Evonne McArthur

This study introduces novel methods for comparing chromatin contact maps and evaluates their performance using Hi-C data and in silico maps, highlighting the need for biologically informed approaches.

The paper develops a method to identify deleterious noncoding variants by analyzing changes in CTCF binding activity across genomic loci and finds that loss of CTCF binding is associated with rare variants.

The paper uses sequence-based machine learning to predict 3D genome contact maps in bonobos and chimpanzees, identifying divergent folding patterns and variants affecting genome structure.

The paper uses machine learning to infer 3D chromatin contact patterns across diverse human populations and identifies regions with significant 3D divergence not explained by sequence variation.

The paper identifies DNA hypomethylated regions (HMRs) across human cell types and developmental stages, showing their role in gene regulation and trait heritability.

This study conducts a genome-wide association study to identify genetic variants linked to musical beat synchronization, finding high polygenicity with 69 significant loci and heritability estimates of 13%-16%.

The paper examines TAD boundary stability across cell types and shows their greater contribution to heritability and evolutionary constraint compared to TADs.