Noah Zaitlen

The paper demonstrates that discarded tumor sequencing reads can be used to impute germline genotypes, enabling the construction of large germline research cohorts from existing clinical data.

The paper analyzes gene expression in African American and Latino children to show ancestry-specific genetic architecture and highlights the benefits of diverse genomic studies.

This paper introduces SBT, a software platform that extracts omics features from clinical tumor sequencing data, identifying prognostic biomarkers in lung, breast, and colorectal cancers.

This study shows that using admixed populations in GWAS increases statistical power and improves cross-population polygenic score accuracy compared to single-ancestry populations.

This study links genomic data with EHRs to identify health outcomes enriched in sub-populations, revealing both known and novel associations.

This paper introduces a method to estimate polygenic adaptation using whole-genome genealogies and GWAS data, finding evidence of directional selection on multiple human traits and correlated responses.

GBAT improves detection of trans-gene regulation by addressing multiple testing, read-mapping biases, and confounders in eQTL analysis.

The paper develops a statistical test to detect polygenic epistasis by analyzing genotype-by-ancestry interactions in mice and yeast, finding significant associations across multiple phenotypes.

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

This paper introduces PAT, a method for joint analysis of multiple traits in GWAS, showing increased power and novel associations in UK Biobank data.

The paper uses whole-genome and RNA sequencing from the human prefrontal cortex to identify genetic variants and transcriptomic changes associated with neurodevelopment and neuropsychiatric disorders.

The paper introduces a model for structured polygenic epistasis called Coordinated Interaction and tests it using the UK Biobank, finding evidence in 14 of 26 traits.

MPRAudit quantifies the fraction of variance explained by unknown features in MPRA data, revealing sequence variation as the main driver of outcome variability but with known categories explaining only part of the variance.

This paper introduces CelFiE, a method to estimate cell type contributions to cfDNA, showing it can detect rare cell types and find increased skeletal muscle cfDNA in ALS patients.

This paper introduces GxEMM, a mixed model for gene-environment interactions, showing it improves heritability estimates and reveals stress reduces heritability of major depression.

This study identifies 588 SNP-metabolite associations using the METSIM cohort, revealing pleiotropy, temporal changes, and age-related genetic control shifts.

The paper uses unsupervised machine learning to identify three molecular subtypes in ALS cortex samples, associated with oxidative stress, retrotransposon activation, and glial activation.

This paper introduces Reverse GWAS (RGWAS) to identify and validate phenotypic subtypes using genetics, finding three novel metabolic subtypes and showing differential treatment effects.

This study identifies genetic and environmental factors that disrupt trait correlations, showing environmental perturbations reduce correlations and genetic factors influence correlation patterns.

This paper introduces multi-level barcoding to address clonal heterogeneity in pooled genetic screens and demonstrates its ability to resolve cellular heterogeneity issues.

The paper identifies a bias in using principal components (PCs) for confounder correction in genomic studies, showing that it can lead to false positives, especially for large genetic effects.

This paper introduces a reference-free method for generating polygenic risk scores (PRS) that avoids over-fitting and computational inefficiencies, and demonstrates applications in pleiotropy detection and mixed-model association testing.

The paper develops ImReP, a computational method to profile TCR and BCR repertoires from RNA-seq data, creating a large atlas of immunological sequences across 53 tissues.

The paper develops GCLiPP to map RNA binding protein occupancy and identifies cis-regulatory elements in T cell 3' UTRs, revealing shared regulatory mechanisms across human and mouse.

The paper introduces ADGLM to model population variance structure, showing it biases genetic association studies and identifies novel genotype-variance associations.

A novel method defines ethnicity-specific reference intervals for lab tests using EHR data, showing better prognostic value than existing intervals for serum creatinine and hemoglobin A1C.

This study identifies pharmacogenetic variants associated with albuterol bronchodilator response in minority children with asthma using whole genome sequencing.

This paper introduces ROP to identify the source of unmapped RNA-Seq reads across diverse tissues, showing it can account for 99.9% of 1 trillion reads and reveals immune and microbial correlations.

The paper introduces demuxlet, an algorithm that uses natural genetic variation to multiplex single-cell RNA-seq samples, enabling accurate sample identification and doublet detection in pooled experiments.

This paper develops a statistical method using genetic ancestry to detect gene-gene and gene-environment interactions in admixed populations, identifying significant interactions in methylation data.