Yale University at ASHG 2026

Studies mitochondrial and nuclear genomes using computational and experimental methods and sequencing data. Develops mitochondrial variant-classification resources for disease-risk prediction.

Analyzes bulk, single-cell, multi-omic and spatial genomic data with statistical and AI methods. Works on genome annotation, disease genomics, protein structure and biomedical privacy.

Generates and analyzes CRISPR, single-cell sequencing, spatial transcriptomics and image-analysis data. Studies DNA damage, mutagenesis and cancer evolution in human cells, tissues and gastrointestinal disease.

Develops statistical methods for GWAS, single-cell, spatial transcriptomics, exome and genome sequencing data. Uses them for disease-gene discovery and genetic risk prediction.

Studies psychiatric genetics with GWAS, sequence-based and epigenetic analyses of biobank data. Works with the Million Veteran Program and Psychiatric Genomics Consortium.

Performs whole-genome and transcriptome RNA sequencing, whole-genome bisulfite sequencing, and single-nuclei RNA, ATAC, and DNA-methylation assays. Studies PTSD and major depression.

Applies big data analytics to molecular, clinical, imaging and electronic-health-record data. Studies psychiatric disease and Gulf War Illness with the Million Veteran Program.

Uses genome-wide sequencing of DNA from present-day and ancient humans. Reconstructs past demographic events and human adaptation.

Analyzes SNP array, whole-exome sequencing and whole-genome sequencing data from human biobanks. Studies genetic risk and etiology of alcohol and substance use disorders.

Develops CRISPR/Cas9 genome and epigenome therapies using patient-derived iPSCs, brain organoids and mutant mice. Targets autism, Angelman, Prader-Willi and related neurodevelopmental disorders.

Develops genome-assembly, alignment and variant-calling algorithms for long-read and Hi-C data. Applies them to pangenome and Darwin Tree of Life projects.

Applies computational methods to genomics, proteomics, electronic health records, biosensors and imaging technologies. Uses data mining, machine learning and disease-process modeling for biomedical interpretation.

Develops chemical and biochemical tools for RNA dynamics, chromatin and gene expression, including TimeLapse chemistry and nucleotide-recoding sequencing. Studies epigenetic memory and regulated gene expression.

We develop population-genetic theory, statistical methods, and computational tools. Biobank GWAS enable analyses of genetic architecture.

Studies human brain development with iPSC-derived brain organoids, RNA-seq, single nuclei RNA-seq, ChIP-seq and ATAC-seq. Research addresses autism, Tourette syndrome and somatic mosaicism.

Develops statistical methods for genome-wide association, single-cell RNA-seq, spatial transcriptomics and whole-exome/whole-genome sequencing data. Uses them for disease-gene discovery, polygenic risk prediction and network modeling.

Builds algorithms and foundation models for population-scale genomics, electronic health records and brain MRI. Uses them for pangenomics, clinical prediction and imaging-genetics GWAS in UK Biobank.

Builds CRISPR perturbation and MPRA tools and analyzes 1000 Genomes data for non-coding regulatory variation. Connects variants to human evolution, disease and traits.

Works in population genetics.

Works in population genetics and reproductive and prenatal genetics.