聚类SNP标记的全基因组连锁分析。

Kaja K Selmer, Kristin Brandal, Ole K Olstad, Bård Birkenes, Dag E Undlien, Thore Egeland
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引用次数: 12

摘要

单核苷酸多态性(snp)近年来已取代微卫星成为连锁分析的首选遗传标记,主要是因为它们更丰富,基因型更易于自动调用。最近推出的链接映射集(LMS)之一是Applied Biosystems Human LMS 4K,它是基于SNPlex技术和集群snp使用的全基因组链接集。在这篇文章中,作者报告了他们使用这套软件和相关基因分型软件GeneMapper 4.0的经验,他们使用该软件对17个假定患有单基因疾病的中至大家庭进行了连锁分析。为了比较方法,他们还使用Affymetrix GeneChip Human Mapping 10K 2.0阵列对17个家族中的1个进行了全基因组连锁分析。结论是,这两种方法在技术上都表现良好,调用率高,可比较的孟德尔不一致率低。然而,与Affymetrix软件相比,GeneMapper 4.0版本的基因分型自动化程度较低,因此更耗时。
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Genome-wide linkage analysis with clustered SNP markers.

Single nucleotide polymorphisms (SNPs) have recently replaced microsatellites as the genetic markers of choice in linkage analysis, primarily because they are more abundant and the genotypes more amenable for automatic calling. One of the most recently launched linkage mapping sets (LMS) is the Applied Biosystems Human LMS 4K, which is a genome-wide linkage set based on the SNPlex technology and the use of clustered SNPs. In this article the authors report on their experience with this set and the associated genotyping software GeneMapper version 4.0, which they have used for linkage analyses in 17 moderate to large families with assumed monogenic disease. For comparison of methods, they also performed a genome-wide linkage analysis in 1 of the 17 families using the Affymetrix GeneChip Human Mapping 10K 2.0 array. The conclusion is that both methods performed technically well, with high call rates and comparable and low rates of Mendelian inconsistencies. However, genotyping is less automated in GeneMapper version 4.0 than in the Affymetrix software and thus more time consuming.

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来源期刊
Journal of Biomolecular Screening
Journal of Biomolecular Screening 生物-分析化学
CiteScore
2.41
自引率
0.00%
发文量
0
审稿时长
4-8 weeks
期刊介绍: Advancing the Science of Drug Discovery: SLAS Discovery reports how scientists develop and utilize novel technologies and/or approaches to provide and characterize chemical and biological tools to understand and treat human disease. SLAS Discovery is a peer-reviewed journal that publishes scientific reports that enable and improve target validation, evaluate current drug discovery technologies, provide novel research tools, and incorporate research approaches that enhance depth of knowledge and drug discovery success. SLAS Discovery emphasizes scientific and technical advances in target identification/validation (including chemical probes, RNA silencing, gene editing technologies); biomarker discovery; assay development; virtual, medium- or high-throughput screening (biochemical and biological, biophysical, phenotypic, toxicological, ADME); lead generation/optimization; chemical biology; and informatics (data analysis, image analysis, statistics, bio- and chemo-informatics). Review articles on target biology, new paradigms in drug discovery and advances in drug discovery technologies. SLAS Discovery is of particular interest to those involved in analytical chemistry, applied microbiology, automation, biochemistry, bioengineering, biomedical optics, biotechnology, bioinformatics, cell biology, DNA science and technology, genetics, information technology, medicinal chemistry, molecular biology, natural products chemistry, organic chemistry, pharmacology, spectroscopy, and toxicology.
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