Timothy Poterba

gnomAD v4 (730,947 exomes) brings LOFTEE-2 loss-of-function annotation (0.90 precision, strict mode), missense-aware constraint, and LLM-mined literature evidence combined with constraint to rank disease genes.

This paper introduces SVCR, a scalable variant call format that reduces file size and enables efficient analysis of large genetic datasets.

The paper quantifies regional missense mutational intolerance using 730,947 exomes to identify constrained gene regions and improve variant interpretation.

Meta-analyzing per-ancestry GWAS of 7,266 UK Biobank traits across six genetic ancestry groups adds 14,676 loci missed by European-only analysis, including ancestry-enriched CAMK2D and G6PD signals.

Rare-variant depletion in 1kb windows of 76,156 gnomAD genomes, versus feature-adjusted mutation expectations, marks regulatory elements and outperforms seven alternative scores at flagging functional non-coding variants.

CHARR estimates DNA contamination from variant-level sequencing data, offering accurate results with reduced computational costs compared to existing methods.

The paper evaluates dominance effects in over 1,000 UK Biobank phenotypes using GWAS, finding no significant dominance contribution to phenotypic variance from common variation.

This study identifies genetic overlaps between psychiatric disorders, language ability, and creativity through a GWAS of school grades.

The paper performs single-variant and gene-based association analyses of 4,529 phenotypes using exome data from 426,370 UK Biobank individuals, finding associations linked to variant frequency and deleteriousness.

Exomes of 24,248 schizophrenia cases and 97,322 controls implicate ultra-rare damaging variants in 10 genes, including GRIN2A and GRIA3, with odds ratios of 3 to 50.

The paper introduces a Bayesian model comparison approach, MRP, for rare variant association studies using summary statistics, demonstrating improved power and discovery of gene-trait associations across multiple phenotypes.

The paper evaluates potential drug targets using loss-of-function genetic variants from gnomAD, highlighting the importance of deep curation and targeted recall studies for drug discovery.

From 125,748 exomes and 15,708 genomes, 443,769 high-confidence predicted loss-of-function variants yield LOEUF, a continuous gene intolerance score tracking mouse lethality, cell essentiality, and disease associations.

The paper develops an expression-based annotation metric to improve rare variant interpretation by filtering false-positive loss-of-function variants and identifying pathogenic variants in highly expressed exons.

Short-read genome sequencing of 14,891 diverse genomes yields 433,371 structural variants (SVs) for gnomAD; loss-constrained genes also resist duplication, and clinical SV carrier rates are benchmarked.

The paper analyzes loss-of-function variants in LRRK2 across large genomic datasets to assess the safety of partial LRRK2 inhibition as a Parkinson's disease therapy.

The paper systematically studies uORF variants in 15,708 genomes, showing they are under strong negative selection and contribute to disease.

The paper analyzes multi-nucleotide variants (MNVs) in 125,748 exomes and 15,708 genomes, identifying their distribution, mutational origins, and functional implications.

The paper identifies LY6A as a novel receptor for AAV-PHP.B vectors, enabling efficient gene delivery across the blood-brain barrier in mice.

The paper investigates polygenic associations between ADHD and literacy/language abilities, finding that genetic confounding with educational attainment masks ADHD-specific reading impairments.

Genome-wide association meta-analysis of 20,183 ADHD cases and 35,191 controls finds 12 significant loci; signal concentrates in conserved regions, constrained genes, and CNS regulatory elements.

The paper analyzes whole genome sequences from 16,324 individuals to identify genetic variants associated with blood lipid levels and develops a polygenic score for LDL-C.

The paper uses whole genome sequencing to identify genetic variants associated with plasma lipoprotein(a) levels and finds high heritability and inter-ethnic differences in Lp(a) risk.

This study finds that ultra-rare disruptive variants in highly constrained genes are associated with reduced educational attainment in the general population.