Chiara Sabatti

The paper identifies a founder variant in ADAP1 associated with increased risk of schizophrenia and bipolar disorder in a Colombian genetic isolate.

This paper introduces a framework for identifying disease-associated features tailored to individual profiles using localized test statistics, applied to single-cell RNA sequencing data for Alzheimer's disease.

The paper shows that European GWAS findings for serious mental illness and cognition are portable to an admixed Latin American population, associating genetic risk scores with multiple symptoms and cognitive traits.

This paper introduces solveblock, a software tool that enables FDR-controlled conditional independence analysis using GWAS summary statistics, leading to additional discoveries in British individuals.

The paper introduces BGE, a sequencing method that combines low-pass whole genome and deep whole exome data, showing high accuracy and cost-effectiveness for genetic variant discovery.

This paper introduces a method to identify causal variants from genome-wide summary statistics, outperforming existing methods and discovering additional loci for complex traits.

This paper introduces GhostKnockoff, a method to improve causal variant identification in GWAS by increasing statistical power and reducing proxy variants.

This paper introduces a statistical framework for GWAS that controls false discovery rate while accounting for population structure and relatedness, validated through simulations and UK Biobank data.

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 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.

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

GLISS integrates spatial gene expression and scRNA-seq data to identify spatially-varying genes and recover cell locations.

The paper investigates how diagnosis and symptom domains affect cognitive performance in a case-control study of severe mental illness, finding distinct cognitive profiles linked to specific symptom domains.

KnockoffZoom identifies causal variants for complex traits at multiple resolutions using artificial genotypes and linkage disequilibrium models, outperforming mixed effects models in the UK Biobank.

This study examines genetic risk for bipolar disorder in extended families, finding that both common and rare variants contribute to susceptibility, with no significant segregation of large-effect variants.

The paper introduces interactive visualization tools for exploring Gene Ontology data, enabling better analysis of high-throughput genomic studies and simulation of single-cell RNA sequencing data.

Exome sequencing in Finnish populations identifies rare trait-associated alleles with higher frequencies and geographical clustering, revealing 30 novel associations with quantitative traits.

The paper analyzes gene expression and splicing changes in human aging using longitudinal RNA sequencing data from the PIVUS study, finding stability in most genetic regulation but dynamic changes in a subset of genes.

The paper analyzes genomic diversity in Latin American population isolates, finding increased genetic diversity and consanguinity compared to the Finnish isolate.

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.

The paper discusses the role of whole genome sequencing in psychiatric disorders through the WGSPD consortium, aiming to assess its utility compared to other genotyping methods.

The paper generates a transcriptome resource and eQTL catalogue in vervet monkeys, revealing sex and age-related expression patterns and key regulatory loci.

This paper introduces a method to control false discoveries in GWAS by pre-screening to adjust for linkage disequilibrium, demonstrating its effectiveness through simulations and real data.

TreeQTL improves error rate control in eQTL studies by using a hierarchical multiple testing approach.