Shadi Zabad

BLocPRS: locus-aware ensembles of polygenic scores provide accurate linear prediction

BLocPRS improves polygenic risk score prediction by combining locus-specific weights from different PRSs, achieving higher accuracy in UK Biobank and extending to non-European populations.

Topological stratification of continuous genetic variation in large biobanks

The paper introduces a topological clustering method for genetic data from biobanks, showing it can reveal population structure and aid in quality control and polygenic score analysis.

PheCode-guided multi-modal topic modeling of electronic health records improves disease incidence prediction and GWAS discovery from UK Biobank

Sparse Polygenic Risk Score Inference with the Spike-and-Slab LASSO

This paper introduces SSLPRS, a Polygenic Risk Score method using Spike-and-Slab LASSO, showing improved variable selection and prediction accuracy over existing methods.

LD Matrix Approximations for Scalable Analysis of High-dimensional Genetic Data

This paper introduces a method to approximate LD matrices with sparse, banded matrices that are positive semi-definite, improving accuracy over existing methods on large genomic data.

Personalized polygenic risk prediction and assessment with a Mixture-of-Experts framework

The paper introduces MoEPRS, a Mixture-of-Experts framework for personalized polygenic risk prediction, showing modest accuracy improvements over single-source models across diverse biobanks.

Towards whole-genome inference of polygenic scores with fast and memory-efficient algorithms

This paper introduces efficient algorithms for polygenic score inference using large-scale genetic data, achieving significant improvements in speed, memory use, and cross-population transferability.

Analysis-ready VCF at Biobank scale using Zarr

The paper introduces VCF Zarr, a format for efficiently storing and processing large-scale genetic variation data, demonstrating improved performance and scalability over traditional VCF formats.

Multi-ancestry polygenic risk scores using phylogenetic regularization

This paper introduces DendroPRS, a machine learning method that improves genotype-to-phenotype prediction across diverse ancestries by incorporating phylogenetic relationships.

On the Genes, Genealogies, and Geographies of Quebec

The paper models French Canadian ancestry using genealogical and genetic data, revealing population structure shaped by geography and migration.

Fast and Accurate Bayesian Polygenic Risk Modeling with Variational Inference

VIPRS improves polygenic risk prediction using variational inference, achieving high accuracy and efficiency in both simulated and real datasets.

Assumptions about frequency-dependent architectures of complex traits bias measures of functional enrichment

This study shows that assumptions in LDSC models can bias functional enrichment estimates in GWAS, suggesting the need for better calibration.