Scholar Profile

John Novembre

Assistant Professor
Ecology and Evolution Department
University of California, Los Angeles
621 Charles E. Young Dr. South
Los Angeles, CA 90095
Voice: 310-825-4065
Email: jnovembre@ucla.edu
Personal Homepage
2009 Searle Scholar

Research Interests

Methods for Spatial Population Genomics: Ancestry Inference and Population Stratification

The central area of interest of the lab is theoretical population genetics and statistical genetics. Specifically, projects focus on developing theory and statistical methods for analyzing genomic-scale population genetic data. Much of this work investigates questions in evolutionary genetics, focusing on human evolutionary history and using data from emerging genotyping and sequencing technologies.

A sample of on-going research interests are:

  • Population genetic methods and theory
  • Methods for studying population structure
  • The impact of population structure on genome-wide association studies and methods to correct for the effects of population structure
  • Inference of the relative strength of selection and dispersal based on the geographic spread of advantageous alleles

Human Population Genetics

  • Patterns of population structure in human populations especially within world regions and finer spatial-scales
  • The interaction of selection and demographic history in human evolutionary history
  • Correcting for population structure in human genome-wide association studies
  • Personalized genomics: Inference of detailed individual ancestry from genetic data

Population Genetics of Canids

In collaboration with Bob Wayne here at UCLA, we are using the Affymetrix canid genotyping array to study the population genetics of canids, especially North American arctic wolves. Arctic wolves, a subspecies of the more broadly distributed Gray Wolf, are distributed across a diversity of habitat types and show evidence of local adapatation to habitat (e.g. tundra vs. taiga forms). The project aims to describe the demographic history of populations from each of these different habitats and scan for regions that may have undergone selection and regions that are associated with ecologically relevant phenotypes.
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