Alkes L Price

This study connects schizophrenia GWAS findings to specific genes, tissues, and regulatory mechanisms through transcriptome-wide and chromatin activity analyses.

This paper introduces Eagle2, a reference-based phasing algorithm that improves accuracy and speed compared to SHAPEIT2 using the Haplotype Reference Consortium panel.

This paper reviews statistical methods that use summary association statistics to analyze genetic data from GWAS and gain insights into complex traits and diseases.

The paper uses UK Biobank and ancient Eurasian data to identify genetic adaptations linked to blood pressure, finding significant selection signals at FUT2, CYP1A2/CSK, and ATXN2/SH2B3.

The paper introduces FastPCA, a method for detecting natural selection by analyzing principal components, and identifies new selection signals including ADH1B in Europeans and East Asians.

This paper estimates transethnic genetic correlations using summary statistics from GWAS data and finds they are significantly less than 1 for gene expression, rheumatoid arthritis, and type-two diabetes.

The paper identifies 2,060 expression STRs (eSTRs) contributing 10%-15% of cis-heritability to gene expression, highlighting their role as regulatory variants.

This paper introduces Eagle, a fast and accurate long-range phasing method applied to the UK Biobank cohort, achieving high accuracy and speed for rare variant imputation.

The paper estimates genetic correlations between 25 traits using GWAS summary statistics, identifying correlations like anorexia nervosa with schizophrenia and educational attainment with diseases.

The paper integrates gene expression and GWAS data to identify novel genes associated with obesity-related traits.

This paper introduces a two variance component model that improves genetic prediction in family data by jointly modeling IBS sharing and pedigree structure, achieving higher prediction accuracy and correcting inflated test statistics.

The paper analyzes the genetic architecture of schizophrenia and nine complex diseases using a fast variance components analysis, finding polygenic patterns and significant genetic correlations.

BOLT-LMM improves genetic association power in large cohorts by using a Bayesian mixture prior to model non-infinitesimal genetic architectures more realistically.

The paper shows regulatory variants explain significantly more heritability than coding variants for 11 diseases, using imputed data across 218 cell types.