藏族人群原发性高血压易感基因全基因组关联分析

目的通过全基因组关联分析(GWAS)发现并鉴定与藏族高血压关联的遗传变异。方法结合高通量芯片分析和高效DNA混合池策略的SNP-Ma P方法,对藏族原发性高血压(EH)患者与藏族正常对照进行了全基因扫描。筛选出有显著差异的位点后,用直接测序和PCR-RFLP的方法,进行群体验证,鉴定出各个基因型和相应等位基因的频率。结果在全基因组的遗传标记中,5个标签位点的等位基因频率在患者组与对照组之间存在显著差异(P9.2×10-8)。用直接测序和PCR-RFLP的方法进行群体验证,发现rs17136827和rs1866525两个位点的基因型和等位基因频率分布在两组间均无差异;rs9865108、rs12541835和rs4547758的基因型频率和等位基因频率在两组间的差异显著,携带C等位基因个体具有较高的EH发病风险。结论证实affymetrix Human SNP芯片和高效混合池策略结合的SNP-Ma P能够有效地大范围分析人类EH易感基因。rs9865108、rs12541835和rs4547758与藏族EH易感相关。     

慢性乙型肝炎易感基因的全基因组关联研究进展

慢性乙型肝炎(chronic hepatitis B,CHB)是一种由遗传、病毒与环境因素共同导致的复杂疾病,具有高度的遗传异质性。自2009年首个CHB全基因组关联研究(genome-wide association studies,GWAS)报道以来,许多GWAS相继开展。文章首先对目前发表的CHB易感基因的GWAS结果进行汇总,发现染色体6p21.32区域中CHB易感位点最多。然而目前GWAS主要基于"常见变异-常见疾病"原则设计,只涉及了频率≥5%的单核苷酸多态性(single nucleotide polymorphism,SNP),没有涵盖人类基因组中重要的低频变异。同时GWAS鉴定到的最显著关联的SNP大多位于内含子、基因间区等非编码区域内,导致功能学研究无法开展。乙型肝炎病毒(hepatitis B virus,HBV)功能性受体钠离子-牛磺胆酸共转运蛋白的发现,加快了HBV感染的机制研究。同时第二代测序技术的发展为CHB易感基因的发现提供了契机。因此今后的研究应将GWAS的发现与功能学研究相结合,以逐步揭示HBV感染的遗传机制。     

DNA混合池技术全基因组关联研究筛查主动脉夹层易感基因的研究

目的筛选主动脉夹层发病机制相关的遗传易感基因。方法选取主动脉夹层患者为病例组（150例）,按照性别、年龄、是否吸烟、合并高血压、糖尿病情况与病例组匹配的原则入选排除主动脉夹层、重大心血管疾病及肿瘤的志愿者为对照组（250例）,均从外周血提取基因组DNA,采用DNA混合池技术为基础的Illumina Human660W—Quad芯片扫描,筛选主动脉夹层发病相关的遗传易感基因。结果对照组女性数量明显多于病例组（P〈0．01）;在年龄、吸烟、高血压、糖尿病人数方面差异均无统计学意义（P〉0．05）。根据芯片扫描结果,挑选silhouettewidth计算值大于0．7,最小等位基因频率（MAF）大于0．05,且SNPs位于已知名称基因上的前5个SNPs位点。结果发现,遗传变异位点SNP rs2970873（位于PPARGClA基因）、SNP rs12678080（位于SGCZ基因）、SNP rs489526（位于UNCl3C基因）、SNP rs6928665（位于TRAM2基因）和SNP rs17837003（位于ACCNl基因上）可能与主动脉夹层的发病机制有关。结论SNPsrs2970873、rs12678080、rs489526、rs6928665和rsl7837003可能与主动脉夹层发病机制相关。     

特应性皮炎全基因组关联研究进展北大核心CSCD

随着测序与分析技术的发展,近年来特应性皮炎(AD)的全基因组关联研究(GWAS)逐渐增加。本文系统地总结了AD的GWAS,发现自2009年以来共报道了11个AD的GWAS,以欧洲人群居多,仅见1个在中国汉族和1个在日本人群的报告。这些GWAS共发现36个染色体区域上76个SNPs与AD在全基因组水平上相关。相关基因主要与表皮皮肤屏障功能和免疫功能相关。这些发现为AD的精准分型和治疗带来新的视角。     

全基因组关联研究的策略及面临的挑战

在人类基因组测序费用还没有足够低廉的情况下,全基因组关联研究( genome-wide association study,GWAS)仍然是复杂疾病易感基因研究的有效策略之一.在此文中,对GWAS的研究设计、遗传分析方法、多重假设检验调整方法等基本原理和面临的挑战等问题进行了系统的阐述.     

多囊卵巢综合征的新候选基因：FABP4

目的首次从基因组水平解释FABP4与多囊卵巢综合征发病的关系。方法选取中国汉族178位PCOS女性患者以及171位健康女性对照的血液样本。利用改良盐析法提取外周血白细胞中的DNA，对FABP4基因的启动子1000bp和全部4个外显子进行特异性扩增。通过对PCR反应产物直接测序进行序列分析。结果在全部样本的测序结果中发现rs16909225，rs3834363，rs16909220三个SNP，其中rs16909225和rs16909220全连锁（r2=1），这两个SNP与PCOS发生和发展没有连锁的关系。与此同时，在PCOS病例样本中rs3834363的-2bp／-2bp基因型与对照样本中有着显著的差异（r=7．39，df=1，P=0．007，OR：1．8095％CI：1．18—2．75）。结论本研究第-次将FABP4基因与PCOS发病及其进程联系起来，并发现其可能的联系途径。     

东亚群体中高血压6WAS阳性9个SNP在中国傣族及蒙古族群体中的验证研究

目的为了验证GwAs研究发现的3个高血压一个基因的相关性。方法本研究选取了3个在东亚群体中被GWAS研究证实了与高血压发生相关的基因（AKAP13,ENPEP和CNNM2）上的9个SNP,在傣族和蒙古族群体中进行验证。对这9个SNPs用SNaPshot的方法在774例傣族群体和409例蒙古族群体中进行了分型,排布单倍型并进行了统计学分析。结果发现统计学上无法证明这9个SNPs与高血压相关。但是在ENPEP基因的一连锁区段中,rs1126483在傣族群体中呈现与收缩压升高弱阳性关联（χ2=4．53,P=0.033）。同时rs3796889在蒙古族群体中也与收缩压的升高呈现弱阳性关联（χ2=4．35,P=0．037）。虽然这一阳性在进行统计学矫正后消失。结论本研究同时在起源不同,生活环境均差异较大的两个群体中均观察到ENPEP基因这一区段与高血压的关联,这也许暗示了此基因确实可能与。高血压发生相关。另一方面本研究的阴性结果,也暗示了中国一些相对封闭的群体可能具有更窄的高血压遗传变异谱。     

中国科学家发现白癜风易感基因

由安徽医科大学张学军教授领衔的研究团队历时5年，运用全基因组关联分析研究（GWAS）成功发现了白癜风易感基因，首次在国际上明确白癜风是自身免疫性疾病，为最终征服白癜风这种复杂疾病奠定了第一步基础。     

建立一种实时荧光PCR对维生素D受体基因单核苷酸多态性快速分型

目的:建立实时荧光PCR快速检测维生素D受体(VDR)基因rs2228570、rs1544410、rs7975232位点多态性。方法:以VDR基因的rs2228570、rs1544410、rs7975232三个SNP位点的变异碱基分别设计并合成特异性引物。11例体检健康儿童血液标本作为检测样本,采用实时荧光PCR方法检测样本中VDR基因型,采用Sanger法对检测结果测序验证。结果:利用所设计的特异性引物对人全血基因组中VDR进行SNP位点特异性扩增,根据7500 FAST实时荧光PCR仪的分析结果得到基因型,与测序结果比对后发现,11个样本采用实时荧光PCR方法基因分型结果与测序结果均一致。结论:实时荧光PCR快速检测VDR基因rs2228570、rs1544410、rs7975232位点多态性的方法或可用于临床VDR基因SNP快速分型。     

LMX1A基因多态性与中国汉族人群先天性脊柱侧凸遗传易感性的关联

背景：先天性脊柱侧凸是由于胚胎期脊柱椎体发育异常引起的脊柱侧凸,其遗传病因学假说开始引起许多学者的重视。 目的：通过候选基因LMX1A上关键单核苷酸多态性位点的筛查,探索LMX1A与中国汉族人群先天性脊柱侧凸及其不同临床表型之间的关联。 方法：入选127例中国汉族先天性脊柱侧凸患者,另选取外伤、感染、炎症性疾病等患者127例为对照组。根据国际人类基因组单体型图计划提供的基因型数据,应用Haploview 4.1软件选取LMX1A的标签和功能单核苷酸多态性位点。根据椎体畸形特点、畸形部位、畸形受累程度、有无合并肋骨畸形和椎管内畸形将病例组进一步分为不同临床表型。所有样本应用SNPstream UHT Genotyping系统对所选单核苷酸多态性位点进行基因型鉴定;进一步进行基于基因型/等位基因频率的关联分析,并用Haploview 4.1软件分析对照组单核苷酸多态性位点间是否存在连锁不平衡。 结果与结论：共筛选6个位点：SNP1(rs1819768)、SNP2(rs12023709)、SNP3(rs16841013)、SNP4(rs4656435)和SNP5(rs4657412)和SNP6(rs4657411),其基因型分布在病例组和对照组中均符合Hardy-Weinberg平衡;在基于基因型的关联分析中发现阳性位点SNP1和SNP2,单位点分析显示两位点基因型在病例组和对照组中的分布频率差异有显著性意义(P=0.026和P=0.026),在进一步非条件Logistic回归分析中发现这两个位点的基因型分布最符合Ressessive(OR=0.38;95%CI=0.15~0.94,P=0.029,AIC=354.9)遗传模型;SNP1、SNP2、SNP3和SNP6处于连锁不平衡状态,SNP4和SNP5也处于完全连锁不平衡状态,但单倍体型与先天性脊柱侧凸的发生风险之间不存在相关性;在进一步与先天性脊柱侧凸临床表型的关联分析中发现SNP1基因型AC型、SNP2基因型AG型、SNP3基因型CT型与有椎体形成障碍先天性脊柱侧凸的易感性升高有关。结果提示在中国汉族人群中LMX1A基因可能和先天性脊柱侧凸的发生、发展相关,是一个重要的易感基因。     

How to deal with the early GWAS data when imputing and combining different arrays is necessary

Genotype imputation has become an essential tool in the analysis of genome-wide association scans. This technique allows investigators to test association at ungenotyped genetic markers, and to combine results across studies that rely on different genotyping platforms. In addition, imputation is used within long-running studies to reuse genotypes produced across generations of platforms. Typically, genotypes of controls are reused and cases are genotyped on more novel platforms yielding a case-control study that is not matched for genotyping platforms. In this study, we scrutinize such a situation and validate GWAS results by actually retyping top-ranking SNPs with the Sequenom MassArray platform. We discuss the needed quality controls (QCs). In doing so, we report a considerable discrepancy between the results from imputed and retyped data when applying recommended QCs from the literature. These discrepancies appear to be caused by extrapolating differences between arrays by the process of imputation. To avoid false positive results, we recommend that more stringent QCs should be applied. We also advocate reporting the imputation quality measure (R(T)(2)) for the post-imputation QCs in publications.     

Statistical Methods for Association Tests of Multiple Continuous Traits in Genome‐Wide Association Studies

Multiple correlated traits are often collected in genetic studies. The joint analysis of multiple traits could have increased power by aggregating multiple weak effects and offer additional insights into the aetiology of complex human diseases by revealing pleiotropic variants. We propose to study multivariate test statistics to detect single nucleotide polymorphism (SNP) association with multiple correlated traits. Most existing methods have been based on the generalized estimating equation (GEE) approach without explicitly modelling the trait correlations. In this article, we explore an alternative likelihood‐based framework to test the multiple trait associations. It is based on the familiar multinomial logistic regression modelling of genotypes, which can be readily implemented using widely available software, and offers very competitive performance. We demonstrate through extensive numerical studies that the proposed method has competitive performance. Its usefulness is further illustrated with application to association analysis of diabetes‐related traits in the Atherosclerosis Risk in Communities (ARIC) Study.     

Polygenic transmission and complex neuro developmental network for attention deficit hyperactivity disorder: Genome‐wide association study of both common and rare variants

Attention‐deficit hyperactivity disorder (ADHD) is a complex polygenic disorder. This study aimed to discover common and rare DNA variants associated with ADHD in a large homogeneous Han Chinese ADHD case–control sample. The sample comprised 1,040 cases and 963 controls. All cases met DSM‐IV ADHD diagnostic criteria. We used the Affymetrix6.0 array to assay both single nucleotide polymorphisms (SNPs) and copy number variants (CNVs). Genome‐wide association analyses were performed using PLINK. SNP‐heritability and SNP‐genetic correlations with ADHD in Caucasians were estimated with genome‐wide complex trait analysis (GCTA). Pathway analyses were performed using the Interval enRICHment Test (INRICH), the Disease Association Protein–Protein Link Evaluator (DAPPLE), and the Genomic Regions Enrichment of Annotations Tool (GREAT). We did not find genome‐wide significance for single SNPs but did find an increased burden of large, rare CNVs in the ADHD sample (P = 0.038). SNP‐heritability was estimated to be 0.42 (standard error, 0.13, P = 0.0017) and the SNP‐genetic correlation with European Ancestry ADHD samples was 0.39 (SE 0.15, P = 0.0072). The INRICH, DAPPLE, and GREAT analyses implicated several gene ontology cellular components, including neuron projections and synaptic components, which are consistent with a neurodevelopmental pathophysiology for ADHD. This study suggested the genetic architecture of ADHD comprises both common and rare variants. Some common causal variants are likely to be shared between Han Chinese and Caucasians. Complex neurodevelopmental networks may underlie ADHD's etiology. © 2013 Wiley Periodicals, Inc.     

Common genetic variants do not associate with CAD in familial hypercholesterolemia

In recent years, multiple loci dispersed on the genome have been shown to be associated with coronary artery disease (CAD). We investigated whether these common genetic variants also hold value for CAD prediction in a large cohort of patients with familial hypercholesterolemia (FH). We genotyped a total of 41 single-nucleotide polymorphisms (SNPs) in 1701 FH patients, of whom 482 patients (28.3%) had at least one coronary event during an average follow up of 66 years. The association of each SNP with event-free survival time was calculated with a Cox proportional hazard model. In the cardiovascular disease risk factor adjusted analysis, the most significant SNP was rs1122608:G>T in the SMARCA4 gene near the LDL-receptor (LDLR) gene, with a hazard ratio for CAD risk of 0.74 (95% CI 0.49–0.99; P-value 0.021). However, none of the SNPs reached the Bonferroni threshold. Of all the known CAD loci analyzed, the SMARCA4 locus near the LDLR had the strongest negative association with CAD in this high-risk FH cohort. The effect is contrary to what was expected. None of the other loci showed association with CAD.     

Genetic Predictors of Cervical Dysplasia in African American HIV-Infected Women: ACTG DACS 268

Abstract

Objective: To examine genome-wide associations in HIV-infected women with a history of cervical dysplasia compared with HIV-infected women with no history of abnormal Papanicolaou (Pap) tests. Design: Case-control study using data from women analyzed for the HIV Controllers Study and enrolled in HIV treatment-naïve studies in the AIDS Clinical Trials Group (ACTG). Methods: Genotyping utilized Illumina HumanHap 650 Y or 1MDuo platforms. After quality control and principal component analysis, ~610,000 significant single nucleotide polymorphisms (SNPs) were tested for association. Threshold for significance was P < 5 × 10^–8 for genome-wide associations. Results: No significant genomic association was observed between women with low-grade dysplasia and controls. The genome-wide association study (GWAS) analysis between women with high-grade dysplasia or invasive cervical cancer and normal controls identified significant SNPs. In the analyses limited to African American women, 11 SNPs were significantly associated with the development of high-grade dysplasia or cancer after correcting for multiple comparisons. The model using significant SNPs alone had improved accuracy in predicting high-grade dysplasia in African American women compared to the use of clinical data (area under the receiver operating characteristic curve for genetic and clinical model = 0.9 and 0.747, respectively). Conclusions: These preliminary data serve as proof of concept that there may be a genetic predisposition to developing high-grade cervical dysplasia in African American HIV-infected women. Given the small sample size, the results need to be validated in a separate cohort.     

Permutation-based approaches do not adequately allow for linkage disequilibrium in gene-wide multi-locus association analysis

Additional information about risk genes or risk pathways for diseases can be extracted from genome-wide association studies through analyses of groups of markers. The most commonly employed approaches involve combining individual marker data by adding the test statistics, or summing the logarithms of their P-values, and then using permutation testing to derive empirical P-values that allow for the statistical dependence of single-marker tests arising from linkage disequilibrium (LD). In the present study, we use simulated data to show that these approaches fail to reflect the structure of the sampling error, and the effect of this is to give undue weight to correlated markers. We show that the results obtained are internally inconsistent in the presence of strong LD, and are externally inconsistent with the results derived from multi-locus analysis. We also show that the results obtained from regression and multivariate Hotelling T(2) (H-T2) testing, but not those obtained from permutations, are consistent with the theoretically expected distributions, and that the H-T2 test has greater power to detect gene-wide associations in real datasets. Finally, we show that while the results from permutation testing can be made to approximate those from regression and multivariate Hotelling T(2) testing through aggressive LD pruning of markers, this comes at the cost of loss of information. We conclude that when conducting multi-locus analyses of sets of single-nucleotide polymorphisms, regression or multivariate Hotelling T(2) testing, which give equivalent results, are preferable to the other more commonly applied approaches.