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.     

Assessment of SNP streak statistics using gene drop simulation with linkage disequilibrium

We describe methods and programs for simulating the genotypes of individuals in a pedigree at large numbers of linked loci when the alleles of the founders are under linkage disequilibrium. Both simulation and estimation of linkage disequilibrium models are shown to be feasible on a genome wide scale. The methods are applied to evaluate the statistical significance of streaks of loci at which sets of related individuals share a common allele. The effects of properly allowing for linkage disequilibrium are shown to be important as they explain many of the large observations. This is illustrated by reanalysis of a previously reported linkage of prostate cancer to chromosome 1p23. Genet. Epidemiol. 34: 119–124, 2010. © 2009 Wiley‐Liss, Inc.     

Effect of linkage disequilibrium between markers in linkage and association analyses

Contributions to Group 17 of the Genetic Analysis Workshop 15 considered dense markers in linkage disequilibrium (LD) in the context of either linkage or association analysis. Three contributions reported on methods for modeling LD or selecting a subset of markers in linkage equilibrium to perform linkage analysis. When all markers were used without modeling LD, inflated evidence for linkage was observed when parental genotypes were missing. All methods for handling LD led to some decreased linkage evidence. Two groups performed a genome-wide association scan using either mixed models to account for known or unknown relatedness between individuals, trend tests or combination statistics. All methods failed to detect four of the eight simulated loci because of low LD in some regions. Three groups performed association analysis using simulated dense markers on chromosome 6, where a simulated HLA-DRB1 locus played a major role in disease susceptibility along with two additional loci of smaller effect. The overall conditional genotype method correctly identified both additional loci while a novel transmission disequilibrium test-statistic to combine studies with non-overlapping markers identified one HLA locus after stratifying on the parental HLA-DRB1 genotypes; LD mapping using the Malécot model mapped two loci in this region, even when using greatly reduced marker density. While LD between markers appears to be a nuisance that may cause spurious linkage results with missing parental genotypes in linkage analysis, association analysis thrives on LD, and disease genes fail to be detected in regions of low LD.     

Characterization of multilocus linkage disequilibrium

Linkage disequilibrium (LD) in the human genome, often measured as pairwise correlation between adjacent markers, shows substantial spatial heterogeneity. Congruent with these results, studies have found that certain regions of the genome have far less haplotype diversity than expected if the alleles at multiple markers were independent, while other sets of adjacent markers behave almost independently. Regions with limited haplotype diversity have been described as "blocked" or "haplotype blocks." In this article, we propose a new method that aims to distinguish between blocked and unblocked regions in the genome. Like some other approaches, the method analyses haplotype diversity. Unlike other methods, it allows for adjacent, distinct blocks and also multiple, independent single nucleotide polymorphisms (SNPs) separating blocks. Based on an approximate likelihood model and a parsimony criterion to penalize for model complexity, the method partitions a genomic region into blocks relatively quickly, and simulations suggest that its partitions are accurate. We also propose a new, efficient method to select SNPs for association analysis, namely tag SNPs. These methods compare favorably to similar blocking and tagging methods using simulations.     

Combined linkage and linkage disequilibrium mapping for genome screens

Linkage analysis and association studies, two major approaches for genetic studies of human diseases, are useful for mapping genes that are highly penetrant, but both use only part of the information that is available for mapping disease genes. Therefore, they provide limited utility when used alone. In this report, we present combined linkage and linkage disequilibrium mapping that simultaneously utilizes linkage and linkage disequilibrium information for mapping human disease genes. Compared with the existing linkage analysis and association study methods, this method has several advantages: 1) it has high statistical power by a joint analysis of linkage and linkage disequilibrium for localizing disease susceptibility loci: 2) it unifies the theory of linkage analysis and linkage disequilibrium mapping, 3) it retains the general framework for linkage analysis and, hence, can be easily incorporated into the existing software for the linkage analysis. The proposed LLDM is applied to familial hemophagocytic lymphohistiocytosis (FHL) disease.     

The TDT reveals linkage and linkage disequilibrium in a rare disease

We used the TDT as the basis for our analysis of data from Problem 1 of GAW9. Among the 360 marker loci on six chromosomes, we searched for any that might show both linkage and allelic association with the disease. We applied the TDT to each allele at every marker locus and found strong evidence for linkage in two regions: one on chromosome 1, another on chromosome 5. ©1995 Wiley-Liss, Inc.     

Fieller's theorem and linkage disequilibrium mapping.

Linkage disequilibrium mapping exploits the fact that at genetic markers close enough to a disease locus on a particular chromosome, we expect to find an association between the disease and marker alleles. Furthermore, the magnitude of the association is expected to follow a unimodal curve when plotted against location, with the peak at the disease location. In practice, for real data, we usually see deviations from such a curve due to other influences such as evolutionary variability, mutation, and selection. Here we propose fitting a quadratic curve to data of this nature, estimating the location of the disease locus by the point at which the curve is maximum. A key feature of our method is the use of transformations of both location and disequilibrium, so that departures from a unimodal curve are incorporated by fitting the curve not to the original location and disequilibrium values but to the transformed values. In addition, we estimate the covariances between the disequilibrium values at linked loci using either a multinomial approximation or a bootstrap procedure. The location estimate from our method is the ratio of two quantities that, in large samples, are normally distributed, and so we use Fieller's theorem to obtain a confidence interval for the disease gene location. We successfully apply our method to data from several published studies in which the true disease gene location is known.     

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.     

Bounds and normalization of the composite linkage disequilibrium coefficient

The composite linkage disequilibrium (LD) measure is often calculated for two-locus genotypic data, especially when coupling and repulsion double heterozygotes cannot be distinguished. This measure was reported to have good statistical properties and was suggested for routine testing of LD, regardless of Hardy-Weinberg equilibrium at either of two loci. However, the bounds for this measure have not been yet reported. These bounds are derived here as functions of one-locus genotype or allele frequencies. They provide standardized measures of composite linkage disequilibrium, defined as the proportion of its maximum attainable value, given observed allele or genotype frequencies.     

Extent and distribution of linkage disequilibrium in the Old Order Amish

Knowledge of the extent and distribution of linkage disequilibrium (LD) is critical to the design and interpretation of gene mapping studies. Because the demographic history of each population varies and is often not accurately known, it is necessary to empirically evaluate LD on a population‐specific basis. Here we present the first genome‐wide survey of LD in the Old Order Amish (OOA) of Lancaster County Pennsylvania, a closed population derived from a modest number of founders. Specifically, we present a comparison of LD between OOA individuals and US Utah participants in the International HapMap project (abbreviated CEU) using a high‐density single nucleotide polymorphism (SNP) map. Overall, the allele (and haplotype) frequency distributions and LD profiles were remarkably similar between these two populations. For example, the median absolute allele frequency difference for autosomal SNPs was 0.05, with an inter‐quartile range of 0.02–0.09, and for autosomal SNPs 10–20 kb apart with common alleles (minor allele frequency≥0.05), the LD measure r² was at least 0.8 for 15 and 14% of SNP pairs in the OOA and CEU, respectively. Moreover, tag SNPs selected from the HapMap CEU sample captured a substantial portion of the common variation in the OOA (～88%) at r²≥0.8. These results suggest that the OOA and CEU may share similar LD profiles for other common but untyped SNPs. Thus, in the context of the common variant‐common disease hypothesis, genetic variants discovered in gene mapping studies in the OOA may generalize to other populations. Genet. Epidemiol. 34: 146–150, 2010. © 2009 Wiley‐Liss, Inc.     

Power consequences of linkage disequilibrium variation between populations

We quantify the degree to which LD differences exist in the human genome and investigates the consequences that variations in patterns of LD between populations can have on the power of case-control or family-trio association studies. Although only a small proportion of SNPs show significant LD differences (0.8-5%), these can introduce artificial signals of associations and reduce the power to detect true associations in case-control designs, even when meta-analytic approaches are used to account for stratification. We show that combining trios from different populations in the presence of significant LD differences can adversely affect power even though the number of trios has increased. Our results have implications on genetic studies conducted in populations with substantial population structure and show that the use of meta-analytic approaches or family-based designs to protect Type 1 error does not prevent loss of power due to differences in LD across populations.     

Model-based and model-free multipoint genome-wide linkage analysis of alcoholism

We analyzed a subset of the Collaborative Study on the Genetics of Alcoholism (COGA) data set as provided by the 11th Genetic Analysis Workshop (GAW11). Linkage analyses were performed using each of the diagnostic criteria for alcoholism included in the data: the COGA criteria (DSM-III-R plus the Feighner criteria) and the narrower World Health Organization diagnosis ICD-10 criteria. Formal segregation analysis using these data was not attempted because only a subset of all the originally ascertained families was made available. Nevertheless, an attempt was made to estimate the best one-locus two-allele genetic model for these data. Model-based multipoint linkage analysis was performed using the results of our trait model fitting, and model-free multipoint linkage analysis was performed with an improved version of the Haseman and Elston linkage method for sib pairs.     

Accounting for haplotype phase uncertainty in linkage disequilibrium estimation

The characterization of linkage disequilibrium (LD) is applied in a variety of studies including the identification of molecular determinants of the local recombination rate, the migration and population history of populations, and the role of positive selection in adaptation. LD suffers from the phase uncertainty of the haplotypes used in its calculation, which reflects limitations of the algorithms used for haplotype estimation. We introduce a LD calculation method, which deals with phase uncertainty by weighting all possible haplotype pairs according to their estimated probabilities as evaluated by PHASE. In contrast to the expectation-maximization (EM) algorithm as implemented in the HAPLOVIEW and GENETICS packages, our method considers haplotypes based on the entire genetic information available for the candidate region. We tested the method using simulated and real genotyping data. The results show that, for all practical purposes, the new method is advantageous in comparison with algorithms that calculate LD using only the most probable haplotype or bilocus haplotypes based on the EM algorithm. The new method deals especially well with low LD regions, which contribute strongly to phase uncertainty. Altogether, the method is an attractive alternative to standard LD calculation procedures, including those based on the EM algorithm. We implemented the method in the software suite R, together with an interface to the popular haplotype calculation package PHASE.     

Asymptotic tests of association with multiple SNPs in linkage disequilibrium

We consider detecting associations between a trait and multiple single nucleotide polymorphisms (SNPs) in linkage disequilibrium (LD). To maximize the use of information contained in multiple SNPs while minimizing the cost of large degrees of freedom (DF) in testing multiple parameters, we first theoretically explore the sum test derived under a working assumption of a common association strength between the trait and each SNP, testing on the corresponding parameter with only one DF. Under the scenarios that the association strengths between the trait and the SNPs are close to each other (and in the same direction), as considered by Wang and Elston [Am. J. Hum. Genet. [2007] 80:353–360], we show with simulated data that the sum test was powerful as compared to several existing tests; otherwise, the sum test might have much reduced power. To overcome the limitation of the sum test, based on our theoretical analysis of the sum test, we propose five new tests that are closely related to each other and are shown to consistently perform similarly well across a wide range of scenarios. We point out the close connection of the proposed tests to the Goeman test. Furthermore, we derive the asymptotic distributions of the proposed tests so that P‐values can be easily calculated, in contrast to the use of computationally demanding permutations or simulations for the Goeman test. A distinguishing feature of the five new tests is their use of a diagonal working covariance matrix, rather than a full covariance matrix as used in the usual Wald or score test. We recommend the routine use of two of the new tests, along with several other tests, to detect disease associations with multiple linked SNPs. Genet. Epidemiol. 33:497–507, 2009. © 2009 Wiley‐Liss, Inc.     

VALID: visualization of association study results and linkage disequilibrium

Promising findings from genetic association studies are commonly presented with two distinct figures: one gives the association study results and the other indicates linkage disequilibrium (LD) between genetic markers in the region(s) of interest. Fully interpreting the results of such studies requires synthesizing the information in these figures, which is generally done in a subjective and unsystematic manner. Here we present a method to formally combine association results and LD and display them in the same figure; we have developed a freely available web‐based application that can be used to generate figures to display the combined data. To demonstrate this approach we apply it to fine mapping data from the prostate cancer 8q24 loci. Combining these two sources of information in a single figure allows one to more clearly assess patterns of association, facilitating the interpretation of genome‐wide and fine mapping data and improving our ability to localize causal variants. Genet. Epidemiol. 33:599–603, 2009. © 2009 Wiley‐Liss, Inc.     

Clonal reproduction and linkage disequilibrium in diploids: a simulation study

Estimating the rate of clonal reproduction in natural population of diploid organisms is recognised as being problematic and even the detection of strictly clonal populations is often controversial. One well-acknowledged signature of clonal reproduction is the generation of non-random associations between loci. Linkage disequilibrium (LD) is thus often used for estimating the amount of clonal reproduction. Here we explore with computer simulations the effect of the rate of clonal reproduction on LD estimates obtained from different estimators within a comprehensive parameter range. None of the LD estimators studied is able to accurately measure the proportion of clonal (or sexual) reproduction on its own, due to strong bias, incoherent behaviour, or huge variances. The joint use of several statistics is thus recommended for the estimation rates of clonal reproduction in natural populations. We hope that our work will provide useful tools for the study of clonal diploids, many of which can only be studied with molecular markers, as it is the case for medically important parasites.     

Incorporation of covariates into multipoint linkage disequilibrium mapping in case-control studies

Case-control designs are commonly adopted in genetic epidemiological studies because they are cost effective and offer powerful tests for genetic and environmental risk factors, as well as their interactions. Previously, we proposed an association mapping approach to estimate the position of an unobserved disease locus as well as measuring its genetic effect on risk. The method provides a confidence interval for the estimated map position to help narrow the chromosomal region potentially harboring a disease locus. However, concerns often rise about case-control designs including possible false positives or bias due to confounders, heterogeneity or interactions among genes and between genes and environments. In the present work, we extended the multipoint linkage disequilibrium mapping approach for case-control studies to incorporate information about factors influencing the effect of causal genes to improve precision and efficiency of the estimated location. The efficiency, bias and coverage probability of this extended approach for locating a disease locus using case-control data with and without additional information on a covariate were compared through simulation. An example of a case-control study for type 2 diabetes was used to illustrate this extended method. In this study, a strong association between diabetes and a candidate gene, SCL2A10, was detected among nonobese subjects, whereas no evidence of association was found for either obese subjects or the whole sample when obesity was ignored. Simulation studies and these diabetes data both demonstrate how the efficiency of the estimated location of a disease gene can be improved substantially by incorporating information on covariates.     

Effect of linkage disequilibrium on the identification of functional variants

We summarize the contributions of Group 9 of Genetic Analysis Workshop 17. This group addressed the problems of linkage disequilibrium and other longer range forms of allelic association when evaluating the effects of genotypes on phenotypes. Issues raised by long-range associations, whether a result of selection, stratification, possible technical errors, or chance, were less expected but proved to be important. Most contributors focused on regression methods of various types to illustrate problematic issues or to develop adaptations for dealing with high-density genotype assays. Study design was also considered, as was graphical modeling. Although no method emerged as uniformly successful, most succeeded in reducing false-positive results either by considering clusters of loci within genes or by applying smoothing metrics that required results from adjacent loci to be similar. Two unexpected results that questioned our assumptions of what is required to model linkage disequilibrium were observed. The first was that correlations between loci separated by large genetic distances can greatly inflate single-locus test statistics, and, whether the result of selection, stratification, possible technical errors, or chance, these correlations seem overabundant. The second unexpected result was that applying principal components analysis to genome-wide genotype data can apparently control not only for population structure but also for linkage disequilibrium.     

梅州地区TLR4基因连锁不平衡与缺血性脑卒中相关性研究

目的 检测梅州地区缺血性脑卒中患者和健康对照人群TLR4基因的（rs10759932、rs11536879、rs11536891、rs1927914）的基因多态性,经连锁不平衡分析其与缺血性脑卒中的相关性。方法 收集2018年1月1日 - 2018年7月31日住院治疗的突发缺血性脑卒中患者作为病例组,同期在体检中心收集健康人群作为对照组。应用Massarray SNP 分型技术检测两组患者TLR4基因的4个位点基因型,进行Hardy - Weinberg（H - W）平衡检测。采用不同模型分析上述位点不同基因型与脑梗死发病风险的相关性,并通过连锁不平衡分析其与缺血性脑卒中的相关性。结果 病例组纳入病例186名,对照组纳入健康人194名;4种SNP位点均符合H - W 平衡。rs1927914位点G/G基因型在对照组出现频率远远高于病例组（χ2 = 9.267,P＜0.05）。rs10759932位点T/T基因型在女性对照组中出现的频率显著高于男性[OR = 0.38 (0.18 - 0.81),P＜0.05]。4个SNP位点之间均存在连锁不平衡,TGCG基因型组合在缺血性卒中男性患者出现的频率显著高于女性[OR = 3.54 (1.17 - 10.69),P＜0.05]。结论 梅州地区rs1927914位点A＞G为保护性基因突变,可以降低缺血性脑卒中发生。4个位点的连锁不平衡与缺血性脑卒中发生存在部分性别差异,TGCG组合为男性人群的危险基因,脑卒中发生率显著升高。