Disease-marker Association Research Articles

In Silico Analysis of Disease-Association Mapping Strategies Using the Coalescent Process and Incorporating Ascertainment and Selection

We present a new method for simulating samples of marker haplotypes, genotypes, or diplotypes in case-control studies in which the markers are linked to a disease locus in any specified region of the genome. The method allows realistic features to be incorporated into the simulations, including selection acting on disease alleles, sample ascertainment of disease chromosomes and polymorphic markers, a genetic dominance model of disease expression that allows incomplete penetrance and phenocopies, and an accurate genetic map of recombination rates and hotspots for recombination in the human genome (or, alternatively, an improved method for simulating the distribution of hotspots). The new method uses an approach that combines simulation of the coalescent process for the sampled chromosomes with a diffusion process used to model the evolution of the disease-mutation frequency over time. Examples illustrate how the method may be used to study the expected power of a marker-disease association study.

Integrating Case-control and TDT Studies

Genetic-association studies are widely expected to unravel the genetic basis of complex diseases. The population-based case-control study, a commonly used approach for association studies, is subject to the problem of population admixture. Consequently, evidence of disease-marker associations obtained from such studies is ideally confirmed by alternative methods. The Transmission/Disequilibrium Test (TDT) is suitable to assess evidence of association obtained from case-control studies. Since data are increasingly available from both case-control and TDT studies of the same disease-marker association, it is useful to obtain a combined estimate of disease-marker association. The odds ratio is a commonly used measure of the magnitude of a disease-marker association that can be easily obtained in case-control studies. Here we show how an odds ratio estimate and its' associated standard error can be obtained from TDT results. Furthermore, we suggest a method for integrating results from case-control studies and the TDT to provide a combined estimate of disease-marker association. Such combined estimates can be used to contrast the results of the two studies and provides an overall picture of the effect size attributable to such polymorphism. An illustrative application is made to a published data set on type 2 diabetes.

Genotype prediction using a dense map of SNPs

The International Haplotype Mapping Project (HapMap) aims to characterize the distribution and extent of linkage disequilibrium (LD) throughout the human genome, thereby facilitating genome-wide association analysis and the search for the genetic determinants of complex diseases. Implicit in the rationale behind the project is the expectation that hidden (unobserved) disease-causing variants will be in significant LD with surrounding typed markers and will thus be amenable to detection using association-based mapping approaches. In order to investigate the validity of this assumption, we examined more than 5,000 SNPs across a 10-MB region of chromosome 20 in a sample of 96 unrelated African-American and 96 unrelated Caucasian individuals. We treated observed loci as surrogates for hidden SNPs by pretending that individuals' genotypes were unknown. We then attempted to predict these genotypes at the surrogate hidden SNP by using information about LD in the region and genotypes at surrounding observed loci. Our method is based on finding the most likely genotype for each individual, given all possible haplotype pairs consistent with observed genotypes for that individual at surrounding loci, and given the frequencies of those haplotypes in an independent sample. Our method performs extremely well in predicting genotypes in areas of high LD. Furthermore, in areas of low LD, our method results in substantial gains in predictive accuracy as compared to pair-wise strategies. These results suggest that pair-wise tests of disease-marker association may be inferior to multipoint methods, which take advantage of the information contained within multi-locus haplotypes.

Linkage Disequilibrium Mapping via Cladistic Analysis of Single-Nucleotide Polymorphism Haplotypes

We present a novel approach to disease-gene mapping via cladistic analysis of single-nucleotide polymorphism (SNP) haplotypes obtained from large-scale, population-based association studies, applicable to whole-genome screens, candidate-gene studies, or fine-scale mapping. Clades of haplotypes are tested for association with disease, exploiting the expected similarity of chromosomes with recent shared ancestry in the region flanking the disease gene. The method is developed in a logistic-regression framework and can easily incorporate covariates such as environmental risk factors or additional unlinked loci to allow for population structure. To evaluate the power of this approach to detect disease-marker association, we have developed a simulation algorithm to generate high-density SNP data with short-range linkage disequilibrium based on empirical patterns of haplotype diversity. The results of the simulation study highlight substantial gains in power over single-locus tests for a wide range of disease models, despite overcorrection for multiple testing.

Assessing Whether an Allele Can Account in Part for a Linkage Signal: The Genotype-IBD Sharing Test (GIST)

To fine map genes, investigators often test for disease-marker association in chromosomal regions with evidence for linkage. Given a marker allele tentatively associated with disease, one would ask if this allele, or one in linkage disequilibrium (LD) with it, could account in part for the observed linkage signal. This question can be addressed by determining if families selected on the basis of the presence of the tentatively associated allele show stronger evidence of linkage as measured by increased allele sharing identical by descent (IBD) by affected family members. However, common selection strategies can be biased for or against linkage in the marker region, even given no disease-marker association. We define unbiased selection schemes and extend the definition to allow weighted selection on the basis of all genotyped family members. For affected-sibship data, we describe three genotype-based weight variables, corresponding to dominant, recessive, and additive models. We then introduce a test for association of a family weight variable with excess IBD sharing. This test allows us to determine if the linkage signal in a region can be attributed in part to the presence of a marker allele, either because of direct involvement in disease etiology or because of LD with a predisposing genetic variant. For samples of 500 affected sib pairs, the tests are powerful in detection of genotype-IBD sharing association, even for disease models with sib relative risk as low as lambda S=1.1, or when evidence for linkage is absent because of sampling variation. This makes our method a new tool for detecting linkage as well as association, especially in regions harboring a candidate gene. We have implemented these methods in the software package GIST (Genotype-IBD Sharing Test).

Increasing the Power and Efficiency of Disease-Marker Case-Control Association Studies through Use of Allele-Sharing Information

Case-control disease-marker association studies are often used in the search for variants that predispose to complex diseases. One approach to increasing the power of these studies is to enrich the case sample for individuals likely to be affected because of genetic factors. In this article, we compare three case-selection strategies that use allele-sharing information with the standard strategy that selects a single individual from each family at random. In affected sibship samples, we show that, by carefully selecting sibships and/or individuals on the basis of allele sharing, we can increase the frequency of disease-associated alleles in the case sample. When these cases are compared with unrelated controls, the difference in the frequency of the disease-associated allele is therefore also increased. We find that, by choosing the affected sib who shows the most evidence for pairwise allele sharing with the other affected sibs in families, the test statistic is increased by >20%, on average, for additive models with modest genotype relative risks. In addition, we find that the per-genotype information associated with the allele sharing-based strategies is increased compared with that associated with random selection of a sib for genotyping. Even though we select sibs on the basis of a nonparametric statistic, the additional gain for selection based on the unknown underlying mode of inheritance is minimal. We show that these properties hold even when the power to detect linkage to a region in the entire sample is negligible. This approach can be extended to more-general pedigree structures and quantitative traits.

Genetic structure of seven Mexican indigenous populations based on five polymarker loci.

This descriptive study investigates the genetic structure of seven Mexican indigenous populations (Mixteca Alta, Mixteca Baja, Otomies, Purepecha, Nahuas-Guerrero, Nahuas-Xochimilco, and Tzeltales) on the basis of five PCR-based polymorphic DNA loci: LDLR, GYPA, HBGG, D7S8, and GC. Genetic distance and diversity analyses indicate that these Mexican indigenous are similar and that more than 96% of the total gene diversity (H(T)) can be attributed to individual variation within populations. Mixteca-Alta, Mixteca-Baja, and Nahuas-Xochimilco show indications of higher admixture with European-derived persons. The demonstration of a relative genetic homogeneity of Mexican Indians for the markers studied suggests that this population is suitable for studying disease-marker associations in the search for candidate genes of complex diseases.

Testing for Population Subdivision and Association in Four Case-Control Studies

Population structure has been presumed to cause many of the unreplicated disease-marker associations reported in the literature, yet few actual case-control studies have been evaluated for the presence of structure. Here, we examine four moderate case-control samples, comprising 3,472 individuals, to determine if detectable population subdivision is present. The four population samples include: 500 U.S. whites and 236 African Americans with hypertension; and 500 U.S. whites and 500 Polish whites with type 2 diabetes, all with matched control subjects. Both diabetes populations were typed for the PPARg Pro12Ala polymorphism, to replicate this well-supported association (Altshuler et al. 2000). In each of the four samples, we tested for structure, using the sum of the case-control allele frequency chi(2) statistics for 9 STR and 35 SNP markers (Pritchard and Rosenberg 1999). We found weak evidence for population structure in the African American sample only, but further refinement of the sample, to include only individuals with U.S.-born parents and grandparents, eliminated the stratification. Our examples provide insight into the factors affecting the replication of association studies and suggest that carefully matched, moderate-sized case-control samples in cosmopolitan U.S. and European populations are unlikely to contain levels of structure that would result in significantly inflated numbers of false-positive associations. We explore the role that extreme differences in power among studies, due to sample size and risk-allele frequency differences, may play in the replication problem.

Extent and Distribution of Linkage Disequilibrium in Three Genomic Regions

The positional cloning of genes underlying common complex diseases relies on the identification of linkage disequilibrium (LD) between genetic markers and disease. We have examined 127 polymorphisms in three genomic regions in a sample of 575 chromosomes from unrelated individuals of British ancestry. To establish phase, 800 individuals were genotyped in 160 families. The fine structure of LD was found to be highly irregular. Forty-five percent of the variation in disequilibrium measures could be explained by physical distance. Additional factors, such as allele frequency, type of polymorphism, and genomic location, explained <5% of the variation. Nevertheless, disequilibrium was occasionally detectable at 500 kb and was present for over one-half of marker pairs separated by <50 kb. Although these findings are encouraging for the prospects of a genomewide LD map, they suggest caution in interpreting localization due to allelic association.

Bayesian Fine-Scale Mapping of Disease Loci, by Hidden Markov Models

We present a new multilocus method for the fine-scale mapping of genes contributing to human diseases. The method is designed for use with multiple biallelic markers-in particular, single-nucleotide polymorphisms for which high-density genetic maps will soon be available. We model disease-marker association in a candidate region via a hidden Markov process and allow for correlation between linked marker loci. Using Markov-chain-Monte Carlo simulation methods, we obtain posterior distributions of model parameter estimates including disease-gene location and the age of the disease-predisposing mutation. In addition, we allow for heterogeneity in recombination rates, across the candidate region, to account for recombination hot and cold spots. We also obtain, for the ancestral marker haplotype, a posterior distribution that is unique to our method and that, unlike maximum-likelihood estimation, can properly account for uncertainty. We apply the method to data for cystic fibrosis and Huntington disease, for which mutations in disease genes have already been identified. The new method performs well compared with existing multi-locus mapping methods.

Relationship between case-control studies and the transmission/disequilibrium test.

Case-control studies provide a powerful approach for detecting disease susceptibility loci that have only a weak to moderate impact on the risk of disease, or markers that are in linkage disequilibrium with such loci. However, since any association detected in a case-control study may result from uncontrolled confounding, evidence for disease-marker associations obtained from such studies must be confirmed by alternative methods. Since studies that use the transmission/disequilibrium test or TDT are frequently employed to confirm disease-marker associations detected in case-control studies, data are increasingly available from both case-control studies and "TDT studies" of the same disease-marker association. It would, therefore, be useful to have a single measure of the magnitude of the disease-marker association that would allow for comparison of results from these two study designs. Such a measure could also be used to estimate minimum sample size requirements for TDT studies of previously reported disease-marker associations. An obvious measure of the disease-marker association in TDT studies is the frequency (T) with which heterozygous parents transmit the putative, high-risk marker allele to affected offspring. In this paper, it is shown that T can also be estimated from case-control data with a minimum of assumptions, and that T is the critical parameter for determining power and estimating sample sizes for the TDT.

A Monte Carlo procedure for two-stage tests with correlated data.

One strategy for mapping disease loci using marker-disease associations is to test for association with case-control samples and follow up a positive result with a family-based test. Using a family-based test in the second stage can help provide protection against false-positive results that can result from use of inappropriate controls and provides assurance that association identified in the first stage is occurring between linked loci. It is crucial for this two-stage strategy that the first stage be as powerful as possible to detect association since only positive results are tested in the second stage. In certain situations, the power of the first-stage test can be increased by combining the case-control and family data. However, this introduces correlation between the first- and second-stage tests, and treating them as independent tests causes a bias. Here we propose a Monte Carlo method that accounts for the correlation and provides the correct significance level for the second-stage test. We also discuss the use of a two-stage procedure when doing a genome scan for the data presented in the Genetic Analysis Workshop 9 study.

Model-Free Analysis and Permutation Tests for Allelic Associations

In this short report, we address some practical problems in performing likelihood-based allelic association analysis of case-control data. Model-free statistics are proposed and their properties assessed by simulation, and procedures based on permutation tests are described for marker-marker as well as marker-disease associations. A memory-efficient algorithm is developed which enables several highly polymorphic markers to be analysed.

Disease-marker associations: power and heterogeneity in independent population samples.

We applied generalized transmission disequilibrium testing (TDT) models in combined replicates 1 through 5 from each of four simulated population samples. All analyses were conducted without knowledge of the generating models. To assess power and consistency of results within and between samples, analyses were repeated in all 25 replicates combined and in each replicate. With the exception of sample-specific findings for locus D, power was generally low to detect linkage in a genome scan or to confirm linkages detected by allele sharing in affected relatives, due to lack of linkage disequilibrium. We proposed likelihood ratio and Wald tests to detect heterogeneity among samples in disease-marker associations. Pooling data across heterogeneous populations may not improve power of the TDT method.

Professor Ching Chun Li, Courageous Scholar and Educator

Professor Ching Chun Li, Courageous Scholar and Educator

Association of the Missense Glu298Asp Variant of the Endothelial Nitric Oxide Synthase Gene With Myocardial Infarction

Objectives. We examined the possible association between the missense Glu298Asp variant of the endothelial nitric oxide synthase (eNOS) gene and myocardial infarction (MI).Background. Endothelium-derived nitric oxide (NO) plays a key role in the regulation of vascular tone. Recently, we reported that a missense Glu298Asp variant in exon 7 of the eNOS gene is a possible genetic factor involved in the pathogenesis of coronary spasm. Endothelium-derived NO also has vasoprotective effects by suppressing platelet aggregation, leukocyte adhesion and smooth muscle cell proliferation.Methods. We screened 285 patients with an MI and 607 control subjects in Kumamoto Prefecture, Japan. Genotypes were determined by polymerase chain reaction–restriction fragment-length polymorphism analysis.Results. The frequency of the missense Glu298Asp variant was significantly higher in the MI group than in the control group (21.1% vs. 13.3%, p = 0.003, odds ratio 1.73 for the dominant effect of the eNOS T allele). Multiple logistic regression analysis showed that the missense Glu298Asp variant was an independent risk factor for MI, as was diabetes mellitus, hypertension, cigarette smoking, hypercholesterolemia and body mass index.Conclusions. There was a significant association of the missense Glu298Asp variant of the eNOS gene with MI. This marker–disease association may be due to the impaired effects of NO on the cardiovascular system: dysregulation of vascular tone, platelet aggregation and leukocyte adhesion and smooth muscle cell proliferation, all of which promote coronary atherosclerosis and thrombosis.

Association Mapping of Disease Loci, by Use of a Pooled DNA Genomic Screen

Genomic screening to map disease loci by association requires automation, pooling of DNA samples, and 3,000-6,000 highly polymorphic, evenly spaced microsatellite markers. Case-control samples can be used in an initial screen, followed by family-based data to confirm marker associations. Association mapping is relevant to genetic studies of complex diseases in which linkage analysis may be less effective and to cases in which multigenerational data are difficult to obtain, including rare or late-onset conditions and infectious diseases. The method can also be used effectively to follow up and confirm regions identified in linkage studies or to investigate candidate disease loci. Study designs can incorporate disease heterogeneity and interaction effects by appropriate subdivision of samples before screening. Here we report use of pooled DNA amplifications-the accurate determination of marker-disease associations for both case-control and nuclear family-based data-including application of correction methods for stutter artifact and preferential amplification. These issues, combined with a discussion of both statistical power and experimental design to define the necessary requirements for detecting of disease loci while virtually eliminating false positives, suggest the feasibility and efficiency of association mapping using pooled DNA screening.

A likelihood ratio test for detecting patterns of disease-marker association.

A likelihood ratio test of disease-marker association is proposed, based on the observation of marker alleles transmitted from parents to affected children. The proposed association test has the advantage of identifying the population pattern of disease-marker association, differentiating between marker alleles that are positively and negatively associated with the disease. The power of the test for detecting association is evaluated and compared with three existing multi-allelic tests for some specific disease-marker association patterns. The power of the parametric tests depends crucially on the pattern of disease-marker association. An over-parameterised association model is less detrimental in terms of power than an under parameterised model.

A likelihood ratio test for detecting patterns of disease-marker association

A likelihood ratio test for detecting patterns of disease-marker association

Randomization tests of disease‐marker associations

A powerful test for population association of a disease with alleles at a bi-allelic marker locus is the transmission/disequilibrium test (TDT). A generalization of the test to multi-allelic marker loci is proposed which utilizes the maximal association of individual alleles with the disease, given by the maximum TDT statistic, TDT(max). To overcome the multiple testing problem encountered when using the maximal association to test the null hypothesis of no disease-marker association, a randomization procedure is developed. An investigation of the power of the test suggests that the randomization procedure performs almost as well as a recently proposed likelihood based test of linkage disequilibrium. The advantage of the new test is that it can be applied sequentially, based on a one-sided version of the TDT statistic, for investigating patterns of association of several individual alleles with the disease.