Alexis J. Battle

This paper improves variant-effect prediction in sequence-to-function models using synthetic MPRA data generated from distilled model predictions.

This paper evaluates how different evaluation designs affect cross-donor generalization in single-cell classification, finding that random cell-level splitting overestimates performance compared to donor-held-out evaluation.

This study creates a spatiomolecular atlas of the human nucleus accumbens using spatial transcriptomics and single-nucleus RNA sequencing, revealing spatial gradients and conserved features across species.

The paper conducts a genome-wide analysis of clinical obesity, identifies distinct genetic loci, and shows that a clinical obesity polygenic score improves cardiovascular risk prediction compared to BMI-based scores.

The paper maps cis- and trans-eQTLs in blood samples to infer gene regulatory networks and prioritize trait-relevant pathways.

The paper identifies key transcription factors and their target proteins in renal cell carcinoma using multi-omic integration of ChIP-seq and GWAS data.

The paper develops a graph neural network framework that integrates biological knowledge to predict phenotypes from proteomics data, outperforming linear models and other architectures.

The paper introduces TWiST, a method for transcriptome-wide association studies at cell state resolution using single-cell eQTL data, showing improved power in detecting dynamic gene-disease associations.

The paper identifies USP18 as a negative regulator of interferon response at a lupus risk locus through trans-eQTL analysis in lymphoblastoid cell lines.

The paper fine-tunes a sequence-to-function model on large proteomic data to improve variant effect prediction, showing better performance with larger sample sizes and rare variants.

This paper introduces PRSFNN, a neural empirical Bayesian method for polygenic prediction that outperforms existing methods on 17 of 18 traits in the UK Biobank.

The paper identifies response eQTLs associated with environmental exposures in human cells and finds they overlap with disease-associated loci not explained by steady-state eQTLs.

The paper integrates long-read sequencing and gene expression to prioritize functional structural variants and tandem repeats in rare disease patients, identifying potential causal variants in a neurodevelopmental disorder.

The paper performs genotype inference from aggregated chromatin accessibility data to identify caQTLs and their links to gene expression and complex traits.

The paper investigates functional rare variants on the X-chromosome and autosomes, showing sex-biased effects and pharmacogenomic implications.

The paper identifies expression quantitative trait loci in 29 cell-types from heterogeneous differentiating cultures and characterizes regulatory dynamics across differentiation trajectories.

This paper introduces GLEANR, a method for sparse matrix factorization of GWAS summary statistics that accounts for sample sharing and identifies interpretable genetic factors.

This study uses CRISPR perturbations in CD4+ T cells to infer a gene regulatory network, revealing trans-regulatory mechanisms linking immune GWAS genes to signaling pathways.

The paper develops a method to quantify somatic mutation burden in whole-genome sequencing data without paired-tissue samples and identifies genetic and phenotypic correlates of clonal hematopoiesis without known driver mutations.

This study identifies pleiotropic variants and trait-specific susceptibility variants using GWAS data from 116 traits, revealing widespread pleiotropy and unique genetic contributions to specific traits.

The paper uses model-X knockoffs to assess false positives in regulatory network inference and identifies causal sufficiency as a major limitation.

The paper analyzes gene expression and splicing variation across diverse human populations, identifying population-specific and shared genetic regulatory elements.

The paper generates a molecular neuroanatomical map of the human prefrontal cortex using spatial transcriptomics and single nucleus RNA-sequencing, revealing novel spatial domains and cell-cell interactions linked to psychiatric disorders.

The paper aggregates RNA-seq data to infer consensus and tissue-specific gene co-expression networks, finding that larger sample sizes improve network quality and trait heritability annotations.

DAESC is a statistical method for differential allele-specific expression analysis in scRNA-seq data, identifying genes with context-specific regulatory effects in endoderm differentiation and type 2 diabetes.

The paper identifies impactful rare variants by integrating multi-omic data from MESA, showing that combining expression and protein data increases the detection of rare stop and frameshift variants.

This study benchmarks expression forecasting methods using 11 perturbation datasets, finding that simple baselines often outperform complex models using mean squared error.

The paper validates novel telomere length regulation genes POP5 and KBTBD6 identified through trans-ancestry GWAS using functional experiments in blood cells.

OTTERS is a TWAS framework that uses summary-level eQTL reference data to estimate eQTL weights and perform omnibus TWAS, showing improved power and applicability.

Jasmine improves SV comparison and analysis by refining SVs, reducing Mendelian discordance, and revealing high-confidence de novo SVs across diverse samples.