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串联重复变异的深度群体参考面板。

A deep population reference panel of tandem repeat variation.

作者信息

Jam Helyaneh Ziaei, Li Yang, DeVito Ross, Mousavi Nima, Ma Nichole, Lujumba Ibra, Adam Yagoub, Maksimov Mikhail, Huang Bonnie, Dolzhenko Egor, Qiu Yunjiang, Kakembo Fredrick Elishama, Joseph Habi, Onyido Blessing, Adeyemi Jumoke, Bakhtiari Mehrdad, Park Jonghun, Javadzadeh Sara, Jjingo Daudi, Adebiyi Ezekiel, Bafna Vineet, Gymrek Melissa

机构信息

Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA.

Department of Medicine, University of California San Diego, La Jolla, CA.

出版信息

bioRxiv. 2023 Mar 12:2023.03.09.531600. doi: 10.1101/2023.03.09.531600.

Abstract

Tandem repeats (TRs) represent one of the largest sources of genetic variation in humans and are implicated in a range of phenotypes. Here we present a deep characterization of TR variation based on high coverage whole genome sequencing from 3,550 diverse individuals from the 1000 Genomes Project and H3Africa cohorts. We develop a method, EnsembleTR, to integrate genotypes from four separate methods resulting in high-quality genotypes at more than 1.7 million TR loci. Our catalog reveals novel sequence features influencing TR heterozygosity, identifies population-specific trinucleotide expansions, and finds hundreds of novel eQTL signals. Finally, we generate a phased haplotype panel which can be used to impute most TRs from nearby single nucleotide polymorphisms (SNPs) with high accuracy. Overall, the TR genotypes and reference haplotype panel generated here will serve as valuable resources for future genome-wide and population-wide studies of TRs and their role in human phenotypes.

摘要

串联重复序列(TRs)是人类遗传变异的最大来源之一,并与一系列表型相关。在此,我们基于来自千人基因组计划和H3Africa队列的3550个不同个体的高覆盖度全基因组测序,对TR变异进行了深入表征。我们开发了一种方法EnsembleTR,以整合来自四种独立方法的基因型,从而在超过170万个TR位点获得高质量基因型。我们的目录揭示了影响TR杂合性的新序列特征,识别了特定人群的三核苷酸扩增,并发现了数百个新的eQTL信号。最后,我们生成了一个分阶段的单倍型面板,可用于从附近的单核苷酸多态性(SNP)中高精度地推断出大多数TR。总体而言,这里生成的TR基因型和参考单倍型面板将为未来全基因组和全人群TR及其在人类表型中的作用研究提供宝贵资源。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e94a/10028971/ef06a9359942/nihpp-2023.03.09.531600v1-f0001.jpg

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