随机SNP在全基因组关联研究人群分层分析中的应用

在复杂疾病的全基因组关联研究中,人群分层现象会增加结果的假阳性率,因此考虑人群遗传结构、控制人群分层是很有必要的。而在人群分层研究中,使用随机选择的SNP的效果还有待进一步探讨。文章利用HapMap Phase2人群中无关个体的Affymetrix SNP 6.0芯片分型数据,在全基因组上随机均匀选择不同数量的SNP,同时利用f值和Fisher精确检验方法筛选祖先信息标记（Ancestry Informative Markers,AIMs）。然后利用HapMap Phase3中的无关个体的数据,以F-statistics和STRUCTURE分析两种方法评估所选出的不同SNP组合对人群的区分效果。研究发现,随机均匀分布于全基因组的SNP可用于识别人群内部存在的遗传结构。文章进一步提示,在全基因组关联研究中,当没有针对特定人群的AIMs时,可在全基因组上随机选择3000以上均匀分布的SNP来控制人群分层。     

全基因组关联研究通路分析方法现状

全基因组关联研究(genome-wide association study, GWAS)自2005年首次发表以来已不断增进人们对疾病遗传机制的认识,结合系统生物学并改进统计分析方法是对GWAS数据进行深度挖掘的重要途径。通路分析(pathway analysis)将GWAS所检测的遗传变异根据一定的生物学含义组合为集合进行分析,有利于发现对疾病单独效应小却在通路中相互关联的遗传变异,更有利于进行生物学解释。当前通路分析在GWAS数据上已有较为广泛的应用并取得初步成果。与此同时,通路分析的统计方法仍在不断发展。本文旨在介绍现有直接以SNP为对象的GWAS通路分析算法,根据方法中是否采用核函数分为非核算法和核算法两大类,其中非核算法主要包括基因功能富集分析(gene set enrichment analysis, GSEA)和分层贝叶斯优取(hierarchical Bayes prioritization, HBP),核算法包括线性核(linear kernel, LIN)、状态认证核(identity-by-status kernel, IBS)和尺度不变核(powered exponential kernel)。通过介绍这些方法的计算原理和优缺点,以期为新算法的构建提供更好的思路,为GWAS领域研究方法的选择提供参考。     

利用紧缩线性模型和贝叶斯模型对猪总产仔数和产活仔数性状的全基因组关联研究

全基因组关联分析策略已逐渐成为家畜重要经济性状研究的强有力工具。文章使用猪60K SNP芯片对一个具多胎繁殖性状记录的商业母猪群(n=820)进行分型检测,共计57 814个SNP通过设定质控标准。主成分分析显示群体内不存在显著的群体分层现象,而后分别运用两种统计模型Compressed Mixed Linear Model(GAPIT程序包)、Bayes CPi(GenSel软件)进行第1和第2胎次总产仔数和产活仔数性状的全基因组关联分析。从两种分析方法所得结果中各取最显著的50个SNP位点进行比较:对于第1胎次总产仔数,两种方法分析结果存在31个重合SNP位点,对于第1胎次产活仔数,有20个重合SNP位点;且两种统计分析结果中最显著的SNP位点都在另一方法中得到验证。与第1胎次总产仔数显著关联的SNP位于1、2、3、7、13、16和18号染色体,与第1胎次产活仔数显著关联的SNP位于1、3、4、13和16号染色体上的11个区域内。在1、3、13和16染色体上共有5个区域同时与这两个性状显著关联。与第2胎次总产仔数和产活仔数显著关联的区域主要位于7、10、12、13、14和16号染色体的6个重叠区域内。     

基于功能基因组数据整合方法研究前列腺癌风险基因

目的:利用生物信息学方法,将高通量基因表达数据与单核酸多肽(SNP)基因型数据进行整合分析,研究并注释前列腺癌风险基因。方法:本文基于EST、SAGE、基因芯片三类功能基因组数据整合的方法研究前列腺癌风险基因。首先,通过三类数据寻找前列腺癌中异常表达和差异表达基因。利用全基因组关联分析得到前列腺癌相关基因的SNPs。定位所得到的前例腺癌差异表达基因与SNPs到染色体,获取与SNPs在同一区段的差异表达基因,并通过各种数据库注释所得基因。结果:通过数据整合分析,最终得到前列腺癌风险基因84个,其中20多个基因已被证实与前列腺癌极其相关。结论:整合前列腺癌高通量表达数据与SNP基因型数据,能够快速有效的获得前列腺癌显著相关基因。此方法可以推广于其它癌症的研究。     

桃基因组及全基因组关/联分析研究进展

桃(Prunus persica [L.] Batsch)是蔷薇科重要的核果类果树, 适应性强, 栽培范围广, 果实口感好, 深受消费者喜欢。提高桃果实品质及增加抗病、抗虫性一直是桃遗传育种者关注的焦点。文章对近年来桃遗传分子标记连锁图谱和物理图谱构建、分子标记开发应用、全基因组和转录组测序工作中所取得的最新成果进行综述, 同时阐述了高密度SNP芯片标记技术在桃以及其它作物上所开展的全基因组关联分析应用实例, 为桃进一步开展全基因组关联分析, 挖掘目标性状QTLs以及高效育种选择标记提供理论基础     

全基因组关联研究现状

在过去的5年中, 全基因组关联研究(Genome-wide association study, GWAS)方法已被证明是研究复杂疾病和性状遗传易感变异的一种有效手段。目前, 各国科学家在多种复杂疾病和性状中开展了大量的GWAS, 对肿瘤、糖尿病、心脏病、神经精神疾病、自身免疫及免疫相关疾病等复杂疾病以及一些常见性状(如身高、体重、血脂、色素等)的遗传易感基因研究取得了重大成果。截止到2010年9月11日, 运用GWAS开展了对近200种复杂疾病/性状的研究, 发现了3 000多个疾病相关的遗传变异。文章就GWAS的发展及其在复杂疾病/性状中的应用做一综述。     

胡萝卜抽薹相关性状全基因组关联分析

【目的】胡萝卜受低温及长日照的影响易发生先期抽薹现象,挖掘与胡萝卜抽薹性状相关联的SNP位点及候选基因,有利于胡萝卜耐抽薹新品种的选育。【方法】以240份胡萝卜种质资源为材料,分别在2021年及2022年调查胡萝卜抽薹(抽薹时间、抽薹率、薹高、抽薹速度)性状,基于质控得到的高质量SNP位点进行抽薹相关性状的GWAS分析。【结果】240份胡萝卜种质资源的抽薹性状具有丰富的遗传多样性,对2年的数据进行分析发现各性状表型均有较大的变异,抽薹率的变异系数最大为144.32%、187.89%,抽薹时间的变异系数同比最小为94.89%和74.63%,BLUE值降低了环境所带来的误差,其变异系数最小为22.53%。相关性分析结果表明,2021年抽薹率与2022年的抽薹性状呈显著正相关,与BLUE值为极显著相关,BLUE值除与2021年的抽薹时间无显著相关外,与其它性状均为极显著相关。GWAS分析共检测到与抽薹性状显著相关的344 个SNP 标记位点,其中有20个多效位点,根据注释信息筛选出29个与抽薹相关的候选基因,主要与光周期途径、春化途径和开花整合子有关。【结论】通过GWAS分析获得多个与抽薹性状相关联的SNP位点,并挖掘到相关候选基因。     

人类基因组致病SNP与良性SNP分布模式探究

基因组变异与生物学功能,乃至人体健康紧密相关.不同种类的基因组变异会对人类健康产生不同的影响,可能出现致病、良性或未知的临床症状.深入研究临床上表现为致病性或良性的基因组变异,能够有效加深我们对基因组变异与疾病发生发展的认识,对评估基因组变异所带来的影响具有一定的意义.本研究选择Clinvar数据库中与疾病相关的SNP...     

普通小麦抗倒伏相关性状的全基因组关联分析

解析小麦抗倒伏遗传机制,发掘抗倒伏位点,选育抗倒伏品种,对于保障我国粮食生产安全具有重要意义.周8425B是我国黄淮麦区重要的小麦骨干亲本,农艺性状优良,其衍生品种具有较好的抗倒伏特性.本研究对周8425B及其衍生品种等62份材料的8个抗倒伏相关性状进行评价,并结合50K SNP芯片数据进行全基因组关联分析(GWAS,...     

小麦根部耐盐性状全基因组关联分析

为了解小麦耐盐相关性状的遗传机理,挖掘与小麦耐盐性显著相关的SNP位点及候选基因,本研究利用浓度200 mmol/L的NaCl溶液和正常营养液对全国300份小麦品种(系)进行耐盐性试验,并利用小麦90 K芯片对分布于小麦全基因组的16650个SNP,采用Q+K关联混合模型对小麦最长根长、根干重、根鲜重、根平均直径、根尖...     

Genome‐wide association studies for hematological traits in swine

Improving immune capacity may increase the profitability of animal production if it enables animals to better cope with infections. Hematological traits play pivotal roles in animal immune capacity and disease resistance. Thus far, few studies have been conducted using a high‐density swine SNP chip panel to unravel the genetic mechanism of the immune capability in domestic animals. In this study, using mixed model‐based single‐locus regression analyses, we carried out genome‐wide association studies, using the Porcine SNP60 BeadChip, for immune responses in piglets for 18 hematological traits (seven leukocyte traits, seven erythrocyte traits, and four platelet traits) after being immunized with classical swine fever vaccine. After adjusting for multiple testing based on permutations, 10, 24, and 77 chromosome‐wise significant SNPs were identified for the leukocyte traits, erythrocyte traits, and platelet traits respectively, of which 10 reached genome‐wise significance level. Among the 53 SNPs for mean platelet volume, 29 are located in a linkage disequilibrium block between 32.77 and 40.59 Mb on SSC6. Four genes of interest are located within the block, providing genetic evidence that this genomic segment may be considered a candidate region relevant to the platelet traits. Other candidate genes of interest for red blood cell, hemoglobin, and red blood cell volume distribution width also have been found near the significant SNPs. Our genome‐wide association study provides a list of significant SNPs and candidate genes that offer valuable information for future dissection of molecular mechanisms regulating hematological traits.     

Current opportunities and challenges: genome-wide association studies on pigmentation and skin cancer

Genome-wide association studies (GWAS) have become a widely used approach for genetic association studies of various human traits. A few GWAS have been conducted with the goal of identifying novel loci for pigmentation traits, melanoma, and non-melanoma skin cancer. Nevertheless, the phenotype variation explained by the genetic markers identified so far is limited. In this review, we discuss the GWAS study design and its application in pigmentation and skin cancer research. Furthermore, we summarize recent developments in post-GWAS activities such as meta-analysis, pathway analysis, and risk prediction.     

PBEAM: A parallel implementation of BEAM for genome-wide inference of epistatic interactions

The software tool PBEAM provides a parallel implementation of the BEAM, which is the first algorithm for large scale epistatic interaction mapping, including genome-wide studies with hundreds of thousands of markers. BEAM describes markers and their interactions with a Bayesian partitioning model and computes the posterior probability of each marker sets via Markov Chain Monte Carlo (MCMC). PBEAM takes the advantage of simulating multiple Markov chains simultaneously. This design can efficiently reduce ~n-fold execution time in the circumstance of n CPUs. The implementation of PBEAM is based on MPI libraries.

Availability

PBEAM is available for download at http://bioinfo.au.tsinghua.edu.cn/pbeam/     

Probability of detecting disease-associated single nucleotide polymorphisms in case-control genome-wide association studies

Some case-control genome-wide association studies (CCGWASs) select promising single nucleotide polymorphisms (SNPs) by ranking corresponding p-values, rather than by applying the same p-value threshold to each SNP. For such a study, we define the detection probability (DP) for a specific disease-associated SNP as the probability that the SNP will be "T-selected," namely have one of the top T largest chi-square values (or smallest p-values) for trend tests of association. The corresponding proportion positive (PP) is the fraction of selected SNPs that are true disease-associated SNPs. We study DP and PP analytically and via simulations, both for fixed and for random effects models of genetic risk, that allow for heterogeneity in genetic risk. DP increases with genetic effect size and case-control sample size and decreases with the number of nondisease-associated SNPs, mainly through the ratio of T to N, the total number of SNPs. We show that DP increases very slowly with T, and the increment in DP per unit increase in T declines rapidly with T. DP is also diminished if the number of true disease SNPs exceeds T. For a genetic odds ratio per minor disease allele of 1.2 or less, even a CCGWAS with 1000 cases and 1000 controls requires T to be impractically large to achieve an acceptable DP, leading to PP values so low as to make the study futile and misleading. We further calculate the sample size of the initial CCGWAS that is required to minimize the total cost of a research program that also includes follow-up studies to examine the T-selected SNPs. A large initial CCGWAS is desirable if genetic effects are small or if the cost of a follow-up study is large.     

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。     

Assessment of the functionality of genome‐wide canine SNP arrays and implications for canine disease association studies

Domestic dogs share a wide range of important disease conditions with humans, including cancers, diabetes and epilepsy. Many of these conditions have similar or identical underlying pathologies to their human counterparts and thus dogs represent physiologically relevant natural models of human disorders. Comparative genomic approaches whereby disease genes can be identified in dog diseases and then mapped onto the human genome are now recognized as a valid method and are increasing in popularity. The majority of dog breeds have been created over the past few hundred years and, as a consequence, the dog genome is characterized by extensive linkage disequilibrium (LD), extending usually from hundreds of kilobases to several megabases within a breed, rather than tens of kilobases observed in the human genome. Genome‐wide canine SNP arrays have been developed, and increasing success of using these arrays to map disease loci in dogs is emerging. No equivalent of the human HapMap currently exists for different canine breeds, and the LD structure for such breeds is far less understood than for humans. This study is a dedicated large‐scale assessment of the functionalities (LD and SNP tagging performance) of canine genome‐wide SNP arrays in multiple domestic dog breeds. We have used genotype data from 18 breeds as well as wolves and coyotes genotyped by the Illumina 22K canine SNP array and Affymetrix 50K canine SNP array. As expected, high tagging performance was observed with most of the breeds using both Illumina and Affymetrix arrays when multi‐marker tagging was applied. In contrast, however, large differences in population structure, LD coverage and pairwise tagging performance were found between breeds, suggesting that study designs should be carefully assessed for individual breeds before undertaking genome‐wide association studies (GWAS).     

Prioritization of SNPs for genome-wide association studies using an interaction model of genetic variation,gene expression,and trait variation

The identification of true causal loci to unravel the statistical evidence of genotype-phenotype correlations and the biological relevance of selected single-nucleotide polymorphisms (SNPs) is a challenging issue in genome-wide association studies (GWAS). Here, we introduced a novel method for the prioritization of SNPs based on p-values from GWAS. The method uses functional evidence from populations, including phenotype-associated gene expressions. Based on the concept of genetic interactions, such as perturbation of gene expression by genetic variation, phenotype and gene expression related SNPs were prioritized by adjusting the p-values of SNPs. We applied our method to GWAS data related to drug-induced cytotoxicity. Then, we prioritized loci that potentially play a role in druginduced cytotoxicity. By generating an interaction model, our approach allowed us not only to identify causal loci, but also to find intermediate nodes that regulate the flow of information among causal loci, perturbed gene expression, and resulting phenotypic variation.