Ziqian Xie

The paper introduces sum-h2, a fast framework for evaluating genetic discovery from deep learning-derived phenotypes using heritability measures, and establishes a Genetic Discovery Arena for benchmarking.

TorchGWAS2 improves genome-wide association testing in related samples by using a deterministic variance-correction algorithm, achieving faster and more powerful results across large-scale omics data.

This study evaluates the replicability of unsupervised deep learning image phenotypes across different models, data splits, and cohorts, finding significant stability and biological relevance.

HiFiMAP provides high-resolution IBD mapping at SNP level, outperforming window-based methods and detecting novel associations in UK Biobank and brain imaging data.

The paper uses deep learning dynamics to uncover epoch-specific genetic architectures of brain morphology from UK Biobank data.

This paper introduces a multimodal contrastive learning framework to derive heritable brain representations from paired T1- and T2-weighted MRIs, improving genetic discovery and revealing shared biological targets.

This study uses deep learning to derive image phenotypes from cardiac MRI and performs GWAS, identifying 323 SNPs and 628 genes associated with cardiac traits and diseases.

This paper introduces a deep learning framework to analyze fractional anisotropy images, identifying genetic variants associated with white matter microstructure and linking them to neurological disorders.

This study introduces a framework that predicts protein-ligand binding affinity by folding proteins, determining binding conformations, and using 3D structures, achieving performance comparable to existing docking-free methods.

This paper uses a vision transformer autoencoder to extract brain imaging phenotypes and identifies 10 novel genetic loci associated with brain structure, 3 of which were previously unreported.

The paper introduces JAGWAS, a multi-phenotype GWAS tool that identifies more genetic loci for brain imaging phenotypes than single-phenotype GWAS.

This paper introduces an unsupervised deep learning method to derive brain imaging phenotypes from MRI data, identifying new genetic loci associated with these endophenotypes.

The paper introduces iGWAS, a method using self-supervised deep learning to extract phenotypes from medical images for GWAS, finding new genetic loci related to retinal vessel development.