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Independent Replication on Genome-Wide Association Study Signals Identifies IRF3 as a Novel Locus for Systemic Lupus Erythematosus.

Feixia Zhang | Yong-Fei Wang | Yan Zhang | Zhiming Lin | Yujie Cao | Huoru Zhang | Zhong-Yi Liu | David L Morris | Yujun Sheng | Yong Cui | Xuejun Zhang | Timothy J Vyse | Yu Lung Lau | Wanling Yang | Yanhui Chen
Frontiers in genetics | 2020

Systemic lupus erythematosus (SLE) is a genetically complex autoimmune disease. Despite the significant progress made in identifying susceptibility genes for SLE, the genetic architecture of the disease is far from being understood. In this study, we set to replicate a number of suggestive association signals found in genome-wide association studies (GWASs) in additional independent cohorts. Replication studies were performed on Han Chinese cohorts from Hong Kong and Anhui, involving a total of 2,269 cases and 5,073 controls. We identified a missense variant in IRF3 (rs7251) reaching genome-wide significance through a joint analysis of GWAS and replication data (OR = 0.876, P = 4.40E-08). A significant correlation was observed between rs7251 and lupus nephritis (LN) by subphenotype stratification (OR = 0.785, P = 0.0128). IRF3 is a key molecule in type I interferon production upon nucleic acid antigen stimulations and may inhibit regulatory T cell differentiation. Further elucidation of the mechanism of this association could help us better understand the pathogenesis of SLE.

Pubmed ID: 32719713

Research resources used in this publication

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Antibodies used in this publication

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Associated grants

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This is a list of tools and resources that we have found mentioned in this publication.


PLINK (tool)

RRID:SCR_001757

Open source whole genome association analysis toolset, designed to perform range of basic, large scale analyses in computationally efficient manner. Used for analysis of genotype/phenotype data. Through integration with gPLINK and Haploview, there is some support for subsequent visualization, annotation and storage of results. PLINK 1.9 is improved and second generation of the software.

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METAL (tool)

RRID:SCR_002013

Software application designed to facilitate meta-analysis of large datasets (such as several whole genome scans) in a convenient, rapid and memory efficient manner. (entry from Genetic Analysis Software)

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1000 Genomes Project and AWS (tool)

RRID:SCR_008801

A dataset containing the full genomic sequence of 1,700 individuals, freely available for research use. The 1000 Genomes Project is an international research effort coordinated by a consortium of 75 companies and organizations to establish the most detailed catalogue of human genetic variation. The project has grown to 200 terabytes of genomic data including DNA sequenced from more than 1,700 individuals that researchers can now access on AWS for use in disease research free of charge. The dataset containing the full genomic sequence of 1,700 individuals is now available to all via Amazon S3. The data can be found at: http://s3.amazonaws.com/1000genomes The 1000 Genomes Project aims to include the genomes of more than 2,662 individuals from 26 populations around the world, and the NIH will continue to add the remaining genome samples to the data collection this year. Public Data Sets on AWS provide a centralized repository of public data hosted on Amazon Simple Storage Service (Amazon S3). The data can be seamlessly accessed from AWS services such Amazon Elastic Compute Cloud (Amazon EC2) and Amazon Elastic MapReduce (Amazon EMR), which provide organizations with the highly scalable compute resources needed to take advantage of these large data collections. AWS is storing the public data sets at no charge to the community. Researchers pay only for the additional AWS resources they need for further processing or analysis of the data. All 200 TB of the latest 1000 Genomes Project data is available in a publicly available Amazon S3 bucket. You can access the data via simple HTTP requests, or take advantage of the AWS SDKs in languages such as Ruby, Java, Python, .NET and PHP. Researchers can use the Amazon EC2 utility computing service to dive into this data without the usual capital investment required to work with data at this scale. AWS also provides a number of orchestration and automation services to help teams make their research available to others to remix and reuse. Making the data available via a bucket in Amazon S3 also means that customers can crunch the information using Hadoop via Amazon Elastic MapReduce, and take advantage of the growing collection of tools for running bioinformatics job flows, such as CloudBurst and Crossbow.

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HARDY (tool)

RRID:SCR_009107

Markov chain Monte Carlo program for association in two-dimensional contingency tables, and for testing Hardy-Weinberg equilibrium. (entry from Genetic Analysis Software)

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