Douglas M. Ruderfer, PhD
Associate Professor , Division of Genetic Medicine, Department of Medicine
Developing analytical tools to elucidate the genetic architecture of psychiatric diseases and translating these findings to improve treatment. The lab has a strong focus in neuropsychiatric phenotypes and interests related to Structural variation and it’s role in regulating expression and disease risk Unraveling the genomic architecture of genetically related diseases Employing genomic information to improve drug design and treatment decisions Utilizing EHR/digital health informatics to objectively quantify mental health traits
Formally trained in computational biology and genetics, Dr. Ruderfer received his BS in computer engineering and MS in computer science from Johns Hopkins University and his PhD in genetics from Cardiff University. Dr. Ruderfer has spent the last ten years applying computational approaches to answering fundamental questions in genetics. He initially worked with Dr. Leonid Kruglyak at Princeton University developing methods and analyzing the genetics of gene expression, proteomics, drug response, populations, and evolution. Since then, he has worked closely with Drs. Shaun Purcell and Pamela Sklar at the Broad Institute, Massachusetts General Hospital, and Icahn School of Medicine at Mount Sinai on elucidating the genetic causes of psychiatric diseases such as schizophrenia and bipolar disorder. His work has contributed substantially to what is currently known about the genetic architecture of these diseases including seminal publications on the polygenic nature of these disorders. In particular, his work has provided integral contributions to the ability to analyze and assess the role of copy number variation to disease risk. He developed some of the earliest methods to analyze these data and demonstrated extensive contribution of this class of variation to schizophrenia risk. More recently, he has sought to integrate the genomic work with more expansive clinical data to identify opportunities for improved treatment utilizing genomic information. For example, his work recently identified a contribution of rare variation to antipsychotic response raising the promise that having genomic information could alter and improve treatment in a subset of patients with schizophrenia.