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Results for “complex diseases”

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At least 595 records · Page 33Linked to original sources
PubMed1997

Detecting linkage for a complex disease using simulated extended pedigrees.

The simulated extended pedigree data of the Genetic Analysis Workshop 10 were used to examine the relationship between several quantitative traits (Q1-Q5), an environmental factor, age and sex and to identify genes contributing to the quantitative traits. A forward selection procedure was used to identify regression models for each trait. Residuals from these regression models were used as quantitative traits in linkage analysis. Two-point sib-pair analysis was performed on Replicate 1 of the data set using SIBPAL. Sixteen regions on 8 chromosomes yielded two-point p-values < 0.005 in Replicate 1. Two strategies for utilizing a second data set were evaluated. In a two-stage approach, only those regions with p-value < 0.005 in Replicate 1 were followed up in the second data set. Nine of these regions had p-values < 0.05 in Replicate 2; four were associated with major genes included in the generating model and the remaining five regions were false positives. An alternative strategy was to perform a repeat genome wide screen in the second data set. This strategy resulted in the identification of 20 regions with p-values < 0.05 in both replicates; five of which included major genes included in the generating model. Although the false positive rate increased when a complete genome screen was performed on both data sets, the two-stage screen, with a more stringent initial criterion for identifying suggestive linkages, had a higher rate of false negatives. For some studies, conducting two complete genome screens in a split-sample design may be worthwhile.

Age Distribution↗
PubMed1999

Exploring the dense mapping of a region of potential linkage in complex disease: an example in multiple sclerosis.

In 1996 we reported the results of a genome screen in multiple sclerosis, in which potential linkage was identified in a total of twenty regions, including the centromeric region of chromosome 5. In order to investigate the efficiency of typing dense arrays of markers in regions of potential linkage, we have typed an additional nineteen microsatellite markers from this chromosome 5 region (D5S623 - D5S428) in the same sibling pair families. The mean additional information extracted per marker typed declined with increasing map density, while inaccuracies in the mapping and the density of genotyping errors increased. Our empirical results suggest that, in linkage-based experiments, there is a limit to the benefits that are gained from typing additional markers in the same families. Increasing map density up to the 2.5-5 cM level efficiently extracts valuable extra information; however, beyond this level efficiency declines while the confounding effects of mapping and genotyping errors accumulate. We, therefore, recommend that extra markers typed in linkage studies be limited to this level of resolution. Mapping regions beyond this density should only be initiated when searching for linkage disequilibrium.

Chromosome Mapping↗
PubMed2001

A general and accurate approach for computing the statistical power of the transmission disequilibrium test for complex disease genes.

Transmission disequilibrium test (TDT) is a nuclear family-based analysis that can test linkage in the presence of association. It has gained extensive attention in theoretical investigation and in practical application; in both cases, the accuracy and generality of the power computation of the TDT are crucial. Despite extensive investigations, previous approaches for computing the statistical power of the TDT are neither accurate nor general. In this paper, we develop a general and highly accurate approach to analytically compute the power of the TDT. We compare the results from our approach with those from several other recent papers, all against the results obtained from computer simulations. We show that the results computed from our approach are more accurate than or at least the same as those from other approaches. More importantly, our approach can handle various situations, which include (1) families that consist of one or more children and that have any configuration of affected and nonaffected sibs; (2) families ascertained through the affection status of parent(s); (3) any mixed sample with different types of families in (1) and (2); (4) the marker locus is not a disease susceptibility locus; and (5) existence of allelic heterogeneity. We implement this approach in a user-friendly computer program: TDT Power Calculator. Its applications are demonstrated. The approach and the program developed here should be significant for theoreticians to accurately investigate the statistical power of the TDT in various situations, and for empirical geneticists to plan efficient studies using the TDT.

Adult↗
PubMed1993

Linkage analysis of a complex disease: application to familial Alzheimer's disease.

Evidence for linkage of the Alzheimer's gene to markers on chromosomes 19 and 21 was assessed using single-locus and two-locus models of inheritance. Families were divided into groups determined by their average age at onset. The youngest group produced higher lod scores for markers on chromosome 21 while an older group showed evidence for linkage to markers on chromosome 19. Two-locus models of disease were used to analyze the youngest group for linkage to pairs of markers on chromosome 21 and an older group with markers on chromosome 19.

Adult↗
PubMed1999

Strategies for detecting susceptibility genes in a complex disease.

One of the current issues in genetic epidemiology is detecting susceptibility genes on the genome. It is common now to undertake systematic screening of the genome using approaches based on a measure of the haplotype sharing in sib pairs. Here, we compare the efficiency of two statistics, the maximum likelihood score (MLS) and the nonparametric linkage score (NPLa) on the simulated data provided for GAW11. A question often raised is whether it is better to perform a single-step or a two-step strategy. For the simulated model, and whatever the strategy used, we show here that the answer is not unequivocal. In both cases, the power to detect susceptibility genes in a single replicate with MLS or NPL is extremely low. With two replicates, only one of the four simulated loci could be detected with reasonable power. When gametic disequilibrium is suspected, methods testing for both linkage and association might be more powerful.

Genetic Linkage↗
PubMed1999

Genetic analysis of a complex disease in the presence of an environmental risk factor.

The role of a gene in a disease may be hidden by the presence of another risk factor such as an environmental factor. In that case, stratifying the data according to this factor strengthens power to detect linkage or association. We followed this strategy on the simulated data provided by GAW11. The transmission/disequilibrium test (TDT) and the maximum likelihood score (MLS) were performed on the first replicate of 100 sib pairs from the population in which the disease risk was significantly influenced by an environmental factor (E1). However, only the TDT was powerful enough to detect one of the four loci involved in the genetic determination of the disease. The MLS showed no evidence for linkage after taking into account the fact that multiple tests were performed. Even when stratifying the sample according to the presence of E1, no additional loci could be detected. Given the simulated models, 100 sib pairs are too low a sample size for a systematic screening of the genome, which in this case was an analysis of 300 markers.

Environment↗
PubMed2001

Modeling complex disease with demographic and environmental covariates and a candidate gene marker.

We randomly chose replicates 28 and 29 of the simulated data sets of Genetic Analysis Workshop 12 to model the dependence of affection status on covariates, quantitative traits, and genes using all living pedigree members. First we explored the relationship of affection status to demographic and environmental factors using logistic regression and the Cox proportional hazards models. In the second stage of our analyses the generalized transmission disequilibrium test (GTDT) was applied to nuclear families with at least two affected siblings to select single markers and high-risk alleles, which were tested in the population association analyses including all pedigree members. Multiple logistic regression models were fitted to investigate the joint contributions of genetic and nongenetic factors and a block-recursive modeling approach was adopted to study inherent hierarchical dependence structure in the data. We found that allele 2 on marker 35 of chromosome 6 is associated with higher risk compared with the other 3 alleles of this marker. In addition to this significant genetic effect, age at exam and four of the five quantitative traits (QT1, QT2, QT4, and QT5) had a significant association with the disease. Our results were obtained without knowledge of the true disease generating models.

Adolescent↗
PubMed2001

Applying data mining techniques to the mapping of complex disease genes.

The simulated sequence data for the Genetic Analysis Workshop 12 were analyzed using data mining techniques provided by SAS ENTERPRISE MINER Release 4.0 in addition to traditional statistical tests for linkage and association of genetic markers with disease status. We examined two ways of combining these approaches to make use of the covariate data along with the genotypic data. The result of incorporating data mining techniques with more classical methods is an improvement in the analysis, both by correctly classifying the affection status of more individuals and by locating more single nucleotide polymorphisms related to the disease, relative to analyses that use classical methods alone.

Chromosome Mapping↗
PubMed2001

Parametric linkage analysis and disequilibrium methods to identify loci for complex disease.

A two-step process was used to find loci contributing to the qualitative disease phenotype in the Genetic Analysis Workshop (GAW) 12 simulated data. The first step used parametric linkage analysis with a limited number of dominant and recessive models to detect linkage to chromosomal regions. Subsequently, a subset of the simulated biallelic sequence polymorphisms was used for transmission/disequilibrium tests and to build haplotypes to fine map the disease-predisposing polymorphism(s). A haplotype, strongly associated with the disease phenotype whose proximal end was within 39 base pairs of the functional allele for simulated major gene 6, was identified in the isolated population.

Alleles↗
PubMed2001

Comparison of three methods for obtaining principal components from family data in genetic analysis of complex disease.

Three multivariate techniques used to derive principal components (PCs) from family data were compared for their ability to model family data and power to detect linkage. Using the simulated data from Genetic Analysis Workshop 12, the five quantitative traits were first adjusted for age, sex, and environmental factors 1 and 2. Then, standard PCs, PCs obtained from between-family covariance, and PCs obtained from within-family genetic covariance were derived and subjected to multivariate sib pair linkage analysis. The standard PCs obtained from the overall correlation matrix allowed identification of key features of the true genetic model more readily than did the other methods. For detection of linkage, standard PCs and PCs obtained from the between-family genetic covariance performed similarly in terms of both power and type 1 error, and both methods performed better than the PCs obtained from within-family genetic covariance.

Chromosome Mapping↗
PubMed2001

Assessing the effect of sampling strategies on the power of linkage analysis to identify pathway-specific loci underlying a complex disease.

Using the simulated general population data sets, we first examined the effect of sampling strategies on the power of identifying linkage by selecting samples with (A) two affected sibs in a nuclear family and (B) one affected sib and one sib with an intermediate trait value in the upper quantiles. Second, we evaluated the improvement in power when analyzing correlated traits simultaneously. Under each selection criteria, 100 replicates of 300 nuclear families were sampled and analyzed with two-point linkage analysis for ten markers (1 cM apart) from each of the candidate regions. Different genes were identified under different sampling strategies. When a gene has a pleitropic effect, it is more powerful to analyze correlated traits simultaneously, either by using a linear combination or the larger value of standardized traits, than to analyze each trait separately.

Chromosome Mapping↗
PubMed2006

A tree-based model for allele-sharing-based linkage analysis in human complex diseases.

By adapting a well-known affected-relative-pair linkage model that can incorporate covariate or sub-phenotype information [Olson, 1999: Am J Hum Genet 65:1760-1769], we have developed a recursive-partitioning (RP) algorithm (tree-based model) for identifying phenotype and covariate groupings that interact with the evidence for linkage. This strategy is designed to identify subgroups of affected relative pairs demonstrating increased evidence for linkage, where subgroups are defined by pair-level or family-level covariates. After growing a full tree, we identified optimal tree size through a form of tree pruning and chose the best covariate at each split by using bootstrap algorithms. Simulation studies showed that power to detect linkage can increase in the presence of gene-environment interactions, depending on the magnitude of the interaction. As expected, however, power can decrease by examining more covariates, despite the pruning to optimize tree size. The RP model correctly identifies tree structure in a large proportion of simulations. We applied the RP model to a dataset of families with bipolar affective disorder (BPAD) where linkage regions on chromosome 18 have been previously identified. Using the all-pairs score in Genehunter, the NPL tests showed no regions with strong linkage evidence on chromosome 18. However, using the RP model, several suggestive regions were found on chromosome 18. Two covariates appeared to influence the degree of linkage: the type II BPAD subtype and a pattern of displaying mania before or after a depressive episode. The RP model has the potential to identify previously unknown gene-environmental interactions; here we have demonstrated the practical utility and potential this new methodology holds.

Algorithms↗