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Steve Horvath

Human Genetics and Biostatistics David Geffen School of Medicine, University of California, Los Angeles, USA

Dr. Horvath's research lies at the intersection of aging research, epidemiology, chronic diseases, epigenetics, genetics, and systems biology. He works on all aspects of biomarker development with a particular focus on genomic biomarkers of aging. He developed a highly accurate multi-tissue biomarker of aging known as the epigenetic clock.  Dr Horvath developed systems biologic approaches such as weighted gene co-expression network analysis which lend themselves for integrating gene genomic data sets. These methods have been used for a broad spectrum of age related diseases including neurodegenerative diseases, cancer, cardiovascular disease. Dr. Horvath received a Ph.D. in Mathematics from the University of North Carolina, Chapel Hill in 1995 and a Doctorate of Science in Biostatistics from the Harvard School of Public Health in 2000.


New Epigenetic Clocks

DNA methylation based biomarkers of aging known as collectively as "epigenetic clock" can be used to measure the age of any human or chimpanzee tissue, cell type, or fluid that contains DNA. DNA methylation age captures aspects of biological age. The skin & blood clock (based on 391 CpGs) is tailor-made for human fibroblasts, keratinocytes, buccal cells, endothelial cells, lymphoblastoid cells, skin, blood, and saliva samples. Gestational age correlates with DNAm age in cord blood. When used on fibroblasts from Hutchinson Gilford Progeria Syndrome patients, this age estimator (referred to as the skin & blood clock) uncovered an epigenetic age acceleration with a magnitude that is below the sensitivity levels of other DNAm-based biomarkers. Arguably the strongest predictor of lifespan, DNAm GrimAge, is a composite biomarker based on seven DNAm surrogates of plasma protein levels and a DNAm-based estimator of smoking pack-years. Using large-scale validation data from thousands of individuals, we demonstrated that DNAm GrimAge stands out among existing epigenetic clocks in terms of its predictive ability for time-to-death ( P=2.0E-75), time-to-coronary heart disease (P=6.2E-24), time-to-cancer (P=1.3E-12), its strong relationship with computed tomography  for fatty liver/excess visceral fat, and age-at-menopause (P=1.6E-12). Overall, these epigenetic biomarkers are expected to find many applications including human anti-aging studies.