Kathryn Roeder, Ph.D.

UPMC Professor of Statistics and Life Sciences and Vice Provost, Carnegie Mellon University

SFARI Investigator Website

Kathryn Roeder is the UPMC Professor of Statistics and Life Sciences in the Department of Statistics and Data Science and vice provost for faculty at Carnegie Mellon University.

Roeder has developed statistical and machine learning methods in a wide spectrum of areas, including high-dimensional data problems. Her work focuses on statistical genetics and the genetic basis of complex disease. Recently, her group developed tools for identifying autism risk genes from de novo mutations as well as associated gene networks.

Roeder 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 and the COPSS Snedecor Award for outstanding work in statistical applications, two of the most prestigious awards in her field.

Funded Projects

Putting genes associated with autism in their neurobiological context by transcriptomic and proteomic analyses

Awarded: 2022
Award Type: Targeted: Genomics of ASD: Pathways to Genetic Therapies

SSC-ASC Whole-Genome Sequencing Consortium (project 2): Development of statistical methods

Awarded: 2018
Award Type: Research

Interactome perturbation screen to identity damaging de novo missense mutations in autism

Awarded: 2018
Award Type: Research

SFARI Funded Publications

Rare coding variation provides insight into the genetic architecture and phenotypic context of autism. Fu J.M., Satterstrom F.K., Peng M., Brand H., Collins R.L., Dong S., Wamsley B., Klei L., Wang L., Hao S.P., Stevens C.R., Cusick C., Babadi M., Banks E., Collins B., Dodge S., Gabriel S.B., Gauthier L., Lee S.K., Liang L., Ljungdahl A., Mahjani B., Sloofman L., Smirnov A., Barbosa M., Betancur C., Brusco A., Chung B.H.Y., Cook E. H., Cuccaro M.L., Domenici E., Ferrero G.B., Gargus J.J., Herman G.E., Hertz-Picciotto I., Maciel P., Manoach D., Passos-Bueno M.R., Persico A.M., Renieri A., Sutcliffe J., Tassone F., Trabetti E., Campos G., Cardaropoli S., Carli D., Chan M.C.Y., Fallerini C., Giorgio E., Girardi A.C., Hansen-Kiss E., Lee S.L., Lintas C., Ludena Y., Nguyen R., Pavinato L., Pericak-Vance M., Pessah I.N., Schmidt R. J., Smith M., Souza C.I.S., Trajkova S., Wang J.Y.T., Yu M.H.C., Autism Sequencing Consortium (ASC), Broad Institute Center for Common Disease Genomics (Broad-CCDG), iPSYCH-BROAD Consortium, Cutler D.J., Rubeis S.D., Buxbaum J., Daly M., Devlin B., Roeder K., Sanders S., Talkowski M.

SCEPTRE improves calibration and sensitivity in single-cell CRISPR screen analysis. Barry T., Wang X., Morris J.A., Roeder K., Katsevich E.

An approach to gene-based testing accounting for dependence of tests among nearby genes. Yurko R., Roeder K., Devlin B., G’Sell M.