基于微流控芯片的鲑科鱼类单核苷酸多态性分型系统构建

为开发针对大规模样本、低通量位点的单核苷酸多态性(Single nucleotide polymorphism, SNP)分型技术,研究依据虹鳟高通量SNP芯片检测鲑科4个属不同物种群体样本的结果,筛选获得了96个高质量共享多态性位点,应用Fluidigm 96.96微流控动态芯片平台,构建了用于鲑科物种增殖放流个体识别的SNP分型系统。以细鳞鲑为例评估芯片分型结果可靠性,分型成功率为98.63%,与Affymetrix高通量芯片分型一致性达到97.92%。基于该芯片分型结果,使用CERVUS 3.0.7软件对96尾细鳞鲑子代样本及其候选亲本和干扰亲本进行亲权鉴定,结果能够准确重现复杂家系的真实系谱,在用于单亲本亲权鉴定时,第一亲本非排除率(Nonexclusion probability for first parent, NE-1P)为4.362×10^–4,用于双亲本亲权鉴定时,双亲非排除率(Nonexclusion probability for parent pair, NE-PP)为6.538×10^–12,完全满足增殖放流回捕个体分...     

高通量基因型分型技术及其在水稻中的应用

作物种质资源遗传基础的深入认识与高效利用,对于品种改良和粮食安全具有重要意义。传统系谱考察的方法对指导育种实践发挥了重要作用,随着分子生物学的发展,基因组学和高通量SNP分子标记等基因型分型方法可以更便捷地对种质资源进行鉴定。就基因型分型的主要方法进行了总结,重点论述了以SNP为核心的下一代高通量测序(NGS)分型方法、竞争性等位基因特异性PCR(KASP)和SNP芯片系统,以及当前主流的SNP分析工具和数据库。同时,介绍了高通量SNP分型技术在水稻研究中的进展,并就分型技术在作物育种等领域的应用和发展趋势进行了展望。     

中国汉族高原肺水肿易感基因的全基因组关联研究

高原肺水肿(High-altitude pulmonary edema, HAPE)是一种特发于高原低氧环境的肺水肿, 是遗传和环境因素共同作用的结果。为了寻找与中国汉族高原肺水肿相关的单核苷酸多态性(Single nucleotide polymorphism, SNP)位点及易感基因, 文章利用Affymetrix SNP Array 6.0芯片, 对2010年5月至2012年7月在青海省玉树地区执行援建任务时来自平原地区的40例HAPE患者和33例健康对照进行全基因组SNP分型, 通过PLINK软件对芯片结果进行全基因组关联分析(Genome-wide association study, GWAS), 筛选出在病例组和对照组中间有显著差异(P < 10E-7)的SNP位点57个, 通过对57个SNP位点附近74个基因进行GO与Pathway富集分析, 发现这些基因与“前列腺素代谢”、“四烯酸代谢”、“氮代谢”显著相关(adjust P < 0.05), 以上代谢过程与HAPE病理生理机制相关。结果表明, 高原肺水肿受遗传多态性影响, 与多个基因以及位点相关。     

牛SNP芯片分型检出率和分型错误率对基因型填充准确率的影响

SNP芯片已被广泛应用于动植物的遗传研究和生产实践,其基因分型的准确性至关重要。但在实际应用中,常有一定数量的基因型因缺失而需要去估计(填充)。此外,由于各种原因,又常常需要在不同芯片的基因型之间相互填充彼此没有的SNP基因型,或从低密度SNP填充到高密度SNP基因型。因此,基因型填充准确率直接影响后续数据分析的准确性和可靠性。为深入了解基因型填充准确率的影响因素,本研究利用20 116头美国荷斯坦牛的50K SNP芯片基因分型数据,在SNP分型检出率与错误率存在相关和没有相关两种情形下,分别评估了上述两个因素对下游基因型填充准确率的影响。当两者不相关时,模拟的SNP分型检出率从100%降低到50%,SNP分型错误率由0%提升到50%。当两者存在相关时,基因分型的检出率和错误率之间的关系是基于一个实际数据中这两个变量之间的线性回归方程来确定,即模拟的SNP分型检出率从100%降低到50%,SNP分型错误率从0%升高到13.35%。最后,采用5折交叉验证的方法评估基因型填充的准确率。结果表明,当原始数据的SNP分型检出率与错误率彼此独立发生时,基因型填充的错误率受原始SNP分型检出率影响不大(P0.05),却随着原始SNP分型错误率的升高而显著提高(P0.01)。当原始数据的SNP分型检出率与错误率存在负相关时,基因型填充的错误率随着原始SNP分型检出率的降低而显著提高(P0.01)。在这两种情形下,建议SNP分型检出率应在90%以上,基因型填充准确率才能不低于98%。该结果可为提升实际的SNP分型和下游数据分析的质控提供参考依据。     

双色荧光杂交芯片在近交系小鼠遗传监测中的应用

应用一种新的高通量SNP检测方法-双色荧光杂交芯片技术进行近交系小鼠遗传监测。应用双色荧光杂交芯片技术对4个品系近交系小鼠的多个基因组DNA 样本进行SNP分型,整合6个SNP位点的芯片杂交信息,对样本所属品系进行判断。研究结果表明SNP检测方法-双色荧光杂交芯片技术能够对选定的6个SNP位点进行高准确率分型;双色荧光杂交芯片技术是一种高通量SNP检测的良好工具,适合于对少量近交系品系来源的大样本量小鼠进行遗传污染监测和品系鉴定,并具有扩大应用的潜力。     

双色荧光杂交芯片在近交系小鼠遗传监测中的应用

应用一种新的高通量SNP检测方法-双色荧光杂交芯片技术进行近交系小鼠遗传监测。应用双色荧光杂交芯片技术对4个品系近交系小鼠的多个基因组DNA样本进行SNP分型,整合6个SNP位点的芯片杂交信息,对样本所属品系进行判断。研究结果表明SNP检测方法-双色荧光杂交芯片技术能够对选定的6个SNP位点进行高准确率分型;双色荧光杂交芯片技术是一种高通量SNP检测的良好工具,适合于对少量近交系品系来源的大样本量小鼠进行遗传污染监测和品系鉴定,并具有扩大应用的潜力。     

基因组拷贝数变异及其突变机理与人类疾病

拷贝数变异(Copy number variation,CNV)是由基因组发生重排而导致的,一般指长度为1 kb以上的基因组大片段的拷贝数增加或者减少,主要表现为亚显微水平的缺失和重复。CNV是基因组结构变异(Structural variation,SV)的重要组成部分。CNV位点的突变率远高于SNP(Single nucleotide polymorphism),是人类疾病的重要致病因素之一。目前,用来进行全基因组范围的CNV研究的方法有:基于芯片的比较基因组杂交技术(array-based comparative genomic hybridization,aCGH)、SNP分型芯片技术和新一代测序技术。CNV的形成机制有多种,并可分为DNA重组和DNA错误复制两大类。CNV可以导致呈孟德尔遗传的单基因病与罕见疾病,同时与复杂疾病也相关。其致病的可能机制有基因剂量效应、基因断裂、基因融合和位置效应等。对CNV的深入研究,可以使我们对人类基因组的构成、个体间的遗传差异、以及遗传致病因素有新的认识。     

双色荧光杂交芯片在检测 P 1A 1M P I 基C 因多态性中的应用

目的：应用一种新的高通量SNP检测方法-双色荧光杂交芯片技术检测CYPIA1 MspI基因多态性。方法：收集江苏汉族人群原发性肺癌患者75例和相应对照77例,应用双色荧光杂交芯片技术检测了152例样本的CYPIAI基因MspI基因多态性,并应用PCR-RFLP技术验证双色荧光杂交芯片的特异性。结果：152例样本的CYPIAI基因双色荧光杂交芯片技术分型结果与PCR-RFLP结果完全相符,两种方法的基因型分型结果具有很好的一致性。结论：双色荧光杂交芯片技术是一个高通量SNP检测的良好工具,特异性高,在大规模人群SNP筛检中具有良好的发展前案。     

基因组选择技术在农业动物育种中的应用

基因组选择(genomic selection, GS)是畜禽经济性状遗传改良的重要方法。随着高密度SNP芯片和二代测序价格的下降,GS技术越来越多被应用于奶牛、猪、鸡等农业动物育种中。然而,降低全基因组SNP分型成本、提高基因组育种值(genomic estimated breeding value,GEBV)估计准确性仍然是GS研究的主要难题。本文从全基因组SNP分型策略和GEBV估计模型两个方面进行了综述,并对目前GS技术在主要畜禽品种中的应用现状进行了介绍,以期为GS在农业动物育种中的深入开展提供借鉴和参考。     

小鼠冷冻胚胎和精子SNP遗传鉴定方法的建立

目的建立小鼠冷冻胚胎和精子SNP(single nucleotide polymorphism)分型方法,用于冷冻胚胎和精子快速遗传鉴定方案。方法以中科院上海实验动物中心(国家啮齿类实验动物种子中心上海分中心)提供的小鼠冷冻胚胎和精子为样本,采用全基因组扩增技术和PCR-LDR分型技术建立小鼠冷冻物SNP遗传鉴定方法。结果全基因组扩增技术能大幅度增加冷冻胚胎样本的DNA总量;PCR-LDR分型方法适用于小鼠全基因组45个SNPs的分型;分型确定C57BL/6,BALB/c,FVB/NJ等胚胎和精子各10种近交系,SNP位点信息与测序结果一致;小鼠冷冻胚胎个数与SNPs检出个数成正比,当胚胎数达到12以上时SNP检出率100%。结论实现近交系小鼠冷冻胚胎和精子快速SNP基因分型及遗传质量鉴定。     

Comparison of four flow cytometric SNP detection assays and their use in plant improvement

Single nucleotide polymorphisms (SNPs) are attractive DNA markers due to their abundance and potential for use in automated high-throughput genotyping. Numerous SNP genotyping assays have been developed, but it is unclear which assays are best suited and most efficient for various types of plant improvement research. The objective of this study was to compare the accuracy, efficiency, and cost of four SNP genotyping assays: single-base extension (SBE), allele-specific primer extension (ASPE), oligonucleotide ligation (OL), and direct hybridization (DH). All four assay methods used the same Luminex 100 flow cytometer platform. Fifty-eight F₂-derived soybean [Glycine max (L.) Merr.] lines from a cross between inbred lines G99-G725 and N00-3350 were genotyped at four SNPs. SBE and ASPE clearly differentiated between the two homozygotes and the heterozygote at each SNP. Results were in agreement with those identified using the SNaPshot minisequencing assay as a control. In contrast, the OL and DH assays were unable to differentiate between genotypes at some of the SNPs. However, when the cost per data point for the four different assays was compared, the cost of OL and DH was only about 70% of that for SBE, with DH requiring the least time of the four assays. On the basis of cost and labor, ASPE is more cost-effective and simpler than SBE, and would therefore be a good method for genetic mapping and diversity studies which require a large number of markers and a high level of multiplexing. DH appears to be the most economical assay for marker-assisted selection, though optimization for DH would be required for some SNP markers.     

Detection of single nucleotide polymorphisms

Single nucleotide polymorphism (SNP) detection technologies are used to scan for new polymorphisms and to determine the allele(s) of a known polymorphism in target sequences. SNP detection technologies have evolved from labor intensive, time consuming, and expensive processes to some of the most highly automated, efficient, and relatively inexpensive methods. Driven by the Human Genome Project, these technologies are now maturing and robust strategies are found in both SNP discovery and genotyping areas. The nearly completed human genome sequence provides the reference against which all other sequencing data can be compared. Global SNP discovery is therefore only limited by the amount of funding available for the activity. Local, target, SNP discovery relies mostly on direct DNA sequencing or on denaturing high performance liquid chromatography (dHPLC). The number of SNP genotyping methods has exploded in recent years and many robust methods are currently available. The demand for SNP genotyping is great, however, and no one method is able to meet the needs of all studies using SNPs. Despite the considerable gains over the last decade, new approaches must be developed to lower the cost and increase the speed of SNP detection.     

Application of single nucleotide polymorphisms to non-model species: a technical review

Single nucleotide polymorphisms (SNPs) have gained wide use in humans and model species and are becoming the marker of choice for applications in other species. Technology that was developed for work in model species may provide useful tools for SNP discovery and genotyping in non-model organisms. However, SNP discovery can be expensive, labour intensive, and introduce ascertainment bias. In addition, the most efficient approaches to SNP discovery will depend on the research questions that the markers are to resolve as well as the focal species. We discuss advantages and disadvantages of several past and recent technologies for SNP discovery and genotyping and summarize a variety of SNP discovery and genotyping studies in ecology and evolution.     

High concordance of bovine single nucleotide polymorphism genotypes generated using two independent genotyping strategies

Single nucleotide polymorphisms (SNPs) represent the most common form of DNA sequence variation in mammalian livestock genomes. While the past decade has witnessed major advances in SNP genotyping technologies, genotyping errors caused, in part, by the biochemistry underlying the genotyping platform used, can occur. These errors can distort project results and conclusions and can result in incorrect decisions in animal management and breeding programs; hence, SNP genotype calls must be accurate and reliable. In this study, 263 Bos spp. samples were genotyped commercially for a total of 16 SNPs. Of the total possible 4,208 SNP genotypes, 4,179 SNP genotypes were generated, yielding a genotype call rate of 99.31% (standard deviation?±?0.93%). Between 110 and 263 samples were subsequently re-genotyped by us for all 16 markers using a custom-designed SNP genotyping platform, and of the possible 3,819 genotypes a total of 3,768 genotypes were generated (98.70% genotype call rate, SD?±?1.89%). A total of 3,744 duplicate genotypes were generated for both genotyping platforms, and comparison of the genotype calls for both methods revealed 3,741 concordant SNP genotype call rates (99.92% SNP genotype concordance rate). These data indicate that both genotyping methods used can provide livestock geneticists with reliable, reproducible SNP genotypic data for in-depth statistical analysis.     

High Concordance of Bovine Single Nucleotide Polymorphism Genotypes Generated Using Two Independent Genotyping Strategies

Single nucleotide polymorphisms (SNPs) represent the most common form of DNA sequence variation in mammalian livestock genomes. While the past decade has witnessed major advances in SNP genotyping technologies, genotyping errors caused, in part, by the biochemistry underlying the genotyping platform used, can occur. These errors can distort project results and conclusions and can result in incorrect decisions in animal management and breeding programs; hence, SNP genotype calls must be accurate and reliable. In this study, 263 Bos spp. samples were genotyped commercially for a total of 16 SNPs. Of the total possible 4,208 SNP genotypes, 4,179 SNP genotypes were generated, yielding a genotype call rate of 99.31% (standard deviation ± 0.93%). Between 110 and 263 samples were subsequently re-genotyped by us for all 16 markers using a custom-designed SNP genotyping platform, and of the possible 3,819 genotypes a total of 3,768 genotypes were generated (98.70% genotype call rate, SD ± 1.89%). A total of 3,744 duplicate genotypes were generated for both genotyping platforms, and comparison of the genotype calls for both methods revealed 3,741 concordant SNP genotype call rates (99.92% SNP genotype concordance rate). These data indicate that both genotyping methods used can provide livestock geneticists with reliable, reproducible SNP genotypic data for in-depth statistical analysis.     

Recent patents on high-throughput single nucleotide polymorphism (SNP) genotyping methods

Single nucleotide polymorphisms (SNPs) are single-base inheritable variations in a given and defined genetic location that occur in at least 1% of the population. SNPs are useful markers for genetic association studies in disease susceptibility or adverse drug reactions, in evolutionary studies and forensic science. Given the potential impact of SNPs, the biotechnology industry has focused on the development of high-throughput methods for SNP genotyping. Many highthroughput SNP genotyping technologies are currently available and many others are being patented recently. Each offers a unique combination of scale, accuracy, throughput and cost. In this review, we described some of the most important recent SNP genotyping methods and also recent patents associated with it.     

Reference-free SNP calling: improved accuracy by preventing incorrect calls from repetitive genomic regions

ABSTRACT: BACKGROUND: Single nucleotide polymorphisms (SNPs) are the most abundant type of genetic variation in eukaryotic genomes and have recently become the marker of choice in a wide variety of ecological and evolutionary studies. The advent of next-generation sequencing (NGS) technologies has made it possible to efficiently genotype a large number of SNPs in the non-model organisms with no or limited genomic resources. Most NGS-based genotyping methods require a reference genome to perform accurate SNP calling. Little effort, however, has yet been devoted to developing or improving algorithms for accurate SNP calling in the absence of a reference genome. RESULTS: Here we describe an improved maximum likelihood (ML) algorithm called iML, which can achieve high genotyping accuracy for SNP calling in the non-model organisms without a reference genome. The iML algorithm incorporates the mixed Poisson/normal model to detect composite read clusters and can efficiently prevent incorrect SNP calls resulting from repetitive genomic regions. Through analysis of simulation and real sequencing datasets, we demonstrate that in comparison with ML or a threshold approach, iML can remarkably improve the accuracy of de novo SNP genotyping and is especially powerful for the reference-free genotyping in diploid genomes with high repeat contents. CONCLUSIONS: The iML algorithm can efficiently prevent incorrect SNP calls resulting from repetitive genomic regions, and thus outperforms the original ML algorithm by achieving much higher genotyping accuracy. Our algorithm is therefore very useful for accurate de novo SNP genotyping in the non-model organisms without a reference genome.     

High-resolution melting analysis for SNP genotyping and mapping in tetraploid alfalfa (Medicago sativa L.)

Single nucleotide polymorphisms (SNPs) represent the most abundant type of genetic polymorphism in plant genomes. SNP markers are valuable tools for genetic analysis of complex traits of agronomic importance, linkage and association mapping, genome-wide selection, map-based cloning, and marker-assisted selection. Current challenges for SNP genotyping in polyploid outcrossing species include multiple alleles per loci and lack of high-throughput methods suitable for variant detection. In this study, we report on a high-resolution melting (HRM) analysis system for SNP genotyping and mapping in outcrossing tetraploid genotypes. The sensitivity and utility of this technology is demonstrated by identification of the parental genotypes and segregating progeny in six alfalfa populations based on unique melting curve profiles due to differences in allelic composition at one or multiple loci. HRM using a 384-well format is a fast, consistent, and efficient approach for SNP discovery and genotyping, useful in polyploid species with uncharacterized genomes. Possible applications of this method include variation discovery, analysis of candidate genes, genotyping for comparative and association mapping, and integration of genome-wide selection in breeding programs.     

Single nucleotide polymorphism genotyping in polyploid wheat with the Illumina GoldenGate assay

Single nucleotide polymorphisms (SNPs) are indispensable in such applications as association mapping and construction of high-density genetic maps. These applications usually require genotyping of thousands of SNPs in a large number of individuals. Although a number of SNP genotyping assays are available, most of them are designed for SNP genotyping in diploid individuals. Here, we demonstrate that the Illumina GoldenGate assay could be used for SNP genotyping of homozygous tetraploid and hexaploid wheat lines. Genotyping reactions could be carried out directly on genomic DNA without the necessity of preliminary PCR amplification. A total of 53 tetraploid and 38 hexaploid homozygous wheat lines were genotyped at 96 SNP loci. The genotyping error rate estimated after removal of low-quality data was 0 and 1% for tetraploid and hexaploid wheat, respectively. Developed SNP genotyping assays were shown to be useful for genotyping wheat cultivars. This study demonstrated that the GoldenGate assay is a very efficient tool for high-throughput genotyping of polyploid wheat, opening new possibilities for the analysis of genetic variation in wheat and dissection of genetic basis of complex traits using association mapping approach. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Whole-genome genotyping with the single-base extension assay

We describe an efficient, accurate and robust whole-genome genotyping (WGG) assay based on a two-color, single-base extension (SBE), single-nucleotide polymorphism (SNP)-scoring step. We report genotyping results for biallelic International HapMap quality control (QC) SNPs using a single probe per locus. We show scalability, throughput and accuracy of the system by resequencing homozygous loci from our 100k Human-1 Genotyping BeadChip.