A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data
A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data
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DOI：
10.1093/bioinformatics/btr509
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发表时间：
2011-11-01
期刊：
影响因子：
5.8
通讯作者：
Li, Heng
中科院分区：
文献类型：
--
作者：
Li, Heng
动机：大多数现有的DNA序列分析方法依赖于准确的序列或基因型。然而，在下一代测序（NGS）的应用中，可能不容易获得准确的基因型（例如，G.多样品低覆盖测序或体细胞突变发现）。这些应用程序的发展压力的新方法，用于分析序列data.Results的不确定性：我们提出了一个统计框架调用SNP，发现体细胞突变，推断人口遗传参数和直接进行关联测试的测序数据的基础上，没有明确的基因分型或基于链接的插补。在真实的数据上，我们证明了我们的方法达到了与估计位点等位基因计数、推断等位基因频谱和关联映射的替代方法相当的精度。我们还强调了使用对称数据集寻找体细胞突变的必要性，并证实了发现罕见事件时，错误映射通常是错误的主要来源。
Motivation: Most existing methods for DNA sequence analysis rely on accurate sequences or genotypes. However, in applications of the next-generation sequencing (NGS), accurate genotypes may not be easily obtained (e. g. multi-sample low-coverage sequencing or somatic mutation discovery). These applications press for the development of new methods for analyzing sequence data with uncertainty.Results: We present a statistical framework for calling SNPs, discovering somatic mutations, inferring population genetical parameters and performing association tests directly based on sequencing data without explicit genotyping or linkage-based imputation. On real data, we demonstrate that our method achieves comparable accuracy to alternative methods for estimating site allele count, for inferring allele frequency spectrum and for association mapping. We also highlight the necessity of using symmetric datasets for finding somatic mutations and confirm that for discovering rare events, mismapping is frequently the leading source of errors.