Benjamin J. Strober

The paper develops PolyPWAS, a framework combining cis- and trans-pQTLs to prioritize disease-critical genes by improving protein prediction with functional annotations and showing better disease heritability explanation.

The paper maps cis- and trans-eQTLs in blood samples to infer gene regulatory networks and prioritize trait-relevant pathways.

This paper introduces J-PEP, a method that clusters GWAS loci using pleiotropy and epigenomics, improving biological interpretation of disease associations.

The paper shows that per-allele effect sizes of genetic variants in European populations are better predicted by African MAF than European MAF.

MultiSuSiE improves multi-ancestry fine-mapping in All of Us whole-genome sequencing data by allowing causal effect sizes to vary across ancestries, achieving higher power and lower computational costs.

This paper introduces PHBC to estimate pleiotropic heritability across diseases, finding that about half of common disease heritability is shared with other diseases.

This paper introduces TGFM, a method for fine-mapping causal tissues and genes at disease-associated loci using GWAS and eQTL data, identifying 147 causal elements per disease with 11% as gene-tissue pairs.

The paper introduces LDSPEC to estimate correlations between causal disease effects of proximal SNPs, finding significant correlations that vary with distance, allele frequency, LD, and functional annotations, implicating stabilizing selection.

DAESC is a statistical method for differential allele-specific expression analysis in scRNA-seq data, identifying genes with context-specific regulatory effects in endoderm differentiation and type 2 diabetes.

The paper identifies impactful rare variants by integrating multi-omic data from MESA, showing that combining expression and protein data increases the detection of rare stop and frameshift variants.

The paper validates novel telomere length regulation genes POP5 and KBTBD6 identified through trans-ancestry GWAS using functional experiments in blood cells.

This paper introduces SURGE, a method to discover context-specific eQTLs from single-cell RNA-seq data, showing its relevance in disease through colocalization analysis.

The paper introduces CAFEH, a method to better identify causal regulatory variants across tissues by accounting for allelic heterogeneity and linkage disequilibrium in eQTL data.

The paper identifies lineage-specific dynamic eQTLs during human cardiomyocyte differentiation using single-cell RNA-seq and deconvolves bulk RNA-seq data to attribute dynamic effects to specific lineages.

The paper uses human embryoid bodies to study inter-individual regulatory differences across diverse cell types using single-cell RNA-sequencing.

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.

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 distinct regulatory patterns and trait associations of lncRNA genes compared to protein-coding genes across human tissues.

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 evaluates the use of whole blood transcriptome sequencing for diagnosing rare diseases, finding an 8.5% diagnostic rate across 56 undiagnosed cases.

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 identifies rare variants contributing to extreme gene expression changes across tissues and introduces RIVER, a Bayesian method to prioritize functional variants.