Alkes L. Price

The paper identifies state-dependent eQTLs in memory T cells by analyzing single-cell RNA and protein data from Peruvians, showing that gene regulation varies with cell states and disease-associated variants are enriched in these contexts.

The paper integrates single-cell RNA-seq, epigenomic maps, and GWAS data to identify disease-critical cell types and processes across multiple diseases and tissues.

The paper introduces CC-GWAS, a method to detect allele frequency differences among cases of psychiatric disorders using summary statistics, identifying 196 loci distinguishing eight disorders.

This study creates a single-cell and spatial atlas of autopsy tissues from COVID-19 patients, revealing altered cellular responses and potential therapeutic targets.

The paper develops a method to identify population-specific causal effect sizes in functionally important regions using trans-ethnic genetic correlations.

This paper introduces cov-LDSC, a method to estimate heritability and its enrichment in tissue-specific gene sets in admixed populations, showing high concordance across populations but population-specific differences for some traits.

The paper assesses and improves Mendelian disease-derived pathogenicity scores for common disease using stratified LD score regression and a gradient boosting framework.

This paper introduces PolyFun, a method that improves fine-mapping accuracy by integrating genome-wide functional data, leading to more causal variant identifications and polygenic localization of trait heritability.

The paper develops ACLR to estimate genomic autocorrelation of causal SNP effects and finds significant negative autocorrelation at short distances in complex traits.

This study evaluates how well deep learning annotations predict disease heritability and finds they provide limited unique information beyond existing functional annotations.

The paper improves deep learning SNP annotations for complex diseases by integrating them with SNP-to-gene linking strategies and gradient boosting, showing increased informativeness for autoimmune diseases.

The paper uses the ABC Model to map enhancer-gene regulation across 131 cell types and tissues, linking GWAS variants to disease genes, particularly in IBD.

GBAT improves detection of trans-gene regulation by addressing multiple testing, read-mapping biases, and confounders in eQTL analysis.

The paper introduces MESC to estimate disease heritability mediated by gene expression, finding that only a small proportion of heritability is explained by cis-genetic expression levels.

LDpred-funct adds baseline-LD functional priors to polygenic scores: average prediction R2 0.144 across 21 UK Biobank traits, a 4.6% relative gain over SBayesR.

This paper introduces a new association method, LT-FH, that combines case-control status and family history to increase power in genetic association studies, outperforming existing methods across multiple diseases.

The paper integrates thousands of epigenetic datasets into 707 cell-type regulatory annotations to improve the trans-ethnic portability of polygenic risk scores by prioritizing variants in IMPACT regulatory elements.

The paper constructs cell-type-specific TF binding annotations by combining sequence-based predictions with chromatin data, showing increased heritability enrichment for 49 diseases.

The paper analyzes 41 diseases and traits to show transposable elements contribute uniquely to disease heritability, with older elements and SINEs showing specific enrichment patterns.

This paper compares S-LDSC and LDAK models, showing the baseline-LD model better fits UK Biobank data and produces more consistent functional enrichment estimates.

The paper assesses gene network connectivity's role in disease heritability using LD score regression and finds that network annotations are enriched but largely explained by regulatory annotations.

IMPACT improves regulatory element annotation by integrating TF binding profiles with epigenomic data, showing better performance in predicting eQTLs and capturing RA heritability.

The paper finds that disease heritability enrichment is higher in ancient, conserved regulatory elements compared to younger or less conserved ones.

The paper quantifies frequency-dependent genetic architectures and negative selection effects across 25 UK Biobank traits using a statistical model and imputed genotypes.

This paper introduces a method to enhance GWAS power by incorporating functional annotations, resulting in increased detection of genome-wide significant loci.

This paper introduces the latent causal variable model to distinguish genetic correlation from causation across 52 diseases and traits, identifying 59 pairs with partial or full genetic causality.

The paper partitions low-frequency and common variant heritability across functional annotations, finding non-synonymous coding variants and conserved regions explain significant portions of low-frequency heritability.

The paper shows that polygenicity of complex traits is explained by negative selection, which flattens genetic signal across the genome.

The paper introduces a method to detect directional effects of transcription factor binding on disease risk using GWAS data and applies it to identify new causal genes and mechanisms.

The paper develops ASMC, a fast method to estimate coalescence times from SNP data, detects selection signals and finds enrichment of disease heritability in regions of background selection.