Steven Brenner, PhD
Adjunct Professor of Bioengineering and Therapeutic Sciences
Department of Molecular & Cell Biology, UC Berkeley
Graduate Program Membership: BMI
Dr. Brenner is a computational biologist with a variety of interests spanning from cell biology to human genetics. Areas of current interest include gene regulation by alternative splicing and nonsense-mediated mRNA decay; prediction of protein function using Bayesian phylogenetics; medical and environmental metagenomis; structural genomics and protein complexes; and application of next generation sequencing in the clinical genetic setting.
Rahul Deo, MD, PhD
Associate Professor of Medicine, Cardiovascular Research Institute
Graduate Program Membership: BMS
Dr. Deo studies how genetic variation contributes to the pathogenesis of cardiovascular and metabolic disease, especially disorders of lipid metabolism and diseases of cardiac muscle (cardiomyopathies). Genetic variants may influence disease predisposition by altering the molecular responses to cellular stimuli, such as circulating lipoproteins, hormones, or fatty acids. To address this hypothesis, he employs machine learning to prioritize likely causal genes from deep sequencing data from family studies; examines epigenetic determinants of mature metabolic cellular phenotypes; deciphers the mechanistic basis for the influence of variation on gene expression; and develops network models to explain the molecular consequences of cellular perturbagens.
Aaron Diaz, PhD
Assistant Professor of Neurological Surgery
Graduate Program Membership: BMS
Dr. Diaz studies the role of neurodevelopmental programs in malignant gliomas, using computational and systems approaches. By contrasting genetic and epigenetic signatures between the developing human cortex and human brain tumors, Dr. Diaz is elucidating developmental pathways that are aberrantly activated during oncogenesis, promoting tumor growth and self-renewal. The Diaz lab is currently using high-throughput, single-cell assays, coupled with modern techniques from machine learning and data science, to identify therapeutic targets within these critical pathways.
Ryan Hernandez, PhD
Associate Professor of Bioengineering and Therapeutic Sciences
Graduate Program Membership: BioE, BMI, BMS, PSPG
Dr. Hernandez studies patterns of genetic variation from populations around the world using large scale sequencing data. Using detailed simulations and population genetic modeling, his laboratory seeks to understand the role that natural selection and demography have had on the patterning of variation throughout our genomes.
Tom Hoffmann, PhD
Associate Professor of Epidemiology & Biostatistics
Graduate Program Membership: ETS
Dr. Hoffmann is interested in statistical design and analysis methods for the genetical basis of a wide variety of human diseases. He has been involved in a number of projects to identify and characterize the genetic basis of common diseases, including the Kaiser Permanente Research Program on Genes, Environment and Health. He helped design and analyze the genotyping arrays used in that project, and has focused on analysis of cardiovascular outcomes including hyperlipidemia and hypertension, and has developing interest in age-related hearing loss. He has also developed new methods for analysis of NGS data.
Katherine Pollard, PhD
Senior Investigator, Gladstone Institutes
Professor of Epidemiology & Biostatistics
Graduate Program Membership: BMI, ETS
Dr. Pollard develops statistical and computational methods for the analysis of genomic datasets. Her research focuses on genome evolution, in particular identifying DNA sequences that differ significantly between or within species, and the sequences’ relationship to biomedical traits. Many of these sequences are non-coding, such as enhancers and RNA genes. The group aims to pinpoint specific DNA alterations in these sequences that are responsible for changes in gene expression. Current projects focus on two major areas: identifying the genetic basis for human-specific traits, such as our susceptibility to AIDS and atherosclerosis; and characterizing the human microbiome through metagenomic data.
Neil Risch, PhD
Lamond Family Foundation Distinguished Professor in Human Genetics
Professor of Epidemiology & Biostatistics
Director, Institute for Human Genetics
Graduate Program Membership: BMI, BMS, ETS, PSPG
Dr. Risch focuses on the development and application of statistical methods to address problems in human population genetics and genetic epidemiology. This has involved numerous projects using linkage analysis and positional cloning to identify novel disease genes, such as the genes causing hemochromatosis and torsion dystonia, as well as methodology for dissection of genetically complex traits including autism, hypertension, and multiple sclerosis. He has also spearheaded the approach of genome-wide association studies, the recent mainstay of human genetic analysis, and developed with investigators at Kaiser Permanente Northern California Division of Research a large genetic epidemiology research cohort on aging.
Stephan Sanders, BMBS, PhD
Assistant Professor, Department of Psychiatry
Graduate Program Membership: Neuroscience
Dr. Sanders is a geneticist and pediatrician who works on the genetics of childhood neurodevelopmental disorders, in particular Autism Spectrum Disorder (ASD). His lab specializes in bioinformatics, including microarray, exome sequencing, and whole-genome sequencing to identify genetic loci, map genomic architecture, and understand the sex bias seen in ASD. One major goal is to identify specific genes that contribute to these disorders, for example the sodium channel gene SCN2A is commonly mutated in ASD, as an entrée into the underlying biology.
Mark Segal, PhD
Professor of Epidemiology and Biostatistics
Director, Center for Bioinformatics and Molecular Biostatistics
Graduate Program Membership: BMI, PSPG, ETS
Dr. Segal focuses on the development and application of statistical methods to address problems in computational biology and genomics. He has devised methods for addressing several aspects of analyzing data deriving from high-throughput biotechnologies, straddling low-level (e.g., pre-processing) to high-level (e.g., linked survival phenotypes, regulatory module elicitation) approaches. He is currently engaged in developing and comparing methods for inferring 3D genome architecture utilizing data from chromatin conformation capture assays.
Jeff Wall, PhD
Dr. Wall’s research spans a wide range of topics in evolutionary and human genetics, including models of speciation, inference of population history from sequence polymorphism data, and analyses of whole genome association study data in admixed populations. Recent studies have focused on the contribution of archaic hominid ancestry in human populations.
John Witte, PhD
Professor of Epidemiology and Biostatistics and of Urology
Co-Leader, Program in Cancer Genetics, Helen Diller Cancer Center
Graduate Program Membership: ETS, iPQB, PSPG
Dr. Witte’s research constitutes applied and methodologic genetic epidemiology, with the aim of deciphering the mechanisms underlying complex diseases. His applied work is primarily focused on prostate cancer, while much of his methodologic work is on association studies and hierarchical modeling. Dr. Witte initiated a series of prostate cancer genetic epidemiology studies, which have had numerous successes toward sorting out the genetic basis of this disease. In particular, using a combination of genome-wide scan, allelic imbalance, and association studies, Dr. Witte and colleagues have isolated distinct chromosomal regions that appear to harbor genes that cause prostate cancer.
Noah Zaitlen, PhD
Dr. Zaitlen develops statistical and computational tools to understand the genetic basis of phenotypes. He is especially interested in human disease, variation in drug/treatment response, and outcomes. Ongoing projects primarily focus on incorporating environmental context into medical genetics. These include developing novel techniques to partition the proportion of phenotype driven by genetic and environmental factors in world-wide populations (Nature versus Nurture), and improving the power to identify disease causing mutations by leveraging gene-expression, meta-genomic, and clinical data (e.g., smoking status, BMI, and age). His work aims to improve the understanding of disease and contribute to human health.