All Publications


  • Chromatin and gene-regulatory dynamics of the developing human cerebral cortex at single-cell resolution. Cell Trevino, A. E., Müller, F., Andersen, J., Sundaram, L., Kathiria, A., Shcherbina, A., Farh, K., Chang, H. Y., Pașca, A. M., Kundaje, A., Pașca, S. P., Greenleaf, W. J. 2021

    Abstract

    Genetic perturbations of cortical development can lead to neurodevelopmental disease, including autism spectrum disorder (ASD). To identify genomic regions crucial to corticogenesis, we mapped the activity of gene-regulatory elements generating a single-cell atlas of gene expression and chromatin accessibility both independently and jointly. This revealed waves of gene regulation by key transcription factors (TFs) across a nearly continuous differentiation trajectory, distinguished the expression programs of glial lineages, and identified lineage-determining TFs that exhibited strong correlation between linked gene-regulatory elements and expression levels. These highly connected genes adopted an active chromatin state in early differentiating cells, consistent with lineage commitment. Base-pair-resolution neural network models identified strong cell-type-specific enrichment of noncoding mutations predicted to be disruptive in a cohort of ASD individuals and identified frequently disrupted TF binding sites. This approach illustrates how cell-type-specific mapping can provide insights into the programs governing human development and disease.

    View details for DOI 10.1016/j.cell.2021.07.039

    View details for PubMedID 34390642

  • Predicting the clinical impact of human mutation with deep neural networks (vol 50, pg 1161, 2018) NATURE GENETICS Sundaram, L., Gao, H., Padigepati, S., McRae, J. F., Li, Y., Kosmicki, J. A., Fritzilas, N., Hakenberg, J., Dutta, A., Shon, J., Xu, J., Batzoglou, S., Li, X., Farh, K. 2019; 51 (2): 364
  • Author Correction: Predicting the clinical impact of human mutation with deep neural networks. Nature genetics Sundaram, L., Gao, H., Padigepati, S. R., McRae, J. F., Li, Y., Kosmicki, J. A., Fritzilas, N., Hakenberg, J., Dutta, A., Shon, J., Xu, J., Batzoglou, S., Li, X., Farh, K. K. 2018

    Abstract

    In the version of this article originally published, the name of author Serafim Batzoglou was misspelled. The error has been corrected in the HTML and PDF versions of the article.

    View details for PubMedID 30559491

  • Predicting the clinical impact of human mutation with deep neural networks NATURE GENETICS Sundaram, L., Gao, H., Padigepati, S., McRae, J. F., Li, Y., Kosmicki, J. A., Fritzilas, N., Hakenberg, J., Dutta, A., Shon, J., Xu, J., Batzloglou, S., Li, X., Farh, K. 2018; 50 (8): 1161-+

    Abstract

    Millions of human genomes and exomes have been sequenced, but their clinical applications remain limited due to the difficulty of distinguishing disease-causing mutations from benign genetic variation. Here we demonstrate that common missense variants in other primate species are largely clinically benign in human, enabling pathogenic mutations to be systematically identified by the process of elimination. Using hundreds of thousands of common variants from population sequencing of six non-human primate species, we train a deep neural network that identifies pathogenic mutations in rare disease patients with 88% accuracy and enables the discovery of 14 new candidate genes in intellectual disability at genome-wide significance. Cataloging common variation from additional primate species would improve interpretation for millions of variants of uncertain significance, further advancing the clinical utility of human genome sequencing.

    View details for PubMedID 30038395

  • Working toward precision medicine: Predicting phenotypes from exomes in the Critical Assessment of Genome Interpretation (CAGI) challenges HUMAN MUTATION Daneshjou, R., Wang, Y., Bromberg, Y., Bovo, S., Martelli, P. L., Babbi, G., Di Lena, P., Casadio, R., Edwards, M., Gifford, D., Jones, D. T., Sundaram, L., Bhat, R., Li, X., Pal, L. R., Kundu, K., Yin, Y., Moult, J., Jiang, Y., Pejaver, V., Pagel, K. A., Li, B., Mooney, S. D., Radivojac, P., Shah, S., Carraro, M., Gasparini, A., Leonardi, E., Giollo, M., Ferrari, C., Tosatto, S. E., Bachar, E., Azaria, J. R., Ofran, Y., Unger, R., Niroula, A., Vihinen, M., Chang, B., Wang, M. H., Franke, A., Petersen, B., Pirooznia, M., Zandi, P., McCombie, R., Potash, J. B., Altman, R. B., Klein, T. E., Hoskins, R. A., Repo, S., Brenner, S. E., Morgan, A. A. 2017; 38 (9): 1182–92

    Abstract

    Precision medicine aims to predict a patient's disease risk and best therapeutic options by using that individual's genetic sequencing data. The Critical Assessment of Genome Interpretation (CAGI) is a community experiment consisting of genotype-phenotype prediction challenges; participants build models, undergo assessment, and share key findings. For CAGI 4, three challenges involved using exome-sequencing data: Crohn's disease, bipolar disorder, and warfarin dosing. Previous CAGI challenges included prior versions of the Crohn's disease challenge. Here, we discuss the range of techniques used for phenotype prediction as well as the methods used for assessing predictive models. Additionally, we outline some of the difficulties associated with making predictions and evaluating them. The lessons learned from the exome challenges can be applied to both research and clinical efforts to improve phenotype prediction from genotype. In addition, these challenges serve as a vehicle for sharing clinical and research exome data in a secure manner with scientists who have a broad range of expertise, contributing to a collaborative effort to advance our understanding of genotype-phenotype relationships.

    View details for DOI 10.1002/humu.23280

    View details for Web of Science ID 000407861100014

    View details for PubMedID 28634997

    View details for PubMedCentralID PMC5600620