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 55 records · Page 3Linked to original sources
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↗
PubMedDate not supplied

The future of path analysis, segregation analysis, and combined models for genetic dissection of complex traits.

Appropriate combined models are discussed for the analysis of complex traits. It is argued that combined models may be necessary for optimally extracting the information from family studies. It is further argued that, especially as we face genes with much smaller effects, our ability to find these genes will depend on how precisely and accurately we are able to model the interrelationships. We need these newer models and methods for optimally extracting the information from family data, and we also need to reorient ourselves as to how we interpret the very information extracted. It is projected that path and segregation analysis, as seen in terms of combined models, will be useful in the new millennium.

Chromosome Segregation↗
PubMed2002

Third international meeting on the genetic epidemiology of complex traits, April 4-6, 2002, Cambridge, UK.

The Third International Meeting on the Genetic Epidemiology of Complex Traits was held at Churchill College, Cambridge, UK on April 4-6, 2002. The event was organized by the Twin Research and Genetic Epidemiology Unit, St Thomas' Hospital, London and sponsored by Roche Genetics and Insightful. It provided an interactive forum for discussion of topical issues relating to the genetic analysis of complex diseases and traits. Topics discussed included linkage disequilibrium mapping and candidate gene analysis, as well as cutting edge advances in both technologies and statistical analysis methods. Details of the meeting can be found at http://www.twin-research.ac.uk/.

Chromosome Mapping↗
PubMed2025

Expanded Chromatin Accessibility Mapping Explains Genetic Variation Associated with Complex Traits in Liver.

Genome-wide association studies (GWAS) have identified thousands of loci associated with a variety of common, complex human traits. Recent efforts have focused on characterizing chromatin accessibility to discover regulatory elements that modify the expression of nearby genes, suggesting that trait associations are mediated through changes in gene regulation. Genetic variants associated with differences in chromatin accessibility, known as chromatin accessibility quantitative trait loci (caQTLs), are established contributors to gene expression differences, providing mechanistic hypotheses for signals identified by GWAS. Using the assay for transposase-accessible chromatin with sequencing (ATAC-seq), we assessed chromatin accessibility in 189 diverse human liver samples, identifying over two million accessible chromatin regions enriched for gene regulatory features and, in 175 of these samples, over 14,000 caQTLs. Focusing subsequently on liver-relevant complex traits, we obtained publicly available blood lipids GWAS data and identified 157 loci where caQTLs, expression quantitative trait loci (eQTLs), and GWAS signals colocalized. This generated specific molecular hypotheses about regulatory elements, affected genes, and, in some cases, implicated transcription factors. Finally, we enumerated the set of blood lipid trait signals that lack an obvious proposed mechanism beyond catalogs of liver caQTLs and eQTLs. After integrating 10 multi-omic QTL regulatory mechanism datasets whilst considering limitations in statistical power, we found that approximately 20% of blood lipid GWAS signals lacked a statistical link to a proposed mechanism. Our results demonstrate the value of integrating multiple genomic datasets to improve understanding of GWAS signals, while emphasizing the need for additional experimental approaches to fully characterize complex trait associations.

Journal Article↗
PubMed2001

Novel selection criteria for genome scans of complex traits.

Due to their oligogenic inheritance, the identification of susceptibility loci for complex traits by classical selection criteria has not been very successful. One way to address this problem is to identify statistics that measure the effect of more than one locus simultaneously. In the approach described here, a p-value is assigned to a combination of loci under the null hypothesis that none of them is linked to the disease locus. In order to examine the power of this method to detect multiple loci, the Genetic Analysis Workshop 12 general population simulated data set was analyzed using variance component methods. Using the described novel selection criteria resulted in an increase of power, however, a rejection of the null hypothesis has to be interpreted with care.

Analysis of Variance↗
PubMed1999

Population genomics in Sardinia: a novel approach to hunt for genomic combinations underlying complex traits and diseases.

The availability of highly polymorphic markers permits testing whether complex traits and diseases result from genomic interactions between nonallelic normal variants at separate loci. Such variants may be identified by deviations from the expected distributions of alleles at a high number of polymorphic loci, when individuals with the phenotype of interest are compared to normal controls of the same breeding unit, provided that both groups share the same remote ancestry and had no ancestors in common for the last three to four generations. The circumstances needed for such studies are ideally met on the island of Sardinia. The recurrent finding of the same type of association in separate breeding units between the phenotype of interest and a given genotype should allow a distinction between true genetic identity by descent and randomly occurring identities, as these will be obviously different in separate breeding units. The availability of several breeding units located in sharply different ecological environments will permit assessment of the role of nature/nurture factors in the degree of manifestation of each newly discovered genotype/phenotype association. A pilot study to evaluate the proposed strategy has been carried out in the Sardinian village of Carloforte, a community of about 8,000 individuals who have remained genetically homogeneous. Fifty-five control samples have been genotyped with six tetranucleotide microsatellites and with a subset of the 400 markers contained in the ABI PRISM linkage mapping panel, version 2. The allele frequencies for these microsatellite markers have been determined for these 55 individuals and compared to those from a random sampling of subsets of these 55 persons. For the six tetranucleotide microsatellites, a subset of as few as 20 people displayed the same allele frequency distributions as observed with the original 55 unrelated individuals. In conclusion, when samples are chosen from the same breeding unit, the number of individuals sufficient to draw the genomic profile of an isolated population can be relatively small. Likewise, the number of probands with the phenotype of interest can be even smaller when they are ascertained with the same genealogical criteria as the normal controls. By comparing the genomic profile of the probands to a fraction of the control samples within each of several separate breeding units of common remote ancestry, the search for genotype/phenotype association for mono- and multifactorial traits and diseases should be simplified and yield unequivocal results.

Aged↗
PubMed1996

Production of congenic mouse strains carrying NOD-derived diabetogenic genetic intervals: an approach for the genetic dissection of complex traits.

Insulin-dependent (Type 1) diabetes (IDD) in the NOD mouse is inherited as a complex polygenic trait making the identification of susceptibility genes difficult. Currently none of the non-MHC IDD susceptibility genes in NOD have been identified. In this paper we describe the congenic mouse approach that we are using for the dissection of complex traits, such as IDD. We produced a series of six congenic strains carrying NOD-derived diabetogenic genomic intervals, which were previously identified by linkage analysis, on a resistant background. These congenic strains were produced for the purpose of characterizing the function of each of these genes, alone and in combinations, in IDD pathogenesis and to allow fine mapping of the NOD IDD susceptibility genes. Histological examination of pancreata from 6 to 8-month-old congenic mice reveals that intervals on Chromosomes (Chrs) 1 and 17, but not 3, 6, and 11, contain NOD-derived genes that can increase the trafficking of mononuclear cells into the pancreas. Insulitis was observed only very rarely, even in older congenic mice, indicating that multiple genes are required for this phenotype. These results demonstrate the utility of this congenic approach for the study of complex genetic traits.

Animals↗
PubMed2002

Association mapping, using a mixture model for complex traits.

Association mapping for complex diseases using unrelated individuals can be more powerful than family-based analysis in many settings. In addition, this approach has major practical advantages, including greater efficiency in sample recruitment. Association mapping may lead to false-positive findings, however, if population stratification is not properly considered. In this paper, we propose a method that makes it possible to infer the number of subpopulations by a mixture model, using a set of independent genetic markers and then testing the association between a genetic marker and a trait. The proposed method can be effectively applied in the analysis of both qualitative and quantitative traits. Extensive simulations demonstrate that the method is valid in the presence of a population structure.

Alleles↗
PubMed2025

Simple scaling laws control the genetic architectures of human complex traits.

Genome-wide association studies have revealed that the genetic architectures of complex traits vary widely, including in terms of the numbers, effect sizes, and allele frequencies of significant hits. However, at present we lack a principled way of understanding the similarities and differences among traits. Here, we describe a probabilistic model that combines the effects of mutation, drift, and stabilizing selection at individual sites with a genome-scale model of phenotypic variation. In this model, the architecture of a trait arises from the distribution of selection coefficients of mutations and from two scaling parameters. We fit this model for 95 highly polygenic quantitative traits of different kinds from the UK Biobank. Notably, we infer that all these traits have fairly similar, though not identical, distributions of selection coefficients. This similarity suggests that differences in architectures of highly polygenic traits arise mainly from the two scaling parameters: the mutational target size and heritability per site, which vary by orders of magnitude among traits. When these two scale factors are accounted for, we find that the architectures of all 95 traits are very similar.

Humans↗
PubMed1990

Linkage strategies for genetically complex traits. I. Multilocus models.

In order to investigate linkage detection strategies for genetically complex traits, multilocus models of inheritance need to be specified. Here, two types of multilocus model are described: (1) a multiplicative model, representing epistasis (interaction) among loci, and (2) an additive model, which is shown to closely approximate genetic heterogeneity, which is characterized by no interlocus interaction. A ratio lambda R of risk for type R relatives that is compared with population prevalence is defined. For a single-locus model, lambda R - 1 decreases by a factor of two with each degree of relationship. The same holds true for an additive multilocus model. For a multiplicative (epistasis) model, lambda R - 1 decreases more rapidly than by a factor of two with degree of relationship. Examination of lambda R values for various classes of relatives can potentially suggest the presence of multiple loci and epistasis. For example, data for schizophrenia suggest multiple loci in interaction. It is shown in the second paper of this series that lambda R is the critical parameter in determining power to detect linkage by using affected relative pairs.

Genetic Linkage↗
PubMed1997

Trade-off between false positives and false negatives in the linkage analysis of complex traits.

This study examines the issue of false positives in genomic scans for detecting complex trait loci using subpair linkage methods and investigates the trade-off between the rate of false positives and the rate of false negatives. It highlights the tremendous cost in terms of power brought about by an excessive control of type I error and, at the same time, confirms that a larger number of false positives can occur otherwise in the course of a genomic scan. Finally, it compares the power and rate of false positives obtained in preplanned replicated studies conducted using a liberal significance level to those for single-step studies that use the same total sample size but stricter levels of significance. For the models considered here, replicate studies were found more attractive as long as one is willing to accept a trade-off, exchanging a much lower rate of false negatives for a slight increase in the rate of false positives.

False Negative Reactions↗
PubMed1995

Statistical methods for linkage analysis of complex traits from high-resolution maps of identity by descent.

A multilocus model for complex traits is described that generalizes the additive and multiplicative models and hence allows simultaneously for both heterogeneity and gene interaction (epistasis). Statistical methods of linkage analysis are discussed under the assumption that identity by descent data from a dense set of polymorphic markers are available. Three methods, single locus search, simultaneous search and conditional search, are described and compared.

Chromosome Mapping↗
PubMed1995

Genetic dissection of complex traits: guidelines for interpreting and reporting linkage results.

Genetic studies are under way for many complex traits, spurred by the recent feasibility of whole genome scans. Clear guidelines for the interpretation of linkage results are needed to avoid a flood of false positive claims. At the same time, an overly cautious approach runs the risk of causing true hints of linkage to be missed. We address this problem by proposing specific standards designed to maintain rigor while also promoting communication.

Genetic Linkage↗
PubMed2001

Variance component methods for detecting complex trait loci.

Variance component-based linkage analysis has become a major statistical tool for the localization and evaluation of quantitative trait loci influencing complex phenotypes. The variance component approach has many benefits--it can, for example, be used to analyze large pedigrees, and it is able to accommodate multiple loci simultaneously in a true oligogenic model. Important biological phenomena such as genotype-environment interaction and epistasis are also examined easily in a variance component framework. In this chapter, we review the basic statistical features of variance component linkage analysis, with an emphasis on its power and robustness to distributional violations.

Analysis of Variance↗
PubMed2001

The genetic structure of recombinant inbred mice: high-resolution consensus maps for complex trait analysis.

BACKGROUND: Recombinant inbred (RI) strains of mice are an important resource used to map and analyze complex traits. They have proved particularly effective in multidisciplinary genetic studies. Widespread use of RI strains has been hampered by their modest numbers and by the difficulty of combining results derived from different RI sets. RESULTS: We have increased the density of typed microsatellite markers two- to five-fold in each of several major RI sets that share C57BL/6 as a parental strain (AXB, BXA, BXD, BXH and CXB). A common set of 490 markers was genotyped in just over 100 RI strains. Genotypes of around 1,100 additional microsatellites in one or more RI sets were generated, collected and checked for errors. Consensus RI maps that integrate genotypes of approximately 1,600 microsatellite loci were assembled. The genomes of individual strains typically incorporate 45-55 recombination breakpoints. The collected RI set - termed the BXN set - contains approximately 5,000 breakpoints. The distribution of recombinations approximates a Poisson distribution and distances between breakpoints average about 0.5 centimorgans (cM). Locations of most breakpoints have been defined with a precision of < 2 cM. Genotypes deviate from Hardy-Weinberg equilibrium in only a small number of intervals. CONCLUSIONS: Consensus maps derived from RI strains conform almost exactly to theoretical expectation and are close to the length predicted by the Haldane-Waddington equation (x3.6 for a 2-3 cM interval between markers). Non-syntenic associations between different chromosomes introduce predictable distortions in quantitative trait locus (QTL) datasets that can be partly corrected using two-locus correlation matrices.

Alleles↗
PubMed1997

Comparison of model-free linkage mapping strategies for the study of a complex trait.

We compared several strategies for identifying and estimating effects from a genetic locus in the etiology of a complex trait. For our analyses we used data from simulated trait 1 and chromosome 5. Results from analysis of the first 20 replicates showed that a components of variance test provided considerably better power for identifying linkage than tests that consider pair differences. We also compared the power from constructing tests with a single marker, an approximate method using five markers jointly, or a multipoint analysis using all 25 markers on chromosome 5 jointly. Results from this analysis showed substantially better power when all markers were jointly used in the analysis. Results from considering all replicates showed that all methods of estimation provided maximal test statistics at the correct marker position, but the components of variance procedure provided more power to detect the correct position than other methods.

Analysis of Variance↗