David V. Conti

HAUDI improves polygenic score construction for admixed populations by efficiently extending the GAUDI method to multi-way admixture with faster computation and better performance.

Admix-kit provides tools for genetic analysis of admixed populations, enabling simulation, association testing, and polygenic scoring.

This study identifies 447 new genetic variants associated with prostate-specific antigen levels across multiple ancestries and shows improved cross-ancestry prediction of PSA, with implications for personalized prostate cancer screening.

This paper introduces HAMSTA, a method to estimate heritability from admixture mapping summary statistics while adjusting for ancestral stratification bias, and applies it to African American datasets.

This study develops a polygenic score to adjust PSA levels, improving the detection of aggressive prostate cancer while reducing unnecessary biopsies.

This study improves polygenic risk scores for colorectal cancer by combining Asian and European ancestry data, enhancing risk prediction across diverse racial and ethnic groups.

This paper introduces mJAM, a multi-population fine-mapping method that improves credible set construction by incorporating population-specific LD structures and demonstrates its effectiveness in real GWAS data.

The paper identifies inverted genomic regions between human reference genome builds that affect imputation accuracy and association testing power, proposing a heuristic to correct these issues.

The paper quantifies similarity in causal effects across local ancestries in admixed individuals and finds high genetic correlation (radmix=0.95) within individuals.

This paper introduces MA-FOCUS, a multi-ancestry fine-mapping framework that improves the identification of causal genes in TWAS by leveraging ancestry-specific LD and eQTL patterns.

This paper develops polygenic risk scores for breast cancer prediction in women of African ancestry by combining African and European ancestry datasets, improving prediction accuracy.

This study identifies novel genetic loci for BMI, height, and WHRadjBMI in Hispanic/Latino populations and shows the benefits of ancestral diversity in GWAS.

This paper introduces a Bayesian variable selection algorithm for Mendelian randomization that identifies genetic variants with minimal pleiotropy, showing significant risk-increasing effects of blood pressure on coronary heart disease.

This study identifies 13 new genetic loci specific to proximal or distal colorectal cancer subtypes, revealing partly distinct genetic architectures for these tumor locations.

This study identifies a genome-wide significant variant for OC traits and shows shared genetic risk between OC traits and OCD.

This paper introduces hJAM, a hierarchical model that improves causal inference in Mendelian randomization and transcriptome-wide association studies by incorporating prior information and accounting for SNP correlations.

HiLDA identifies mutational patterns and their frequencies in cancer data, showing significant associations with mutation timing in colon cancer.

This study quantifies genetic correlations between six solid cancers and other diseases using GWAS data, finding shared heritability and functional enrichment in regulatory regions.

This study integrates a large prostate cancer GWAS with gene expression data to identify 235 genes associated with risk, prioritizing causal genes through Bayesian credible sets.