Seva Kashin

This study shows that somatic DNA-repeat expansions in cortical and striatal neurons of Huntington's disease initiate similar pathological changes, but lead to distinct gene expression outcomes.

The paper analyzes single-nucleus RNA-seq data from the human striatum and prefrontal cortex to study inter-individual variation in cell abundances and gene expression, finding age-related changes and genetic associations.

The paper shows that HTT CAG repeats expand in striatal neurons over time, leading to gene expression changes and neurodegeneration in Huntington disease.

The paper identifies a neuron-astrocyte gene expression program, SNAP, that declines in aging and schizophrenia, linking it to cognitive function and genetic risk factors.

The paper analyzes single-nucleus RNA sequencing data from marmoset brain cells to show that neuronal identity is more influenced by developmental origin than functional class.

The paper introduces SBARRO, a method that maps synaptic connections and RNA expression in neurons using barcoded rabies virus and single-cell RNA sequencing, revealing cell-type-specific synaptic networks in mouse brain cells.

This study performs whole genome sequencing of human embryonic stem cells to identify genetic variants and provides resources for cell line selection.

The paper presents a large ASE resource from GTEx v8 with 431 million SNP-level and 153 million haplotype-level measurements, and extends phASER to estimate cis regulatory variant effect sizes.

The paper uses bulk RNA-seq data to estimate cell type abundances and identifies cell type interaction QTLs that provide finer resolution of tissue specificity than bulk cis-eQTLs.

The paper examines telomere length variation across 6,391 tissue samples from 952 individuals, showing tissue-specific differences, positive correlations between tissues, and associations with age and ancestry.

Integrating expression, allelic expression and splicing across tissues identifies rare variants with relevance to traits.

The paper quantifies protein levels across 32 human tissues and compares them to transcriptome data, revealing insights into post-transcriptional regulation and disease mechanisms.

The paper identifies dispersed duplications and de novo structural mutations using whole-genome sequencing data from 706 families by analyzing IBD relationships and copy number variations.

GTEx v8 maps expression and splicing QTLs in 49 tissues from 838 donors: cis-eQTLs cover 94.7% of protein-coding genes, and cross-tissue QTL sharing mirrors cell-type composition.

This study examines the role of noncoding variation in autism spectrum disorder using whole genome sequencing of 2,076 genomes, finding no significant association after correction for multiple testing.

The paper develops S-PrediXcan to compute gene expression associations from GWAS summary data and applies it to 44 GTEx tissues and 100+ phenotypes, finding tissue-specific effects and colocalized associations.