Analytical challenges in omics research on asthma and allergy: A National Institute of Allergy and Infectious Diseases workshop

Supinda Bunyavanich, Patrice M. Becker, Matthew C. Altman, Jessica Lasky-Su, Carole Ober, Karsten Zengler, Evgeny Berdyshev, Richard Bonneau, Talal Chatila, Nilanjan Chatterjee, Kian Fan Chung, Colleen Cutcliffe, Wendy Davidson, Gang Dong, Gang Fang, Patricia Fulkerson, Blanca E. Himes, Liming Liang, Rasika A. Mathias, Shuji OginoJoseph Petrosino, Nathan D. Price, Eric Schadt, James Schofield, Max A. Seibold, Hanno Steen, Lisa Wheatley, Hongmei Zhang, Alkis Togias, Kohei Hasegawa

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Studies of asthma and allergy are generating increasing volumes of omics data for analysis and interpretation. The National Institute of Allergy and Infectious Diseases (NIAID) assembled a workshop comprising investigators studying asthma and allergic diseases using omics approaches, omics investigators from outside the field, and NIAID medical and scientific officers to discuss the following areas in asthma and allergy research: genomics, epigenomics, transcriptomics, microbiomics, metabolomics, proteomics, lipidomics, integrative omics, systems biology, and causal inference. Current states of the art, present challenges, novel and emerging strategies, and priorities for progress were presented and discussed for each area. This workshop report summarizes the major points and conclusions from this NIAID workshop. As a group, the investigators underscored the imperatives for rigorous analytic frameworks, integration of different omics data types, cross-disciplinary interaction, strategies for overcoming current limitations, and the overarching goal to improve scientific understanding and care of asthma and allergic diseases.

Original languageEnglish
Pages (from-to)954-968
Number of pages15
JournalJournal of Allergy and Clinical Immunology
Issue number4
StatePublished - Apr 2024


  • Omics
  • allergic diseases
  • allergy
  • asthma
  • causal inference
  • epigenomics
  • genomics
  • integrative
  • lipidomics
  • metabolomics
  • microbiome
  • proteomics
  • systems biology
  • transcriptomics


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