Hong-Wen Deng, PhD

Professor, Aron Family Endowed Chair

Director, Center for Biomedical Informatics & Genomics, Director, Tulane Integrated Institute of Data & Health Sciences (TIIDHS)
(504) 988-1310
Office Address
1440 Canal Street, Suite 1621, New Orleans, LA 70112
School of Medicine
Hong-Wen Deng, PhD

Education & Affiliations

Postdoctoral research, molecular and statistical population/quantitative genetics, Human Genetics Center, University of Texas, Houston, TX
PhD, Quantitative Genetics, University of Oregon, Eugene, OR
MS, Mathematical Statistics, University of Oregon, Eugene, OR
MS, Ecology & Entomology, Peking University, Beijing, P. R. China
BS, Ecology & Environmental Biology, Peking University, Beijing, P. R. China

Areas of Expertise

Aging and age-related diseases of the skeleton such as osteoporosis


Dr. Hong-Wen Deng is a researcher/educator with extensive multi-/inter-disciplinary expertise in biostatistics and bioinformatics methodology research, big data, genomics, transcriptomics, epigenomics, proteomics, genetic epidemiology, complex traits and diseases (especially osteoporosis, sarcopenia, and obesity), system biology, endocrinology, bone biology and recently metabolomics and metagenomics. Dr. Deng’s work is published in nearly 600 peer-reviewed publications including journals such as Nature, New England Journal of Medicine, American Journal of Human Genetics, Endocrine Review, Plos Genetics, Human Molecular Genetics, Molecular Psychiatry, Bioinformatics, and Molecular Cell Proteomics. As of September 2020, these publications have been cited more than 21,382 times and his H-index is 72 and i10 index is 411. Dr, Deng’s primary research interests include all those areas that are related addressing the question: What and how genetic and environmental factors incur higher risk of, or better protection against, complex diseases, such as osteoporosis, in different sex and ethnic groups. Theoretical (e.g., methodology development in biostatistics/bioinformatics) and empirical (e.g., clinical data collection and wet/dry labs data collection/management/processing/analyses) approaches are used. He is also interested in generating, analyzing and integrating big data of various omics in vivo in humans. The purpose is to elucidate how DNA variants affect gene expression/regulation and protein expression/modification in the form of functional networks/modules/pathways and how the knowledge gained on these molecular mechanisms in humans would translate into better prediction/intervention/precision medicine and drug development. Dr. Deng has won numerous NIH grants, including R01 grants and a U19 program grant and has advised/mentored more than 100 graduate students and faculty. Many of his mentees have become tenured professors winning their own grants with their own research centers/programs and some become academic and industrial administrators such as associate deans, department chairs and vice president of genomics companies.


  • Member, Genetics Society of American
  • Member, American Society of Human Genetics (ASHG)
  • Member, American Society for Bone and Mineral Research (ASBMR)
  • Member, International Genetic Epidemiology Society
  • Member, Great Plains States Society for Molecular Biology and Genetics
  • Member, International Chinese Hard Tissue Society (ICHTS)
  • Member, International Society of Musculoskeletal and Neuronal Interactions (ISMNI)
  • Member, International Society of Clinical Densitometry (ISCD)

Level of Instruction:



  • Multi-omics (Including Metagenomics) and Trans-omics
  • Biostatistics and Bioinformatics
  • Genetics of Complex Diseases
  • Single Cell Sequencing
  • Big Data
  • Machine Learning (Including Deep Learning)
  • Data Analytics
  • Osteoporosis, Sarcopenia, Obesity, Alzheimer's Disease, and Other Complex Diseases
  • Data Sciences, AI
  • Electronic Health Records
  • Single cell seq and spatial omics

Center Section: Center Director
Our research group is interested in genetic dissection of human complex diseases using the state-of-the-art multi- and inter-disciplinary approaches of genomic technologies, and statistical and bioinformatical methods. The complex disease we are focusing on is osteoporosis, which is a prevalent, debilitating disorder characterized by bone fragility and an increased risk of low-trauma fractures. The approaches we are using involve genome-wide association analyses, genome-wide transcriptome analyses, proteome-wide protein expression profiling and in vivo and in vitro functional analyses of specific genes of interest. Currently, we are funded by several NIH grants for genetic, genomic and proteomic research of osteoporosis.

Using the same genomic strategies, we are extending our research to other human complex diseases, including obesity, sarcopenia and periodontitis, and other human complex traits, including age at menarche/menopause and behavior traits, e.g., smoking, drinking and exercise. We are also extending our research of complex diseases from genome to epigenome, e.g., we are performing epigenome-wide profiling of osteoporosis at micro-RNA, DNA phosphorylation and histone modification levels. In particular, we are interested in developing novel statistical methods and bioinformatics tools for analyzing and managing large, complex datasets in genomic and epigenomic research.

Through Louisiana Osteoporosis Study (LOS), we are building a large research cohort and database for human complex disease studies. The LOS will enroll >20,000 subjects of different ethnicities in New Orleans, Baton Rouge and their surrounding areas. Each subject will be phenotyped for body composition (including bone mineral density, lean and fat mass), muscle function, and blood pressure etc., and assayed for important health-related and life-style information. Their blood samples will be collected for extraction of DNA, RNA and proteins and for cell isolation and biobanking. The LOS will become a sample pool for selecting subjects of extreme phenotypes (e.g., extremely high vs. low bone mass) for our ongoing funded and future genomic and epigenomic studies.  



Omics, Biostatistics and Bioinformatics, Genetics of Complex Diseases