Evidence for an association between genetic variants of the fatty acid desaturase 1 fatty acid desaturase 2 ( FADS1 FADS2) gene cluster and the fatty acid composition of erythrocyte membranes

The present study gives further evidence for the recently found association between variants of the fatty acid desaturase 1 fatty acid desaturase 2 (FADS1 FADS2) gene cluster and PUFA in blood phospholipids and explores this association for cellular fatty acids in erythrocyte membranes. In a subgroup of adults participating in the Bavarian Nutrition Survey II, a cross-sectional population-based study conducted in Bavaria, Germany, allelic variation in three selected loci of the FADS1 FADS2 gene cluster was analysed and used for haplotype construction. Associations with plasma phospholipid PUFA (n 163) and PUFA in erythrocyte membranes (n 535) were investigated by regression analysis. All haplotypes of the original five-loci haplotypes of our previous study could be replicated. In addition, associations with serum phospholipid PUFA were confirmed in the present data set. Although less pronounced, associations between FADS1 FADS2 haplotypes and PUFA in erythrocyte membranes, particularly arachidonic and dihomo-gamma-linolenic acid, could be established. We provide the first replication of the association of the FADS1 FADS2 gene cluster with PUFA in blood phospholipids. For the first time, such associations were also shown for PUFA in cell membranes.     

Haplotype associations with quantitative traits in the presence of complex multilocus and heterogeneous effects

In genetic mapping of complex traits, scored haplotypes are likely to represent only a subset of all causal polymorphisms. At the extreme of this scenario, observed polymorphisms are not themselves functional, and only linked to causal ones via linkage disequilibrium (LD). We will demonstrate that due to such incomplete knowledge regarding the underlying genetic mechanism, the variance of a trait may become different between the scored haplotypes. Thus, unequal variances between haplotypes may be indicative of additional functional polymorphisms affecting the trait. Methods accounting for such haplotype-specific variance may also provide an increased power to detect complex associations. We suggest ways to estimate and test these haplotypic variance contrasts, and incorporate them into the haplotypic tests for association. We further extend this approach to data with unknown gametic phase via likelihood-based simultaneous estimation of haplotypic effects and their frequencies. We find our approach to provide additional power, especially under the following types of models: (a) where scored and unobserved variants are epistatically interacting with each other; and (b) under heterogeneity models, where multiple unobserved mutations are linked to non-functional observed polymorphisms via LD. An illustrative example of usefulness of the method is discussed, utilizing analysis of multilocus effects within the catechol-O-methyltransferase gene.     

On selecting markers for association studies: patterns of linkage disequilibrium between two and three diallelic loci

Association studies depend on linkage disequilibrium (LD) between a causative mutation and linked marker loci. Selecting markers that give the best chance of showing useful levels of LD with the causative mutation will increase the chances of successfully detecting an association. This report examines the variation in the extent of LD between a disease locus and one or two diallelic marker loci (termed single nucleotide polymorphisms or SNPs). We use a simulation method based on the neutral coalescent in a population of variable size to find the distribution of LD as a function of allele frequencies, the recombination rate, and the population history. Given that LD exists, the allele frequencies determine if a site will be useful for detecting an association with the disease mutation. We show that there is extensive variation in LD even for closely linked loci, implying that several markers may be needed to detect a disease locus. The distribution of LD between common variants is strongly influenced by ancestral population size. We show that in general, best results will be obtained if the frequencies of marker alleles are at least as large as the frequency of the causative mutation. Haplotypes of two or more SNPs generally have a higher probability than individual SNPs of showing useful LD with a disease mutation, although exceptions are described.     

Power-based, phase-informed selection of single nucleotide polymorphisms for disease association screens

Single nucleotide polymorphisms (SNPs) are becoming widely used as genotypic markers in genetic association studies of common, complex human diseases. For such association screens, a crucial part of study design is determining what SNPs to prioritize for genotyping. We present a novel power-based algorithm to select a subset of tag SNPs for genotyping from a map of available SNPs. Blocks of markers in strong linkage disequilibrium (LD) are identified, and SNPs are selected to represent each block such that power to detect disease association with an underlying disease allele in LD with block members is preserved; all markers outside of blocks are also included in the tagging subset. A key, novel element of this method is that it incorporates information about the phase of LD observed among marker pairs to retain markers likely to be in coupling phase with an underlying disease locus, thus increasing power compared to a phase-blind approach. Power calculations illustrate important issues regarding LD phase and make clear the advantages of our approach to SNP selection. We apply our algorithm to genotype data from the International HapMap Consortium and demonstrate that considerable reduction in SNP genotyping may be attained while retaining much of the available power for a disease association screen. We also demonstrate that these tag SNPs effectively represent underlying variants not included in the LD analysis and SNP selection, by using leave-one-out tests to show that most (approximately 90%) of the "untyped" variants lying in blocks are in coupling-phase LD with a tag SNP. Additional performance tests using the HapMap ENCyclopedia of DNA Elements (ENCODE) regions show that the method compares well with the popular r2 bin tagging method. This work is a concrete example of how empirical LD phase may be used to benefit study design.     

Using Phenotypic Heterogeneity to Increase the Power of Genome‐Wide Association Studies: Application to Age at Onset of Ischaemic Stroke Subphenotypes

Genome‐wide association studies (GWAS) have been successful in identifying common variants related to complex disorders. However, some disorders have proved resistant to this strategy with few associations confirmed, despite evidence from twin and family studies of a genetic component. Sophisticated strategies that account for phenotypic heterogeneity may be required to uncover these genetic contributions. Age at onset is an example of a potential source of this heterogeneity in ischaemic stroke. We explore the contribution of age at onset in the Wellcome Trust Case‐Control Consortium 2 ischaemic stroke study. We first examine four established stroke loci in younger onset cases. We extend this to all single‐nucleotide polymorphisms (SNPs) genome‐wide, testing for stronger association signals in younger subsets of cases. Finally, we estimate the pseudoheritability accounted for by common SNPs present on genome‐wide genotyping arrays for cases stratified by age at onset. We find evidence for stronger associations in younger onset cases for the four established stroke loci. Genome‐wide, in cardioembolic and small vessel stroke subphenotypes, a significant number of SNPs show stronger association P‐values when the oldest cases are removed. Finally, we show that the pseudoheritability estimated by common SNPs in cardioembolic stroke increased from 16.5% for older onset cases to 28.5% for younger onset cases. Our results indicate that age at onset is a valuable measure for case ascertainment and in analysis of GWAS in ischaemic stroke: focussing on younger cases who may have a stronger genetic predisposition increases power to detect associations.     

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.     

Effects of single SNPs, haplotypes, and whole-genome LD maps on accuracy of association mapping

We describe an association mapping approach that utilizes linkage disequilibrium (LD) maps in LD units (LDU). This method uses composite likelihood to combine information from all single marker tests, and applies a model with a parameter for the location of the causal polymorphism. Previous analyses of the poor drug metabolizer phenotype provided evidence of the substantial utility of LDU maps for disease gene association mapping. Using LDU locations for the 27 single nucleotide polymorphisms (SNPs) flanking the CYP2D6 gene on chromosome 22, the most common functional polymorphism within the gene was located at 15 kb from its true location. Here, we examine the performance of this mapping approach by exploiting the high-density LDU map constructed from the HapMap data. Expressing the locations of the 27 SNPs in LDU from the HapMap LDU map, analysis yielded an estimated location that is only 0.3 kb away from the CYP2D6 gene. This supports the use of the high marker density HapMap-derived LDU map for association mapping even though it is derived from a much smaller number of individuals compared to the CYP2D6 sample. We also examine the performance of 2-SNP haplotypes. Using the same modelling procedures and composite likelihood as for single SNPs, the haplotype data provided much poorer localization compared to single SNP analysis. Haplotypes generate more autocorrelation through multiple inclusions of the same SNPs, which could inflate significance in association studies. The results of the present study demonstrate the great potential of the genome HapMap LDU maps for high-resolution mapping of complex phenotypes.     

Principal component analysis for selection of optimal SNP-sets that capture intragenic genetic variation

Candidate gene association studies often utilize one single nucleotide polymorphism (SNP) for analysis, with an initial report typically not being replicated by subsequent studies. The failure to replicate may result from incomplete or poor identification of disease-related variants or haplotypes, possibly due to naive SNP selection. A method for identification of linkage disequilibrium (LD) groups and selection of SNPs that capture sufficient intra-genic genetic diversity is described. We assume all SNPs with minor allele frequency above a pre-determined frequency have been identified. Principal component analysis (PCA) is applied to evaluate multivariate SNP correlations to infer groups of SNPs in LD (LD-groups) and to establish an optimal set of group-tagging SNPs (gtSNPs) that provide the most comprehensive coverage of intra-genic diversity while minimizing the resources necessary to perform an informative association analysis. This PCA method differs from haplotype block (HB) and haplotype-tagging SNP (htSNP) methods, in that an LD-group of SNPs need not be a contiguous DNA fragment. Results of the PCA method compared well with existing htSNP methods while also providing advantages over those methods, including an indication of the optimal number of SNPs needed. Further, evaluation of the method over multiple replicates of simulated data indicated PCA to be a robust method for SNP selection. Our findings suggest that PCA may be a powerful tool for establishing an optimal SNP set that maximizes the amount of genetic variation captured for a candidate gene using a minimal number of SNPs.     

Genetic polymorphisms in host antiviral genes: associations with humoral and cellular immunity to measles vaccine

Host antiviral genes are important regulators of antiviral immunity and plausible genetic determinants of immune response heterogeneity after vaccination. We genotyped and analyzed 307 common candidate tagSNPs from 12 antiviral genes in a cohort of 745 schoolchildren immunized with two doses of measles-mumps-rubella (MMR) vaccine. Associations between SNPs/haplotypes and measles virus-specific immune outcomes were assessed using linear regression methodologies in Caucasians and African-Americans.Genetic variants within the DDX58/RIG-I gene, including a coding polymorphism (rs3205166/Val800Val), were associated as single-SNPs (p ≤ 0.017; although these SNPs did not remain significant after correction for false discovery rate/FDR) and in haplotype-level analysis, with measles-specific antibody variations in Caucasians (haplotype allele p-value = 0.021; haplotype global p-value = 0.076). Four DDX58 polymorphisms, in high LD, demonstrated also associations (after correction for FDR) with variations in both measles-specific IFN-γ and IL-2 secretion in Caucasians (p ≤ 0.001, q = 0.193). Two intronic OAS1 polymorphisms, including the functional OAS1 SNP rs10774671 (p = 0.003), demonstrated evidence of association with a significant allele-dose-related increase in neutralizing antibody levels in African-Americans. Genotype and haplotype-level associations demonstrated the role of ADAR genetic variants, including a non-synonymous SNP (rs2229857/Arg384Lys; p = 0.01), in regulating measles virus-specific IFN-γ Elispot responses in Caucasians (haplotype global p-value = 0.017). After correction for FDR, 15 single-SNP associations (11 SNPs in Caucasians and 4 SNPs in African-Americans) still remained significant at the q-value < 0.20.In conclusion, our findings strongly point to genetic variants/genes, involved in antiviral sensing and antiviral control, as critical determinants, differentially modulating the adaptive immune responses to live attenuated measles vaccine in Caucasians and African-Americans.     

A novel method to express SNP-based genetic heterogeneity, Psi, and its use to measure linkage disequilibrium for multiple SNPs, D(g), and to estimate absolute maximum of haplotype frequency

Single nucleotide polymorphisms (SNPs) are important markers to investigate genetic heterogeneity of population and to perform linkage disequilibrium (LD) mapping. We propose a new method, Psi, to express frequency of 2(N(s)) haplotypes for N(s) di-allelic SNPs. Using the new expression of haplotype frequency, we propose a novel measure of LD, D(g), not only for SNP pairs but also for multiple markers. The values of D(g) for SNP pairs were revealed to be similar to values of conventional pairwise LD indices, D' and r(2), and it was revealed that D(g) quantitated components of LD that were not measured by conventional LD indices for SNP pairs. Also we propose a distinct method, D(g)-based absolute estimation, to infer the absolute maximum estimates of haplotype frequency. The result of the D(g)-based absolute estimation of haplotype frequency for SNP pairs were compared with the conventional expectation-maximization (EM) algorithm and reported that the new method gave better inference than the EM algorithm which converged infrequently to a local extreme.     

Genetic associations of the vitamin D and antiviral pathways with natural resistance to HIV-1 infection are influenced by interpopulation variability

Vitamin D (VitD) may modulate anti-HIV-1 responses modifying the risk to acquire the HIV-1-infection. We performed a nested case-control exploratory study involving 413 individuals; HIV-1-exposed seropositives (cases) and seronegatives (HESN) (controls) from three cohorts: sexually-exposed from Colombia and Italy and parenterally-exposed from Spain. The association and interactions of 139 variants in 9 VitD pathway genes, and in 14 antiviral genes with resistance/susceptibility (R/S) to HIV-1 infection was evaluated. Associations between variants and mRNA levels were also analyzed in the Colombian samples.Variants and haplotypes in genes of VitD and antiviral pathways were associated with R/S, but specific associations were not reproduced in all cohorts. Allelic heterogeneity could explain such inconsistency since the associations found in all cohorts were consistently in the same genes: VDR and RXRA of the VitD pathway genes and in TLR2 and RNASE4.Remarkably, the multi-locus genotypes (interacting variants) observed in genes of VitD and antiviral pathways were present in most HESNs of all cohorts. Finally, HESNs carrying resistance-associated variants had higher levels of VitD in plasma, of VDR mRNA in blood cells, and of ELAFIN and defensins mRNA in the oral mucosa.In conclusion, despite allelic heterogeneity, most likely due to differences in the genetic history of the populations, the associations were locus dependent suggesting that genes of the VitD pathway might act in concert with antiviral genes modulating the resistance phenotype of the HESNs. Although these associations were significant after permutation test, only haplotype results remained statistically significant after Bonferroni test, requiring further replications in larger cohorts and functional analyzes to validate these conclusions.     

A general method for linkage disequilibrium correction for multipoint linkage and association

Lately, many different methods of linkage, association or joint analysis for family data have been invented and refined. Common to most of those is that they require a map of markers that are in linkage equilibrium. However, at the present day, high-density single nucleotide polymorphisms (SNPs) maps are both more inexpensive to create and they have lower genotyping error. When marker data is incomplete, the crucial and computationally most demanding moment in the analysis is to calculate the inheritance distribution at a certain position on the chromosome. Recently, different ways of adjusting traditional methods of linkage analysis to denser maps of SNPs in linkage disequilibrium (LD) have been proposed. We describe a hidden Markov model which generalizes the Lander-Green algorithm. It combines Markov chain for inheritance vectors with a Markov chain modelling founder haplotypes and in this way takes account for LD between SNPs. It can be applied to association, linkage or combined association and linkage analysis, general phenotypes and arbitrary score functions. We also define a joint likelihood for linkage and association that extends an idea of Kong and Cox (1997 Am. J. Hum. Genet. 61: 1179-1188) for pure linkage analysis.     

A robust and powerful two‐step testing procedure for local ancestry adjusted allelic association analysis in admixed populations

Genetic association studies in admixed populations allow us to gain deeper understanding of the genetic architecture of human diseases and traits. However, population stratification, complicated linkage disequilibrium (LD) patterns, and the complex interplay of allelic and ancestry effects on phenotypic traits pose challenges in such analyses. These issues may lead to detecting spurious associations and/or result in reduced statistical power. Fortunately, if handled appropriately, these same challenges provide unique opportunities for gene mapping. To address these challenges and to take these opportunities, we propose a robust and powerful two‐step testing procedure Local Ancestry Adjusted Allelic (LAAA) association. In the first step, LAAA robustly captures associations due to allelic effect, ancestry effect, and interaction effect, allowing detection of effect heterogeneity across ancestral populations. In the second step, LAAA identifies the source of association, namely allelic, ancestry, or the combination. By jointly modeling allele, local ancestry, and ancestry‐specific allelic effects, LAAA is highly powerful in capturing the presence of interaction between ancestry and allele effect. We evaluated the validity and statistical power of LAAA through simulations over a broad spectrum of scenarios. We further illustrated its usefulness by application to the Candidate Gene Association Resource (CARe) African American participants for association with hemoglobin levels. We were able to replicate independent groups’ previously identified loci that would have been missed in CARe without joint testing. Moreover, the loci, for which LAAA detected potential effect heterogeneity, were replicated among African Americans from the Women's Health Initiative study. LAAA is freely available at https://yunliweb.its.unc.edu/LAAA .     

Aspects of observing and claiming allele flips in association studies

Significant allele flipping, where associations for the same disease occur at opposite alleles of the same bi‐allelic locus, is increasing. But when is a significant allele flip genuine? We address the statistical issues of claiming and observing genuine allele flips in actual samples. We show that unless an allele flip is genuine, the probability of observing a significant allele flip in samples ascertained similarly from a common population is negligible. We derive expressions for the expected values of commonly used measures of association, which confirm previous findings that the underlying mechanism of a genuine allele flip is variation in the haplotype frequencies and show further how this variation interacts with variation in the genetic effects to impact allele flipping. We show that for association testing at proxy SNPs, common in genome‐wide association studies, variation in haplotype frequencies must coincide with a reversal in the sign of linkage disequilibrium (LD) to trigger genuine allele flips. Using HapMap data and r, rather than r², to highlight previously unobserved effects, we show that unless genetic effects are large, variation in LD is unlikely to cause genuine allele flips in samples drawn from the same population. However, as populations diverge, it is an increasingly viable cause of a genuine allele flip for sufficiently large genetic effect and/or sample sizes. We conclude that evidence of variation in local patterns of LD, ancestral composition of study samples, and environmental exposures between study populations can provide compelling practical evidence in defense of a genuine allele flip. Genet. Epidemiol. 34: 266–274, 2010. © 2009 Wiley‐Liss, Inc.     

Assessment and implications of linkage disequilibrium in genome-wide single-nucleotide polymorphism and microsatellite panels

Linkage disequilibrium (LD) between markers is more likely to exist in dense genome-wide single-nucleotide polymorphism (SNP) panels than in microsatellite panels. As part of Genetic Analysis Workshop 14 (GAW14), the extent of LD in the Illumina linkage panel III and the Affymetrix Genechip 10 K mapping array was assessed, using data from the Collaborative Study on the Genetics of Alcoholism (COGA). The impact of LD on linkage results was examined in COGA and simulated data, and characteristics of SNPs were assessed for their ability to detect population substructure and predict haplotypes. The authors of the papers summarized here observed greater LD in the Affymetrix than in the Illumina panel, possibly due to increased marker density in the Affymetrix panel, and found greater LD on chromosome X than on the autosomes. Simulation analyses suggest that intermarker LD can cause an upward bias in linkage statistics; however, the impact of LD on linkage analysis depends on the proportion of ungenotyped founders and the extent of LD. No large effect of LD on linkage peaks was observed in COGA analyses. In addition, the papers summarized here found that SNPs with high minor allele frequencies were the most informative compared with microsatellites for the detection of population substructure, and that SNPs in higher LD, and small numbers of SNPs, were the most reliable for haplotype prediction. As ease of genotyping continues to increase, study design and SNP selection for linkage and association studies (including genome-wide association studies) will be improved with consideration of LD in the particular populations studied.     

TSC1、TSC2基因多态性和儿童孤独症的家系研究

目的 探讨结节性硬化症(tuberous sclerosis complex,TSC)相关基因TSC1、TSC2基因多态性与儿童孤独症之间的关联。 方法 利用SNaPshot基因分型技术,在97例孤独症核心家系中,对TSC1、TSC2基因上的8个标签SNP,即rs3761840、rs2809244、rs1050700、rs739441、rs2074968、rs2074969、rs2072314、rs8063461进行分型;通过FBAT软件及Haploview软件进行基于家系的单倍型分析。 结果 1)基于家系的关联分析发现8个SNPs等位基因中有2个SNPs的等位基因倾向于过传递(rs1050700 A:Z=2.708,P=0.006769;rs2074968 G:Z=3.244,P=0.001180),并且经过FDR校正后,2个SNPs仍显示出与孤独症之间存在显著关联性(校正P值分别为0.027,0.014)。2)rs3761840-rs2809244基因型的单体型A-C显示出显著的传递不平衡,双亲较少传递给子女(Z=-2.297,P=0.021629)。rs2074968-rs2072314基因型的2种单体型即 G-C及C-C均显示出显著的传递不平衡,单体型G-C能从双亲过传递给子女(Z=2.596,P=0.009444),单体型C-C则相反(Z=-3.657,P=0.000256)。 结论 TSC1、TSC2基因可能与儿童孤独症的发生存在关联。     

Are rare variants really independent?

Recent advances in genotyping with high‐density markers allow researchers access to genomic variants including rare ones. Linkage disequilibrium (LD) is widely used to provide insight into evolutionary history. It is also the basis for association mapping in humans and other species. Better understanding of the genomic LD structure may lead to better‐informed statistical tests that can improve the power of association studies. Although rare variant associations with common diseases (RVCD) have been extensively studied recently, there is very limited understanding, and even controversial view of LD structures among rare variants and between rare and common variants. In fact, many popular RVCD tests make the assumptions that rare variants are independent. In this report, we show that two commonly used LD measures are not capable of detecting LD when rare variants are involved. We present this argument from two perspectives, both the LD measures themselves and the computational issues associated with them. To address these issues, we propose an alternative LD measure, the polychoric correlation, that was originally designed for detecting associations among categorical variables. Using simulated as well as the 1000 Genomes data, we explore the performances of LD measures in detail and discuss their implications in association studies.     

Multiple linear combination (MLC) regression tests for common variants adapted to linkage disequilibrium structure

By jointly analyzing multiple variants within a gene, instead of one at a time, gene‐based multiple regression can improve power, robustness, and interpretation in genetic association analysis. We investigate multiple linear combination (MLC) test statistics for analysis of common variants under realistic trait models with linkage disequilibrium (LD) based on HapMap Asian haplotypes. MLC is a directional test that exploits LD structure in a gene to construct clusters of closely correlated variants recoded such that the majority of pairwise correlations are positive. It combines variant effects within the same cluster linearly, and aggregates cluster‐specific effects in a quadratic sum of squares and cross‐products, producing a test statistic with reduced degrees of freedom (df) equal to the number of clusters. By simulation studies of 1000 genes from across the genome, we demonstrate that MLC is a well‐powered and robust choice among existing methods across a broad range of gene structures. Compared to minimum P‐value, variance‐component, and principal‐component methods, the mean power of MLC is never much lower than that of other methods, and can be higher, particularly with multiple causal variants. Moreover, the variation in gene‐specific MLC test size and power across 1000 genes is less than that of other methods, suggesting it is a complementary approach for discovery in genome‐wide analysis. The cluster construction of the MLC test statistics helps reveal within‐gene LD structure, allowing interpretation of clustered variants as haplotypic effects, while multiple regression helps to distinguish direct and indirect associations.     

Localization of breast cancer susceptibility loci by genome-wide SNP linkage disequilibrium mapping

We studied the feasibility of a novel approach to localize breast cancer susceptibility genes, using a low-density genome-wide panel of single-nucleotide polymorphisms and taking advantage of large regions of linkage disequilibrium (LD) flanking Jewish disease genes in high-risk cases. With Affymetrix GeneChip arrays, we genotyped 8,576 polymorphisms in three sets of Ashkenazi Jewish breast cancer cases: a "validation" set of 27 breast cancer cases, all of whom carried the BRCA2*6174delT founder mutation; a "field" set of 19 breast cancer cases from male breast cancer kindreds, which simulated conditions for finding new genes; and a "test" set of 57 probands from breast cancer kindreds (4 or more cases/kindred), in which mutations in BRCA1 and BRCA2 had been excluded. To identify associations, we compared the frequency of genotypes and haplotypes in cases vs. controls by the Fisher's exact test and a maximum likelihood ratio test. In the "validation" set, we demonstrated the presence of a region of linkage disequilibrium on BRCA2*6174delT chromosomes that spanned over 5 million bases. In the "field" set, we showed that this large region of linkage disequilibrium flanking BRCA2 was detectable despite the presence of heterogeneity in the sample set. Finally, in the "test" set, at least three regions of interest emerged that could contain novel breast cancer genes, one of which had been identified previously by linkage analysis. While these results demonstrate the feasibility of genome-wide association strategies, further application of this approach will critically depend on optimizing the density and distribution of SNPs and the size and type of study design.