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.     

Haplotypes and haplotype-tagging single-nucleotide polymorphism: presentation Group 8 of Genetic Analysis Workshop 14

Moderately dense maps of single-nucleotide polymorphism (SNP) markers across the human genome for both the simulated data set and data from the Collaborative Study of the Genetics of Alcoholism were available at Genetic Analysis Workshop 14 for the first time. This allowed examination of various novel and existing methods for haplotype analyses. Three contributors applied Mantel statistics in different ways for both linkage and association analysis by using the shared length between two haplotypes at a marker locus as a measure of genetic similarity. The results indicate that haplotype-sharing based on Mantel statistics can be a powerful approach and needs further methodological evaluation. Four contributors investigated haplotype-tagging SNP (htSNP) selection procedures, two contributors examined the use of multilocus haplotypes compared to single loci in association tests, and two contributors compared the accuracy of various methods for reconstructing haplotypes and estimating haplotype frequencies for both pedigree data and data from unrelated individuals. For all three different tasks, software packages and procedures gave similar results in regions of high linkage disequilibrium (LD). However, they were not as consistent in regions of moderate to low LD. One coalescence-based approach for estimating haplotype frequencies, coupled with a Markov chain Monte Carlo technique, outperformed the other haplotype frequency estimation methods in regions of low LD. In conclusion, regardless of the task, results were similar in chromosomal regions of high LD. However, based on the differing results observed here, methodological improvements are required for chromosomal regions of low to moderate 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.     

Characteristics of genetic markers and maps for cost-effective genome screens using diallelic markers

New technologies for genotyping diallelic markers (SNPs) were recently developed that may be lower in cost, and more easily automated than microsatellite markers (STRPs). The reduction in genotyping costs resulting from such automation may significantly impact the overall cost of studies of complex traits, which generally require large sample sizes. Use of multiple SNPs in linkage analysis can recapture the linkage information otherwise lost with such markers. Here we derive a measure of the multilocus polymorphic information content (MPIC) in the context of linkage analysis for a cluster of SNPs, and we explore the characteristics of uniform vs. clustered SNP maps, relative to STRP maps. Issues addressed in comparing the map structures include the information content for clustered or single markers, and map accuracy. To be cost-effective, SNPs should have a common allele frequency between 0.5-0.75. No more than five loci per cluster are needed. Some linkage disequilibrium between loci in a cluster is tolerable. In the ideal case, a uniformly spaced SNP map is more cost-effective than one composed of clustered loci. However, the genotyping cost per marker for diallelic markers can be at most 60% of the genotyping cost per marker for STRPs. The consequences of using clustered vs. uniform SNP maps are considered in the context of map inaccuracy and use of multipoint vs. pairwise linkage analysis. Overall, when marker information, map accuracy, and flexibility of analysis are jointly considered, an optimal solution may be use of maps with 2-3 SNPs per cluster.     

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.     

Measuring and partitioning the high-order linkage disequilibrium by multiple order Markov chains

A map of the background levels of disequilibrium between nearby markers can be useful for association mapping studies. In order to assess the background levels of linkage disequilibrium (LD), multilocus LD measures are more advantageous than pairwise LD measures because the combined analysis of pairwise LD measures is not adequate to detect simultaneous allele associations among multiple markers. Various multilocus LD measures based on haplotypes have been proposed. However, most of these measures provide a single index of association among multiple markers and does not reveal the complex patterns and different levels of LD structure. In this paper, we employ non-homogeneous, multiple order Markov Chain models as a statistical framework to measure and partition the LD among multiple markers into components due to different orders of marker associations. Using a sliding window of multiple markers on phased haplotype data, we compute corresponding likelihoods for different Markov Chain (MC) orders in each window. The log-likelihood difference between the lowest MC order model (MC0) and the highest MC order model in each window is used as a measure of the total LD or the overall deviation from the gametic equilibrium for the window. Then, we partition the total LD into lower order disequilibria and estimate the effects from two-, three-, and higher order disequilibria. The relationship between different orders of LD and the log-likelihood difference involving two different orders of MC models are explored. By applying our method to the phased haplotype data in the ENCODE regions of the HapMap project, we are able to identify high/low multilocus LD regions. Our results reveal that the most LD in the HapMap data is attributed to the LD between adjacent pairs of markers across the whole region. LD between adjacent pairs of markers appears to be more significant in high multilocus LD regions than in low multilocus LD regions. We also find that as the multilocus total LD increases, the effects of high-order LD tends to get weaker due to the lack of observed multilocus haplotypes. The overall estimates of first, second, third, and fourth order LD across the ENCODE regions are 64, 23, 9, and 3%.     

Entropy‐supported marker selection and Mantel statistics for haplotype sharing analysis

Haplotype sharing analysis is a well‐established option for the investigation of the etiology of complex diseases. The statistical power of haplotype association methods depends strongly on how the information of unobserved haplotypes can be captured by multilocus genotypes. In this study we combine an entropy‐based marker selection algorithm (EMS), with a haplotype sharing‐based Mantel statistics into a new algorithm. Genetic markers are iteratively selected by their multilocus linkage disequilibrium (LD), which is assessed by a normalized entropy difference. The initial marker set is gradually enlarged to increase the available information on the amount of sharing around a potential susceptibility marker. Markers are rejected from joint phasing if they do not increase the multilocus LD. In simulated candidate gene studies, the Mantel statistics combined with the new EMS performs as well or better at detecting the disease single nucleotide polymorphism—or in indirect association analysis its flanking markers—than the Mantel statistics without selection of markers prior to haplotype estimation and the Mantel statistics using sliding windows of size five. It is therefore appealing to apply our selection approach for haplotype‐based association analysis, since marker selection driven by the observed data avoids both the arbitrary choice of markers when using a fixed window size, as well as the estimation of haplotype block structure. Genet. Epidemiol. 34: 354–363, 2010. © 2010 Wiley‐Liss, Inc.     

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.     

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.     

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.     

Tag SNP selection for Finnish individuals based on the CEPH Utah HapMap database

The pattern and nature of linkage disequilibrium in the human genome is being studied and catalogued as part of the International HapMap Project [:2003 Nature 426:789-796]. A key goal of the HapMap Project is to enable identification of tag single nucleotide polymorphisms (SNPs) that capture a substantial portion of common human genetic variability while requiring only a small fraction of SNPs to be genotyped [International HapMap Consortium, 2005: Nature 437:1299-1320]. In the current study, we examined the effectiveness of using the CEU HapMap database to select tag SNPs for a Finnish sample. We selected SNPs in a 17.9-Mb region of chromosome 14 based on pairwise linkage disequilibrium (r(2)) estimates from the HapMap CEU sample, and genotyped 956 of these SNPs in 1,425 Finnish individuals. An excess of SNPs showed significantly different allele frequencies between the HapMap CEU and the Finnish samples, consistent with population-specific differences. However, we observed strong correlations between the two samples for estimates of allele frequencies, r(2) values, and haplotype frequencies. Our results demonstrate that the HapMap CEU samples provide an adequate basis for tag SNP selection in Finnish individuals, without the need to create a map specifically for the Finnish population, and suggest that the four-population HapMap data will provide useful information for tag SNP selection beyond the specific populations from which they were sampled.     

Linkage disequilibrium assessment via log-linear modeling of SNP haplotype frequencies

Analyses of high-density single-nucleotide polymorphism (SNP) data, such as genetic mapping and linkage disequilibrium (LD) studies, require phase-known haplotypes to allow for the correlation between tightly linked loci. However, current SNP genotyping technology cannot determine phase, which must be inferred statistically. In this paper, we present a new Bayesian Markov chain Monte Carlo (MCMC) algorithm for population haplotype frequency estimation, particularly in the context of LD assessment. The novel feature of the method is the incorporation of a log-linear prior model for population haplotype frequencies. We present simulations to suggest that 1) the log-linear prior model is more appropriate than the standard coalescent process in the presence of recombination (>0.02 cM between adjacent loci), and 2) there is substantial inflation in measures of LD obtained by a "two-stage" approach to the analysis by treating the "best" haplotype configuration as correct, without regard to uncertainty in the recombination process.     

Haplotype block structures show significant variation among populations

Recent studies suggest that haplotypes tend to have block-like structures throughout the human genome. Several methods were proposed for haplotype block partitioning and for tagging single-nucleotide polymorphism (SNP) identification. In population genetics studies, several research groups compared block structures across human populations. However, the measures used to quantify population similarity are either less than satisfactory or nonexistent. In this article, we propose several similarity measures to facilitate the comparisons of haplotype structures, namely block boundaries and tagging SNPs, across populations. With these measures, we can more objectively compare haplotype block structures and tagging SNP sets between different populations. In addition, these measures allow us to compare the results of different methods for block partition and tagging SNP identification. When we applied these measures to a real data set on chromosome 10 in 16 worldwide populations, we found that in this genome region: 1) haplotype block boundaries vary among populations, with European and some African populations showing similar boundaries but other populations showing other patterns; 2) tagging SNP sets are generally similar for populations with similar haplotype block structures but differ if the block structures differ; and 3) all but one of the block finding methods we tested yield consistent results, although variations exist regarding consistency. Our tentative results show that at least in the genome region studied, it is unlikely that a common haplotype pattern exists for all human populations: many populations, even in the same geographical region, may have different haplotype patterns.     

Population structure of hyperinvasive serotype 12F, clonal complex 218 Streptococcus pneumoniae revealed by multilocus boxB sequence typing

At least four outbreaks of invasive disease caused by serotype 12F, clonal complex 218 Streptococcus pneumoniae have occurred in the United States over the past two decades. We studied the population structure of this clonal complex using a sample of 203 outbreak and surveillance isolates that were collected over 22 years from 34 US states and eight other countries. Conventional multilocus sequence typing identified five types and distinguished a single outbreak from the others. To improve typing resolution, multilocus boxB sequence typing (MLBT) was developed from 10 variable boxB minisatellite loci. MLBT identified 86 types and distinguished between each of the four outbreaks. Diversity across boxB loci tended to be positively correlated with repeat array size and, overall, best fit the infinite alleles mutation model. Multilocus linkage disequilibrium was strong, but pairwise disequilibrium decreased with the physical distance between loci and was strongest in one large region of the chromosome, indicating recent recombinations. Two major clusters were identified in the sample, and they were differentiated geographically, as western and more easterly US clusters, and temporally, as clusters that predominated before and after the licensure of pneumococcal conjugate vaccines. The diversity and linkage disequilibrium within these two clusters also differed, suggesting different population dynamics. MLBT revealed hidden aspects of the population structure of these hyperinvasive pneumococci, and it may provide a useful adjunct tool for outbreak investigations, surveillance, and population genetics studies of other pneumococcal clonal complexes.     

广东人群PP2A-Aα亚基基因5′-侧翼区单体型分析

目的探讨蛋白磷酸酶2A(PP2A)-Aα亚基基因启动子区多态性的人群单体型分布特征。方法采用Haploview软件分析部分广东汉族人群PPP2R1A基因5′-侧翼区筛查到的7个多态性位点的遗传学特征、连锁不平衡(LD)、标签(tag)SNP和单体型(域)分布。结果各多态性位点基因型频率均符合H-W平衡(P>0.05);各位点在该人群的杂合度(π)不同,且在-568G>A和+87T>C与HapMap中的不同人群存在明显差异(P<0.05);-1039G>T(+Ins)与+87T>C和+108A>G、-568G>A与-241-/G位点之间呈强LD,+87T>C与+108A>G之间为完全LD;构建该人群中的5种单体型(H1～H5),频率分布为野生单体型(H1)53%、其余4种变异单体型(H2～H5)为44%;得到两个单体型域,筛选出-1039G>T(+Ins)、-512G>A、-241-/G、+107-/C为4个tag SNP,并确定了2个单体型域内标签SNPs(htSNP)及其分别代表的单体型。结论首次确定并报道中国广东汉族健康人群PPP2R1A基因5′-侧翼区的标签SNP和单体型(域)分布。     

Reprioritizing genetic associations in hit regions using LASSO-based resample model averaging

Significance testing one SNP at a time has proven useful for identifying genomic regions that harbor variants affecting human disease. But after an initial genome scan has identified a "hit region" of association, single-locus approaches can falter. Local linkage disequilibrium (LD) can make both the number of underlying true signals and their identities ambiguous. Simultaneous modeling of multiple loci should help. However, it is typically applied ad hoc: conditioning on the top SNPs, with limited exploration of the model space and no assessment of how sensitive model choice was to sampling variability. Formal alternatives exist but are seldom used. Bayesian variable selection is coherent but requires specifying a full joint model, including priors on parameters and the model space. Penalized regression methods (e.g., LASSO) appear promising but require calibration, and, once calibrated, lead to a choice of SNPs that can be misleadingly decisive. We present a general method for characterizing uncertainty in model choice that is tailored to reprioritizing SNPs within a hit region under strong LD. Our method, LASSO local automatic regularization resample model averaging (LLARRMA), combines LASSO shrinkage with resample model averaging and multiple imputation, estimating for each SNP the probability that it would be included in a multi-SNP model in alternative realizations of the data. We apply LLARRMA to simulations based on case-control genome-wide association studies data, and find that when there are several causal loci and strong LD, LLARRMA identifies a set of candidates that is enriched for true signals relative to single locus analysis and to the recently proposed method of Stability Selection.     

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.     

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.     

Contrasting linkage disequilibrium as a multilocus family‐based association test

Linkage disequilibrium (LD) of genetic loci is routinely estimated and graphically illustrated in genetic association studies. It has been suggested that the information in LD is also useful for association mapping and genetic association can be detected by comparing LD patterns between cases and controls. Here, we extend this idea to analyze case‐parents data by comparing LD patterns between transmitted and nontransmitted genotypes. We provide the condition when contrasting LD is valid for testing gene‐gene interactions. A permutation procedure is given to assess statistical significance. One advantage of our proposed methods is that haplotype information is not required. Thus, the implementation of our methods is straightforward and the resulted tests are free from potential bias caused by assumptions made to estimate haplotypes in silico. Since our test statistics use pairwise LD measurements, they are less affected by missing data than many other multilocus methods. With simulated data, we demonstrate that examining LD patterns of case‐parents data is a useful multilocus association mapping strategy and it complements existing association mapping methods. The application of our methods to a Crohn's disease data set shows that our methods can detect multilocus association that might be missed by other association methods. Our permutation procedure can also be modified to allow multiple offspring from a family to be analyzed. Genet. Epidemiol. 2011. © 2011 Wiley‐Liss, Inc. 35: 487‐498, 2011     

Haplotype-Based Methods for Detecting Uncommon Causal Variants With Common SNPs

Detecting uncommon causal variants (minor allele frequency [MAF] < 5%) is difficult with commercial single-nucleotide polymorphism (SNP) arrays that are designed to capture common variants (MAF > 5%). Haplotypes can provide insights into underlying linkage disequilibrium (LD) structure and can tag uncommon variants that are not well tagged by common variants. In this work, we propose a wei-SIMc-matching test that inversely weights haplotype similarities with the estimated standard deviation of haplotype counts to boost the power of similarity-based approaches for detecting uncommon causal variants. We then compare the power of the wei-SIMc-matching test with that of several popular haplotype-based tests, including four other similarity-based tests, a global score test for haplotypes (global), a test based on the maximum score statistic over all haplotypes (max), and two newly proposed haplotype-based tests for rare variant detection. With systematic simulations under a wide range of LD patterns, the results show that wei-SIMc-matching and global are the two most powerful tests. Among these two tests, wei-SIMc-matching has reliable asymptotic P-values, whereas global needs permutations to obtain reliable P-values when the frequencies of some haplotype categories are low or when the trait is skewed. Therefore, we recommend wei-SIMc-matching for detecting uncommon causal variants with surrounding common SNPs, in light of its power and computational feasibility.