Computational human genomics and data science @MountSinai

The Do lab uses computational genomic approaches to understand the genomic, biological and clinical basis of human diseases

We pursue this research by developing and applying methods in statistical computing, machine learning, genetic epidemiology, statistical genetics and population genetics to large-scale human genotyping, sequencing, functional and clinical datasets. Research areas of interest include: 1) empowering discovery of disease-associated genomic loci through development and application of statistical methods; 2) inferring causal relationships between risk factors and disease using Mendelian randomization; 3) evaluating genetic pleiotropy; 4) evaluating the impact of natural selection and genetic load on human biology and disease; 5) interpreting genetic association links between genes and diseases in electronic health record-linked biobanks; 6) informing drug outcomes using human genomics; 7) evaluating clinical utility of genetic risk to disease; 8) evaluating disease prediction using multi-modal data, amongst others.