Variable number tandem repeats mediate the expression of proximal genes.
Variable number tandem repeats mediate the expression of proximal genes.
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DOI：
10.1038/s41467-021-22206-z
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发表时间：
2021-04-06
影响因子：
16.6
通讯作者：
Bafna V
中科院分区：
文献类型：
--
作者：
Bakhtiari M;Park J;Ding YC;Shleizer-Burko S;Neuhausen SL;Halldórsson BV;Stefánsson K;Gymrek M;Bafna V
可变数量串联重复序列 (VNTR) 是许多生物体中显着遗传变异的原因。在人类中，VNTR 与孟德尔疾病和复杂疾病有关，但由于基因分型的复杂性和计算费用而在很大程度上被基因组管道忽略。我们描述了 adVNTR-NN，一种使用浅层神经网络在 18 秒内对 55X 全基因组数据进行 VNTR 基因分型的方法，同时保持高精度。我们使用 adVNTR-NN 对 652 个 GTEx 个体中的 10,264 个 VNTR 进行基因分型。将 VNTR 长度与 46 种组织中的基因表达联系起来，我们鉴定出 163 个“eVNTR”。在血液中可获得独立数据的 22 个 eVNTR 中，有 21 个 (95%) 在重要性和关联方向方面得到了重复。 49% 的 eVNTR 位点对基因表达显示出强烈且可能的因果影响，80% 的最大效应大小至少为 0.3。受影响的基因与阿尔茨海默氏症、肥胖症和家族性癌症等疾病有关，这凸显了 VNTR 对于了解复杂疾病的遗传基础的重要性。可变数目串联重复序列（VNTR）与人类疾病有关，但很难进行计算分析。在这里，作者描述了一种神经网络方法 adVNTR-NN，该方法允许从大型全基因组测序数据集中快速准确地对 VNTR 进行基因分型。
Variable number tandem repeats (VNTRs) account for significant genetic variation in many organisms. In humans, VNTRs have been implicated in both Mendelian and complex disorders, but are largely ignored by genomic pipelines due to the complexity of genotyping and the computational expense. We describe adVNTR-NN, a method that uses shallow neural networks to genotype a VNTR in 18 seconds on 55X whole genome data, while maintaining high accuracy. We use adVNTR-NN to genotype 10,264 VNTRs in 652 GTEx individuals. Associating VNTR length with gene expression in 46 tissues, we identify 163 “eVNTRs”. Of the 22 eVNTRs in blood where independent data is available, 21 (95%) are replicated in terms of significance and direction of association. 49% of the eVNTR loci show a strong and likely causal impact on the expression of genes and 80% have maximum effect size at least 0.3. The impacted genes are involved in diseases including Alzheimer’s, obesity and familial cancers, highlighting the importance of VNTRs for understanding the genetic basis of complex diseases. Variable number tandem repeats (VNTRs) are implicated in human diseases yet have been difficult to analyse computationally. Here, the authors describe a neural network method, adVNTR-NN, that allows rapid and accurate genotyping of VNTRs from large whole genome sequencing datasets.
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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：
4.5
作者：
Gylfe AE;Katainen R;Kondelin J;Tanskanen T;Cajuso T;Hänninen U;Taipale J;Taipale M;Renkonen-Sinisalo L;Järvinen H;Mecklin JP;Kilpivaara O;Pitkänen E;Vahteristo P;Tuupanen S;Karhu A;Aaltonen LA
通讯作者：
Aaltonen LA
影响因子：
3.9
作者：
Borel, Christelle;Migliavacca, Eugenia;Antonarakis, Stylianos E.
通讯作者：
Antonarakis, Stylianos E.
影响因子：
7
作者：
Bakhtiari, Mehrdad;Shleizer-Burko, Sharona;Bafna, Vineet
通讯作者：
Bafna, Vineet
影响因子：
30.8
作者：
Gudbjartsson, Daniel F.;Helgason, Hannes;Stefansson, Kari
通讯作者：
Stefansson, Kari