Recently the largest exome sequencing study to date of autism spectrum disorder (ASD) implicated 102 genes in risk. An innovative statistical approach was required to obtain this breakthrough for a disorder that has been unusually challenging to unravel. 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, both single cell and bulk RNA-sequencing of brain tissues, can be a critical step in such investigations. We describe our statistical approaches to understand how cells develop in the brain, identifying both when and where these risk genes are primarily active. Together, our methods and results can broaden our understanding of the neurobiology of ASD.
Kathryn Roeder is the UPMC Professor of Statistics and Life Sciences in the Departments of Statistics and Data Science and Computational Biology. Dr. Roeder 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). She is an elected fellow of the American Statistical Association, the Institute of Mathematical Statistics and AAAS. In 2019 she was elected to the National Academy of Sciences.