中国美利奴羊MHC-DRB1 exon2单倍型构建及与布鲁氏菌病易感性关联分析

为研究中国美利奴羊MHC-DRB1基因exon2单倍型与布鲁氏菌易感性的关联性,本实验采用PCR直接测序法对40例布鲁氏菌血清检测阳性和阴性个体MHC-DRB1 exon2的单核苷酸多态性(SNPs)进行检测,而后运用SHEsis在线软件对筛选的SNPs构建单倍型并进行单倍型关联分析.结果显示,在270 bp的序列内共检测到41个SNPs,经Hardy-Weinberg平衡检测筛选出符合条件的SNPs有29个,连锁不平衡发现9个连锁不平衡域,而且每个block中的SNPs两两之间存在强连锁不平衡.单倍型分析显示,由于连锁不平衡存在,仅构建9种单倍型,其中只有Hap8和Hap9两种单倍型在病例-对照组中比较差异有统计学意义(P0.05).     

Lymphotoxin-alpha gene and risk of myocardial infarction in 6,928 cases and 2,712 controls in the ISIS case-control study

Lymphotoxin-alpha (LTA) is a pro-inflammatory cytokine that plays an important role in the immune system and local inflammatory response. LTA is expressed in atherosclerotic plaques and has been implicated in the pathogenesis of atherosclerosis and coronary heart disease (CHD). Polymorphisms in the gene encoding lymphotoxin-alpha (LTA) on Chromosome 6p21 have been associated with susceptibility to CHD, but results in different studies appear to be conflicting. We examined the association of seven single nucleotide polymorphisms (SNPs) across the LTA gene, and their related haplotypes, with risk of myocardial infarction (MI) in the International Study of Infarct Survival (ISIS) case-control study involving 6,928 non-fatal MI cases and 2,712 unrelated controls. The seven SNPs (including the rs909253 and rs1041981 SNPs previously implicated in the risk of CHD) were in strong linkage disequilibrium with each other and contributed to six common haplotypes. Some of the haplotypes for LTA were associated with higher plasma concentrations of C-reactive protein (p = 0.004) and lower concentrations of albumin (p = 0.023). However, none of the SNPs or related haplotypes were significantly associated with risk of MI. The results of the ISIS study were considered in the context of six previously published studies that had assessed this association, and this meta-analysis found no significant association with CHD risk using a recessive model and only a modest association using a dominant model (with narrow confidence intervals around these risk estimates). Overall, these studies provide reliable evidence that these common polymorphisms for the LTA gene are not strongly associated with susceptibility to coronary disease.     

Lymphotoxin-α Gene and Risk of Myocardial Infarction in 6,928 Cases and 2,712 Controls in the ISIS Case-Control Study

Lymphotoxin-α (LTA) is a pro-inflammatory cytokine that plays an important role in the immune system and local inflammatory response. LTA is expressed in atherosclerotic plaques and has been implicated in the pathogenesis of atherosclerosis and coronary heart disease (CHD). Polymorphisms in the gene encoding lymphotoxin-α (LTA) on Chromosome 6p21 have been associated with susceptibility to CHD, but results in different studies appear to be conflicting. We examined the association of seven single nucleotide polymorphisms (SNPs) across the LTA gene, and their related haplotypes, with risk of myocardial infarction (MI) in the International Study of Infarct Survival (ISIS) case-control study involving 6,928 non-fatal MI cases and 2,712 unrelated controls. The seven SNPs (including the rs909253 and rs1041981 SNPs previously implicated in the risk of CHD) were in strong linkage disequilibrium with each other and contributed to six common haplotypes. Some of the haplotypes for LTA were associated with higher plasma concentrations of C-reactive protein (p = 0.004) and lower concentrations of albumin (p = 0.023). However, none of the SNPs or related haplotypes were significantly associated with risk of MI. The results of the ISIS study were considered in the context of six previously published studies that had assessed this association, and this meta-analysis found no significant association with CHD risk using a recessive model and only a modest association using a dominant model (with narrow confidence intervals around these risk estimates). Overall, these studies provide reliable evidence that these common polymorphisms for the LTA gene are not strongly associated with susceptibility to coronary disease.     

Haplotype-based case-control study of CYP4A11 gene and myocardial infarction

CYP4A11, which is a member of the cytochrome P450 family, acts mainly as an enzyme that converts arachidonic acid to 20-hydroxyeicosatetraenoic acid (20-HETE), a metabolite involved in the maintenance of cardiovascular health. Recently, it was reported that many subfamilies of CYP genes have an association with myocardial infarction (MI). The aim of the present study was to assess the association between the human CYP4A11 gene and MI, using a haplotype-based case-control study with a separate analysis of the gender groups. A total of 239 MI patients and 285 controls were genotyped for 3 single-nucleotide polymorphisms (SNPs) of the human CYP4A11 gene (rs2269231, rs1126742, rs9333025). The data obtained via haplotype-based case-control studies were assessed for 3 separate groups: total subjects, men, and women. For the total, men and women groups, the distribution of the genotypes and alleles of the 3 SNPs did not show any significant difference between the MI patients and the control subjects. For the total and the men groups, the overall distribution of the haplotypes constructed with the 3 SNPs significantly differed between the MI patients and control subjects (P < 0.001). Also, for the total and for the men, the frequency of the T-T-A haplotype constructed with the 3 SNPs was significantly lower for the MI patients than for the control subjects (both P 相似文献   

Adaptive two-stage analysis of genetic association in case-control designs

We study a two-stage analysis of genetic association for case-control studies. In the first stage, we compare Hardy-Weinberg disequilibrium coefficients between cases and controls and, in the second stage, we apply the Cochran- Armitage trend test. The two analyses are statistically independent when Hardy-Weinberg equilibrium holds in the population, so all the samples are used in both stages. The significance level in the first stage is adaptively determined based on its conditional power. Given the level in the first stage, the level for the second stage analysis is determined with the overall Type I error being asymptotically controlled. For finite sample sizes, a parametric bootstrap method is used to control the overall Type I error rate. This two-stage analysis is often more powerful than the Cochran-Armitage trend test alone for a large association study. The new approach is applied to SNPs from a real study.     

Angiotensin converting enzyme gene polymorphism and myocardial infarction a large association and linkage study

The DD genotype of the angiotensin converting enzyme (ACE) polymorphism has been associated with myocardial infarction (MI). However, sample sizes of many case-control studies showing positive association were small and data were inconsistent. Furthermore, no family-based study is available.In a case-control study frequencies of the ACE genotypes were compared in 1319 unrelated patients with previous MI before 60 years of age (616 from the MONICA Augsburg region and 703 from rehabilitation centers in south Germany) and in 2381 population controls from the MONICA Augsburg study region). Furthermore, linkage and association of the ACE I/D polymorphism with MI were tested in 246 informative families using the sib-transmission/disequilibrium test (S-TDT).Overall, no excess of the D allele was found in MI patients (frequency 0.53 versus 0.57 in the general population; P=0.2). The ACE DD genotype was even slightly less frequent in groups with MI compared to the general population controls (0.26 versus 0.33 in women and 0.28 versus 0.33 in men). Similar results were also obtained in 247 men with low cardiovascular risk. In the family-based study, the frequency of the D allele was not different in siblings with or without previous MI (0.53 versus 0.50, respectively; S-TDT P=0.15) indicating no linkage or association of the D allele with MI.In a case-control study of MI patients and controls from the general population as well as a family study neither association nor linkage of the ACE D allele with MI was detected despite sample sizes that were among the largest samples studied so far.     

Polymorphism study of seven SNPs at ADH genes in 15 Chinese populations

It has been shown that the variants of alcohol dehydrogenase (ADH) genes exhibit great diversities among various populations and are associated with susceptibility to alcoholism. To investigate the distribution of SNPs at ADH genes in Chinese populations and the genetic relationship of these groups, we collected 467 individuals from 15 groups distributing widely from north to south in China and genotyped 7 SNPs at ADH genes respectively. The statistic analyses of allele frequencies, estimated haplotype frequencies, pairwise linkage disequilibrium, AMOVA (analysis of molecular variance), pairwise Fst', and cluster analysis indicated (1) that six of these seven SNPs showed great variations in the 15 Chinese populations, and three of them (RsaI, SspI, EcoRI), were confirmed to be informative SNPs. However, the causative SNP ADH1B Arg47His confirmed in case-control studies could not act as significant indicator to distinguish bibulous groups from non-bibulous groups in healthy individuals; (2) haplotypes constructed with ADH SNPs could be used as markers to discern different populations in China, and six-allele haplotype "221211" was the most common one defined in present study; (3) on the basis of SNPs analysis of ADH genes, the 15 populations were grouped into northern groups and southern groups. Moreover, the origin relationship among the populations was indicated according to the results of cluster analysis.     

The Null Distributions of Test Statistics in Genomewide Association Studies

Genomewide association (GWA) studies assay hundreds of thousands of single nucleotide polymorphisms (SNPs) simultaneously across the entire genome and associate them with diseases, other biological or clinical traits. The association analysis usually tests each SNP as an independent entity and ignores the biological information such as linkage disequilibrium. Although the Bonferroni correction and other approaches have been proposed to address the issue of multiple comparisons as a result of testing many SNPs, there is a lack of understanding of the distribution of an association test statistic when an entire genome is considered together. In other words, there are extensive efforts in hypothesis testing, and almost no attempt in estimating the density under the null hypothesis. By estimating the true null distribution, we can apply the result directly to hypothesis testing; better assess the existing approaches of multiple comparisons; and evaluate the impact of linkage disequilibrium on the GWA studies. To this end, we estimate the empirical null distribution of an association test statistic in GWA studies using simulated population data. We further propose a convenient and accurate method based on adaptive spline to estimate the empirical value in GWA studies and validate our findings using a real data set. Our method enables us to fully characterize the null distribution of an association test that not only can be used to test the null hypothesis of no association, but also provides important information about the impact of density of the genetic markers on the significance of the tests. Our method does not require users to perform computationally intensive permutations, and hence provides a timely solution to an important and difficult problem in GWA studies.     

Association analysis of the RGS4 gene in Han Chinese and Scottish populations with schizophrenia

We investigated the RGS4 as a susceptibility gene for schizophrenia in Chinese Han (184 trios and 138 sibling pairs, a total of 322 families) and Scottish (580 cases and 620 controls) populations using both a family trio and case-control design. Both the samples had statistical power greater than 70% to detect a heterozygote genotype relative risk of >1.2 for frequent RGS4-risk alleles. We genotyped four single nucleotide polymorphisms (SNPs) which have previously been associated with schizophrenia as either individually or part of haplotypes. Allele frequencies and linkage disequilibrium between the SNPs was similar in the two populations. In the Chinese sample, no individual SNPs or any of their haplotypes were associated with schizophrenia. In the Scottish population, one SNP (SNP7) was significantly over-represented in the cases compared with the controls (0.44 vs. 0.38; A allele; chi(2) 7.08, P = 0.011 after correction for correlation between markers by permutation testing). One two-marker haplotype, composed of alleles T and A of SNP4 and SNP7, respectively, showed individual significance after correction by permutation testing (chi(2) 6.8; P = 0.04). None of the full four-marker haplotypes showed association, including the G-G-G-G haplotype previously associated with schizophrenia in more than one sample and the A-T-A-A haplotype. Thus, our data do not directly replicate previous associations of RGS4, but association with SNP 7 in the Scottish population provides some support for a role in schizophrenia susceptibility. We cannot conclusively exclude RGS4, as associated haplotypes are likely to be surrogates for unknown causative alleles, whose relationship with overlying haplotypes may differ between the population groups. Differences in the association seen across the two populations could result from methodological factors such as diagnostic differences but most likely result from ethnic differences in haplotype structures within RGS4.     

A fast method for computing high-significance disease association in large population-based studies

Because of rapid progress in genotyping techniques, many large-scale, genomewide disease-association studies are now under way. Typically, the disorders examined are multifactorial, and, therefore, researchers seeking association must consider interactions among loci and between loci and other factors. One of the challenges of large disease-association studies is obtaining accurate estimates of the significance of discovered associations. The linkage disequilibrium between SNPs makes the tests highly dependent, and dependency worsens when interactions are tested. The standard way of assigning significance (P value) is by a permutation test. Unfortunately, in large studies, it is prohibitively slow to compute low P values by this method. We present here a faster algorithm for accurately calculating low P values in case-control association studies. Unlike with several previous methods, we do not assume a specific distribution of the traits, given the genotypes. Our method is based on importance sampling and on accounting for the decay in linkage disequilibrium along the chromosome. The algorithm is dramatically faster than the standard permutation test. On data sets mimicking medium-to-large association studies, it speeds up computation by a factor of 5,000-100,000, sometimes reducing running times from years to minutes. Thus, our method significantly increases the problem-size range for which accurate, meaningful association results are attainable.     

Prioritize and select SNPs for association studies with multi-stage designs.

Large-scale whole genome association studies are increasingly common, due in large part to recent advances in genotyping technology. With this change in paradigm for genetic studies of complex diseases, it is vital to develop valid, powerful, and efficient statistical tools and approaches to evaluate such data. Despite a dramatic drop in genotyping costs, it is still expensive to genotype thousands of individuals for hundreds of thousands single nucleotide polymorphisms (SNPs) for large-scale whole genome association studies. A multi-stage (or two-stage) design has been a promising alternative: in the first stage, only a fraction of samples are genotyped and tested using a dense set of SNPs, and only a small subset of markers that show moderate associations with the disease will be genotyped in later stages. Multi-stage designs have also been used in candidate gene association studies, usually in regions that have shown strong signals by linkage studies. To decide which set of SNPs to be genotyped in the next stage, a common practice is to utilize a simple test (such as a chi2 test for case-control data) and a liberal significance level without corrections for multiple testing, to ensure that no true signals will be filtered out. In this paper, I have developed a novel SNP selection procedure within the framework of multi-stage designs. Based on data from stage 1, the method explicitly explores correlations (linkage disequilibrium) among SNPs and their possible interactions in determining the disease phenotype. Comparing with a regular multi-stage design, the approach can select a much reduced set of SNPs with high discriminative power for later stages. Therefore, not only does it reduce the genotyping cost in later stages, it also increases the statistical power by reducing the number of tests. Combined analysis is proposed to further improve power, and the theoretical significance level of the combined statistic is derived. Extensive simulations have been performed, and results have shown that the procedure can reduce the number of SNPs required in later stages, with improved power to detect associations. The procedure has also been applied to a real data set from a genome-wide association study of the sporadic amyotrophic lateral sclerosis (ALS) disease, and an interesting set of candidate SNPs has been identified.     

Association of the insulin-like growth factor1 gene with myocardial infarction in Japanese subjects

During adult life, the insulin/insulin-like growth factor1 (IGF1) signaling pathway plays an important role in cardiovascular function. Several reports have suggested that low baseline levels of IGF1 increase the risk of fatal ischemic heart disease. Thus, IGF1 may be involved in cardiovascular disease. The aim of the present study was to investigate the relationship between the human IGF1 gene and myocardial infarction (MI) in the Japanese population via the use of single nucleotide polymorphisms (SNPs). After selecting six SNPs in the human IGF1 gene (rs2162679, rs7956547, rs2288378, rs2072592, rs978458 and rs6218), we performed a case-control study using each of the SNPs and haplotypes in 320 MI patients and 307 non-MI controls. Multiple logistic regression analysis demonstrated that the GG+GA variant of rs2162679 (p=0.009) and the AA+GA variant of rs2072592 (p=0.026) exhibited a resistant effect for MI. The haplotype-based case-control study revealed that the frequency of the A-T-G-G haplotype for rs2162679-rs7956547-rs2072592-rs978458 was significantly higher in the MI group (47.3%) as compared to the non-MI group (41.4%) (p=0.037, odds ratio=1.270). The frequency of the A-T-G-T haplotype for rs2162679-rs7956547-rs978458-rs6218 was also significantly higher in the MI group (47.3%) as compared to the non-MI group (41.3%) (p=0.033, odds ratio=1.276). The current results suggest that specific SNPs and haplotypes can be utilized as genetic markers for MI risk or MI resistance. In addition, IGF1 or a neighboring gene might be associated with increased or decreased susceptibility to MI.     

Single nucleotide polymorphisms of the purinergic 1 receptor are not associated with myocardial infarction in a Latvian population

The purinergic 1 receptor (P2RY1) has been implicated in development of heart disease and in individual pharmacodynamic response to anticoagulant therapies. However, the association of polymorphisms in the P2RY1 gene with myocardial infarction (MI), and its associated conditions, has yet to be reported in the literature. We evaluated seven known SNPs in P2RY1 for association with MI in a Latvian population. Seven independent parameters that are related to MI [body mass index (BMI), type 2 diabetes (T2D), angina pectoris, hypertension, hyperlipidemia, atrial fibrillation and heart failure] were investigated. No significant association with MI was observed for any of the polymorphisms. Those SNPs for which the P value was close to significance were located in coding or promoter regions. Intriguingly, carriers of the minor allele in the P2RY1 gene locus showed a tendency towards higher onset age for MI, suggesting a possible protective effect of these SNPs against MI or their contribution in progression as opposed to onset. Finally, a linkage disequilibrium (LD) plot was generated for these polymorphisms in the Latvian population. The results of this study suggest that the role of P2RY1 in individuals from Latvian population is likely to be principally involved in platelet aggregation and thromboembolic diseases, and not as a significant contributing factor to the global metabolic syndrome.     

Association studies in candidate genes: strategies to select SNPs to be tested

OBJECTIVE: When numerous single nucleotide polymorphisms (SNPs) have been identified in a candidate gene, a relevant and still unanswered question is to determine how many and which of these SNPs should be optimally tested to detect an association with the disease. Testing them all is expensive and often unnecessary. Alleles at different SNPs may be associated in the population because of the existence of linkage disequilibrium, so that knowing the alleles carried at one SNP could provide exact or partial knowledge of alleles carried at a second SNP. We present here a method to select the most appropriate subset of SNPs in a candidate gene based on the pairwise linkage disequilibrium between the different SNPs. METHOD: The best subset is identified through power computations performed under different genetic models, assuming that one of the SNPs identified is the disease susceptibility variant. RESULTS: We applied the method on two data sets, an empirical study of the APOE gene region and a simulated study concerning one of the major genes (MG1) from the Genetic Analysis Workshop 12. For these two genes, the sets of SNPs selected were compared to the ones obtained using two other methods that need the reconstruction of multilocus haplotypes in order to identify haplotype-tag SNPs (htSNPs). We showed that with both data sets, our method performed better than the other selection methods.     

Identifying the susceptibility gene(s) in a set of trait-linked genes using genotype data

There are generally three steps to isolate a disease linkage-susceptibility gene: genome-wide scan, fine mapping, and, last, positional cloning. The last step is time consuming and involves intensive laboratory work. In some cases, fine mapping cannot proceed further on a set of markers because they are tightly linked. For years, genetic statisticians have been trying different ways to narrow the fine-mapping results to provide some guidance for the next step of laboratory work. Although these methods are practical and efficient, most of them are based on IBD data, which usually can be inferred only from the genotype data with some uncertainty. The corresponding methods thus have no greater power than one using genotype data directly. Also, IBD-based methods apply only to relative pair data. Here, using genotype data, we have developed a statistical hypothesis-testing method to pinpoint a SNP, or SNPs, suspected of responsibility for a disease trait linkage among a set of SNPs tightly linked in a region. Our method uses genotype data of affected individuals or case-control studies, which are widely available in the laboratory. The testing statistic can be constructed using any genotype-based disease-marker disequilibrium measure and is asymptotically distributed as a chi-square mixture. This method can be used for singleton data, relative pair data, or general pedigree data. We have applied the method to simulated data as well as a real data set; it gives satisfactory results.     

Association Test Based on SNP Set: Logistic Kernel Machine Based Test vs. Principal Component Analysis

GWAS has facilitated greatly the discovery of risk SNPs associated with complex diseases. Traditional methods analyze SNP individually and are limited by low power and reproducibility since correction for multiple comparisons is necessary. Several methods have been proposed based on grouping SNPs into SNP sets using biological knowledge and/or genomic features. In this article, we compare the linear kernel machine based test (LKM) and principal components analysis based approach (PCA) using simulated datasets under the scenarios of 0 to 3 causal SNPs, as well as simple and complex linkage disequilibrium (LD) structures of the simulated regions. Our simulation study demonstrates that both LKM and PCA can control the type I error at the significance level of 0.05. If the causal SNP is in strong LD with the genotyped SNPs, both the PCA with a small number of principal components (PCs) and the LKM with kernel of linear or identical-by-state function are valid tests. However, if the LD structure is complex, such as several LD blocks in the SNP set, or when the causal SNP is not in the LD block in which most of the genotyped SNPs reside, more PCs should be included to capture the information of the causal SNP. Simulation studies also demonstrate the ability of LKM and PCA to combine information from multiple causal SNPs and to provide increased power over individual SNP analysis. We also apply LKM and PCA to analyze two SNP sets extracted from an actual GWAS dataset on non-small cell lung cancer.     

基因水平的关联分析方法

全基因组关联研究(Genome wide association study, GWAS)已经在国内外的医学遗传学研究中得到广泛应用, 但是GWAS数据中所蕴含的与多基因复杂性状疾病机制相关的丰富信息尚未得到深度挖掘。近年来, 研究者采用生物网络分析和生物通路分析等生物信息学和生物统计学手段分析GWAS数据, 并探索潜在的疾病机制。生物网络分析和生物通路分析主要是以基因为单位进行的, 因此必须在分析前将基因上全部或者部分单个单核苷酸多态性(Single nucleotide polymorphism, SNP)的遗传关联结果综合起来, 即基因水平的关联分析。基因水平的关联分析需要考虑单个SNP的遗传关联、基因上SNP数量和SNP之间的连锁不平衡结构等多种因素, 因此不仅在遗传学的概念上也在统计方法方面具有一定的复杂性和挑战性。文章对基因水平的关联分析的研究进展、原理和应用进行了综述。     

A new approach to account for the correlations among single nucleotide polymorphisms in genome: wide association studies

In genetic association studies, such as genome-wide association studies (GWAS), the number of single nucleotide polymorphisms (SNPs) can be as large as hundreds of thousands. Due to linkage disequilibrium, many SNPs are highly correlated; assuming they are independent is not valid. The commonly used multiple comparison methods, such as Bonferroni correction, are not appropriate and are too conservative when applied to GWAS. To overcome these limitations, many approaches have been proposed to estimate the so-called effective number of independent tests to account for the correlations among SNPs. However, many current effective number estimation methods are based on eigenvalues of the correlation matrix. When the dimension of the matrix is large, the numeric results may be unreliable or even unobtainable. To circumvent this obstacle and provide better estimates, we propose a new effective number estimation approach which is not based on the eigenvalues. We compare the new method with others through simulated and real data. The comparison results show that the proposed method has very good performance.     

Analysis of 17,576 potentially functional SNPs in three case-control studies of myocardial infarction

Myocardial infarction (MI) is a common complex disease with a genetic component. While several single nucleotide polymorphisms (SNPs) have been reported to be associated with risk of MI, they do not fully explain the observed genetic component of MI. We have been investigating the association between MI and SNPs that are located in genes and have the potential to affect gene function or expression. We have previously published studies that tested about 12,000 SNPs for association with risk of MI, early-onset MI, or coronary stenosis. In the current study we tested 17,576 SNPs that could affect gene function or expression. In order to use genotyping resources efficiently, we staged the testing of these SNPs in three case-control studies of MI. In the first study (762 cases, 857 controls) we tested 17,576 SNPs and found 1,949 SNPs that were associated with MI (P<0.05). We tested these 1,949 SNPs in a second study (579 cases and 1159 controls) and found that 24 SNPs were associated with MI (1-sided P<0.05) and had the same risk alleles in the first and second study. Finally, we tested these 24 SNPs in a third study (475 cases and 619 controls) and found that 5 SNPs in 4 genes (ENO1, FXN (2 SNPs), HLA-DPB2, and LPA) were associated with MI in the third study (1-sided P<0.05), and had the same risk alleles in all three studies. The false discovery rate for this group of 5 SNPs was 0.23. Thus, we have identified 5 SNPs that merit further examination for their potential association with MI. One of these SNPs (in LPA), has been previously shown to be associated with risk of cardiovascular disease in other studies.     

Enhanced statistical tests for GWAS in admixed populations: assessment using African Americans from CARe and a Breast Cancer Consortium

While genome-wide association studies (GWAS) have primarily examined populations of European ancestry, more recent studies often involve additional populations, including admixed populations such as African Americans and Latinos. In admixed populations, linkage disequilibrium (LD) exists both at a fine scale in ancestral populations and at a coarse scale (admixture-LD) due to chromosomal segments of distinct ancestry. Disease association statistics in admixed populations have previously considered SNP association (LD mapping) or admixture association (mapping by admixture-LD), but not both. Here, we introduce a new statistical framework for combining SNP and admixture association in case-control studies, as well as methods for local ancestry-aware imputation. We illustrate the gain in statistical power achieved by these methods by analyzing data of 6,209 unrelated African Americans from the CARe project genotyped on the Affymetrix 6.0 chip, in conjunction with both simulated and real phenotypes, as well as by analyzing the FGFR2 locus using breast cancer GWAS data from 5,761 African-American women. We show that, at typed SNPs, our method yields an 8% increase in statistical power for finding disease risk loci compared to the power achieved by standard methods in case-control studies. At imputed SNPs, we observe an 11% increase in statistical power for mapping disease loci when our local ancestry-aware imputation framework and the new scoring statistic are jointly employed. Finally, we show that our method increases statistical power in regions harboring the causal SNP in the case when the causal SNP is untyped and cannot be imputed. Our methods and our publicly available software are broadly applicable to GWAS in admixed populations.