Haplotyping RAD loci: an efficient method to filter paralogs and account for physical linkage
Haplotyping RAD loci: an efficient method to filter paralogs and account for physical linkage
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单倍型 RAD 位点：过滤旁系同源物和解释物理连锁的有效方法
DOI：
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发表时间：
2017
影响因子：
7.7
通讯作者：
D. Portnoy
中科院分区：
文献类型：
--
作者：
S. Willis;C. Hollenbeck;Jonathan B. Puritz;J. Gold;D. Portnoy
降低代表性基因组文库的下一代测序为非模式物种个体中的数千个单核苷酸多态(SNPs)提供了一种强大的基因分型方法。然而，在没有参考基因组的情况下利用基因数据带来了许多挑战。一个主要的挑战是在将单个基因座的等位基因分裂成单独的簇(基因座)、创造夸大的纯合性和将多个基因座聚集到单个重叠群(基因座)、创造人工制品和夸大杂合度之间的权衡。这个问题主要是通过在序列聚类中使用相似截止点来解决的。在这里，两种常用的后聚类过滤方法(读取深度和过度杂合度)用于识别错误组装的基因座，并与另一种后聚类过滤方法单倍型进行比较。使用模拟和经验数据集验证了三种方法单独识别错误拼接的轨迹；当三种方法结合使用时，效果更佳。结果证实，在群体遗传数据集中包括不正确组装的基因座会夸大杂合性的估计，并降低群体分化的估计。此外，在种群分化程度较低的情况下，在假设标记是独立的分析中，一个基因座内的SNP之间的物理联系造成了人为的聚集。在一个基因座内单倍化SNP有效地中和了物理连锁问题，而不必将数据精简到每个基因座一个SNP。我们介绍了一个Perl脚本，它使用来自单端或成对端读取的数据对多态进行单倍化，并识别潜在的有问题的基因座。
Next‐generation sequencing of reduced‐representation genomic libraries provides a powerful methodology for genotyping thousands of single‐nucleotide polymorphisms (SNPs) among individuals of nonmodel species. Utilizing genotype data in the absence of a reference genome, however, presents a number of challenges. One major challenge is the trade‐off between splitting alleles at a single locus into separate clusters (loci), creating inflated homozygosity, and lumping multiple loci into a single contig (locus), creating artefacts and inflated heterozygosity. This issue has been addressed primarily through the use of similarity cut‐offs in sequence clustering. Here, two commonly employed, postclustering filtering methods (read depth and excess heterozygosity) used to identify incorrectly assembled loci are compared with haplotyping, another postclustering filtering approach. Simulated and empirical data sets were used to demonstrate that each of the three methods separately identified incorrectly assembled loci; more optimal results were achieved when the three methods were applied in combination. The results confirmed that including incorrectly assembled loci in population‐genetic data sets inflates estimates of heterozygosity and deflates estimates of population divergence. Additionally, at low levels of population divergence, physical linkage between SNPs within a locus created artificial clustering in analyses that assume markers are independent. Haplotyping SNPs within a locus effectively neutralized the physical linkage issue without having to thin data to a single SNP per locus. We introduce a Perl script that haplotypes polymorphisms, using data from single or paired‐end reads, and identifies potentially problematic loci.
使用多位点基因型数据推断群体结构：连锁位点和相关等位基因频率。
影响因子：
3.3
作者：
Falush,Daniel;Stephens,Matthew;Pritchard,JonathanK
通讯作者：
Pritchard,JonathanK