About

Our group is housed in the departments of Biomedical Data Science and Statistics at Stanford. We are interested in the statistical challenges presented by high-throughput biomedical data. We are members of the center for Computational, Evolutionary and Human Genomics, BioX, and the Stanford Cancer Institute. Chiara is also an associate director of the institute for Human-centered AI and the Data Science BS.

Research topics

• Biology
• Genetics
• Computer Science
• Medicine
• Pathology
• Cell biology
• Computational biology
• Internal medicine
• Machine Learning
• Data Mining
• Biophysics
• Immunology
• Statistics
• Biochemistry
• Virology
• Chemistry
• Materials science
• Neuroscience
• Nanotechnology
• Physics
• Evolutionary biology
• Mathematics

Selected publications

• Revealing enzyme functional architecture via high-throughput microfluidic enzyme kinetics

  Science · 2021 · 229 citations

  • Computer Science
  • Chemistry
  • Biochemistry

  Systematic and extensive investigation of enzymes is needed to understand their extraordinary efficiency and meet current challenges in medicine and engineering. We present HT-MEK (High-Throughput Microfluidic Enzyme Kinetics), a microfluidic platform for high-throughput expression, purification, and characterization of more than 1500 enzyme variants per experiment. For 1036 mutants of the alkaline phosphatase PafA (phosphate-irrepressible alkaline phosphatase of Flavobacterium), we performed more than 670,000 reactions and determined more than 5000 kinetic and physical constants for multiple substrates and inhibitors. We uncovered extensive kinetic partitioning to a misfolded state and isolated catalytic effects, revealing spatially contiguous regions of residues linked to particular aspects of function. Regions included active-site proximal residues but extended to the enzyme surface, providing a map of underlying architecture not possible to derive from existing approaches. HT-MEK has applications that range from understanding molecular mechanisms to medicine, engineering, and design.

  DOI

• Population-scale tissue transcriptomics maps long non-coding RNAs to complex disease

  Cell · 2021 · 212 citations

  • Biology
  • Genetics
  • Computational biology
  PublisherOA PDFDOI

• Determinants of telomere length across human tissues

  Science · 2020 · 486 citations

  • Biology
  • Genetics
  • Evolutionary biology

  Telomere shortening is a hallmark of aging. Telomere length (TL) in blood cells has been studied extensively as a biomarker of human aging and disease; however, little is known regarding variability in TL in nonblood, disease-relevant tissue types. Here, we characterize variability in TLs from 6391 tissue samples, representing >20 tissue types and 952 individuals from the Genotype-Tissue Expression (GTEx) project. We describe differences across tissue types, positive correlation among tissue types, and associations with age and ancestry. We show that genetic variation affects TL in multiple tissue types and that TL may mediate the effect of age on gene expression. Our results provide the foundational knowledge regarding TL in healthy tissues that is needed to interpret epidemiological studies of TL and human health.

  DOI

• Multi-resolution localization of causal variants across the genome

  Nature Communications · 2020 · 79 citations

  Senior authorCorresponding
  • Computer Science
  • Data Mining
  • Machine Learning

  In the statistical analysis of genome-wide association data, it is challenging to precisely localize the variants that affect complex traits, due to linkage disequilibrium, and to maximize power while limiting spurious findings. Here we report on KnockoffZoom: a flexible method that localizes causal variants at multiple resolutions by testing the conditional associations of genetic segments of decreasing width, while provably controlling the false discovery rate. Our method utilizes artificial genotypes as negative controls and is equally valid for quantitative and binary phenotypes, without requiring any assumptions about their genetic architectures. Instead, we rely on well-established genetic models of linkage disequilibrium. We demonstrate that our method can detect more associations than mixed effects models and achieve fine-mapping precision, at comparable computational cost. Lastly, we apply KnockoffZoom to data from 350k subjects in the UK Biobank and report many new findings.

  DOI

• Progenitor identification and SARS-CoV-2 infection in human distal lung organoids

  Nature · 2020 · 417 citations

  • Biology
  • Cell biology
  • Pathology
  PublisherOA PDFDOI

• Cell type–specific genetic regulation of gene expression across human tissues

  Science · 2020 · 583 citations

  • Biology
  • Genetics
  • Cell biology

  The Genotype-Tissue Expression (GTEx) project has identified expression and splicing quantitative trait loci in cis (QTLs) for the majority of genes across a wide range of human tissues. However, the functional characterization of these QTLs has been limited by the heterogeneous cellular composition of GTEx tissue samples. We mapped interactions between computational estimates of cell type abundance and genotype to identify cell type-interaction QTLs for seven cell types and show that cell type-interaction expression QTLs (eQTLs) provide finer resolution to tissue specificity than bulk tissue cis-eQTLs. Analyses of genetic associations with 87 complex traits show a contribution from cell type-interaction QTLs and enables the discovery of hundreds of previously unidentified colocalized loci that are masked in bulk tissue.

  DOI

• Progenitor identification and SARS-CoV-2 infection in long-term human distal lung organoid cultures

  bioRxiv (Cold Spring Harbor Laboratory) · 2020 · 27 citations

  • Virology
  • Medicine
  • Biology

  basal cells and resided in clusters within terminal bronchioles. To model COVID-19 distal lung disease, we everted the polarity of basal and alveolar organoids to rapidly relocate differentiated club and ciliated cells from the organoid lumen to the exterior surface, thus displaying the SARS-CoV-2 receptor ACE2 on the outwardly-facing apical aspect. Accordingly, basal and AT2 apical-out organoids were infected by SARS-CoV-2, identifying club cells as a novel target population. This long-term, feeder-free organoid culture of human distal lung alveolar and basal stem cells, coupled with single cell analysis, identifies unsuspected basal cell functional heterogeneity and exemplifies progenitor identification within a slowly proliferating human tissue. Further, our studies establish a facile in vitro organoid model for human distal lung infectious diseases including COVID-19-associated pneumonia.

  PublisherOA PDFDOI

Recent grants

• NIH Grant R01HG006695

  NIH · $1.0M · 2017

• The pursuit of genetic causal mechanisms

  NIH · $875k · 2021–2023

• Discovering What Matters: Informative and Reproducible Variable Selection with Applications to Genomics

  NSF · $420k · 2017–2020

• Scientific Findings across Multiple Environments: Replication, Robustness, and Equity in Genetic Association Studies

  NSF · $250k · 2022–2025

• NIH Grant R01MH101782

  NIH · $1.3M · 2018

Frequent coauthors

• Nelson B. Freimer

  University of California, Los Angeles

  29 shared

• Neil Risch

  University of California, San Francisco

  29 shared

• Emmanuel J. Candès

  29 shared

• Harley I. Kornblum

  25 shared

• Koppany Visnyei

  23 shared

• Eduard H. Panosyan

  Harbor–UCLA Medical Center

  22 shared

• Robert Damoiseaux

  22 shared

• Syuzanna Petrosyan

  22 shared

Education

• PhD, Statistics

  Stanford University

  1998