Search PubMed⌕ Search

SEARCH · Search PubMed

Results for “complex trait”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 145 records · Page 8Linked to original sources
PubMed1996

A general statistical model for detecting complex-trait loci by using affected relative pairs in a genome search.

Scanning of the human genome by use of affected relative pairs and dense sets of highly polymorphic markers or by emerging techniques such as genomic mismatch scanning. (GMS) is making it possible to identify the genetic etiology of a disease through detection of susceptibility loci. We present a general statistical model and test to detect disease genes, using affected relative pairs and either markers or GMS technologies in a genome search. There are an exact test and large-sample normal approximation that control for the elevated probability of false detection of linkage in a genome search. The approach can be used to determine the sample size needed to obtain a prespecified power to detect a disease gene in the presence of etiologic heterogeneity for a single class or mixture of relative classes, with any number of markers, or clones, markers PIC values, or mapping function. The approach is used to examine differences in performance of markers and GMS technologies in a common statistical framework and to provide practical information for designing studies of complex traits.

Alleles↗
PubMed2004

Coalescent-based association mapping and fine mapping of complex trait loci.

We outline a general coalescent framework for using genotype data in linkage disequilibrium-based mapping studies. Our approach unifies two main goals of gene mapping that have generally been treated separately in the past: detecting association (i.e., significance testing) and estimating the location of the causative variation. To tackle the problem, we separate the inference into two stages. First, we use Markov chain Monte Carlo to sample from the posterior distribution of coalescent genealogies of all the sampled chromosomes without regard to phenotype. Then, averaging across genealogies, we estimate the likelihood of the phenotype data under various models for mutation and penetrance at an unobserved disease locus. The essential signal that these models look for is that in the presence of disease susceptibility variants in a region, there is nonrandom clustering of the chromosomes on the tree according to phenotype. The extent of nonrandom clustering is captured by the likelihood and can be used to construct significance tests or Bayesian posterior distributions for location. A novelty of our framework is that it can naturally accommodate quantitative data. We describe applications of the method to simulated data and to data from a Mendelian locus (CFTR, responsible for cystic fibrosis) and from a proposed complex trait locus (calpain-10, implicated in type 2 diabetes).

Alleles↗
PubMed2003

Schizophrenia as a complex trait: evidence from a meta-analysis of twin studies.

CONTEXT: There are many published twin studies of schizophrenia. Although these studies have been reviewed previously, to our knowledge, no review has provided quantitative summary estimates of the impact of genes and environment on liability to schizophrenia that also accounted for the different ascertainment strategies used. OBJECTIVE: To calculate meta-analytic estimates of heritability in liability and shared and individual-specific environmental effects from the pooled twin data. DATA SOURCES: We used a structured literature search to identify all published twin studies of schizophrenia, including MEDLINE, dissertation, and books-in-print searches. STUDY SELECTION: Of the 14 identified studies, 12 met the minimal inclusion criteria of systematic ascertainment. DATA SYNTHESIS: By using a multigroup twin model, we found evidence for substantial additive genetic effects-the point estimate of heritability in liability to schizophrenia was 81% (95% confidence interval, 73%-90%). Notably, there was consistent evidence across these studies for common or shared environmental influences on liability to schizophrenia-joint estimate, 11% (95% confidence interval, 3%-19%). CONCLUSIONS: Despite evidence of heterogeneity across studies, these meta-analytic results from 12 published twin studies of schizophrenia are consistent with a view of schizophrenia as a complex trait that results from genetic and environmental etiological influences. These results are broadly informative in that they provide no information about the specific identity of these etiological influences, but they do provide a component of a unifying empirical basis supporting the rationality of searches for underlying genetic and common environmental etiological factors.

Diseases in Twins↗
PubMed2004

Genotyping pooled DNA on microarrays: a systematic genome screen of thousands of SNPs in large samples to detect QTLs for complex traits.

Large samples and systematic screens of thousands of DNA markers are needed to detect quantitative trait loci (QTLs) of small effect size. One approach to conduct systematic genome scans for association is to use microarrays which, although expensive and non-reusable, simultaneously genotype thousands of single-nucleotide polymorphisms (SNPs). This brief report provides proof of principle that groups of pooled DNA (for example cases and controls) can be genotyped reliably on a microarray. DNA was pooled for 105 Caucasian males and genotyped three times on microarrays for more than 10,000 SNPs (Affymetrix GeneChip Mapping 10K Array Xba 131). The average correlation was 0.973 between the allele frequency estimates for the three microarrays using the same DNA pool. The correlation was 0.923 between the average of the three microarray estimates using pooled DNA and individual genotyping estimates for a Caucasian population as provided by Affymetrix (NetAffx). Thus, genotyping pooled DNA on microarrays can provide a systematic and powerful approach for identifying QTL associations for complex traits including behavioral dimensions and disorders.

DNA↗
PubMed2025

Orthobunyavirus neurovirulence is a complex trait involving all three genome segments.

La Crosse orthobunyavirus (LACV) is a tri-segmented negative sense RNA virus and is the leading cause of pediatric arboviral encephalitis in the USA. The viral factors that mediate LACV's ability to replicate and cause damage and disease in the brain (neurovirulence) are not fully understood. We previously characterized the neurovirulence of LACV and closely related Inkoo virus (INKV) and discovered they have opposing neurovirulence phenotypes in mice and human neuronal cells: LACV has high neurovirulence and INKV has low neurovirulence. We therefore generated reassortant viruses between LACV and INKV to map the genome segments that mediate LACV's high neurovirulence phenotype. We recovered all six possible reassortant viruses of the L, M, and S genome segments using coinfection and reverse genetics approaches. We evaluated the neurovirulence of these reassortant viruses in mice in vivo and in human neuronal cells in vitro. Our results show that no single LACV genome segment alone was sufficient to cause wildtype LACV-like neurological disease in mice, and in fact all six reassortant viruses were attenuated from wildtype LACV. We found that the LACV M and S segments together were the primary drivers of neurological disease in mice, whereas the LACV L segment played a minor role. Our in vitro results indicate that the LACV M segment is crucial for efficient replication in neurons, but the LACV L segment appears to mediate slightly more efficient neuronal replication than the INKV L segment. The LACV M and S segments together induced wildtype LACV-like levels of neuronal death, indicating the LACV M and S are the primary mediators of neuronal death, and the L segment is not required. Together, these results indicate that LACV neurovirulence is a complex trait mediated by viral proteins on all three genome segments.

Journal Article↗
PubMed2006

Allele-specific KRT1 expression is a complex trait.

The differential expression of alleles occurs commonly in humans and is likely an important genetic factor underlying heritable differences in phenotypic traits. Understanding the molecular basis of allelic expression differences is thus an important challenge. Although many genes have been shown to display differential allelic expression, this is the first study to examine in detail the cumulative effects of multiple cis-regulatory polymorphisms responsible for allele-specific expression differences. We have used a variety of experimental approaches to identify and characterize cis-regulatory polymorphisms responsible for the extreme allele-specific expression differences of keratin-1 (KRT1) in human white blood cells. The combined data from our analyses provide strong evidence that the KRT1 allelic expression differences result from the haplotypic combinations and interactions of five cis-regulatory single nucleotide polymorphisms (SNPs) whose alleles differ in their affinity to bind transcription factors and modulate KRT1 promoter activity. Two of these cis-regulatory SNPs bind transcriptional activators with the alleles on the high-expressing KRT1 haplotype pattern having a higher affinity than the alleles on the low-expressing haplotype pattern. In contrast, the other three cis-regulatory SNPs bind transcriptional inhibitors with the alleles on the low-expressing haplotype pattern having a higher affinity than the alleles on the high-expressing haplotype pattern. Our study provides important new insights into the degree of complexity that the cis-regulatory sequences responsible for allele-specific transcriptional regulation have. These data suggest that allelic expression differences result from the cumulative contribution of multiple DNA sequence polymorphisms, with each having a small effect, and that allele-specific expression can thus be viewed as a complex trait.

Base Sequence↗
PubMed2005

The IgA nephropathy Biobank. An important starting point for the genetic dissection of a complex trait.

BACKGROUND: IgA nephropathy (IgAN) or Berger's disease, is the most common glomerulonephritis in the world diagnosed in renal biopsied patients. The involvement of genetic factors in the pathogenesis of the IgAN is evidenced by ethnic and geographic variations in prevalence, familial clustering in isolated populations, familial aggregation and by the identification of a genetic linkage to locus IGAN1 mapped on 6q22-23. This study seems to imply a single major locus, but the hypothesis of multiple interacting loci or genetic heterogeneity cannot be ruled out. The organization of a multi-centre Biobank for the collection of biological samples and clinical data from IgAN patients and relatives is an important starting point for the identification of the disease susceptibility genes. DESCRIPTION: The IgAN Consortium organized a Biobank, recruiting IgAN patients and relatives following a common protocol. A website was constructed to allow scientific information to be shared between partners and to divulge obtained data (URL: http://www.igan.net). The electronic database, the core of the website includes data concerning the subjects enrolled. A search page gives open access to the database and allows groups of patients to be selected according to their clinical characteristics. DNA samples of IgAN patients and relatives belonging to 72 multiplex extended pedigrees were collected. Moreover, 159 trios (sons/daughters affected and healthy parents), 1068 patients with biopsy-proven IgAN and 1040 healthy subjects were included in the IgAN Consortium Biobank. Some valuable and statistically productive genetic studies have been launched within the 5th Framework Programme 1998-2002 of the European project No. QLG1-2000-00464 and preliminary data have been published in "Technology Marketplace" website: http://www.cordis.lu/marketplace. CONCLUSION: The first world IgAN Biobank with a readily accessible database has been constituted. The knowledge gained from the study of Mendelian diseases has shown that the genetic dissection of a complex trait is more powerful when combined linkage-based, association-based, and sequence-based approaches are performed. This Biobank continuously expanded contains a sample size of adequately matched IgAN patients and healthy subjects, extended multiplex pedigrees, parent-child trios, thus permitting the combined genetic approaches with collaborative studies.

Databases, Nucleic Acid↗
PubMed1999

Genetic mapping of complex traits.

Statistical genetic mapping methods are powerful tools for finding genes that contribute to complex human traits. Mapping methods combine knowledge of the biological mechanisms of inheritance and the randomness inherent in those mechanisms to locate, with increasing precision, trait genes on the human genome. We provide an overview of the two major classes of mapping methods, genetic linkage analysis and linkage disequilibrium analysis, and related concepts of genetic inheritance.

Chromosome Mapping↗
PubMedDate not supplied

Gene-environment interaction and the mapping of complex traits: some statistical models and their implications.

The manifestation of many complex diseases or traits is very likely the result of an inextricable interplay of the biological and the environmental. Yet the role of environmental effect has traditionally been played down, for various reasons. In this paper, some simple statistical models that incorporate gene-environment interaction (GEI) have been proposed and their behavior and implications investigated. These implications concern the conditional independence assumption in likelihood calculation of pedigree data, the fine-tuning of the sib pair method for mapping quantitative traits, apportioning of disease or trait variation due to specific causes. In addition, they concern properties of gene mapping methods that do not take GEI into account, and they bring into question the utility of commonly used measures of genetic effects such as recurrence risk ratio for relative pairs, twin concordance rates, and heritability coefficients. In the presence of GEI, all these measures are functions not only of genetic effects and gene frequency, but also of environmental effects, the distribution of environmental factors in the population, and of GEI. Above all, these measures are all measures of familial aggregation, since they can be significant even in the absence of any genetic component of the disease. Thus their use as indicators of the genetic basis of complex diseases is cast into doubt.

Chromosome Mapping↗
PubMed2004

Epigenetic regulation of translation reveals hidden genetic variation to produce complex traits.

Phenotypic plasticity and the exposure of hidden genetic variation both affect the survival and evolution of new traits, but their contributing molecular mechanisms are largely unknown. A single factor, the yeast prion [PSI(+)], may exert a profound effect on both. [PSI(+)] is a conserved, protein-based genetic element that is formed by a change in the conformation and function of the translation termination factor Sup35p, and is transmitted from mother to progeny. Curing cells of [PSI(+)] alters their survival in different growth conditions and produces a spectrum of phenotypes in different genetic backgrounds. Here we show, by examining three plausible explanations for this phenotypic diversity, that all traits tested involved [PSI(+)]-mediated read-through of nonsense codons. Notably, the phenotypes analysed were genetically complex, and genetic re-assortment frequently converted [PSI(+)]-dependent phenotypes to stable traits that persisted in the absence of [PSI(+)]. Thus, [PSI(+)] provides a temporary survival advantage under diverse conditions, increasing the likelihood that new traits will become fixed by subsequent genetic change. As an epigenetic mechanism that globally affects the relationship between genotype and phenotype, [PSI(+)] expands the conceptual framework for phenotypic plasticity, provides a one-step mechanism for the acquisition of complex traits and affords a route to the genetic assimilation of initially transient epigenetic traits.

Biological Evolution↗
PubMed2000

Complex trait analysis in the mouse: The strengths, the limitations and the promise yet to come.

In 1990, David Baltimore predicted that the 1990s would be the decade of the mouse (). This certainly proved to be true: The mouse has contributed immensely to biological research through transgenic, embryonic stem cell (ES) knockout, and classical genetic technologies. But its usefulness as a model organism is by no means over; indeed it is still rising to its peak: The mouse as a model mammalian organism still has much to offer. This article reviews use of the mouse to dissect complex genetic traits using quantitative trait analysis, with a particular emphasis on medically important diseases.

Animals↗
PubMed2004

Quantitative trait locus mapping based on resampling in a vast maize testcross experiment and its relevance to quantitative genetics for complex traits.

From simulation studies it is known that the allocation of experimental resources has a crucial effect on power of QTL detection as well as on accuracy and precision of QTL estimates. In this study, we used a very large experimental data set composed of 976 F(5) maize testcross progenies evaluated in 19 environments and cross-validation to assess the effect of sample size (N), number of test environments (E), and significance threshold on the number of detected QTL, the proportion of the genotypic variance explained by them, and the corresponding bias of estimates for grain yield, grain moisture, and plant height. In addition, we used computer simulations to compare the usefulness of two cross-validation schemes for obtaining unbiased estimates of QTL effects. The maximum, validated genotypic variance explained by QTL in this study was 52.3% for grain moisture despite the large number of detected QTL, thus confirming the infinitesimal model of quantitative genetics. In both simulated and experimental data, the effect of sample size on power of QTL detection as well as on accuracy and precision of QTL estimates was large. The number of detected QTL and the proportion of genotypic variance explained by QTL generally increased more with increasing N than with increasing E. The average bias of QTL estimates and its range were reduced by increasing N and E. Cross-validation performed well with respect to yielding asymptotically unbiased estimates of the genotypic variance explained by QTL. On the basis of our findings, recommendations for planning of QTL mapping experiments and allocation of experimental resources are given.

Chromosome Mapping↗
PubMed2007

Complex trait genetics of refractive error.

Refractive errors (myopia, hyperopia, and astigmatism) are complex heterogeneous disorders of the human eye and are ideal for genetic investigation. Moderate to severe refractive errors can predispose individuals to poor visual development, various types of glaucoma, misshapen corneal surfaces, premature cataracts, and loss of retinal integrity, which can lead to detachment. Knowledge of genetic mechanisms involved in refractive error susceptibility may allow treatment to prevent progression or to further examine gene-environment interactions. Early genetic predisposition detection for developing severe refractive errors may be useful for efficient and cost-effective screening program design. This review explores the genetic mechanisms associated with nonsyndromic refractive error development known to date.

Animals↗
PubMed2006

Complex genetics of complex traits: the case of primary open-angle glaucoma.

Glaucoma, which is a complex heterogeneous disease, presents an ideal case for genetic investigation. Primary open-angle glaucoma (POAG) is the commonest subtype and will be the focus of this review. When detected early, POAG is amenable to therapeutic intervention. Unfortunately, current population-based clinical screening lacks efficacy. If individuals with a genetic predisposition for developing POAG can be identified, then efficient and cost-effective population-based screening programs could be designed. Although considerable inroads have been made in understanding the natural history of POAG caused by mutations in the myocilin and optineurin genes, other POAG genes accounting for most cases remain to be identified. This review explores the genetic mechanisms that have been unequivocally linked to the glaucomatous process and then discusses potential avenues for future breakthroughs.

Cell Cycle Proteins↗
PubMed2000

Twins. Novel uses to study complex traits and genetic diseases.

The challenge faced by research into the genetic basis of complex disease is to identify genes of small relative effect against a background of substantial genetic and environmental variation. This has focused interest on a classical epidemiological design: the study of twins. Through their precise matching for age, the common family environment and background environmental variation, studying diseases in non-identical twins provides a means to enhance the power of conventional strategies to detect genetic influence through linkage and association. The unique matching of identical twins provides researchers with ways to isolate the function of individual genes involved in disease together with approaches to understanding how genes and the environment interact.

Diseases in Twins↗
PubMed2003

Using haplotype blocks to map human complex trait loci.

Understanding of linkage disequilibrium (LD) in human populations could facilitate the discovery of genes that influence complex human diseases. The "HapMap" project is now underway to characterize patterns of LD in the human genome. A pilot study showed "haplotype blocks" in 51 regions scattered throughout the genome. These intriguing results raise important questions about the nature of recombination, and highlight practical issues of marker collection, the influence of statistical modelling on apparent block structure, and the levels of genotyping necessary for studies of common diseases. Knowledge of local disequilibrium patterns may help identify common polymorphisms involved in complex disease, but completely new analytical methods and experimental designs will be required to identify important rare variants.

Chromosome Mapping↗
PubMed1996

Modern molecular genetic approaches to complex traits: implications for psychiatric disorders.

The majority of common psychiatric disorders pose problems for geneticists because of their complex and non-Mendelian modes of inheritance. Early attempts to map genes for mental illness have so far largely overlooked this and sought genes of major effect in multiplex families using the lod score method of linkage analysis. However it seems that major genes are likely to be at best rare causes of common mental disorders, and the majority of cases probably reflect the interaction of several and perhaps many genes of comparatively small effect. There are two complementary sets of strategies that allow such genes to be identified. The first is to perform linkage analysis based on allele sharing in pairs of affected relatives. The second is to carry out association studies on samples of unrelated individuals. These methods and their applicability to psychiatric disorders are described. Psychiatric genetics has traditionally focussed on categorical phenotypes, but if valid continuous measures can be developed, powerful quantitative trait loci (QTL) approaches may also become feasible. Another important area is likely to be the study of relevant models in animals such as rodents in which genetic studies have many advantages. Finally we should not overlook the possibility that there are molecular explanations for irregular patterns of transmission such as mitochondrial inheritance, genomic imprinting and dynamic mutations.

Genetic Diseases, Inborn↗
PubMed2003

Automated detection of informative combined effects in genetic association studies of complex traits.

There is a growing body of evidence suggesting that the relationships between gene variability and common disease are more complex than initially thought and require the exploration of the whole polymorphism of candidate genes as well as several genes belonging to biological pathways. When the number of polymorphisms is relatively large and the structure of the relationships among them complex, the use of data mining tools to extract the relevant information is a necessity. Here, we propose an automated method for the detection of informative combined effects (DICE) among several polymorphisms (and nongenetic covariates) within the framework of association studies. The algorithm combines the advantages of the regressive approaches with those of data exploration tools. Importantly, DICE considers the problem of interaction between polymorphisms as an effect of interest and not as a nuisance effect. We illustrate the method with three applications on the relationship between (1). the P-selectin gene and myocardial infarction, (2). the cholesteryl ester transfer protein gene and plasma high-density-lipoprotein cholesterol concentration, and (3). genes of the renin-angiotensin-aldosterone system and myocardial infarction. The applications demonstrated that the method was able to recover results already found using other approaches, but in addition detected biologically sensible effects not previously described.

Algorithms↗