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Mo A, Nagpal S, Gettler K, Haritunians T, Giri M, Haberman Y, Karns R, Prince J, Arafat D, Hsu NY, Chuang LS, Argmann C, Kasarskis A, Suarez-Farinas M, Gotman N, Mengesha E, Venkateswaran S, Rufo PA, Baker SS, Sauer CG, Markowitz J, Pfefferkorn MD, Rosh JR, Boyle BM, Mack DR, Baldassano RN, Shah S, LeLeiko NS, Heyman MB, Griffiths AM, Patel AS, Noe JD, Davis Thomas S, Aronow BJ, Walters TD, McGovern DPB, Hyams JS, Kugathasan S, Cho JH, Denson LA, Gibson G. Stratification of risk of progression to colectomy in ulcerative colitis via measured and predicted gene expression. Am J Hum Genet 2021; 108:1765-1779. [PMID: 34450030 DOI: 10.1016/j.ajhg.2021.07.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2021] [Accepted: 07/26/2021] [Indexed: 12/13/2022] Open
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.
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Affiliation(s)
- Angela Mo
- Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Sini Nagpal
- Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Kyle Gettler
- Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA
| | - Talin Haritunians
- F. Widjaja Foundation Inflammatory Bowel and Immunobiology Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Mamta Giri
- Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA
| | - Yael Haberman
- Cincinnati Children's Hospital Medical Center and the University of Cincinnati College of Medicine, Cincinnati, OH 45229, USA; Sheba Medical Center, Tel Hashomer, Tel Aviv University, Tel Aviv 5265601, Israel
| | - Rebekah Karns
- Cincinnati Children's Hospital Medical Center and the University of Cincinnati College of Medicine, Cincinnati, OH 45229, USA
| | | | - Dalia Arafat
- Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Nai-Yun Hsu
- Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA
| | - Ling-Shiang Chuang
- Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA
| | - Carmen Argmann
- Icahn Institute for Data Science and Genomic Technology, and Department of Population Health Science and Policy, Mount Sinai School of Medicine, New York City, NY 10029, USA
| | - Andrew Kasarskis
- Icahn Institute for Data Science and Genomic Technology, and Department of Population Health Science and Policy, Mount Sinai School of Medicine, New York City, NY 10029, USA
| | - Mayte Suarez-Farinas
- Icahn Institute for Data Science and Genomic Technology, and Department of Population Health Science and Policy, Mount Sinai School of Medicine, New York City, NY 10029, USA
| | - Nathan Gotman
- University of North Carolina, Chapel Hill, NC 27516, USA
| | - Emebet Mengesha
- F. Widjaja Foundation Inflammatory Bowel and Immunobiology Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | | | - Paul A Rufo
- Harvard University-Children's Hospital Boston, Boston, MA 02115, USA
| | - Susan S Baker
- Women & Children's Hospital of Buffalo, Buffalo, NY 14222, USA
| | | | - James Markowitz
- Cohen Children's Medical Center of New York, New Hyde Park, NY 11040, USA
| | | | - Joel R Rosh
- Goryeb Children's Hospital-Atlantic Health, Morristown, NJ 07960, USA
| | | | - David R Mack
- Children's Hospital of East Ontario, Ottawa, ON K1P 1J1, Canada
| | | | - Sapana Shah
- Children's Hospital of Pittsburgh of UPMC, Pittsburgh, PA 15224, USA
| | - Neal S LeLeiko
- Department of Pediatrics, Columbia University, New York City, NY 10032, USA
| | - Melvin B Heyman
- University of California at San Francisco, San Francisco, CA 94143, USA
| | | | | | - Joshua D Noe
- Medical College of Wisconsin, Milwaukee, WI 53226, USA
| | | | - Bruce J Aronow
- Cincinnati Children's Hospital Medical Center and the University of Cincinnati College of Medicine, Cincinnati, OH 45229, USA
| | | | - Dermot P B McGovern
- F. Widjaja Foundation Inflammatory Bowel and Immunobiology Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Jeffrey S Hyams
- Connecticut Children's Medical Center, Hartford, CT 06106, USA
| | | | - Judy H Cho
- Charles Bronfman Institute of Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA
| | - Lee A Denson
- Cincinnati Children's Hospital Medical Center and the University of Cincinnati College of Medicine, Cincinnati, OH 45229, USA
| | - Greg Gibson
- Georgia Institute of Technology, Atlanta, GA 30332, USA.
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Shan N, Xie Y, Song S, Jiang W, Wang Z, Hou L. A novel transcriptional risk score for risk prediction of complex human diseases. Genet Epidemiol 2021; 45:811-820. [PMID: 34245595 DOI: 10.1002/gepi.22424] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 06/08/2021] [Accepted: 06/24/2021] [Indexed: 11/06/2022]
Abstract
Recently polygenetic risk score (PRS) has been successfully used in the risk prediction of complex human diseases. Many studies incorporated internal information, such as effect size distribution, or external information, such as linkage disequilibrium, functional annotation, and pleiotropy among multiple diseases, to optimize the performance of PRS. To leverage on multiomics datasets, we developed a novel flexible transcriptional risk score (TRS), in which messenger RNA expression levels were imputed and weighted for risk prediction. In simulation studies, we demonstrated that single-tissue TRS has greater prediction power than LDpred, especially when there is a large effect of gene expression on the phenotype. Multitissue TRS improves prediction accuracy when there are multiple tissues with independent contributions to disease risk. We applied our method to complex traits, including Crohn's disease, type 2 diabetes, and so on. The single-tissue TRS method outperformed LDpred and AnnoPred across the tested traits. The performance of multitissue TRS is trait-dependent. Moreover, our method can easily incorporate information from epigenomic and proteomic data upon the availability of reference datasets.
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Affiliation(s)
- Nayang Shan
- Center for Statistical Science, Department of Industrial Engineering, Tsinghua University, Beijing, China
| | - Yuhan Xie
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA
| | - Shuang Song
- Center for Statistical Science, Department of Industrial Engineering, Tsinghua University, Beijing, China
| | - Wei Jiang
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA
| | - Zuoheng Wang
- Department of Biostatistics, Yale School of Public Health, New Haven, Connecticut, USA
| | - Lin Hou
- Center for Statistical Science, Department of Industrial Engineering, Tsinghua University, Beijing, China.,MOE Key Laboratory of Bioinformatics, School of Life Sciences, Tsinghua University, Beijing, China
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