新一代高通量测序技术在卫生检验检疫的应用

众多分子诊断方法中,DNA序列测定和分析无疑是最权威的金标准。传统的Sanger测序技术虽已成熟,但其速度和成本的限制满足不了大规模测序的要求。新一代高通量测序技术是对传统测序技术的革命性变革可以一次完成数十万到数百万条DNA分子的序列测定,具有分析结果准确、快速、高灵敏度和高自动化的特点。本文概述新一代测序平台技术原理、操作步骤及应用,并展望了新技术在出入境卫生检验检疫应用前景。     

测序技术在无创产前筛查中的应用及进展

测序技术是现代分子生物学研究中的重要技术,1977年桑格测序技术（一代测序技术）出现。30年后,二代测序技术也应用于科研及临床;近年国外出现了以单分子测序为特点的第三代测序技术。测序技术促进了无创产前筛查技术的进步。相对于传统的羊膜腔穿刺术和绒毛活检,高通量测序技术通过对妊娠妇女血浆中胎儿游离DNA进行测序而获得胎儿的染色体信息,避免直接接触和伤害生长中的胎儿。研究人员利用大规模并行测序技术发现无创产前筛查技术相对于标准筛查技术拥有较高敏感度和特异度。多国卫生机构相继公布了产前检测的指导意见。2013年,单细胞测序开始应用于产前辅助生殖技术的临床试验。国内外通过对单个卵细胞的测序有望大幅度提高辅助生殖的活产率。高通量测序技术在产前诊断领域具有广阔的应用前景。     

Resequencing of pooled DNA for detecting disease associations with rare variants

A combination of common and rare variants is thought to contribute to genetic susceptibility to complex diseases. Recently, next‐generation sequencers have greatly lowered sequencing costs, providing an opportunity to identify rare disease variants in large genetic epidemiology studies. At present, it is still expensive and time consuming to resequence large number of individual genomes. However, given that next‐generation sequencing technology can provide accurate estimates of allele frequencies from pooled DNA samples, it is possible to detect associations of rare variants using pooled DNA sequencing. Current statistical approaches to the analysis of associations with rare variants are not designed for use with pooled next‐generation sequencing data. Hence, they may not be optimal in terms of both validity and power. Therefore, we propose here a new statistical procedure to analyze the output of pooled sequencing data. The test statistic can be computed rapidly, making it feasible to test the association of a large number of variants with disease. By simulation, we compare this approach to Fisher's exact test based either on pooled or individual genotypic data. Our results demonstrate that the proposed method provides good control of the Type I error rate, while yielding substantially higher power than Fisher's exact test using pooled genotypic data for testing rare variants, and has similar or higher power than that of Fisher's exact test using individual genotypic data. Our results also provide guidelines on how various parameters of the pooled sequencing design affect the efficiency of detecting associations. Genet. Epidemiol. 34: 492–501, 2010. © 2010 Wiley‐Liss, Inc.     

Deep resequencing of Trichinella spiralis reveals previously un-described single nucleotide polymorphisms and intra-isolate variation within the mitochondrial genome

The phylogeny and historical dispersal of Trichinella spp. have been studied, in part, by sequencing portions of the mitochondrial genome. Such studies rely on two untested beliefs: that variation in a portion is representative of the entire mitochondrial genome, and that each isolate is characterized by only one mitochondrial haplotype. We have used next generation DNA sequencing technology to obtain the complete mitochondrial genome sequence from a second isolate of T. spiralis. By aligning it to the only previously sequenced genome, we sought to establish whether the exceptionally deep sequencing coverage provided by such an approach could detect regions of the genome which had been misassembled, or nucleotide positions which may vary within an isolate. The new data broadly confirm the gene order and sequence assembly for protein-coding regions. However, in the repetitive non-coding region, alignment to the previously published genome sequence proved difficult. Such discrepancies may represent true biological variation, but may rather result from methodological or algorithmic sources. Within the 13,902 bp protein-coding region, 7 polymorphisms were identified. Six of these polymorphisms occurred within protein-coding genes and three alter an amino acid sequence, one occurred in a tRNA-Ile sequence, and four were found to vary within our isolate. Thus, comparing only two isolates of T. spiralis has enabled the discovery of previously unrecognized variation within the species. Characterizing diversity within and among the mitochondrial genomes of additional species of Trichinella would undoubtedly yield further insights into the diversification history of the genus. Our study affirms that next generation DNA sequencing technology can reliably characterize a complete mitochondrial genome.     

高通量测序技术在五类疾病分子诊断中的应用及质量管理策略

高通量测序技术(high-throughput sequencing)又称下一代测序(next generation sequencing, NGS)能一次并行对成千上万个基因进行测序,极大地提高了DNA测序效率并降低了成本。近年来,NGS在医学研究和临床诊断中的应用日益广泛,在无创产前筛查,肿瘤筛查、诊断和治疗,微生物病原体检测,遗传性疾病的筛查与诊断及器官移植排斥筛查等多个领域具有广泛的应用前景。NGS技术作为一项新兴技术,在相关临床应用中也存在诸多问题,其质量管理体系尚不完善,还面临许多挑战,需要医学实验室和相关部门共同努力,以促进NGS在临床诊断中规范开展。     

Efficient unified rare variant association test by modeling the population genetic distribution in case‐control studies

Recent advancements in next‐generation DNA sequencing technologies have made it plausible to study the association of rare variants with complex diseases. Due to the low frequency, rare variants need to be aggregated in association tests to achieve adequate power with reasonable sample sizes. Hierarchical modeling/kernel machine methods have gained popularity among many available methods for testing a set of rare variants collectively. Here, we propose a new score statistic based on a hierarchical model by additionally modeling the distribution of rare variants under the case‐control study design. Results from extensive simulation studies show that the proposed method strikes a balance between robustness and power and outperforms several popular rare‐variant association tests. We demonstrate the performance of our method using the Dallas Heart Study.     

Next generation amplicon sequencing improves detection of Blastocystis mixed subtype infections

Blastocystis is a highly prevalent enteric protist parasite of humans and animals. Transmission occurs via the fecal-oral route through ingestion of contaminated food or water. Genetic diversity studies have identified numerous subtypes (STs) within the genus Blastocystis based on polymorphism at the SSU rRNA gene. Although there is evidence of frequent mixed subtype infections, the extent of within-host subtype diversity remains largely unexplored. Accurate assessment of Blastocystis ST diversity is crucial to understand epidemiology and sources of Blastocystis transmission to humans. Here, we report the application of next generation sequencing (NGS) for detection and characterization of Blastocystis subtypes to investigate intra-host Blastocystis diversity. A total of 75 specimens obtained from cattle feces, previously identified as Blastocystis positive, were examined using next generation amplicon sequencing. A fragment of the SSU rRNA gene was amplified using Blastocystis-specific primers and resulting amplicons were used for NGS. Comparison of Sanger and NGS results suggest greater sensitivity using the NGS approach. Using Sanger sequencing, mixed infections were suspected in 18 specimens but only confirmed through cloning in three, while NGS identified 49 mixed infections (16 times more). In addition, NGS revealed greater diversity of subtypes with 14 detected compared to 11 by Sanger. Nine more infections with potentially zoonotic STs were detected by NGS than Sanger. Indeed, subtype 3, the most common subtype found in humans, was found in 37% (28) of specimens tested by NGS but in only four specimens using Sanger. Our findings indicate that mixed Blastocystis infections may be far more common than previously thought due to the limitations of current detection methods. This next generation amplicon sequencing strategy improves detection of mixed subtype infections and low abundance subtypes and represents a valuable resource for future Blastocystis studies to improve our understanding of its epidemiology.     

Regularized Rare Variant Enrichment Analysis for Case‐Control Exome Sequencing Data

Rare variants have recently garnered an immense amount of attention in genetic association analysis. However, unlike methods traditionally used for single marker analysis in GWAS, rare variant analysis often requires some method of aggregation, since single marker approaches are poorly powered for typical sequencing study sample sizes. Advancements in sequencing technologies have rendered next‐generation sequencing platforms a realistic alternative to traditional genotyping arrays. Exome sequencing in particular not only provides base‐level resolution of genetic coding regions, but also a natural paradigm for aggregation via genes and exons. Here, we propose the use of penalized regression in combination with variant aggregation measures to identify rare variant enrichment in exome sequencing data. In contrast to marginal gene‐level testing, we simultaneously evaluate the effects of rare variants in multiple genes, focusing on gene‐based least absolute shrinkage and selection operator (LASSO) and exon‐based sparse group LASSO models. By using gene membership as a grouping variable, the sparse group LASSO can be used as a gene‐centric analysis of rare variants while also providing a penalized approach toward identifying specific regions of interest. We apply extensive simulations to evaluate the performance of these approaches with respect to specificity and sensitivity, comparing these results to multiple competing marginal testing methods. Finally, we discuss our findings and outline future research.     

On optimal pooling designs to identify rare variants through massive resequencing

The advent of next‐generation sequencing technologies has facilitated the detection of rare variants. Despite the significant cost reduction, sequencing cost is still high for large‐scale studies. In this article, we examine DNA pooling as a cost‐effective strategy for rare variant detection. We consider the optimal number of individuals in a DNA pool to detect an allele with a specific minor allele frequency (MAF) under a given coverage depth and detection threshold. We found that the optimal number of individuals in a pool is indifferent to the MAF at the same coverage depth and detection threshold. In addition, when the individual contributions to each pool are equal, the total number of individuals across different pools required in an optimal design to detect a variant with a desired power is similar at different coverage depths. When the contributions are more variable, more individuals tend to be needed for higher coverage depths. Our study provides general guidelines on using DNA pooling for more cost‐effective identifications of rare variants. Genet. Epidemiol. 35:139‐147, 2011. © 2011 Wiley‐Liss, Inc.     

高通量测序技术检测自然流产绒毛染色体异常的准确性分析

目的:探讨高通量测序技术检测自然流产绒毛染色体异常的准确性。方法:选择2018年3月-2019年3月在本院行自然流产术的39例患者,提取39份绒毛标本,采用高通量测序技术对绒毛组织的基因组DNA进行分析,并与细胞培养染色体核型分析结果进行比较。结果:细胞培养染色体核型分析发现:染色体异常21例(53.85%),染色体未见明显异常14例(35.90%),检测失败4例(10.26%)。在21例染色体异常样本中,非整倍体11例(52.38%),单体共5例(23.81%),四体3例(14.29%),染色体结构异常2例(9.52%)。高通量测序检测发现:染色体异常25例(64.10%),染色体未见明显异常14例(35.90%)。其中细胞培养染色体核型分析未见明显异常的14例标本中,发现1例三体嵌合体、2例拷贝数变化(CNVs);4例检测失败标本中,发现1例CNVs。2种检测方法检出率差异无统计学意义(χ~2=2.250,P=0.134)。结论:高通量测序检测技术是一项有助于明确自然流产遗传学因素的高效遗传学检测手段,可以作为细胞培养染色体核型分析检查的有力补充。     

Confounded by sequencing depth in association studies of rare alleles

Next‐generation DNA sequencing technologies are facilitating large‐scale association studies of rare genetic variants. The depth of the sequence read coverage is an important experimental variable in the next‐generation technologies and it is a major determinant of the quality of genotype calls generated from sequence data. When case and control samples are sequenced separately or in different proportions across batches, they are unlikely to be matched on sequencing read depth and a differential misclassification of genotypes can result, causing confounding and an increased false‐positive rate. Data from Pilot Study 3 of the 1000 Genomes project was used to demonstrate that a difference between the mean sequencing read depth of case and control samples can result in false‐positive association for rare and uncommon variants, even when the mean coverage depth exceeds 30× in both groups. The degree of the confounding and inflation in the false‐positive rate depended on the extent to which the mean depth was different in the case and control groups. A logistic regression model was used to test for association between case‐control status and the cumulative number of alleles in a collapsed set of rare and uncommon variants. Including each individual's mean sequence read depth across the variant sites in the logistic regression model nearly eliminated the confounding effect and the inflated false‐positive rate. Furthermore, accounting for the potential error by modeling the probability of the heterozygote genotype calls in the regression analysis had a relatively minor but beneficial effect on the statistical results. Genet. Epidemiol. 35: 261‐268, 2011. © 2011 Wiley‐Liss, Inc.     

Design of association studies with pooled or un‐pooled next‐generation sequencing data

Most common hereditary diseases in humans are complex and multifactorial. Large‐scale genome‐wide association studies based on SNP genotyping have only identified a small fraction of the heritable variation of these diseases. One explanation may be that many rare variants (a minor allele frequency, MAF <5%), which are not included in the common genotyping platforms, may contribute substantially to the genetic variation of these diseases. Next‐generation sequencing, which would allow the analysis of rare variants, is now becoming so cheap that it provides a viable alternative to SNP genotyping. In this paper, we present cost‐effective protocols for using next‐generation sequencing in association mapping studies based on pooled and un‐pooled samples, and identify optimal designs with respect to total number of individuals, number of individuals per pool, and the sequencing coverage. We perform a small empirical study to evaluate the pooling variance in a realistic setting where pooling is combined with exon‐capturing. To test for associations, we develop a likelihood ratio statistic that accounts for the high error rate of next‐generation sequencing data. We also perform extensive simulations to determine the power and accuracy of this method. Overall, our findings suggest that with a fixed cost, sequencing many individuals at a more shallow depth with larger pool size achieves higher power than sequencing a small number of individuals in higher depth with smaller pool size, even in the presence of high error rates. Our results provide guidelines for researchers who are developing association mapping studies based on next‐generation sequencing. Genet. Epidemiol. 34: 479–491, 2010. © 2010 Wiley‐Liss, Inc.     

Bioinformatics tools and databases for whole genome sequence analysis of Mycobacterium tuberculosis

Tuberculosis (TB) is an infectious disease of global public health importance caused by Mycobacterium tuberculosis complex (MTC) in which M. tuberculosis (Mtb) is the major causative agent. Recent advancements in genomic technologies such as next generation sequencing have enabled high throughput cost-effective generation of whole genome sequence information from Mtb clinical isolates, providing new insights into the evolution, genomic diversity and transmission of the Mtb bacteria, including molecular mechanisms of antibiotic resistance. The large volume of sequencing data generated however necessitated effective and efficient management, storage, analysis and visualization of the data and results through development of novel and customized bioinformatics software tools and databases. In this review, we aim to provide a comprehensive survey of the current freely available bioinformatics software tools and publicly accessible databases for genomic analysis of Mtb for identifying disease transmission in molecular epidemiology and in rapid determination of the antibiotic profiles of clinical isolates for prompt and optimal patient treatment.     

胚胎植入前遗传学筛查的临床应用

随着辅助生殖技术和遗传学分析技术的发展,胚胎植入前遗传学筛查应用于胚胎染色体数目异常（非整倍体）检测,以期改善体外受精-胚胎移植的妊娠结局。新方法、新技术不断出现并应用于胚胎植入前遗传学筛查中,如囊胚期活检、比较基因组杂交技术、微阵列技术、第二代测序技术等,显著增加了诊断准确性,减少误诊风险。同时,胚胎植入前遗传学筛查的广泛应用也面临许多挑战。综述该领域的应用进展和面临的挑战。     

A Weighted U‐Statistic for Genetic Association Analyses of Sequencing Data

With advancements in next‐generation sequencing technology, a massive amount of sequencing data is generated, which offers a great opportunity to comprehensively investigate the role of rare variants in the genetic etiology of complex diseases. Nevertheless, the high‐dimensional sequencing data poses a great challenge for statistical analysis. The association analyses based on traditional statistical methods suffer substantial power loss because of the low frequency of genetic variants and the extremely high dimensionality of the data. We developed a Weighted U Sequencing test, referred to as WU‐SEQ, for the high‐dimensional association analysis of sequencing data. Based on a nonparametric U‐statistic, WU‐SEQ makes no assumption of the underlying disease model and phenotype distribution, and can be applied to a variety of phenotypes. Through simulation studies and an empirical study, we showed that WU‐SEQ outperformed a commonly used sequence kernel association test (SKAT) method when the underlying assumptions were violated (e.g., the phenotype followed a heavy‐tailed distribution). Even when the assumptions were satisfied, WU‐SEQ still attained comparable performance to SKAT. Finally, we applied WU‐SEQ to sequencing data from the Dallas Heart Study (DHS), and detected an association between ANGPTL 4 and very low density lipoprotein cholesterol.     

Characterisation of taeniid cestodes by DNA analysis

This review describes how the application of molecular technology is providing improved methods for characterising and identifying taeniid cestodes. Emphasis has been placed on the use of DNA approaches for specific diagnosis of the various taeniid life-cycle stages, unambiguous species and strain discrimination, determination of evolutionary and phylogenetic relationships and the assignment of function to key macromolecules based on inferred amino acid sequences corresponding to cloned cDNA sequences. In addition, the review describes methods for isolating DNA from taeniids, approaches for cloning genomic and mitochondrial DNA from these organisms and the use of DNA methods for strain characterisation and egg detection. Lastly, the review focuses on the polymerase chain reaction (PCR) which can provide the basis for exquisitely sensitive assays diagnostic for individual taeniid species. Coupled with direct sequencing techniques, PCR can also provide a valuable new method for examining genetic variation between and within the various taeniid species.     

The Viable Microbiome of Human Milk Differs from the Metataxonomic Profile

Bacteria in human milk contribute to the establishment of the infant gut microbiome. As such, numerous studies have characterized the human milk microbiome using DNA sequencing technologies, particularly 16S rRNA gene sequencing. However, such methods are not able to differentiate between DNA from viable and non-viable bacteria. The extent to which bacterial DNA detected in human milk represents living, biologically active cells is therefore unclear. Here, we characterized both the viable bacterial content and the total bacterial DNA content (derived from viable and non-viable cells) of fresh human milk (n = 10). In order to differentiate the living from the dead, a combination of propidium monoazide (PMA) and full-length 16S rRNA gene sequencing was used. Our results demonstrate that the majority of OTUs recovered from fresh human milk samples (67.3%) reflected DNA from non-viable organisms. PMA-treated samples differed significantly in their bacterial composition compared to untreated samples (PERMANOVA p < 0.0001). Additionally, an OTU mapping to Cutibacterium acnes had a significantly higher relative abundance in PMA-treated (viable) samples. These results demonstrate that the total bacterial DNA content of human milk is not representative of the viable human milk microbiome. Our findings raise questions about the validity of conclusions drawn from previous studies in which viability testing was not used, and have broad implications for the design of future work in this field.