Profiling variable-number tandem repeat variation across populations using repeat-pangenome graphs.
Profiling variable-number tandem repeat variation across populations using repeat-pangenome graphs.
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DOI：
10.1038/s41467-021-24378-0
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发表时间：
2021-07-12
影响因子：
16.6
通讯作者：
Chaisson MJP
中科院分区：
文献类型：
--
作者：
Lu TY;Human Genome Structural Variation Consortium;Chaisson MJP
可变数目串联重复序列（VNTR）是由重复序列数目和组成高度可变的连续重复DNA组成。它们包括蛋白质编码序列和与临床疾病的关联。在使用短读序测序的疾病研究中很难结合VNTR分析，因为映射到人类参考的传统方法对于重复和发散序列不太有效。在这项工作中，我们解决了VNTR映射的短读段与重复泛基因组图（RPGG），一种数据结构，编码的人口多样性和重复结构的VNTR基因座从多个单倍型解析组件。我们开发软件来构建RPGG，并使用RPGG来估计具有短读段的VNTR组成。我们利用这一点来发现VNTR的长度分层的大陆人口，和表达数量性状位点，表明RPGG分析的VNTR的多样性和疾病的未来研究将是至关重要的。在疾病研究中，可变数目串联重复序列（VNTR）很难通过短读段测序进行分析。在这里，作者描述了使用重复泛基因组图进行短读段分析的VNTR映射策略。这种方法将有助于阐明VNTR对多样性和疾病的贡献。
Variable number tandem repeats (VNTRs) are composed of consecutive repetitive DNA with hypervariable repeat count and composition. They include protein coding sequences and associations with clinical disorders. It has been difficult to incorporate VNTR analysis in disease studies that use short-read sequencing because the traditional approach of mapping to the human reference is less effective for repetitive and divergent sequences. In this work, we solve VNTR mapping for short reads with a repeat-pangenome graph (RPGG), a data structure that encodes both the population diversity and repeat structure of VNTR loci from multiple haplotype-resolved assemblies. We develop software to build a RPGG, and use the RPGG to estimate VNTR composition with short reads. We use this to discover VNTRs with length stratified by continental population, and expression quantitative trait loci, indicating that RPGG analysis of VNTRs will be critical for future studies of diversity and disease. Variable number tandem repeats (VNTRs) are difficult to analyze by short-read sequencing in disease studies. Here, the authors describe a VNTR mapping strategy for short-read analyses using a repeat pangenome graph. This method will help elucidate the contribution of VNTRs to diversity and disease.
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遗传对人体组织基因表达的影响。
影响因子：
64.8
作者：
GTEx Consortium;Laboratory, Data Analysis &Coordinating Center (LDACC)—Analysis Working Group;Statistical Methods groups—Analysis Working Group;Enhancing GTEx (eGTEx) groups;NIH Common Fund;NIH/NCI;NIH/NHGRI;NIH/NIMH;NIH/NIDA;Biospecimen Collection Source Site—NDRI;Biospecimen Collection Source Site—RPCI;Biospecimen Core Resource—VARI;Brain Bank Repository—University of Miami Brain Endowment Bank;Leidos Biomedical—Project Management;ELSI Study;Genome Browser Data Integration &Visualization—EBI;Genome Browser Data Integration &Visualization—UCSC Genomics Institute, University of California Santa Cruz;Lead analysts:;Laboratory, Data Analysis &Coordinating Center (LDACC):;NIH program management:;Biospecimen collection:;Pathology:;eQTL manuscript working group:;Battle A;Brown CD;Engelhardt BE;Montgomery SB
通讯作者：
Montgomery SB
影响因子：
16.6
作者：
Bakhtiari M;Park J;Ding YC;Shleizer-Burko S;Neuhausen SL;Halldórsson BV;Stefánsson K;Gymrek M;Bafna V
通讯作者：
Bafna V
影响因子：
30.8
作者：
通讯作者：
--
影响因子：
9.5
作者：
Du, Zhenglin;Ma, Liang;Zeng, Changqing
通讯作者：
Zeng, Changqing
影响因子：
7
作者：
Bakhtiari, Mehrdad;Shleizer-Burko, Sharona;Bafna, Vineet
通讯作者：
Bafna, Vineet