Eleina M. England

The Rare Genomes Project provides remote genomic sequencing to rare disease families, achieving a 23.3% diagnostic solve rate and highlighting the role of research in overcoming clinical diagnostic barriers.

The paper analyzes over 800,000 human genomes to assess penetrance of clinically relevant variants, finding that 95% of pLoF variants in severe diseases have explanations for lack of disease manifestation.

The paper identifies ABI2 missense variants linked to neurodevelopmental disorders with intellectual disability, epilepsy, and brain malformations through exome sequencing.

The paper develops a cell type-aware framework to identify non-coding variants in Mendelian disorders by integrating single-cell multi-omic data and applying it to congenital cranial dysinnervation disorders.

The paper identifies three novel candidate genes for ocular congenital cranial dysinnervation disorders using zebrafish CRISPR screening and protein binding microarrays to assess variant functionality.

The paper detects and classifies copy number variants in exome data from 6,633 families, identifying causal CNVs in 173 families and improving classification standards.

The paper identifies a novel neurodevelopmental disorder caused by de novo deletion of the lncRNA CHASERR, showing its role in regulating CHD2 expression and causing developmental and epileptic encephalopathy.

Rare-variant depletion in 1kb windows of 76,156 gnomAD genomes, versus feature-adjusted mutation expectations, marks regulatory elements and outperforms seven alternative scores at flagging functional non-coding variants.

The paper develops a method to infer compound heterozygosity from exome sequencing data, achieving high accuracy in phasing rare variants for recessive disease diagnosis.

This paper presents a framework to reduce false positives in predicted loss-of-function variants by analyzing LoF evasion mechanisms and adjusting ACMG/AMP guidelines.

This study evaluates genome sequencing's diagnostic yield in a cohort of undiagnosed rare disease patients, finding it solves 29.3% of cases with specific variant types not detectable by exome sequencing.

seqr is a web-based tool for analyzing rare disease genomic data, enabling variant filtering, annotation, and collaboration, with over 10,000 families analyzed and 300 novel disease genes discovered.

The paper functionally tests ASD missense variants in Drosophila, finding 38% caused functional alterations and identifying GLRA2 as a cause of neurodevelopmental phenotypes.

The paper describes the role of the Centers for Mendelian Genomics in advancing gene discovery through data sharing, collaboration, and resource development over the past decade.

The paper introduces MATK, an automated variant ranking tool for Mendelian disease diagnosis, showing it identifies most expert solutions and finds additional variants in unsolved cases.

The paper evaluates penetrance and variable expressivity of monogenic metabolic conditions using exome data from 77,184 individuals, finding that monogenic variants have larger effect sizes than polygenic scores but low penetrance.

From 125,748 exomes and 15,708 genomes, 443,769 high-confidence predicted loss-of-function variants yield LOEUF, a continuous gene intolerance score tracking mouse lethality, cell essentiality, and disease associations.