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慢性阻塞性肺疾病(COPD)遗传关联结果与转录定量性状位点之间的重叠。

Overlap between COPD genetic association results and transcriptional quantitative trait loci.

作者信息

Saferali Aabida, Kim Wonji, Chase Robert P, Vollmers Chris, Silverman Edwin K, Cho Michael H, Castaldi Peter J, Hersh Craig P

出版信息

medRxiv. 2024 Jul 8:2024.07.08.24310079. doi: 10.1101/2024.07.08.24310079.

Abstract

RATIONALE

Genome-wide association studies (GWAS) have identified multiple genetic loci associated with chronic obstructive pulmonary disease (COPD). When integrated with GWAS results, expression quantitative trait locus (eQTL) studies can provide insight into biological mechanisms involved in disease by identifying single nucleotide polymorphisms (SNPs) that contribute to whole gene expression. However, there are multiple genetically driven regulatory and isoform-specific effects which cannot be detected in traditional eQTL analyses. Here, we identify SNPs that are associated with alternative splicing (sQTL) in addition to eQTLs to identify novel functions for COPD associated genetic variants.

METHODS

We performed RNA sequencing on whole blood from 3743 subjects in the COPDGene Study. RNA sequencing data from lung tissue of 1241 subjects from the Lung Tissue Research Consortium (LTRC), and whole genome sequencing data on all subjects. Associations between all SNPs within 1000 kb of a gene (cis-) and splice and gene expression quantifications were tested using tensorQTL. In COPDGene a total of 11,869,333 SNPs were tested for association with 58,318 splice clusters, and 8,792,206 SNPs were tested for association with 70,094 splice clusters in LTRC. We assessed colocalization with COPD-associated SNPs from a published GWAS[1].

RESULTS

After adjustment for multiple statistical testing, we identified 28,110 splice-sites corresponding to 3,889 unique genes that were significantly associated with genotype in COPDGene whole blood, and 58,258 splice-sites corresponding to 10,307 unique genes associated with genotype in LTRC lung tissue. We found 7,576 sQTL splice-sites corresponding to 2,110 sQTL genes were shared between whole blood and lung, while 20,534 sQTL splice-sites in 3,518 genes were unique to blood and 50,682 splice-sites in 9,677 genes were unique to lung. To determine what proportion of COPD-associated SNPs were associated with transcriptional splicing, we performed colocalization analysis between COPD GWAS and sQTL data, and found that 38 genomic windows, corresponding to 38 COPD GWAS loci had evidence of colocalization between QTLs and COPD. The top five colocalizations between COPD and lung sQTLs include , , , and .

CONCLUSIONS

A total of 38 COPD GWAS loci contain evidence of sQTLs, suggesting that analysis of sQTLs in whole blood and lung tissue can provide novel insights into disease mechanisms.

摘要

原理

全基因组关联研究(GWAS)已确定了多个与慢性阻塞性肺疾病(COPD)相关的基因位点。当与GWAS结果相结合时,表达定量性状位点(eQTL)研究可以通过识别影响全基因表达的单核苷酸多态性(SNP),深入了解疾病涉及的生物学机制。然而,存在多种基因驱动的调控和异构体特异性效应,这些效应在传统的eQTL分析中无法检测到。在这里,我们除了识别eQTL外,还识别与可变剪接相关的SNP(sQTL),以确定COPD相关基因变异的新功能。

方法

我们对COPDGene研究中3743名受试者的全血进行了RNA测序。来自肺组织研究联盟(LTRC)的1241名受试者肺组织的RNA测序数据,以及所有受试者的全基因组测序数据。使用tensorQTL测试基因1000 kb内所有SNP(顺式)与剪接和基因表达定量之间的关联。在COPDGene中,共测试了11,869,333个SNP与58,318个剪接簇的关联,在LTRC中测试了8,792,206个SNP与70,094个剪接簇的关联。我们评估了与已发表的GWAS[1]中COPD相关SNP的共定位情况。

结果

在对多重统计检验进行校正后,我们在COPDGene全血中确定了28,110个剪接位点,对应3,889个独特基因,这些基因与基因型显著相关;在LTRC肺组织中确定了58,258个剪接位点,对应10,307个独特基因与基因型相关。我们发现全血和肺之间共有7,576个sQTL剪接位点,对应2,110个sQTL基因,而3,518个基因中的20,534个sQTL剪接位点是血液特有的,9,677个基因中的50,682个剪接位点是肺特有的。为了确定COPD相关SNP中有多大比例与转录剪接相关,我们在COPD GWAS和sQTL数据之间进行了共定位分析,发现38个基因组窗口,对应38个COPD GWAS位点,在QTL和COPD之间有共定位证据。COPD与肺sQTL之间的前五个共定位包括 , , 和 。

结论

共有38个COPD GWAS位点包含sQTL证据,表明对全血和肺组织中的sQTL进行分析可以为疾病机制提供新的见解。

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