Rebecca Keener

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

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.

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

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

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

The paper develops a nanopore sequencing method to measure telomere length and finds chromosome-specific and conserved telomere lengths across individuals.

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

This study identifies 59 sentinel variants and two novel genes associated with telomere length using whole genome sequences from 109,122 trans-ethnic individuals.

The paper shows that Tel1 activation by the MRX complex regulates telomere length but not the DNA damage response in S. cerevisiae, challenging existing models.