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SNP-噬菌体:高通量单核苷酸多态性发现流程

SNP-PHAGE: high-throughput SNP discovery pipeline.

作者信息

Aransay Ana M, Matthiesen Rune, Regueiro Manuela M

机构信息

Functional Genomics Unit, Parque Technológico de Bizkaia, Derio, Spain.

出版信息

Methods Mol Biol. 2010;593:49-65. doi: 10.1007/978-1-60327-194-3_3.

Abstract

High-throughput genotyping technologies have become popular in studies that aim to reveal the genetics behind polygenic traits such as complex disease and the diverse response to some drug treatments. These technologies utilize bioinformatics tools to define strategies, analyze data, and estimate the final associations between certain genetic markers and traits. The strategy followed for an association study depends on its efficiency and cost. The efficiency is based on the assumed characteristics of the polymorphisms' allele frequencies and linkage disequilibrium for putative casual alleles. Statistically significant markers (single mutations or haplotypes) that cause a human disorder should be validated and their biological function elucidated. The aim of this chapter is to present a subset of bioinformatics tools for haplotype inference, tag SNP selection, and genome-wide association studies using a high-throughput generated SNP data set.

摘要

高通量基因分型技术在旨在揭示复杂疾病等多基因性状背后的遗传学以及对某些药物治疗的不同反应的研究中已变得很流行。这些技术利用生物信息学工具来定义策略、分析数据,并估计某些遗传标记与性状之间的最终关联。关联研究采用的策略取决于其效率和成本。效率基于假定的多态性等位基因频率特征以及假定的致病等位基因的连锁不平衡。导致人类疾病的具有统计学意义的标记(单突变或单倍型)应得到验证,并阐明其生物学功能。本章的目的是介绍一部分生物信息学工具,用于利用高通量生成的单核苷酸多态性(SNP)数据集进行单倍型推断、标签SNP选择和全基因组关联研究。

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