Mengyuan Kan1, Avantika R Diwadkar1, Haoyue Shuai1, Jaehyun Joo1, Alberta L Wang2, Mei-Sing Ong3, Joanne E Sordillo3, Carlos Iribarren4, Meng X Lu4, Natalia Hernandez-Pacheco5, Javier Perez-Garcia6, Mario Gorenjak7, Uroš Potočnik8, Esteban G Burchard9, Maria Pino-Yanes10, Ann Chen Wu3, Blanca E Himes11. 1. Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pa. 2. Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Mass; Division of Allergy and Clinical Immunology, Brigham and Women's Hospital, Boston, Mass. 3. Precision Medicine Translational Research Center, Department of Population Medicine, Harvard Pilgrim Health Care Institute and Harvard Medical School, Boston, Mass. 4. Kaiser Permanente Division of Research, Kaiser Permanente, Oakland, Calif. 5. Department of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden; CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain. 6. Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna, San Cristóbal de La Laguna, Spain. 7. Center for Human Molecular Genetics and Pharmacogenomics, Faculty of Medicine, University of Maribor, Maribor, Slovenia. 8. Center for Human Molecular Genetics and Pharmacogenomics, Faculty of Medicine, University of Maribor, Maribor, Slovenia; Laboratory for Biochemistry, Molecular Biology, and Genomics, Faculty of Chemistry and Chemical Engineering, University of Maribor, Maribor, Slovenia. 9. Department of Medicine, University of California, San Francisco, Calif; Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, Calif. 10. CIBER de Enfermedades Respiratorias, Instituto de Salud Carlos III, Madrid, Spain; Genomics and Health Group, Department of Biochemistry, Microbiology, Cell Biology and Genetics, Universidad de La Laguna, San Cristóbal de La Laguna, Spain; Instituto de Tecnologías Biomédicas, Universidad de La Laguna, Faculty of Health Sciences, San Cristóbal de La Laguna, Spain. 11. Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pa. Electronic address: bhimes@pennmedicine.upenn.edu.
Abstract
BACKGROUND: Inhaled corticosteroid (ICS) response among patients with asthma is influenced by genetics, but biologically actionable insights based on associations have not been found. Various glucocorticoid response omics data sets are available to interrogate their biological effects. OBJECTIVE: We sought to identify functionally relevant ICS-response genetic associations by integrating complementary multiomics data sets. METHODS: Variants with P values less than 10-4 from a previous ICS-response genome-wide association study were reranked on the basis of integrative scores determined from (1) glucocorticoid receptor- and (2) RNA polymerase II-binding regions inferred from ChIP-Seq data for 3 airway cell types, (3) glucocorticoid response element motifs, (4) differentially expressed genes in response to glucocorticoid exposure according to 20 transcriptomic data sets, and (5) expression quantitative trait loci from GTEx. Candidate variants were tested for association with ICS response and asthma in 6 independent studies. RESULTS: Four variants had significant (q value < 0.05) multiomics integrative scores. These variants were in a locus consisting of 52 variants in high linkage disequilibrium (r2 ≥ 0.8) near glucocorticoid receptor-binding sites by the gene BIRC3. Variants were also BIRC3 expression quantitative trait loci in lung, and 2 were within/near putative glucocorticoid response element motifs. BIRC3 had increased RNA polymerase II occupancy and gene expression, with glucocorticoid exposure in 2 ChIP-Seq and 13 transcriptomic data sets. Some BIRC3 variants in the 52-variant locus were associated (P < .05) with ICS response in 3 independent studies and others with asthma in 1 study. CONCLUSIONS: BIRC3 should be prioritized for further functional studies of ICS response.
BACKGROUND: Inhaled corticosteroid (ICS) response among patients with asthma is influenced by genetics, but biologically actionable insights based on associations have not been found. Various glucocorticoid response omics data sets are available to interrogate their biological effects. OBJECTIVE: We sought to identify functionally relevant ICS-response genetic associations by integrating complementary multiomics data sets. METHODS: Variants with P values less than 10-4 from a previous ICS-response genome-wide association study were reranked on the basis of integrative scores determined from (1) glucocorticoid receptor- and (2) RNA polymerase II-binding regions inferred from ChIP-Seq data for 3 airway cell types, (3) glucocorticoid response element motifs, (4) differentially expressed genes in response to glucocorticoid exposure according to 20 transcriptomic data sets, and (5) expression quantitative trait loci from GTEx. Candidate variants were tested for association with ICS response and asthma in 6 independent studies. RESULTS: Four variants had significant (q value < 0.05) multiomics integrative scores. These variants were in a locus consisting of 52 variants in high linkage disequilibrium (r2 ≥ 0.8) near glucocorticoid receptor-binding sites by the gene BIRC3. Variants were also BIRC3 expression quantitative trait loci in lung, and 2 were within/near putative glucocorticoid response element motifs. BIRC3 had increased RNA polymerase II occupancy and gene expression, with glucocorticoid exposure in 2 ChIP-Seq and 13 transcriptomic data sets. Some BIRC3 variants in the 52-variant locus were associated (P < .05) with ICS response in 3 independent studies and others with asthma in 1 study. CONCLUSIONS: BIRC3 should be prioritized for further functional studies of ICS response.
Authors: Amber Dahlin; Joanne E Sordillo; John Ziniti; Carlos Iribarren; Meng Lu; Scott T Weiss; Kelan G Tantisira; Quan Lu; Mengyuan Kan; Blanca E Himes; Eric Jorgenson; Ann Chen Wu Journal: J Allergy Clin Immunol Date: 2018-12-20 Impact factor: 10.793
Authors: Mark N Kvale; Stephanie Hesselson; Thomas J Hoffmann; Yang Cao; David Chan; Sheryl Connell; Lisa A Croen; Brad P Dispensa; Jasmin Eshragh; Andrea Finn; Jeremy Gollub; Carlos Iribarren; Eric Jorgenson; Lawrence H Kushi; Richard Lao; Yontao Lu; Dana Ludwig; Gurpreet K Mathauda; William B McGuire; Gangwu Mei; Sunita Miles; Michael Mittman; Mohini Patil; Charles P Quesenberry; Dilrini Ranatunga; Sarah Rowell; Marianne Sadler; Lori C Sakoda; Michael Shapero; Ling Shen; Tanu Shenoy; David Smethurst; Carol P Somkin; Stephen K Van Den Eeden; Lawrence Walter; Eunice Wan; Teresa Webster; Rachel A Whitmer; Simon Wong; Chia Zau; Yiping Zhan; Catherine Schaefer; Pui-Yan Kwok; Neil Risch Journal: Genetics Date: 2015-06-19 Impact factor: 4.562
Authors: Kelan G Tantisira; Ross Lazarus; Augusto A Litonjua; Barbara Klanderman; Scott T Weiss Journal: Pharmacogenet Genomics Date: 2008-08 Impact factor: 2.089
Authors: Natalia Hernandez-Pacheco; Mario Gorenjak; Staša Jurgec; Almudena Corrales; Andrea Jorgensen; Leila Karimi; Susanne J Vijverberg; Vojko Berce; Maximilian Schieck; Marialbert Acosta-Herrera; Martin Kerick; Lesly-Anne Samedy-Bates; Roger Tavendale; Jesús Villar; Somnath Mukhopadhyay; Munir Pirmohamed; Katia M C Verhamme; Michael Kabesch; Daniel B Hawcutt; Steve Turner; Colin N Palmer; Esteban G Burchard; Anke H Maitland-van der Zee; Carlos Flores; Uroš Potočnik; Maria Pino-Yanes Journal: Allergy Date: 2020-09-16 Impact factor: 13.146
Authors: Michelle M Cloutier; Alan P Baptist; Kathryn V Blake; Edward G Brooks; Tyra Bryant-Stephens; Emily DiMango; Anne E Dixon; Kurtis S Elward; Tina Hartert; Jerry A Krishnan; Robert F Lemanske; Daniel R Ouellette; Wilson D Pace; Michael Schatz; Neil S Skolnik; James W Stout; Stephen J Teach; Craig A Umscheid; Colin G Walsh Journal: J Allergy Clin Immunol Date: 2020-12 Impact factor: 10.793