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At least 73 records · Page 4Linked to original sources
PubMed1997

A twin-pronged attack on complex traits.

Before one starts the hunt for quantitative trait loci (QTLs) for a complex trait, it is necessary to show that the trait is genetically influenced. This evidence is most likely to come from the classical twin study--the demonstration that monozygotic twins are more similar for the trait than dizygotic twins. The strengths and weaknesses of twin studies are discussed, and it is suggested that, far from becoming irrelevant with advances in molecular biology, they can improve the efficiency of QTL detection and play an important role in unravelling developmental genetic mechanisms.

Diseases in Twins↗
PubMed2004

Genetic structure of the LXS panel of recombinant inbred mouse strains: a powerful resource for complex trait analysis.

The set of LXS recombinant inbred (RI) strains is a new and exceptionally large mapping panel that is suitable for the analysis of complex traits with comparatively high power. This panel consists of 77 strains-more than twice the size of other RI sets--and will typically provide sufficient statistical power (beta = 0.8) to map quantitative trait loci (QTLs) that account for approximately 25% of genetic variance with a genomewide p < 0.05. To characterize the genetic architecture of this new set of RI strains, we genotyped 330 MIT microsatellite markers distributed on all autosomes and the X Chromosome and assembled error-checked meiotic recombination maps that have an average F2-adjusted marker spacing of approximately 4 cM. The LXS panel has a genetic structure consistent with random segregation and subsequent fixation of alleles, the expected 3-4 x map expansion, a low level of nonsyntenic association among loci, and complete independence among all 77 strains. Although the parental inbred strains-Inbred Long-Sleep (ILS) and Inbred Short-Sleep (ISS)--were derived originally by selection from an 8-way heterogeneous stock selected for differential sensitivity to sedative effects of ethanol, the LXS panel is also segregating for many other traits. Thus, the LXS panel provides a powerful new resource for mapping complex traits across many systems and disciplines and should prove to be of great utility in modeling the genetics of complex diseases in human populations.

Alleles↗
PubMed2006

Genetic and environmental effects on complex traits in mice.

The interaction between genotype and environment is recognized as an important source of experimental variation when complex traits are measured in the mouse, but the magnitude of that interaction has not often been measured. From a study of 2448 genetically heterogeneous mice, we report the heritability of 88 complex traits that include models of human disease (asthma, type 2 diabetes mellitus, obesity, and anxiety) as well as immunological, biochemical, and hematological phenotypes. We show that environmental and physiological covariates are involved in an unexpectedly large number of significant interactions with genetic background. The 15 covariates we examined have a significant effect on behavioral and physiological tests, although they rarely explain >10% of the variation. We found that interaction effects are more frequent and larger than the main effects: half of the interactions explained >20% of the variance and in nine cases exceeded 50%. Our results indicate that assays of gene function using mouse models should take into account interactions between gene and environment.

Animals↗
PubMed2006

Genome-wide genetic association of complex traits in heterogeneous stock mice.

Difficulties in fine-mapping quantitative trait loci (QTLs) are a major impediment to progress in the molecular dissection of complex traits in mice. Here we show that genome-wide high-resolution mapping of multiple phenotypes can be achieved using a stock of genetically heterogeneous mice. We developed a conservative and robust bootstrap analysis to map 843 QTLs with an average 95% confidence interval of 2.8 Mb. The QTLs contribute to variation in 97 traits, including models of human disease (asthma, type 2 diabetes mellitus, obesity and anxiety) as well as immunological, biochemical and hematological phenotypes. The genetic architecture of almost all phenotypes was complex, with many loci each contributing a small proportion to the total variance. Our data set, freely available at http://gscan.well.ox.ac.uk, provides an entry point to the functional characterization of genes involved in many complex traits.

Animals↗
PubMed2001

Improved power of sib-pair linkage analysis using measures of complex trait dynamics.

The influence of epistasis on a quantitative trait can reduce the power of linkage analysis to identify the underlying loci. In the present study, we simulated a complex trait derived from a dynamic one-locus gene expression system with epistasis arising from feedback regulation and tested the power of sib-pair linkage analysis methods for detecting the underlying quantitative trait locus (QTL). Using this simple genetic architecture, we demonstrate that the power of sib-pair linkage analysis can be greatly improved if measures of complex trait dynamics are considered.

Computer Simulation↗
PubMed1995

High-resolution genetic mapping of complex traits.

Positional cloning requires high-resolution genetic mapping. To plan a positional cloning project, one needs to know how many informative meioses will be required to narrow the search for a disease gene to an acceptably small region. For a simple Mendelian trait studied with linkage analysis, the answer is straightforward. In this paper, we address the situation of a complex trait studied with affected-relative-pair methods. We derive mathematical formulas for the size of an appropriate confidence region, as a function of the relative risk attributable to the gene. Using these results, we provide graphs showing the number of relative pairs required to narrow the gene hunt to an interval of a given size. For example, we show that localizing a gene to 1 cM requires a median of 200 sib pairs for a locus causing a fivefold increased risk to an offspring and 700 sib pairs for a locus causing a twofold increased risk. We discuss the implications of these results for the positional cloning of genes underlying complex traits.

Alleles↗
PubMed2006

Revealing complex traits with small molecules and naturally recombinant yeast strains.

Here we demonstrate that natural variants of the yeast Saccharomyces cerevisiae are a model system for the systematic study of complex traits, specifically the response to small molecules. As a complement to artificial knockout collections of S. cerevisiae widely used to study individual gene function, we used 314- and 1932-member libraries of mutant strains generated by meiotic recombination to study the cumulative, quantitative effects of natural mutations on phenotypes induced by 23 small-molecule perturbagens (SMPs). This approach reveals synthetic lethality between SMPs, and genetic mapping studies confirm the involvement of multiple quantitative trait loci in the response to two SMPs that affect respiratory processes. The systematic combination of natural variants of yeast and small molecules that modulate evolutionarily conserved cellular processes can enable a better understanding of the general features of complex traits.

Biosensing Techniques↗
PubMed2005

Genetic approaches to studying common diseases and complex traits.

Most common diseases and most quantitative traits that can be measured in human populations are complex genetic traits. That is, many genetic and nongenetic factors interact to determine the final phenotype, whether that phenotype is susceptibility to disease, or a quantifiable trait such as height, weight, serum cholesterol, or blood pressure. Identifying the genes that underlie the population variation in these phenotypes has been challenging. Recently, databases of common genetic variants, recognition of the patterns of genetic variation, and rapid genotyping methodologies have emerged, and the combination of these tools and resources will greatly facilitate genetic association studies, a potentially powerful method to map the genes for complex traits. However, care will be required in performing and interpreting these association studies. Until genome-wide studies are feasible, choosing candidate genes will be necessary. In addition, the choice of phenotype will likely influence the success of these gene mapping efforts. Finally, population genetic methods, including searching for genes under selection, may provide clues to the location of the genes for common disease and complex traits.

Alanine↗
PubMed2006

Novel integrative genomics strategies to identify genes for complex traits.

Forward genetics is a common approach to dissecting complex traits like common human diseases. The ultimate aim of this approach was the identification of genes that are causal for disease or other phenotypes of interest. However, the forward genetics approach is by definition restricted to the identification of genes that have incurred mutations over the course of evolution or that incurred mutations as a result of chemical mutagenesis, and that as a result lead to disease or to variations in other phenotypes of interest. Genes that harbour no such mutations, but that play key roles in parts of the biological network that lead to disease, are systematically missed by this class of approaches. Recently, a class of novel integrative genomics approaches has been devised to elucidate the complexity of common human diseases by intersecting genotypic, molecular profiling, and clinical data in segregating populations. These novel approaches take a more holistic view of biological systems and leverage the vast network of gene-gene interactions, in combination with DNA variation data, to establish causal relationships among molecular profiling traits and between molecular profiling and disease (or other classic phenotypes). A number of novel genes for disease phenotypes have been identified as a result of these approaches, highlighting the utility of integrating orthogonal sources of data to get at the underlying causes of disease.

Animals↗
PubMed1994

Genetic dissection of complex traits.

Medical genetics was revolutionized during the 1980s by the application of genetic mapping to locate the genes responsible for simple Mendelian diseases. Most diseases and traits, however, do not follow simple inheritance patterns. Genetics have thus begun taking up the even greater challenge of the genetic dissection of complex traits. Four major approaches have been developed: linkage analysis, allele-sharing methods, association studies, and polygenic analysis of experimental crosses. This article synthesizes the current state of the genetic dissection of complex traits--describing the methods, limitations, and recent applications to biological problems.

Alleles↗
PubMed2007

The quest for genes causing complex traits in ocular medicine: successes, interpretations, and challenges.

Gene mapping and positional cloning have gained acceptance as state-of-the-art methods to identify molecules that cause common complex diseases. However, the use of specialized technology, varying study designs, and misconceptions about the role of novel findings in genetics has led to confusion among basic scientists and health care professionals alike regarding the importance of these findings in molecular diagnostics and individualized medicine. To alleviate this confusion, the successes achieved in the past few years in mapping of genes for complex traits such as age-related macular degeneration and glaucoma are interpreted in the context of the appropriate population biology framework. The current article veers away from propagating the overly simplistic belief of a linear relationship between a specific gene and age-related macular degeneration, particularly one that equates possession of a specific risk allele as the only precursor to end-stage disease. Ascribing predictive properties to a single gene without consideration of its network partners, timing of action, or environmental correlates argues for a static view of gene action. Modern viewpoints of the mechanisms of action of a gene are contextual and encompass more cohesive frameworks, ranging from the developmental timing of action, to the genomic and environmental milieu. In this regard, gene mapping studies that have been so immensely successful in the gene detection phase of a study provide biased perspectives on the importance of these genes and the corresponding risk alleles in the general population because of their limited sample size and constrained design. To move the field of gene-based diagnosis forward, it will be necessary to conduct additional cohort and longitudinal studies using the original gene finding studies as a knowledge base to develop predictive models. In summary, while we have achieved great successes in finding genes for complex traits, the application of these findings to clinical medicine is not straightforward. The key question of who will develop disease in the future remains.

Chromosome Mapping↗
PubMed2003

Combining gene expression and molecular marker information for mapping complex trait genes: a simulation study.

A method for mapping complex trait genes using cDNA microarray and molecular marker data jointly is presented and illustrated via simulation. We introduce a novel approach for simulating phenotypes and genotypes conditionally on real, publicly available, microarray data. The model assumes an underlying continuous latent variable (liability) related to some measured cDNA expression levels. Partial least-squares logistic regression is used to estimate the liability under several scenarios where the level of gene interaction, the gene effect, and the number of cDNA levels affecting liability are varied. The results suggest that: (1) the usefulness of microarray data for gene mapping increases when both the number of cDNA levels in the underlying liability and the QTL effect decrease and when genes are coexpressed; (2) the correlation between estimated and true liability is large, at least under our simulation settings; (3) it is unlikely that cDNA clones identified as significant with partial least squares (or with some other technique) are the true responsible cDNAs, especially as the number of clones in the liability increases; (4) the number of putatively significant cDNA levels increases critically if cDNAs are coexpressed in a cluster (however, the proportion of true causal cDNAs within the significant ones is similar to that in a no-coexpression scenario); and (5) data reduction is needed to smooth out the variability encountered in expression levels when these are analyzed individually.

Chromosome Mapping↗
PubMed2002

Evaluating the results of genomewide linkage scans of complex traits by locus counting.

The evaluation of results from primary genomewide linkage scans of complex human traits remains an area of importance and considerable debate. Apart from the usual assessment of statistical significance by use of asymptotic and empirical calculations, an additional means of evaluation--based on counting the number of distinct regions showing evidence of linkage--is possible. We have explored the characteristics of such a locus-counting method over a range of experimental conditions typically encountered during genomewide scans for complex trait loci. Under the null hypothesis, factors that have an impact on the informativeness of the data--such as map density, availability of parental data, and completeness of genotyping--are seen to markedly influence the number of regions of excess allele sharing and the empirically derived genomewide significance of the associated LOD score thresholds. In some circumstances, the expected number of regions is less than one-quarter of that predicted under the assumption of a dense map and complete extraction of inheritance information. We have applied this method to a previously analyzed data set--the Warren 2 genome scan for type 2-diabetes susceptibility--and demonstrate that more regions showing evidence for linkage were observed in the primary genome scan than would be expected by chance, across the whole range of LOD scores, even though no single linkage result achieved empirical genomewide statistical significance. Locus counting may be useful in assessing the results from genome scans for complex traits in general, especially because relatively few scans generate evidence for linkage reaching genomewide significance by dense-map criteria. By taking account of the effects of reduced data informativeness on the expected number of regions showing evidence for linkage, a more meaningful, and less conservative, evaluation of the results from such linkage studies is possible.

Computer Simulation↗
PubMed2005

Epistatic pleiotropy and the genetic architecture of covariation within early and late-developing skull trait complexes in mice.

The role of epistasis as a source of trait variation is well established, but its role as a source of covariation among traits (i.e., as a source of "epistatic pleiotropy") is rarely considered. In this study we examine the relative importance of epistatic pleiotropy in producing covariation within early and late-developing skull trait complexes in a population of mice derived from an intercross of the Large and Small inbred strains. Significant epistasis was found for several pairwise combinations of the 21 quantitative trait loci (QTL) affecting early developing traits and among the 20 QTL affecting late-developing traits. The majority of the epistatic effects were restricted to single traits but epistatic pleiotropy still contributed significantly to covariances. Because of their proportionally larger effects on variances than on covariances, epistatic effects tended to reduce within-group correlations of traits and reduce their overall degree of integration. The expected contributions of single-locus and two-locus epistatic pleiotropic QTL effects to the genetic covariance between traits were analyzed using a two-locus population genetic model. The model demonstrates that, for single-locus or epistatic pleiotropy to contribute to trait covariances in the study population, both traits must show the same pattern of single-locus or epistatic effects. As a result, a large number of the cases where loci show pleiotropic effects do not contribute to the covariance between traits in this population because the loci show a different pattern of effect on the different traits. In general, covariance patterns produced by single-locus and epistatic pleiotropy predicted by the model agreed well with actual values calculated from the QTL analysis. Nearly all single-locus and epistatic pleiotropic effects contributed positive components to covariances between traits, suggesting that genetic integration in the skull is achieved by a complex combination of pleiotropic effects.

Age Factors↗
PubMed1998

Mapping of complex traits by single-nucleotide polymorphisms.

Molecular geneticists are developing the third-generation human genome map with single-nucleotide polymorphisms (SNPs), which can be assayed via chip-based microarrays. One use of these SNP markers is the ability to locate loci that may be responsible for complex traits, via linkage/linkage-disequilibrium analysis. In this communication, we describe a semiparametric method for combined linkage/linkage-disequilibrium analysis using SNP markers. Asymptotic results are obtained for the estimated parameters, and the finite-sample properties are evaluated via a simulation study. We also applied this technique to a simulated genome-scan experiment for mapping a complex trait with two major genes. This experiment shows that separate linkage and linkage-disequilibrium analyses correctly detected the signals of both major genes; but the rates of false-positive signals seem high. When linkage and linkage-disequilibrium signals were combined, the analysis yielded much stronger and clearer signals for the presence of two major genes than did two separate analyses.

Biosensing Techniques↗
PubMed1998

Efficient, robust, and unified method for mapping complex traits (I): two-point linkage analysis.

The completion of a preliminary human genome map and development of molecular methods have enabled researchers to assay a large number of polymorphic markers that are evenly spaced along the entire human genome. Among many applications, marker data are valuable for mapping complex traits through linkage or linkage-disequilibrium analysis, the former of which is the focus of this paper, the first in a series on this subject. Formalizing the concept and computation for linkage analysis, Elston and Stewart [1971; Human Heredity 21:523-542] introduced a likelihood function to capture relevant genetic information and a recursive algorithm for computing the likelihood function. However, the computing burden is prohibitive in processing complex pedigrees. Since that fundamental development, improving the computational algorithm and extending the method has been a dynamic area of research. The primary objective of this communication is to introduce a semiparametric method for linkage analysis. It is a particularly suitable approach with desirable properties for mapping complex traits that may be binary, continuous, and partially observed (i.e., censored). It incorporates candidate genes, environmental factors, and their interactions with the putative gene and is expected to be robust and efficient in comparison with likelihood-based methods. The properties of the estimates have been studied in finite samples with a limited simulation study. This method is illustrated with an application to family data contributed to the Breast Cancer Consortium.

Algorithms↗
PubMed2004

Replication in genetic studies of complex traits.

Disappointments in replicating initial findings in gene mapping for complex traits are often attributed to small sample sizes and inadequate techniques to determine the threshold value. This is clearly not the whole truth. More fundamental reasons lie in the inherent heterogeneity related to disease, including genetic heterogeneity, differences in allele frequencies, and context-dependency in genetic architecture. There are also other reasons related to the data collection and analysis. Replication may remain a source of frustration unless more emphasis is put on controlling these sources of heterogeneity between studies.

Bayes Theorem↗