Kathryn Roeder

About

Kathryn Roeder began her career as a biologist but transitioned into statistics because every question that interested her required solving intriguing statistical puzzles. Her work, both theoretical and applied, remains motivated by her scientific training. She has enjoyed collaborative research primarily in the area of statistical genetics and genomics. Currently, she is particularly interested in applying statistical tools to genetic and genomic data to understand the workings of the human brain and its interplay with genetic variation. For many years, a primary goal of her research group has been to develop statistical tools for finding associations between patterns of genetic variation and complex diseases. Recently, her collaborative work has focused on understanding the genetic etiology of autism and other neuropsychiatric disorders. Her group develops new tools for analyzing rare genetic variants in the genome, single-cell RNA sequencing data, and other multi-omic data. These methods utilize various statistical and machine learning techniques, including graphical modeling, network community estimation and latent space embedding, sparse PCA, and high-dimensional nonparametric methods.

Research topics

• Biology
• Genetics
• Computational biology
• Artificial Intelligence
• Evolutionary biology
• Developmental psychology
• Data Mining
• Computer Science
• Neuroscience
• Theoretical computer science

Selected publications

• Large-Scale Exome Sequencing Study Implicates Both Developmental and Functional Changes in the Neurobiology of Autism

  Cell · 2020 · 2401 citations

  We present the largest exome sequencing study of autism spectrum disorder (ASD) to date (n = 35,584 total samples, 11,986 with ASD). Using an enhanced analytical framework to integrate de novo and case-control rare variation, we identify 102 risk genes at a false discovery rate of 0.1 or less. Of these genes, 49 show higher frequencies of disruptive de novo variants in individuals ascertained to have severe neurodevelopmental delay, whereas 53 show higher frequencies in individuals ascertained t…

  DOI

• Rare coding variation provides insight into the genetic architecture and phenotypic context of autism

  Nature Genetics · 2022 · 602 citations

  PublisherOA PDFDOI

• The huge Package for High-dimensional Undirected Graph Estimation in R

  arXiv (Cornell University) · 2020 · 487 citations

  We describe an R package named huge which provides easy-to-use functions for estimating high dimensional undirected graphs from data. This package implements recent results in the literature, including Friedman et al. (2007), Liu et al. (2009, 2012) and Liu et al. (2010). Compared with the existing graph estimation package glasso, the huge package provides extra features: (1) instead of using Fortan, it is written in C, which makes the code more portable and easier to modify; (2) besides fitting…

  PublisherOA PDFDOI

• Whole-Genome and RNA Sequencing Reveal Variation and Transcriptomic Coordination in the Developing Human Prefrontal Cortex

  Cell Reports · 2020 · 153 citations

  Gene expression levels vary across developmental stage, cell type, and region in the brain. Genomic variants also contribute to the variation in expression, and some neuropsychiatric disorder loci may exert their effects through this mechanism. To investigate these relationships, we present BrainVar, a unique resource of paired whole-genome and bulk tissue RNA sequencing from the dorsolateral prefrontal cortex of 176 individuals across prenatal and postnatal development. Here we identify common…

  DOI

• Causal Inference for Genomic Data with Multiple Heterogeneous Outcomes

  Journal of the American Statistical Association · 2025-02-24 · 3 citations

  articleOpen accessSenior authorCorresponding

  With the evolution of single-cell RNA sequencing techniques into a standard approach in genomics, it has become possible to conduct cohort-level causal inferences based on single-cell-level measurements. However, the individual gene expression levels of interest are not directly observable; instead, only repeated proxy measurements from each individual's cells are available, providing a derived outcome to estimate the underlying outcome for each of many genes. In this paper, we propose a generic…

  PublisherOA PDFDOI

Recent grants

• Methods for single-cell CRISPR screens and multiomic data: constructing powerful well-calibrated tests, circumventing unmeasured confounding, and accounting for denoising and imputation

  NIH · $3.8M · 2020–2029

• NIH Grant R01MH109900

  NIH · $578k · 2019

Frequent coauthors

• Bernie Devlin

  University of Pittsburgh

  193 shared

• Joseph D. Buxbaum

  Icahn School of Medicine at Mount Sinai

  101 shared

• Mark J. Daly

  Massachusetts General Hospital

  91 shared

• Lambertus Klei

  University of Pittsburgh

  89 shared

• Benjamin M. Neale

  Massachusetts General Hospital

  57 shared

• Michael E. Talkowski

  Harvard University

  50 shared

• Stephan Sanders

  University of California, San Francisco

  46 shared

• Catalina Betancur

  Institut de Biologie Paris-Seine

  39 shared

Education

• PhD, Statistics

  Pennsylvania State University

  1988

Similar researchers at Carnegie Mellon University

• Jian Mah-60
• Min Xuh-48
• Carl Kingsfordh-40