免疫调节相关基因及其多态性与慢性乙型肝炎病毒感染的关系

机体对HBV的免疫耐受导致感染慢性化.已有大量研究发现,在慢性HBV感染过程中,免疫调节相关基因如人类白细胞抗原(HLA)、TNF-α、细胞凋亡因子-1(PD-1)、Bcl2家族凋亡协助蛋白(BIM)等基因的表达状态与肝脏炎症程度有关.动物模型中也观察到,封闭某些基因表达可以阻断免疫炎症,更进一步证实这一点.因此,涉及基因表达的宿主遗传背景对免疫应答的影响,成为新近关注的热点问题.人类基因组变异形式之一的基因拷贝数变异(CNVs)已被证实与多种免疫性疾病的发生与发展密切相关,虽然目前尚未证实哪些基因的CNVs与HBV感染转归有关,但已有的相关研究结果对研究HBV感染后疾病进程有借鉴作用.此文就HLA、TNF-α、PD-1、BIM等基因的多态性及基因表达差异对HBV感染的影响,以及近年来对CNVs与免疫性疾病关系研究所取得的进展进行了综述.     

344例超声异常胎儿全基因组拷贝数变异分析

目的分析在全基因组水平胎儿超声异常表型与染色体拷贝数变异(CNVs)的相关性。方法收集2019年7月—2021年1月共计344例孕妇,经胎儿系统超声提示异常,且自愿接受胎儿全基因组CNVs检测。在传统的染色体核型分析基础上,对胎儿样本进行染色体微阵列分析(CMA)。结果344例超声异常胎儿中,有结构异常胎儿223例,占64.83%,确诊染色体非整倍体变异20例,发现27例存在CNVs,检出变异CNVs 29个;有非结构异常胎儿121例,占35.17%,确诊染色体非整倍体变异共13例,发现5例存在CNVs,检出变异CNVs 6个。结论胎儿超声异常表型发生时,应采用全基因组CNVs补充检测,以降低缺陷儿出生率,尤其针对结构异常胎儿,全基因组CNVs检测可发挥较高的补充检测价值。     

基因组拷贝数变异与妇科恶性肿瘤的相关性研究进展

<正>人类基因组中存在大量大于1 kb但小于3 Mb的DNA片段多态,包括片段的插入、缺失、重复等,这种多态被称为拷贝数变异(CNV)。CNVs可能和SNPs一样影响基因的表达、表型变异和适应~([1-2]),一些出现在健康人群的基因组中,导致基因表达量改变,但不引起疾病;还有一些通过改变基因组的结构     

基因芯片在产前诊断胎儿先天性心脏病中的应用价值

目的探讨单核苷酸多态性基因芯片(SNP array)在产前诊断胎儿先天性心脏病(CHD)中的应用价值。方法选取2017年11月-2018年11月间在丽水市妇幼保健院行产前诊断,经超声心动图检测诊断是CHD而核型分析未见异常的胎儿51例,依据胎儿有无心外结构异常分成两组,分别为心外结构异常CHD组和单纯CHD组,依据德国Qiagen公司生产的DNA提取试剂盒说明书对脐带血或者羊水的基因组DNA进行提取,SNP array行遗传学检测,根据基因组拷贝数变异(CNVs)所检测出性质,分成VOUS、良性CNVs及致病性CNVs。结果 51例CHD胎儿检测结果含有CNVs异常17例,其中5例(9. 80%)为致病性CNVs、1例(1. 96%)为VOUS、11例(21. 57%)为良性CNVs。同时SNP array检测含有致病性CNVs胎儿的父母,结果为1例为遗传自表型正常母亲(最终为终止妊娠),4例为CNVs的新发突变。合并心脏结构异常CHD胎儿其致病性CNVs为1例(5. 56%),单纯CHD胎儿其致病性CNVs为4例(12. 12%)。左室流出道梗阻、锥干畸形及间隔缺损胎儿其致病性CNVs检出率分别为25. 00%、10. 53%及9. 09%;致病性CNVs10例分别为Phelan-McDermid综合征1例、1q末端缺损综合征1例、17q12重复综合征1例、猫叫综合征1例、Miller-Dieker综合征1例、威廉综合征1例及Decipher数据库内所包含先天性心脏或者片段相关致病基因4例。结论 CMA技术对CHD胎儿产前诊断有较高应用价值,可使胎儿检出致病性CNVs概率明显提升。采用CMA检测核型正常CHD胎儿,可为出生后干预、临床处理和遗传咨询提取更多遗传学的依据。     

350例超声异常胎儿的染色体及全基因组拷贝数变异分析

目的 探讨胎儿超声异常与胎儿染色体核型及全基因组拷贝数变异(CNVs)的相关性.方法 选择2017年7月—2019年6月在哈尔滨医科大学附属第二医院产前诊断中心就诊并自愿行羊水穿刺的孕妇为研究对象,对其中350例超声提示胎儿异常的孕妇进行常规染色体核型分析高通量测序CNVs检测,比较不同超声异常指标所对应的染色体异常核...     

智力障碍/发育迟缓患儿的染色体核型和基因拷贝数变异分析

目的了解染色体核型和基因拷贝数变异(CNVs)在不明原因智力障碍/发育迟缓(ID/DD)遗传病因诊断中的作用。方法对2016年1月-2017年12月在该院就诊的14岁以下不明原因ID/DD患儿分别进行染色体核型和CNVs分析,比较两者的阳性检出率。结果 89例患儿进行了染色体核型分析,结果检出异常核型7例,阳性检出率为7. 87%,其中以21-三体综合征最多,占57. 14%。29例患儿进行了基因CNVs检测,结果检出8例致病性CNVs,检出率为27. 59%,包括了1例相对多见的5p缺失,还有1例1p32. 3-p31. 3缺失。利用χ~2检验对分别进行染色体核型分析和基因CNVs分析的两组患儿进行比较,结果显示CNVs的阳性检出率明显高于染色体核型分析(P0. 05),两组患儿在性别、智力障碍程度、伴发畸形、伴发其他疾病等方面比较差异无统计学意义(P0. 05)。结论染色体核型分析在不明原因ID/DD患儿中的阳性检出率较低,基因CNVs分析的阳性检出率明显高于染色体核型分析,且可以提供更加精确的定位信息,在不明原因ID/DD患儿的病因诊断中具有重要价值。     

同胞对连锁分析的三种统计方法比较

在很多物种中,无论是低等的酵母,还是高等的人类,许多基因的表达水平都展示出丰富的自然变异.而目前,很多科研工作都集中于研究疾病发展不同阶段的基因表达水平,较少研究健康人群基因表达的自然变化.事实上,健康人群个体间的遗传变异常常和人类复杂疾病的易感性及复杂疾病性状有密切联系.而人类基因组中广泛存在并被广泛应用的第三代基因遗传标记,即SNP标记,是一种能够稳定遗传的早期突变,研究者可以通过对SNP的相关分析和高密度的SNP图谱来定位一系列复杂疾病的相关基因.     

基因组拷贝数变异在单纯性先天性心脏病中的研究进展

先天性心脏病(CHDs)是我国最常见的一种出生缺陷,其潜在的病因至今未明。近年来,一些科学研究和临床诊断试验表明基因拷贝数变异(CNVs)已成为先天性遗传性疾病的一个重要因素,例如该病因在单纯性先心病中约占3%~10%。然而,拷贝数变异在单纯性CHDs形成中的遗传机制尚不明确。因此本文就CNVs与单纯性CHDs关系的研究进展作一综述。     

应用微阵列比较基因组杂交技术对55例智力低下/发育迟缓患儿基因组拷贝数变异的分析

目的 应用高分辨微阵列比较基因组杂交技术(array-comparative genomic hybridization, aCGH)对55例不明原因的智力低下或发育迟缓(mental retardation or developmental delay, MR/DD)患儿进行拷贝数变异(copy number variations, CNVs)检测, 寻求与遗传学相关的致病因素, 探讨aCGH对不明原因MR/DD患儿可能的分子病因诊断的作用。方法 收集2013年6月-2013 年12月到本院儿科初步诊断为MR/DD的患儿55例, 应用25~50 K CytoScan HD芯片检测全基因组CNVs, 联合生物信息学分析手段分析致病性CNVs。结果 在55例不明原因MR/DD患者中共检测到21例存在罕见CNVs。通过比对数据库, 21处CNVs确认为致病性CNVs。19例患者携带与MR/DD相关的CNVs。2例为已知综合征患者, 其中1例为Turner综合征, 1例为1p36缺失综合征。结论 基因组CNVs相关的微缺失或微重复是不明原因MR/DD的病因之一, 这些片段均无法被常规染色体G带检查所识别。aCGH可以提高对不明原因MR/DD患儿的分子病因诊断水平, 对深入研究MR/DD病因机制有重要意义, 为患儿预后和家庭再发风险评估提供指导。     

SIRT1、SIRT1SNP与年龄相关性疾病研究进展

正单核苷酸多态性(SNPs)指在基因水平上由单个核苷酸变异所引起的DNA序列多态性,是人类可遗传变异中最常见的一种,是人类基因组中广泛而稳定分布的多态性位点。随着人类基因组计划的完成,人类基因组单体型图(Haplotype map,简称Hapmap)计划的开展,SNP与复杂的多基因疾病(如心血管疾病、糖尿病等年龄相关疾病)的关     

Identifying a Deletion Affecting Total Lung Capacity Among Subjects in the COPDGene Study Cohort

Chronic obstructive pulmonary disease (COPD) is a progressive disease with both environmental and genetic risk factors. Genome‐wide association studies (GWAS) have identified multiple genomic regions influencing risk of COPD. To thoroughly investigate the genetic etiology of COPD, however, it is also important to explore the role of copy number variants (CNVs) because the presence of structural variants can alter gene expression and can be causal for some diseases. Here, we investigated effects of polymorphic CNVs on quantitative measures of pulmonary function and chest computed tomography (CT) phenotypes among subjects enrolled in COPDGene, a multisite study. COPDGene subjects consist of roughly one‐third African American (AA) and two‐thirds non‐Hispanic white adult smokers (with or without COPD). We estimated CNVs using PennCNV on 9,076 COPDGene subjects using Illumina's Omni‐Express genome‐wide marker array. We tested for association between polymorphic CNV components (defined as disjoint intervals of copy number regions) for several quantitative phenotypes associated with COPD within each racial group. Among the AAs, we identified a polymorphic CNV on chromosome 5q35.2 located between two genes (FAM153B and SIMK1, but also harboring several pseudo‐genes) giving genome‐wide significance in tests of association with total lung capacity (TLC_CT) as measured by chest CT scans. This is the first study of genome‐wide association tests of polymorphic CNVs and TLC_CT. Although the ARIC cohort did not have the phenotype of TLC_CT, we found similar counts of CNV deletions and amplifications among AA and European subjects in this second cohort.     

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Copy number variants (CNVs) play an important role in a number of human diseases, but the accurate calling of CNVs remains challenging. Most current approaches to CNV detection use raw read alignments, which are computationally intensive to process. We use a regression tree-based approach to call germline CNVs from whole-genome sequencing (WGS, >18x) variant call sets in 6,898 samples across four European cohorts, and describe a rich large variation landscape comprising 1,320 CNVs. Eighty-one percent of detected events have been previously reported in the Database of Genomic Variants. Twenty-three percent of high-quality deletions affect entire genes, and we recapitulate known events such as the GSTM1 and RHD gene deletions. We test for association between the detected deletions and 275 protein levels in 1,457 individuals to assess the potential clinical impact of the detected CNVs. We describe complex CNV patterns underlying an association with levels of the CCL3 protein (MAF = 0.15, p = 3.6x10⁻¹²) at the CCL3L3 locus, and a novel cis-association between a low-frequency NOMO1 deletion and NOMO1 protein levels (MAF = 0.02, p = 2.2x10⁻⁷). This study demonstrates that existing population-wide WGS call sets can be mined for germline CNVs with minimal computational overhead, delivering insight into a less well-studied, yet potentially impactful class of genetic variant.     

Candidate single nucleotide polymorphism selection using publicly available tools: a guide for epidemiologists

Single nucleotide polymorphisms (SNPs) are the most common form of human genetic variation, with millions present in the human genome. Because only 1% might be expected to confer more than modest individual effects in association studies, the selection of predictive candidate variants for complex disease analyses is formidable. Technologic advances in SNP discovery and the ever-changing annotation of the genome have led to massive informational resources that can be difficult to master across disciplines. A simplified guide is needed. Although methods for evaluating nonsynonymous coding SNPs are known, several other publicly available computational tools can be utilized to assess polymorphic variants in noncoding regions. As an example, the authors applied multiple methods to select SNPs in DNA double-strand break repair genes. They chose to evaluate SNPs that occurred among a preexisting set of 57 validated assays and to justify new assay development for 83 potential SNPs in the DNA-dependent protein kinase catalytic subunit. Of the 140 SNPs, the authors eliminated 119 variants with low or neutral predictions. The existing computational methods they used and the semiquantitative relative ranking strategy they developed can be adapted to a priori SNP selection or post hoc evaluation of variants identified in whole genome scans or within haplotype blocks associated with disease. The authors show a "real world" application of some existing bioinformatics tools for use in large epidemiologic studies and genetic analyses. They also reviewed alternative approaches that provide related information.     

Critical evaluation of copy number variant calling methods using DNA methylation

Recent technological and methodological developments have enabled the use of array-based DNA methylation data to call copy number variants (CNVs). ChAMP, Conumee, and cnAnalysis450k are popular methods currently used to call CNVs using methylation data. However, so far, no studies have analyzed the reliability of these methods using real samples. Data from a cohort of individuals with genotype and DNA methylation data generated using the HumanMethylation450 and MethylationEPIC BeadChips were used to assess the consistency between the CNV calls generated by methylation and genotype data. We also took advantage of repeated measures of methylation data collected from the same individuals to compare the reliability of CNVs called by ChAMP, Conumee, and cnAnalysis450k for both the methylation arrays. ChAMP identified more CNVs than Conumee and cnAnalysis450k for both the arrays and, as a consequence, had a higher overlap (~62%) with the calls from the genotype data. However, all methods had relatively low reliability. For the MethylationEPIC array, Conumee had the highest reliability (57.6%), whereas for the HumanMethylation450 array, cnAnalysis450k had the highest reliability (43.0%). Overall, the MethylationEPIC array provided significant gains in reliability for CNV calling over the HumanMethylation450 array but not for overlap with CNVs called using genotype data.     

Estimating the effects of copy-number variants on intelligence using hierarchical Bayesian models

It is challenging to estimate the phenotypic impact of the structural genome changes known as copy-number variations (CNVs), since there are many unique CNVs which are nonrecurrent, and most are too rare to be studied individually. In recent work, we found that CNV-aggregated genomic annotations, that is, specifically the intolerance to mutation as measured by the pLI score (probability of being loss-of-function intolerant), can be strong predictors of intellectual quotient (IQ) loss. However, this aggregation method only estimates the individual CNV effects indirectly. Here, we propose the use of hierarchical Bayesian models to directly estimate individual effects of rare CNVs on measures of intelligence. Annotation information on the impact of major mutations in genomic regions is extracted from genomic databases and used to define prior information for the approach we call HBIQ. We applied HBIQ to the analysis of CNV deletions and duplications from three datasets and identified several genomic regions containing CNVs demonstrating significant deleterious effects on IQ, some of which validate previously known associations. We also show that several CNVs were identified as deleterious by HBIQ even if they have a zero pLI score, and the converse is also true. Furthermore, we show that our new model yields higher out-of-sample concordance (78%) for predicting the consequences of carrying known recurrent CNVs compared with our previous approach.     

Rare variant association test with multiple phenotypes

Although genome‐wide association studies (GWAS) have now discovered thousands of genetic variants associated with common traits, such variants cannot explain the large degree of “missing heritability,” likely due to rare variants. The advent of next generation sequencing technology has allowed rare variant detection and association with common traits, often by investigating specific genomic regions for rare variant effects on a trait. Although multiple correlated phenotypes are often concurrently observed in GWAS, most studies analyze only single phenotypes, which may lessen statistical power. To increase power, multivariate analyses, which consider correlations between multiple phenotypes, can be used. However, few existing multivariant analyses can identify rare variants for assessing multiple phenotypes. Here, we propose Multivariate Association Analysis using Score Statistics (MAAUSS), to identify rare variants associated with multiple phenotypes, based on the widely used sequence kernel association test (SKAT) for a single phenotype. We applied MAAUSS to whole exome sequencing (WES) data from a Korean population of 1,058 subjects to discover genes associated with multiple traits of liver function. We then assessed validation of those genes by a replication study, using an independent dataset of 3,445 individuals. Notably, we detected the gene ZNF620 among five significant genes. We then performed a simulation study to compare MAAUSS's performance with existing methods. Overall, MAAUSS successfully conserved type 1 error rates and in many cases had a higher power than the existing methods. This study illustrates a feasible and straightforward approach for identifying rare variants correlated with multiple phenotypes, with likely relevance to missing heritability.     

Identification of Grouped Rare and Common Variants via Penalized Logistic Regression

In spite of the success of genome‐wide association studies in finding many common variants associated with disease, these variants seem to explain only a small proportion of the estimated heritability. Data collection has turned toward exome and whole genome sequencing, but it is well known that single marker methods frequently used for common variants have low power to detect rare variants associated with disease, even with very large sample sizes. In response, a variety of methods have been developed that attempt to cluster rare variants so that they may gather strength from one another under the premise that there may be multiple causal variants within a gene. Most of these methods group variants by gene or proximity, and test one gene or marker window at a time. We propose a penalized regression method (PeRC) that analyzes all genes at once, allowing grouping of all (rare and common) variants within a gene, along with subgrouping of the rare variants, thus borrowing strength from both rare and common variants within the same gene. The method can incorporate either a burden‐based weighting of the rare variants or one in which the weights are data driven. In simulations, our method performs favorably when compared to many previously proposed approaches, including its predecessor, the sparse group lasso [Friedman et al., 2010].     

智力障碍共患癫痫儿童的临床特征及基因组拷贝数变异情况分析

目的 分析智力障碍共患癫痫(ID-E)儿童的临床特征及基因组拷贝数变异(CNVs)情况,为ID-E的临床治疗提供参考依据。方法 选取2013年1月—2017年1月唐山市妇幼保健院收治的60例ID-E患儿为研究对象,收集患儿的一般临床资料,采集患儿及其父母外周血进行全外显子CNVs检测,并对数据进行分析。结果 ID-E患儿中男性较多(61.67%),年龄主要集中在1~3岁(31.67%),大多数患儿BMI正常或偏高(93.33%)、出生体重正常(86.67%)、顺产(90.00%),大多数呈重度ID受损(83.33%)、临床表型以生长发育迟缓(76.67%)和语言表达障碍(68.33%)为主;治疗药物主要为丙戊酸钠,31例(51.67%)患儿双联治疗,预后良好率为46.67%;60例ID-E患儿中检出6例(10.00%)异常CNVs,其中2例诊断为已知综合征(Wolf-Hirschhorn综合征、Smith-Magenis综合征),2例诊断为致病性CNVs,1例为可疑CNVs,1例为临床意义不明CNVs;父母起源分析,仅有1例临床意义不明的患儿为母源性,其他均为新发性改变。结论 ID-E患儿具有一定临床特点,CNVs可作为ID-E遗传病学病因分析的重要检测手段,帮助明确ID-E病因,指导疾病诊治。