UTCS Colloquia/AI - Alexis Battle/Stanford University, "Combining data and networks to unravel the genetics of complex traits," ACES 2.302

Contact Name: 
Jenna Whitney
May 4, 2012 11:00am - 12:00pm



Type o

f Talk: UTCS Colloquia/AI

Speaker/Affiliation: Alexis Battle/Stanford


Talk Audience: UTCS Faculty, Graduate Students, Undergrad

uate Students and Outside Interested Parties

Date/Time: Friday, May 4

, 2012, 11:00 am

Location: ACES 2.302

Host: Ray Mooney


k Title: Combining data and networks to unravel the genetics of complex tra


Talk Abstract: Complex traits, including many human diseases, ar

e affected by multiple genetic elements, often working together in intrica

te networks and pathways. Recent technologies have allowed us to collect de

tailed genetic profiles on a large scale, opening up the possibility of id

entifying the genes and pathways disrupted in many diseases. However, unta

ngling genetic factors from such data has presented significant statistical
challenges. In particular, available studies are often underpowered, wit

h up to millions of candidate genetic elements, but only hundreds or thous

ands of individuals.

In this talk, I will discuss machine learning me

thods for inferring the effects of genetic variation on complex traits, ba

sed on structured probabilistic models. We model the effects of multiple ge

netic elements jointly on a given trait, and leverage known relationships

among genes, incorporating gene networks as structured prior distributions

. Thus, our models will preferentially identify multiple genetic elements

which are known to be functionally connected, reflecting a more biological

ly plausible model of many complex traits, and providing improved statisti

cal power. I will discuss the use of these methods in identifying genetic v

ariants relevant to autoimmune diseases, and in identifying important inte

ractions between genes in cancer survival.

Speaker Bio: Alexis Battle

is a PhD candidate in Computer Science at Stanford University. Her research
in computational biology focuses on machine learning and probabilistic mod

els for the genetics of complex traits. Alexis received her BS in Symbolic

Systems from Stanford, and spent four years as a member of the technical s

taff at Google. She is the recipient of an NSF Graduate Research Fellowship
and an NIH NIGMS training award.