Analysis of brain cells using RNA-seq provides insights into autism

Seminar Series

Friday, January 22, 2021 - 03:30
Zoom Conference
Kathryn Roeder, PhD

Abstract: Recently the largest exome sequencing study to date of autism spectrum disorder (ASD) implicated 102 genes in risk. This risk gene set serves as a springboard for additional explorations into the etiological pathways of ASD, which can guide in the hunt for therapeutics.  Quantification of gene expression using single cell RNA-sequencing of brain tissues, can be a critical step in such investigations.   We describe statistical challenges encountered analyzing developing brain cells, including new methods for transfer learning and hierarchical reconstruction via reconciliation of multi-resolution cluer trees. 

Speaker: Kathryn Roeder, PhD
Professor of Statistics and Data Science
Carnegie Mellon University

Bio:  Dr. Roeder is the UPMC Professor of Statistics and Life Sciences in the Departments of Statistics and Data Science and Computational Biology. She has developed statistical and machine learning methods in a wide spectrum of areas, including high dimensional data problems in genetics. Her work focuses on statistical methods to reveal the genetic basis of complex disease. She is one of the leaders of the Autism Sequencing Consortium, an international organization dedicated to discovering the genetic etiology of autism. She received the Committee of Presidents of Statistical Societies (COPSS) Presidents’ Award (1997), Snedecor Award for outstanding work in statistical applications (1997) and Distinguished Achievement Award and Lectureship (2020). In 2013, she received the Janet L. Norwood Award for outstanding achievement by a woman in statistical sciences. She is an elected fellow of the American Statistical Association, the Institute of Mathematical Statistics and AAAS. In 2019 Dr. Roeder was elected to the National Academy of Sciences.

A primary goal of her research group is to develop statistical tools for finding associations between patterns of genetic variation and complex disease. To solve biologically relevant problems, they utilize modern statistical methods such as high dimensional statistics, statistical machine learning, nonparametric methods and networks. Data arises from primarily from Next Generation Sequencing and gene expression arrays. Their methodological work is motivated by our studies of schizophrenia, autism and other genetic disorders.


Meeting ID: 915 4596 8925

Passcode:  301685