Identifying high-impact variants and genes in exomes of Ashkenazi Jewish inflammatory bowel disease patients

Wu, Yiming; Gettler, Kyle; Kars, Meltem Ece; Giri, Mamta; Li, Dalin; Bayrak, Cigdem Sevim; Zhang, Peng; Jain, Aayushee; Maffucci, Patrick; Sabic, Ksenija; Van Vleck, Tielman; Nadkarni, Girish; Denson, Lee A.; Ostrer, Harry; Levine, Adam P.; Schiff, Elena R.; Segal, Anthony W.; Kugathasan, Subra; Stenson, Peter D.; Cooper, David N.; Schumm, L. Philip

doi:10.6082/04khd-xhz94

Published April 20, 2023 | Version v1

Journal article Open

Identifying high-impact variants and genes in exomes of Ashkenazi Jewish inflammatory bowel disease patients

1. Icahn School of Medicine at Mount Sinai
2. Yale University
3. Cedars-Sinai Medical Center
4. Rockefeller University
5. Cincinnati Children's Hospital
6. Albert Einstein College of Medicine
7. University College London
8. Emory University
9. Cardiff University
10. University of Chicago

Inflammatory bowel disease (IBD) is a group of chronic digestive tract inflammatory conditions whose genetic etiology is still poorly understood. The incidence of IBD is particularly high among Ashkenazi Jews. Here, we identify 8 novel and plausible IBD-causing genes from the exomes of 4453 genetically identified Ashkenazi Jewish IBD cases (1734) and controls (2719). Various biological pathway analyses are performed, along with bulk and single-cell RNA sequencing, to demonstrate the likely physiological relatedness of the novel genes to IBD. Importantly, we demonstrate that the rare and high impact genetic architecture of Ashkenazi Jewish adult IBD displays significant overlap with very early onset-IBD genetics. Moreover, by performing biobank phenome-wide analyses, we find that IBD genes have pleiotropic effects that involve other immune responses. Finally, we show that polygenic risk score analyses based on genome-wide high impact variants have high power to predict IBD susceptibility.

Notes

Due to the large number of authors, only the first 20 and the University of Chicago author is included on the above author list. Please download the article for the complete list of authors.

Data availability

The single-cell RNA-seq raw data used in this study are available at the NCBI GEO database under accession code GSE134809. The RNA data used in this study is available in dbGaP Study Accession: phs001642.v1.p1 (https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs001642.v1.p1). The DNA data used in this study is available in Gene Expression Omnibus (GEO) Series GSE57945. The Mutation Significance Cutoff database for selecting high-impact variants is available at https://lab.rockefeller.edu/casanova/MSC. The gene- and variant-level association data generated in this study are provided in the supplementary data. The raw sequencing data and raw bulk RNA-seq data are protected and are not available due to data privacy laws, which can be available based on reasonable request to IBDGC consortium (https://www.ibdgc.org/).

The python code for prioritizing candidate genes can be found at https://lab.rockefeller.edu/casanova/HGC. The in-house script for running gene level PheWAS can be found at https://gitlab.com/wym0072003/multi-phewas. Standard software guidelines were followed for other scripts used in this study.

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