying these poorly understood traits.

Animals↗
PubMed1999

Genetic dissection of Plasmodium falciparum blood infection levels and other complex traits related to human malaria infection.

There is accumulating evidence of host genetic control in malaria infection and, in humans, some genes have been associated with severe malaria. Nevertheless, other important genes controlling blood infection levels, malarial disease and immune responses are likely to be identified. In this paper, we focus on segregation and linkage analyses of blood infection levels in an urban population living in Burkina Faso. We found evidence of a complex genetic control and a linkage to chromosome 5q31-q33. The identification of genes controlling complex traits related to malaria infection should be helpful in understanding protective mechanisms and the relationship between infection, malaria attacks and severe malaria.

Age Factors↗
PubMed2003

The inherited basis of diabetes mellitus: implications for the genetic analysis of complex traits.

Diabetes encompasses a heterogeneous group of diseases, each with a substantial genetic component. We review the division of diabetes into different subtypes based on clinical phenotype, the fruitful pursuit of genes underlying monogenic forms of the disease, the successes and drawbacks of whole-genome linkage scans in type 1 and type 2 diabetes, and the recent identification of several diabetes genes by large association studies. We use the lessons learned from this extensive body of evidence to illustrate general implications for the genetic analysis of complex traits.

Diabetes Mellitus, Type 1↗
PubMed2006

Functional mapping - how to map and study the genetic architecture of dynamic complex traits.

The development of any organism is a complex dynamic process that is controlled by a network of genes as well as by environmental factors. Traditional mapping approaches for analysing phenotypic data measured at a single time point are too simple to reveal the genetic control of developmental processes. A general statistical mapping framework, called functional mapping, has been proposed to characterize, in a single step, the quantitative trait loci (QTLs) or nucleotides (QTNs) that underlie a complex dynamic trait. Functional mapping estimates mathematical parameters that describe the developmental mechanisms of trait formation and expression for each QTL or QTN. The approach provides a useful quantitative and testable framework for assessing the interplay between gene actions or interactions and developmental changes.

Animals↗
PubMed2001

Epistasis, complex traits, and mapping genes.

Using a three-locus model wherein two loci regulate a third, candidate locus, I examine physiological epistasis from the 'gene's eye view' of the regulated locus. I show that, depending upon genetic background at the regulatory loci, an allele at the candidate locus can be dominant, additive, recessive, neutral, over-dominant, or under-dominant in its effects on fitness. This kind of variation in allelic effect caused by variation in genetic background from population to population, from time to time in the same population, or sample to sample makes finding and mapping the genes underlying a complex phenotype difficult. The rate of evolution of such genes can also be slowed, especially in genetically subdivided metapopulations with migration. Nevertheless, understanding how variation in genetic background causes variation in allelic effects permits the genetic architecture of such complex traits to be dissected into the interacting component genes. While some backgrounds diminish allelic effects and make finding and mapping genes difficult, other backgrounds enhance allelic effects and facilitate gene mapping.

Alleles↗
PubMed1995

Detection of genome similarity as an exploratory tool for mapping complex traits.

For one- and two-trait-locus models, we show that the lod score based on affected relative pairs or trios is a monotonically increasing function of the genome similarity measured by the proportion of alleles shared identical by descent (IBD) conditional on observed marker data. These results can be generalized to multitrait-locus models. Thus, we can use conditional probability of genes shared IBD as a tool to reveal chromosomal segments that are likely to harbor the genes underlying the complex traits.

Alleles↗
PubMed2003

Mapping complex traits using Random Forests.

Random Forest is a prediction technique based on growing trees on bootstrap samples of data, in conjunction with a random selection of explanatory variables to define the best split at each node. In the case of a quantitative outcome, the tree predictor takes on a numerical value. We applied Random Forest to the first replicate of the Genetic Analysis Workshop 13 simulated data set, with the sibling pairs as our units of analysis and identity by descent (IBD) at selected loci as our explanatory variables. With the knowledge of the true model, we performed two sets of analyses on three phenotypes: HDL, triglycerides, and glucose. The goal was to approach the mapping of complex traits from a multivariate perspective. The first set of analyses mimics a candidate gene approach with a high proportion of true genes among the predictors while the second set represents a genome scan analysis using microsatellite markers. Random Forest was able to identify a few of the major genes influencing the phenotypes, such as baseline HDL and triglycerides, but failed to identify the major genes regulating baseline glucose levels.

Chromosome Mapping↗
PubMed2005

Enhanced phenotyping of complex traits with a circadian clock model.

Models of biological systems are increasingly used to generate and test predictions in silico. This article explores the basic workings of a multifeedback network model of a circadian clock. In a series of in silico experiments, we investigated the influence of the number of feedbacks by adding and removing one or more. We further explore the possibilities of testing in silico models in classic "circadian" protocols. In addition, we performed an in silico mutagenesis screen (by altering parameters throughout the network), creating a library of mutants (based on "phenotype," not "genotype"), and subjected them to a variety of straightforward "circadian" protocols. The results of this mutant "taxonomy" are surprising. While most mutants can be identified (separated) using a limited set of experimental protocols, some resist such a separation, even when "mutations" are at vastly different locations within the complex model. Furthermore, some protocols distinguish similar alleles of the same component, which would be counterproductive. The described taxonomy invites experimental verification, in vivo, and may ultimately streamline genotyping of complex traits, which may have been based previously on imprecise phenotypes.

Circadian Rhythm↗