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Webinar: Biostatistics and Bioinformatics Seminar – “Modeling Regulatory Network Topology Improves Genome-Wide Analyses of Complex Human Traits”
April 15, 12:00 pm to 1:00 pm
“Modeling Regulatory Network Topology Improves Genome-Wide Analyses of Complex Human Traits” will be presented by Xiang Zhu, PhD, assistant professor, Department of Statistics, Huck Institutes of the Life Sciences, Penn State.
Genome-wide association studies (GWAS) have cataloged many significant associations between genetic variants and complex traits. However, most of these findings have unclear biological significance, because they often have small effects and occur in non-coding regions. Integration of GWAS with gene regulatory networks addresses both issues by aggregating weak genetic signals within regulatory programs. In this presentation, Dr. Zhu describes the development of a Bayesian framework that integrates GWAS summary statistics with regulatory networks to infer genetic enrichments and associations simultaneously. This method improves upon existing approaches by explicitly modeling network topology to assess enrichments, and by automatically leveraging enrichments to identify associations. Applying this method to 18 human traits and 38 regulatory networks shows that genetic signals of complex traits are often enriched in interconnections specific to trait-relevant cell types or tissues. Prioritizing variants within enriched networks identifies known and new trait-associated genes revealing novel biological and therapeutic insights.
Participants may also join by calling 929-205-6099, meeting ID 96786475339. If prompted, enter passcode 013520.