Yongtao Guan

Bayes factor for linear mixed model in genetic association studies

The paper introduces a method to compute Bayes factors for linear mixed models in genetic association studies using Bayesian regression and demonstrates its equivalence to BLUP predictions.

Reflection Knockoffs via Householder Reflection: Applications in Proteomics and Genetic Fine Mapping

This paper introduces reflection knockoffs for feature selection in highly correlated datasets, showing improved performance in proteomics and genetic fine mapping.

Abundant Parent-of-origin Effect eQTL in Humans: The Framingham Heart Study

This study identifies over 2,000 POE eQTL in whole blood gene expression from the Framingham Heart Study, showing their abundance and complexity, and highlights implications for GWAS and Mendelian randomization.

Estimation of inbreeding and kinship coefficients via latent identity-by-descent states

This paper introduces Kindred, a method for estimating inbreeding and kinship coefficients by modeling latent identity-by-descent states, showing high accuracy and non-negativity in kinship estimates.

Asymptotically exact fit for linear mixed model

This paper introduces IDUL and IDUL{dagger}, efficient and asymptotically exact methods for fitting linear mixed models in genetic association studies, particularly for multiomics data.

Human microbiome sequences in the light of the Nubeam

The paper introduces Nubeam, a reference-free method for analyzing sequencing reads, and applies it to human microbiome data to analyze unmapped reads and their contributions.

Tissue-specific Gene Expression Prediction Associates Vitiligo with SUOX through an Active Enhancer

The paper identifies SUOX as a vitiligo-associated gene by integrating GWAS data with tissue-specific gene expression prediction and experimental validation of an enhancer.

MVP: predicting pathogenicity of missense variants by deep learning

MVP uses deep learning to predict pathogenicity of missense variants, outperforming previous methods in prioritizing pathogenic variants.