| Title: | Stratification of risk of progression to colectomy in ulcerative colitis via measured and predicted gene expression |
| Journal: | American Journal of Human Genetics |
| Published: | 26 Aug 2021 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/34450030/ |
| DOI: | https://doi.org/10.1016/j.ajhg.2021.07.013 |
| URL: | http://www.cell.com/article/S0002929721002962/pdf |
| Citations: | 8 (7 in last 2 years) as of 8 Aug 2024 |
Publication 7298
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Abstract
An important goal of clinical genomics is to be able to estimate the risk of adverse disease outcomes. Between 5% and 10% of individuals with ulcerative colitis (UC) require colectomy within 5 years of diagnosis, but polygenic risk scores (PRSs) utilizing findings from genome-wide association studies (GWASs) are unable to provide meaningful prediction of this adverse status. By contrast, in Crohn disease, gene expression profiling of GWAS-significant genes does provide some stratification of risk of progression to complicated disease in the form of a transcriptional risk score (TRS). Here, we demonstrate that a measured TRS based on bulk rectal gene expression in the PROTECT inception cohort study has a positive predictive value approaching 50% for colectomy. Single-cell profiling demonstrates that the genes are active in multiple diverse cell types from both the epithelial and immune compartments. Expression quantitative trait locus (QTL) analysis identifies genes with differential effects at baseline and week 52 follow-up, but for the most part, differential expression associated with colectomy risk is independent of local genetic regulation. Nevertheless, a predicted polygenic transcriptional risk score (PPTRS) derived by summation of transcriptome-wide association study (TWAS) effects identifies UC-affected individuals at 5-fold elevated risk of colectomy with data from the UK Biobank population cohort studies, independently replicated in an NIDDK-IBDGC dataset. Prediction of gene expression from relatively small transcriptome datasets can thus be used in conjunction with TWASs for stratification of risk of disease complications.41 Authors
- Angela Mo
- Sini Nagpal
- Kyle Gettler
- Talin Haritunians
- Mamta Giri
- Yael Haberman
- Rebekah Karns
- Jarod Prince
- Dalia Arafat
- Nai-Yun Hsu
- Ling-Shiang Chuang
- Carmen Argmann
- Andrew Kasarskis
- Mayte Suarez-Farinas
- Nathan Gotman
- Emebet Mengesha
- Suresh Venkateswaran
- Paul A Rufo
- Susan S Baker
- Cary G Sauer
- James Markowitz
- Marian D Pfefferkorn
- Joel R Rosh
- Brendan M Boyle
- David R Mack
- Robert N Baldassano
- Sapana Shah
- Neal S LeLeiko
- Melvin B Heyman
- Anne M Griffiths
- Ashish S Patel
- Joshua D Noe
- Sonia Davis Thomas
- Bruce J Aronow
- Thomas D Walters
- Dermot P B McGovern
- Jeffrey S Hyams
- Subra Kugathasan
- Judy H Cho
- Lee A Denson
- Greg Gibson
1 Application
| Application ID | Title |
|---|---|
| 17984 | Genotype-environment interactions and sub-classification of disease |
Enabling scientific discoveries that improve human health