Weekly Seminar: Amy Williams

January 24, 2025

Amy Williams

Details

When: January 30th, 2025

Where: TMCB 1170

Speaker: Amy Williams

Talk Title

Developing algorithms to aid DNA-based family history research

Abstract

Human genetic datasets have recently undergone an explosive growth in size, providing opportunities to study genetic diseases and to identify large numbers of genetic relatives. Many computational methods exist to infer relatives from genetic data, and we undertook studies to learn best practices for detecting pairs of relatives from DNA. All these methods estimate how much DNA individuals share in identity-by-descent (IBD) segments—regions they co-inherited from a common ancestor. We found that algorithms that analyze IBD segments explicitly, although slower than methods that consider many individual variants, perform better at classifying all but the closest relatives. Building on these insights, we developed algorithms that combine IBD segments from multiple relatives to (a) more precisely detect distant relatives in the software DRUID, and (b) distinguish relationships that have the same average amount of IBD in CREST (e.g., half-sibling, uncle-niece, and grandparent-grandchild pairs). Another less-prominent signal close relatives harbor in IBD segments is the sex of their shared ancestor, and CREST can also infer whether half-siblings and grandparent-grandchild pairs are maternally or paternally related, with accuracies >93%. The talk concludes with an outline of future plans to develop methods to better reconstruct genealogical trees for large numbers of genetic relatives.

Biography

Amy L. Williams is a Senior Scientist at 23andMe in the Product Research and Development department. Prior to joining industry in 2022, she was an Associate Professor of Computational Biology at Cornell University. She received her PhD (2010) and SM (2005) degrees in Computer Science from Massachusetts Institute of Technology, and BS (2003) in Computer Science and in Mathematics from the University of Utah. From 2009-2013 she worked as a postdoctoral research fellow at Harvard Medical School, and from 2013-2014 she was a postdoctoral research associate at Columbia University. Her research interests span the intersection of computer science and genetics and she is especially interested in characterizing genetic relatives in large datasets.