SUMMARY
Wenan Chen, Ph.D., has an interdisciplinary background in biostatistics, bioinformatics and computer science. Research areas include statistical genetics, statistical genomics and computational biology. Dr. Chen's main research focus is on creating proper statistical and computational methods and tools for analyzing high-dimensional molecular data arising from biomedical areas. The goal is to identify and understand the links between underlying molecular markers and rare diseases, cancer and other complex conditions.
To make direct and substantial contributions to biology and medicine, Dr. Chen actively seeks close collaborations with basic scientists and clinicians. During these collaborations, he contributes to study design, helps plan analysis, evaluates different analysis methods, and finds new methods and tools to learn from the data.
Focus areas
- Collaborative omics data analyses. Collaborations in wet-lab-oriented basic science research, patient-oriented clinical research, and dry-lab-oriented quantitative or computational modeling and analysis research are critical for understanding and curing unsolved diseases. Dr. Chen has rich collaborative experience examining diverse types of omics data, including:
- Genomics data, such as germline and cancer genetics.
- Transcriptomic data, such as bulk and single-cell RNA sequencing.
- Epigenetics, such as methylation data.
Dr. Chen welcomes all collaborations — either within Mayo Clinic or with researchers at other institutions.
- Discovery of disease predisposition genes. Genes can predispose risk of certain disease, especially early-onset rare diseases and cancer. Dr. Chen discovers new disease predisposition genes for rare diseases and cancer. Examples of these conditions include pediatric cancers, congenital diseases and adult cancers such as breast cancer. To identify related genes, he combines large sample sizes of sequencing data, made up of both cases and controls. He also finds and uses proper genetic analysis methods, especially gene-based rare variant analysis.
- Functional data analysis of genetic variant effects. Identifying the effects of genetic variants is key to solving unsolved rare diseases and finding genes associated with disease risk. Gene editing technology lets scientists introduce all potential variations in a high throughput manner and study their effects on cell survival or fitness. Through collaborations with basic scientists, Dr. Chen evaluates and creates ways to analyze functional data. These methods maximize the power of these data to tell functional from nonfunctional genetic variants and to classify pathogenic and benign variants.
- Integration of multi-omics data. Different omics data may provide complementary information about the underlying biological states and different resolutions of distinct features. Integrating multiple types of omics data has the potential to provide a comprehensive approach for:
- Understanding the biological states and the mechanisms of diseases.
- Maximizing the power to detect associations between molecular data and phenotypes.
Dr. Chen aims to evaluate and create methods to integrate relevant multi-omics data for analyzing associations and predicting disease risks, prognoses or other important clinical metrics.
Significance to patient care
Collaboration on omics data analysis can provide new insight in understanding and assessing disease mechanisms and predicting treatment effects.
Discovering genes that predispose people to disease can help identify genetic causes and genetic risks of rare diseases and cancer. This also could help guide risk stratification and personalized treatment.
Functional analysis of genetic variants can aid in genetic variant classification. This can be used to assess people's risk of diseases such as breast cancer or prostate cancer. And it can guide further disease prevention or treatment.
Integration of multi-omics data brings all available omics technologies together to predict people's disease risk, provide accurate disease diagnosis, personalize treatment and advance precision medicine.