Caroline Fiona Wright

IMPROVE-DD integrates multiple phenotype data sources to improve variant evaluation in developmental disorders, showing better diagnostic accuracy and fewer false positives.

The paper evaluates nine in silico pathogenicity prediction tools for small in-frame indels using a dataset from gnomAD, ClinVar, and DDD, finding VEST-indel performs best with AUCs of 0.93 and 0.87.

The paper estimates penetrance of monogenic diabetes variants across different ascertainment contexts, finding age-related variability for HNF1A/HNF4A variants but consistent penetrance for GCK variants.

The paper identifies de novo gain-of-function mutations in KCNK3 causing a developmental disorder with sleep apnea by disrupting X-gating of TASK-1 channels.

The paper estimates diagnostic noise in genomic analysis using gene panels by analyzing variant prioritization rates in different panel sizes and disease categories.

The paper reports diagnostic outcomes from a large-scale genomic study of developmental disorders, identifying factors that influence diagnostic success.

This paper provides recommendations for interpreting non-coding variants in clinical settings, adapting existing guidelines to account for their role in disease.

The paper identifies genetic variants linked to ovarian ageing and shows they increase offspring de novo mutation rates, demonstrating heritability of female mutation rate.

The paper investigates the phenotypic effects of rare variants in developmental disorder genes in the general population, finding milder related traits in carriers.

The paper describes the formation of the Gene Curation Coalition to standardize gene-disease validity terminology and create a unified database for curated gene-disease assertions.

This paper introduces HMC, a high-resolution genetic constraint score for missense variants, improving variant prioritization and gene discovery for developmental disorders.

InDelible detects cryptic structural variants in exome sequencing data, improving diagnostic yield for developmental disorders by identifying variants missed by standard methods.

The paper identifies non-coding variants upstream of MEF2C causing severe developmental disorder through three distinct loss-of-function mechanisms, showing their diagnostic importance in existing exome datasets.

The paper identifies common genetic variants affecting fetal birth weight that are near genes causing rare developmental disorders, using GWAS data and simulations.

This study assesses the clinical and psychosocial outcomes of genetic diagnoses in developmental disorders using data from the DDD study, finding substantial positive impacts for most patients and families.

The paper evaluates pathogenicity predictors using two clinically-relevant variant datasets, finding that newer meta-predictors outperform older tools but still have limited accuracy in clinical data.

The paper integrates healthcare and research exome sequences to identify 28 novel developmental disorder-associated genes using a simulation-based statistical test.

The paper identifies 299 new genes associated with developmental disorders using exome data from 31,058 trios and finds many more likely remain undiscovered.

This study evaluates SNP-chip accuracy for detecting rare pathogenic variants and finds low precision for very rare variants, highlighting risks for DTC genetic testing.

This study uses in silico structural analysis to assess the impact of missense variants in MEN1, finding a stability threshold to predict pathogenicity.

VEP-G2P filters genomic variants for diagnostic purposes, showing higher sensitivity but lower precision for de novo and inherited variants, with significant background noise in controls.

The paper analyzes exome sequencing data to assess the contribution of splice site mutations to developmental disorders and estimates their pathogenicity and underrepresentation in public databases.

This study uses UK Biobank data to assess the penetrance and pathogenicity of rare variants, finding reduced penetrance and variable expressivity for some clinically relevant variants.

The paper estimates the contribution of recessive coding variants to developmental disorders using exome data from 7,448 families, finding low prevalence in European ancestry but higher in Pakistani ancestry.

The paper shows that common genetic variants contribute to risk and clinical variation in severe neurodevelopmental disorders, even in individuals with known monogenic diagnoses.

The paper identifies de novo mutations in fetal-brain active regulatory elements as a cause of 1-3% of neurodevelopmental disorders in patients without coding variants.