Research projects in the Ramachandran Lab
Research in the lab lies in the fields of evolutionary biology and population genetics. We use mathematical modeling, applied statistical methods, and computer simulations to make inferences about aspects of population histories from extant individuals’ genetic variation. The lab is currently pursuing many projects, including those in the following subtopics:
Human population genetic variation and the inference of human evolutionary history
Genotypes in extant humans contain signatures of events throughout our history as a species. We are interested in the geographic distribution of human genetic variation. We apply and develop statistical methods to learn about how, for example, humans peopled the globe after leaving Africa, and migrated in the Americas. Currently we are interested in the inference of local genomic ancestry for admixed individuals, as our collaboration with St. Jude Children’s Research Hospital has shown that genomic ancestry at certain loci can be a helpful prognostic for some traits related to cancer therapy outcome in children. Currently we are studying relationships between genetic and linguistic differentiation across the globe.
The evolution and population genetics of the X chromosome
The X chromosome is a particularly interesting chromosome due to its haplo-diploid existence in human populations; males carry one X chromosome, inherited from their mothers, while females carry two X chromosomes. Differences in patterns of genetic variation among the X and autosomes may reflect past differences between males and females in demographic parameters such as population size and migration rate; X-linked genes likely experience different levels of selection when in males compared to females. Currently we are interested in investigating evolutionary processes that shape X-linked genetic variation across different time-depths (e.g., comparing species, as well as different human populations).
The sequential Markovian coalescent
The coalescent is a retrospective model of population genetics, and provides a mathematical framework to describe the evolutionary history of a sample of chromosomes within a population. Currently, in collaboration with John Wakeley, we are developing methods to infer changes in effective population size and migration rates from multilocus sequence data.