Health Research and Policy


DATE: March 7, 2013
TIME: 1:15 - 3:00 pm
LOCATION: Medical School Office Building, Rm x303
TITLE: Human Genetic Geography - Evolving Statistical Learning and Prediction
SPEAKER: Mathew Barber, Statistical Geneticist, in San Francisco

Genealogy, the study of family history, is the second most popular hobby in the US.
Decreases in the costs of genetic testing technologies mean that genealogical discovery via genotyping data is now affordable for the vast majority of genealogical researchers.

Genetic data provides unique insights into family history.
Firstly, people can identify relatives that they did not previously know about (and may never have a paper trail to). Secondly, people can discover connections with regions of the world that they share ancestry with, which we call genetic geography.

In the presentation we will focus on the statistical methods for genetic geography.
We will describe the background of the problem we are trying to solve with genetic geography, the challenges that are faced, the nuances of the available genotyping data and the reference populations data, and finally the statistical models that have been already successfully applied.

The statistical subjects that will be touched on are the uses of PCA, clustering algorithms, high dimensional optimization and penalized likelihood.

Suggested readings:
Novembre, J., Ramachandran, S., Perspectives on Human Population Structure at the Cusp of the Sequencing Era> Annual Review of Genomics and Human Genetics 12:, 245-274 (2011).

(Note: We will not be talking about sequence data, but this is also a great review of the results from SNP genotyping).

Stanford Medicine Resources:

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