EE Student Information

EE Student Information, Spring Quarter 19-20: FAQs and Updated EE Course List.

Updates will be posted on this page, as well as emailed to the EE student mail list.

Please see Stanford University Health Alerts for course and travel updates.

As always, use your best judgement and consider your own and others' well-being at all times.

Workshop in Biostatistics welcomes Prof. Jonathan Pritchard

Topic: 
Why are human complex traits so enormously polygenic? Lessons from molecular biomarker traits.
Thursday, April 30, 2020 - 1:30pm
Venue: 
Zoom
Speaker: 
Prof. Jonathan Pritchard (Stanford Genetics and Biology)
Abstract / Description: 

One of the central challenges in genetics is to understand the mapping from genetic variation to phenotypic variation. During the past 15 years, genome-wide association studies (GWAS) have been used to study the genetic basis of a wide variety of complex diseases and other traits. One striking finding from this work has been that for a wide range of complex traits such as height or schizophrenia, even the most important loci in the genome contribute just a small fraction of the phenotypic variance. Instead, most of the variance comes from tiny contributions from tens to hundreds of thousands of variants spread across most of the genome. Our group has argued that these observations do not fit neatly into standard conceptual models of genetics. In recent papers, we have proposed one model to explain this, which we refer to as the "omnigenic" model.

In this talk, I will review our past work in this area, and describe new work that we have done on the genetic basis of three molecular traits---urate, IGF-1, and testosterone---that are biologically simpler than most diseases, and for which we know a great deal in advance about the core genes and pathways. For these molecular traits, we observe huge enrichment of significant signals near genes involved in the relevant biosynthesis, transport, or signaling pathways. However, even these molecular traits are highly polygenic, with most of the variance coming not from core genes, but from thousands to tens of thousands of variants spread across most of the genome. In summary, our models help to illustrate why so many variants affect risk for any given disease.


Contact kkanagaw@stanford.edu for required meeting password.