Timothy A. Thornton

This paper proposes a method to correct for multiple testing in IBD mapping for case-control studies, identifies six genome-wide significant AD risk loci, and shows positive selection can confound results.

This study shows that adjusting for principal components in GWAS of admixed populations can lead to biased results and spurious associations due to collider bias.

The paper introduces SBAT, a method for joint testing of rare variant burden scores, showing it is well calibrated and outperforms existing tests in some scenarios.

The paper compares the effectiveness of whole genome sequencing, whole exome sequencing with imputation, and array genotyping in detecting genetic associations across 100 complex traits in the UK Biobank, finding that WES+IMP provides similar signal yield to WGS with greater efficiency.

This paper presents the largest catalog of human protein-coding variation from 985,830 individuals, identifying rare variants and their functional implications for precision medicine.

The paper conducts an X-chromosome association study in Latin American cohorts, identifying new loci linked to Parkinson Disease and replicating previous findings.

GAUDI improves polygenic risk prediction in admixed populations by modeling ancestry-specific and shared effects, outperforming existing methods in simulations and real data.

The paper genotypes and sequences 140,000 adults from the Mexico City Prospective Study, revealing ancestry diversity and developing imputation resources for genetic studies.

The paper constructs and validates a polygenic risk score for Parkinson's disease in a Latino cohort, finding ancestry-related biases and haplotype associations at the SNCA locus.

The paper identifies ancestry-derived loci associated with CKD traits and risk factors in U.S. Hispanic/Latino individuals of Central American origin using admixture mapping.

The paper develops genetic predictors of plasma protein abundance in diverse populations and demonstrates their utility in proteome-wide association studies (PWAS) for complex traits.

The paper introduces REHE and reREHE methods for fast and robust variance components estimation in linear mixed models, providing non-negative estimates comparable to REML.

This study conducts the first GWAS of Parkinson's disease in Latinos, identifies SNCA as significant, and finds genetic overlap with Europeans.

This study uses admixture mapping to identify ancestry-specific loci associated with eGFR and CKD in U.S. Hispanics/Latinos and validates findings in African Americans.

The paper analyzes copy number variants in Latin American Parkinson's disease patients and finds enrichment of variants overlapping known Parkinson's genes, particularly PARK2, associated with early-onset disease.

This study identifies maternal-fetal genetic interactions and imprinting effects associated with placental abruption risk using a large cohort of Peruvian mother-infant pairs.

The paper tests gene expression prediction models' performance across populations and finds poor generalizability to African Americans compared to European Americans.

The paper addresses variance stratification in pooled whole genome sequencing data, showing how study-specific phenotypic variances affect analysis and introducing a method to improve statistical power and reduce false positives.

This study uses TOPMed whole genome sequences to improve imputation quality and detect rare variant associations in admixed African and Hispanic/Latino populations, identifying two novel associations with hematological traits.

This study conducts a GWAS on QRS duration in Hispanic/Latino populations, identifying two new loci and confirming most previously known SNPs.

This study evaluates PrediXcan's gene expression prediction accuracy across diverse populations and finds significant variation in performance between African and European samples.

This study compares methods for constructing genetic risk scores in Hispanics/Latinos using European ancestry GWAS results and finds that using Hispanic/Latino GWASs for SNP weights improves performance.

This paper introduces LMM-OPS, a linear mixed model method that improves calibration and power in genetic association studies with ancestry heterogeneity, demonstrating its effectiveness in a GWAS of white blood cell count in Hispanic/Latino women.