From Big Data to Big Mind
Building Data-Driven Frameworks to Solve Complex Diseases
Personal Genomes + Clinical Records + Individual Lifestyles + Emerging Technologies
We use multi-omics approaches to delineate the convergent molecular networks in neurological diseases, and develop deep learning frameworks to directly predict clinical outcomes from personal genomes.
Li, J. et al. Identification of human neuronal protein complexes reveals biochemical activities and convergent mechanisms of action in autism spectrum disorders. Cell Systems 2015, 1(5): 361-374.
Li, J. et al. Integrated systems analysis reveals a molecular network underlying autism spectrum disorders. Molecular Systems Biology 2014, 10:774.
Preterm birth affects 15 million pregnancies worldwide every year, and is the leading cause for neonatal morbidity and mortality. This condition has a strong genetic component, and is hallmarked by a strong population disparity, e.g. affecting ~18% in African Americans, 6% in East Asians, and ~10% on average across the nation. We use genomic approaches to identify genetic elements in spontaneous preterm birth, and analyze their population differentiation. Integrating genomic information, health records and socioeconomic data, we aim to solve the stark health disparity problem , and achieve precision management for human pregnancies.
Li, J. et al. Fetal de novo mutations and preterm birth. PLoS Genetics 2017 13(4): e1006689.
Li, J. et al. Exome sequencing of neonatal blood spots identifies genes implicated in bronchopulmonary dysplasia. American Journal of Respiratory and Critical Care Medicine (AJRCCM) 2015, 192:589-596 (the Blue Journal).
In addition to our clinical research, we have keen interests in many fundamental questions in genome sciences. We study the structural organization of genes on biological networks, and use ChIP-Seq to delineate the epigenome landscapes of the developing brain and heart in humans. We also investigate many understudied areas in genomic medicine, with a focus on genomic alterations at the RNA level. We previously demonstrated the RNA binding specificity of human Argonaute proteins, and are now elucidating the mutational effects on RNA secondary structure and protein-RNA interaction.
Li, J., et al. Identifying mRNA sequence elements for target recognition by human Argonaute proteins. Genome Research 2014, 24(5):775-785.
Li, J. et al. Exploiting the determinants of stochastic gene expression in S. cerevisiae for genome-wide prediction of expression noise. PNAS 2010, 107(23): 10472-10477.
We build machine learning models to define personal genome baselines for the risk of cardiovascular diseases, and quantify the contribution from lifestyle adjustments to risk reduction. We also actively work on congenital heart diseases for a direct translation from genome sequences to clinical manifestations.
Li, J. et al. Decoding the genomics of abdominal aortic aneurysm. Cell 2018, 174: 1361-1372.
Each genomic locus has its own evolutionary trajectory, and we trace the evolutionary origin of disease-associated mutations back to archaic humans, primates, as well as distantly related vertebrate species. We are particularly interested in understanding the evolutionary trade-offs associated with local adaptation events during modern human evolution and migration, and these evolutionary "costs" will help us understand the origin of human diseases. We combine genomic analysis and iPSC techniques to model the mutational impacts on disease onset and progression.
Li, J. et al. Natural selection has differentiated the progesterone receptor among human populations. American Journal of Human Genetics (AJHG) 2018,103:1-13.
Li, J. et al. microRNA regulatory variation in human evolution. Trends in Genetics 2013, 29(2): 116-124.
Precision Health Management
Integrating genomic data and digitized physiological and behavioral profiles, we aim to detect diseases before symptoms emerge.
Li , J. et al. Gene-environment interaction in the era of precision medicine. Cell 2019, 177(1):38-44.