Fine‐Mapping Additive and Dominant SNP Effects Using Group‐LASSO and Fractional Resample Model Averaging

Genomewide association studies (GWAS) sometimes identify loci at which both the number and identities of the underlying causal variants are ambiguous. In such cases, statistical methods that model effects of multiple single‐nucleotide polymorphisms (SNPs) simultaneously can help disentangle the observed patterns of association and provide information about how those SNPs could be prioritized for follow‐up studies. Current multi‐SNP methods, however, tend to assume that SNP effects are well captured by additive genetics; yet when genetic dominance is present, this assumption translates to reduced power and faulty prioritizations. We describe a statistical procedure for prioritizing SNPs at GWAS loci that efficiently models both additive and dominance effects. Our method, LLARRMA‐dawg, combines a group LASSO procedure for sparse modeling of multiple SNP effects with a resampling procedure based on fractional observation weights. It estimates for each SNP the robustness of association with the phenotype both to sampling variation and to competing explanations from other SNPs. In producing an SNP prioritization that best identifies underlying true signals, we show the following: our method easily outperforms a single‐marker analysis; when additive‐only signals are present, our joint model for additive and dominance is equivalent to or only slightly less powerful than modeling additive‐only effects; and when dominance signals are present, even in combination with substantial additive effects, our joint model is unequivocally more powerful than a model assuming additivity. We also describe how performance can be improved through calibrated randomized penalization, and discuss how dominance in ungenotyped SNPs can be incorporated through either heterozygote dosage or multiple imputation.     

A multiple testing correction method for genetic association studies using correlated single nucleotide polymorphisms

Multiple testing is a challenging issue in genetic association studies using large numbers of single nucleotide polymorphism (SNP) markers, many of which exhibit linkage disequilibrium (LD). Failure to adjust for multiple testing appropriately may produce excessive false positives or overlook true positive signals. The Bonferroni method of adjusting for multiple comparisons is easy to compute, but is well known to be conservative in the presence of LD. On the other hand, permutation-based corrections can correctly account for LD among SNPs, but are computationally intensive. In this work, we propose a new multiple testing correction method for association studies using SNP markers. We show that it is simple, fast and more accurate than the recently developed methods and is comparable to permutation-based corrections using both simulated and real data. We also demonstrate how it might be used in whole-genome association studies to control type I error. The efficiency and accuracy of the proposed method make it an attractive choice for multiple testing adjustment when there is high intermarker LD in the SNP data set.     

Mining gold dust under the genome wide significance level: a two-stage approach to analysis of GWAS

We propose a two-stage approach to analyze genome-wide association data in order to identify a set of promising single-nucleotide polymorphisms (SNPs). In stage one, we select a list of top signals from single SNP analyses by controlling false discovery rate. In stage two, we use the least absolute shrinkage and selection operator (LASSO) regression to reduce false positives. The proposed approach was evaluated using simulated quantitative traits based on genome-wide SNP data on 8,861 Caucasian individuals from the Atherosclerosis Risk in Communities (ARIC) Study. Our first stage, targeted at controlling false negatives, yields better power than using Bonferroni-corrected significance level. The LASSO regression reduces the number of significant SNPs in stage two: it reduces false-positive SNPs and it reduces true-positive SNPs also at simulated causal loci due to linkage disequilibrium. Interestingly, the LASSO regression preserves the power from stage one, i.e., the number of causal loci detected from the LASSO regression in stage two is almost the same as in stage one, while reducing false positives further. Real data on systolic blood pressure in the ARIC study was analyzed using our two-stage approach which identified two significant SNPs, one of which was reported to be genome-significant in a meta-analysis containing a much larger sample size. On the other hand, a single SNP association scan did not yield any significant results.     

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.     

Detecting genetic association through shortest paths in a bidirected graph

Genome‐wide association studies (GWASs) commonly use marginal association tests for each single‐nucleotide polymorphism (SNP). Because these tests treat SNPs as independent, their power will be suboptimal for detecting SNPs hidden by linkage disequilibrium (LD). One way to improve power is to use a multiple regression model. However, the large number of SNPs preclude simultaneous fitting with multiple regression, and subset regression is infeasible because of an exorbitant number of candidate subsets. We therefore propose a new method for detecting hidden SNPs having significant yet weak marginal association in a multiple regression model. Our method begins by constructing a bidirected graph locally around each SNP that demonstrates a moderately sized marginal association signal, the focal SNPs. Vertexes correspond to SNPs, and adjacency between vertexes is defined by an LD measure. Subsequently, the method collects from each graph all shortest paths to the focal SNP. Finally, for each shortest path the method fits a multiple regression model to all the SNPs lying in the path and tests the significance of the regression coefficient corresponding to the terminal SNP in the path. Simulation studies show that the proposed method can detect susceptibility SNPs hidden by LD that go undetected with marginal association testing or with existing multivariate methods. When applied to real GWAS data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), our method detected two groups of SNPs: one in a region containing the apolipoprotein E (APOE) gene, and another in a region close to the semaphorin 5A (SEMA5A) gene.     

Inclusion of biological knowledge in a Bayesian shrinkage model for joint estimation of SNP effects

With the aim of improving detection of novel single‐nucleotide polymorphisms (SNPs) in genetic association studies, we propose a method of including prior biological information in a Bayesian shrinkage model that jointly estimates SNP effects. We assume that the SNP effects follow a normal distribution centered at zero with variance controlled by a shrinkage hyperparameter. We use biological information to define the amount of shrinkage applied on the SNP effects distribution, so that the effects of SNPs with more biological support are less shrunk toward zero, thus being more likely detected. The performance of the method was tested in a simulation study (1,000 datasets, 500 subjects with ～200 SNPs in 10 linkage disequilibrium (LD) blocks) using a continuous and a binary outcome. It was further tested in an empirical example on body mass index (continuous) and overweight (binary) in a dataset of 1,829 subjects and 2,614 SNPs from 30 blocks. Biological knowledge was retrieved using the bioinformatics tool Dintor, which queried various databases. The joint Bayesian model with inclusion of prior information outperformed the standard analysis: in the simulation study, the mean ranking of the true LD block was 2.8 for the Bayesian model versus 3.6 for the standard analysis of individual SNPs; in the empirical example, the mean ranking of the six true blocks was 8.5 versus 9.3 in the standard analysis. These results suggest that our method is more powerful than the standard analysis. We expect its performance to improve further as more biological information about SNPs becomes available.     

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.     

Bayesian variable and model selection methods for genetic association studies

Variable selection is growing in importance with the advent of high throughput genotyping methods requiring analysis of hundreds to thousands of single nucleotide polymorphisms (SNPs) and the increased interest in using these genetic studies to better understand common, complex diseases. Up to now, the standard approach has been to analyze the genotypes for each SNP individually to look for an association with a disease. Alternatively, combinations of SNPs or haplotypes are analyzed for association. Another added complication in studying complex diseases or phenotypes is that genetic risk for the disease is often due to multiple SNPs in various locations on the chromosome with small individual effects that may have a collectively large effect on the phenotype. Hence, multi-locus SNP models, as opposed to single SNP models, may better capture the true underlying genotypic-phenotypic relationship. Thus, innovative methods for determining which SNPs to include in the model are needed. The goal of this article is to describe several methods currently available for variable and model selection using Bayesian approaches and to illustrate their application for genetic association studies using both real and simulated candidate gene data for a complex disease. In particular, Bayesian model averaging (BMA), stochastic search variable selection (SSVS), and Bayesian variable selection (BVS) using a reversible jump Markov chain Monte Carlo (MCMC) for candidate gene association studies are illustrated using a study of age-related macular degeneration (AMD) and simulated data.     

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.     

POLARIS: Polygenic LD‐adjusted risk score approach for set‐based analysis of GWAS data

Polygenic risk scores (PRSs) are a method to summarize the additive trait variance captured by a set of SNPs, and can increase the power of set‐based analyses by leveraging public genome‐wide association study (GWAS) datasets. PRS aims to assess the genetic liability to some phenotype on the basis of polygenic risk for the same or different phenotype estimated from independent data. We propose the application of PRSs as a set‐based method with an additional component of adjustment for linkage disequilibrium (LD), with potential extension of the PRS approach to analyze biologically meaningful SNP sets. We call this method POLARIS: POlygenic Ld‐Adjusted RIsk Score. POLARIS identifies the LD structure of SNPs using spectral decomposition of the SNP correlation matrix and replaces the individuals' SNP allele counts with LD‐adjusted dosages. Using a raw genotype dataset together with SNP effect sizes from a second independent dataset, POLARIS can be used for set‐based analysis. MAGMA is an alternative set‐based approach employing principal component analysis to account for LD between markers in a raw genotype dataset. We used simulations, both with simple constructed and real LD‐structure, to compare the power of these methods. POLARIS shows more power than MAGMA applied to the raw genotype dataset only, but less or comparable power to combined analysis of both datasets. POLARIS has the advantages that it produces a risk score per person per set using all available SNPs, and aims to increase power by leveraging the effect sizes from the discovery set in a self‐contained test of association in the test dataset.     

Interpreting Joint SNP Analysis Results: When Are Two Distinct Signals Really Two Distinct Signals?

In genetic association studies, much effort has focused on moving beyond the initial single‐nucleotide polymorphism (SNP)‐by‐SNP analysis. One approach is to reanalyze a chromosomal region where an association has been detected, jointly analyzing the SNP thought to best represent that association with each additional SNP in the region. Such joint analyses may help identify additional, statistically independent association signals. However, it is possible for a single genetic effect to produce joint SNP results that would typically be interpreted as two distinct effects (e.g., both SNPs are significant in the joint model). We present a general approach that can (1) identify conditions under which a single variant could produce a given joint SNP result, and (2) use these conditions to identify variants from a list of known SNPs (e.g., 1000 Genomes) as candidates that could produce the observed signal. We apply this method to our previously reported joint result for smoking involving rs16969968 and rs588765 in CHRNA5. We demonstrate that it is theoretically possible for a joint SNP result suggestive of two independent signals to be produced by a single causal variant. Furthermore, this variant need not be highly correlated with the two tested SNPs or have a large odds ratio. Our method aids in interpretation of joint SNP results by identifying new candidate variants for biological causation that would be missed by traditional approaches. Also, it can connect association findings that may seem disparate due to lack of high correlations among the associated SNPs.     

A scan statistic for identifying chromosomal patterns of SNP association

We have developed a single nucleotide polymorphism (SNP) association scan statistic that takes into account the complex distribution of the human genome variation in the identification of chromosomal regions with significant SNP associations. This scan statistic has wide applicability for genetic analysis, whether to identify important chromosomal regions associated with common diseases based on whole-genome SNP association studies or to identify disease susceptibility genes based on dense SNP positional candidate studies. To illustrate this method, we analyzed patterns of SNP associations on chromosome 19 in a large cohort study. Among 2,944 SNPs, we found seven regions that contained clusters of significantly associated SNPs. The average width of these regions was 35 kb with a range of 10-72 kb. We compared the scan statistic results to Fisher's product method using a sliding window approach, and detected 22 regions with significant clusters of SNP associations. The average width of these regions was 131 kb with a range of 10.1-615 kb. Given that the distances between SNPs are not taken into consideration in the sliding window approach, it is likely that a large fraction of these regions represents false positives. However, all seven regions detected by the scan statistic were also detected by the sliding window approach. The linkage disequilibrium (LD) patterns within the seven regions were highly variable indicating that the clusters of SNP associations were not due to LD alone. The scan statistic developed here can be used to make gene-based or region-based SNP inferences about disease association.     

Power of genome-wide association studies in the presence of interacting loci

Though multiple interacting loci are likely involved in the etiology of complex diseases, early genome-wide association studies (GWAS) have depended on the detection of the marginal effects of each locus. Here, we evaluate the power of GWAS in the presence of two linked and potentially associated causal loci for several models of interaction between them and find that interacting loci may give rise to marginal relative risks that are not generally considered in a one-locus model. To derive power under realistic situations, we use empirical data generated by the HapMap ENCODE project for both allele frequencies and linkage disequilibrium (LD) structure. The power is also evaluated in situations where the causal single nucleotide polymorphisms (SNPs) may not be genotyped, but rather detected by proxy using a SNP in LD. A common simplification for such power computations assumes that the sample size necessary to detect the effect at the tSNP is the sample size necessary to detect the causal locus directly divided by the LD measure r(2) between the two. This assumption, which we call the "proportionality assumption", is a simplification of the many factors that contribute to the strength of association at a marker, and has recently been criticized as unreasonable (Terwilliger and Hiekkalinna [2006] Eur J Hum Genet 14(4):426-437), in particular in the presence of interacting and associated loci. We find that this assumption does not introduce much error in single locus models of disease, but may do so in so in certain two-locus models.     

Multilocus LD measure and tagging SNP selection with generalized mutual information

Linkage disequilibrium (LD) plays a central role in fine mapping of disease genes and, more recently, in characterizing haplotype blocks. Classical LD measures, such as D' and r(2), are frequently used to quantify relationship between two loci. A pairwise "distance" matrix among a set of loci can be constructed using such a measure, and based upon which a number of haplotype block detection and tagging single nucleotide polymorphism (SNP) selection algorithms have been devised. Although successful in many applications, the pairwise nature of these measures does not provide a direct characterization of joint linkage disequilibrium among multiple loci. Consequently, applications based on them may lead to loss of important information. In this report, we propose a multilocus LD measure based on generalized mutual information, which is also known as relative entropy or Kullback-Leibler distance. In essence, this measure seeks to quantify the distance between the observed haplotype distribution and the expected distribution assuming linkage equilibrium. We can show that this measure is approximately equal to r(2) in the special case with two loci. Based on this multilocus LD measure and an entropy measure that characterizes haplotype diversity, we propose a class of stepwise tagging SNP selection algorithms. This represents a unified approach for SNP selection in that it takes into account both the haplotype diversity and linkage disequilibrium objectives. Applications to both simulated and real data demonstrate the utility of the proposed methods for handling a large number of SNPs. The results indicate that multilocus LD patterns can be captured well, and informative and nonredundant SNPs can be selected effectively from a large set of loci.     

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.     

SNPs, haplotypes, and model selection in a candidate gene region: the SIMPle analysis for multilocus data

Modern molecular techniques make discovery of numerous single nucleotide polymorphims (SNPs) in candidate gene regions feasible. Conventional analysis relies on either independent tests with each variant or the use of haplotypes in association analysis. The first technique ignores the dependencies between SNPs. The second, though it may increase power, often introduces uncertainty by estimating haplotypes from population data. Additionally, as the number of loci expands for a haplotype, ambiguity in interpretation increases for determining the underlying genetic components driving a detected association. Here, we present a genotype-level analysis to jointly model the SNPs via a SNP interaction model with phase information (SIMPle) to capture the underlying haplotype structure. This analysis estimates both the risk associated with each variant and the importance of phase between pairwise combinations of SNPs. Thus, rather than selecting between genotype- or haplotype-level approaches, the SIMPle method frames the analysis of multilocus data in a model selection paradigm, the aim to determine which SNPs, phase terms, and linear combinations best describe the relation between genetic variation and a trait of interest. To avoid unstable estimation due to sparse data and to incorporate both the dependencies among terms and the uncertainty in model selection, we propose a Bayes model averaging procedure. This highlights key SNPs and phase terms and yields a set of best representative models. Using simulations, we demonstrate the utility of the SIMPle model to identify crucial SNPs and underlying haplotype structures across a variety of causal models and genetic architectures.     

Smooth‐Threshold Multivariate Genetic Prediction with Unbiased Model Selection

We develop a new genetic prediction method, smooth‐threshold multivariate genetic prediction, using single nucleotide polymorphisms (SNPs) data in genome‐wide association studies (GWASs). Our method consists of two stages. At the first stage, unlike the usual discontinuous SNP screening as used in the gene score method, our method continuously screens SNPs based on the output from standard univariate analysis for marginal association of each SNP. At the second stage, the predictive model is built by a generalized ridge regression simultaneously using the screened SNPs with SNP weight determined by the strength of marginal association. Continuous SNP screening by the smooth thresholding not only makes prediction stable but also leads to a closed form expression of generalized degrees of freedom (GDF). The GDF leads to the Stein's unbiased risk estimation (SURE), which enables data‐dependent choice of optimal SNP screening cutoff without using cross‐validation. Our method is very rapid because computationally expensive genome‐wide scan is required only once in contrast to the penalized regression methods including lasso and elastic net. Simulation studies that mimic real GWAS data with quantitative and binary traits demonstrate that the proposed method outperforms the gene score method and genomic best linear unbiased prediction (GBLUP), and also shows comparable or sometimes improved performance with the lasso and elastic net being known to have good predictive ability but with heavy computational cost. Application to whole‐genome sequencing (WGS) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) exhibits that the proposed method shows higher predictive power than the gene score and GBLUP methods.     

Imputation Without Doing Imputation: A New Method for the Detection of Non‐Genotyped Causal Variants

Genome‐wide association studies allow detection of non‐genotyped disease‐causing variants through testing of nearby genotyped SNPs. This approach may fail when there are no genotyped SNPs in strong LD with the causal variant. Several genotyped SNPs in weak LD with the causal variant may, however, considered together, provide equivalent information. This observation motivates popular but computationally intensive approaches based on imputation or haplotyping. Here we present a new method and accompanying software designed for this scenario. Our approach proceeds by selecting, for each genotyped “anchor” SNP, a nearby genotyped “partner” SNP, chosen via a specific algorithm we have developed. These two SNPs are used as predictors in linear or logistic regression analysis to generate a final significance test. In simulations, our method captures much of the signal captured by imputation, while taking a fraction of the time and disc space, and generating a smaller number of false‐positives. We apply our method to a case/control study of severe malaria genotyped using the Affymetrix 500K array. Previous analysis showed that fine‐scale sequencing of a Gambian reference panel in the region of the known causal locus, followed by imputation, increased the signal of association to genome‐wide significance levels. Our method also increases the signal of association from to . Our method thus, in some cases, eliminates the need for more complex methods such as sequencing and imputation, and provides a useful additional test that may be used to identify genetic regions of interest.     

Unidentified genetic variants influence pancreatic cancer risk: an analysis of polygenic susceptibility in the PanScan study

Genome-wide association (GWA) studies have identified several pancreatic cancer (PanCa) susceptibility loci. Methods for assessment of polygenic susceptibility can be employed to detect the collective effect of additional association signals for PanCa. Using data on 492,651 autosomal single nucleotide polymorphisms (SNPs) from the PanScan GWA study (2,857 cases, 2,967 controls), we employed polygenic risk score (PRS) cross-validation (CV) methods to (a) confirm the existence of unidentified association signals, (b) assess the predictive value of PRSs, and (c) assess evidence for polygenic effects in specific genomic locations (genic vs. intergenic). After excluding SNPs in known PanCa susceptibility regions, we constructed PRS models using a training GWA dataset and then tested the model in an independent testing dataset using fourfold CV. We also employed a "power-replication" approach, where power to detect SNP associations was calculated using a training dataset, and power was tested for association with "replication status" in a testing dataset. PRS scores constructed using ≥ 10% of genome-wide SNPs showed significant association with PanCa (P< 0.05) across the majority of CV analyses. Associations were stronger for PRSs restricted to genic SNPs compared to intergenic PRSs. The power-replications approach produced weaker associations that were not significant when restricting to SNPs with low pairwise linkage disequilibrium, whereas PRS results were robust to such restrictions. Although the PRS approach will not dramatically improve PanCa prediction, it provides strong evidence for unidentified association signals for PanCa. Our results suggest that focusing association studies on genic regions and conducting larger GWA studies can reveal additional PanCa susceptibility loci.     

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.