As an Imperial College Research Fellow, Nicky uses bioinformatics and large genomic datasets to study the contribution of rare variants to human disease.
Nicky is a member of the Cardiovascular Genetics and Genomics research team.
Nicky is also a visiting scientist at both the Wellcome Trust Sanger Institute in Cambridge and the Broad Institute of MIT and Harvard in Boston, where she is a member of the analysis team for the Genome Aggregation Database (gnomAD).
Using large scale genomics datasets including gnomAD, the Genomics England 100,000 Genomes project and the UK Biobank, Nicky applies bioinformatics and statistical approaches to identify novel variants with a role in rare disease. Nicky is particularly interested in identifying disease-causing variants in the non-protein-coding regions of the genome, and elucidating the mechanisms through which they lead to disease.
Most recently, Nicky used sequence constraint and signals of negative selection in gnomAD to identify deleterious variants in 5 prime untranslated regions (5'UTRs) with effects of translation efficiency and a role in disease.
Other areas of interest include:
- Using large reference datasets to identify variants too common to cause disease
- Increasing consistency and reproducibility in interpretation of genetic variants associated with inherited heart conditions (IHCs; cardioclassifier.org)
If you are a student and interested in working with us, please get in touch!
Prior to her current role, Nicky was a PostDoc in the group. During this time she developed tools and resources to aid clinical interpretation of variants identified in patients with IHCs. Nicky also led development of the bioinformatics infrastructure to establish a new clinical diagnostic service for IHCs within the Royal Brompton Hospital.
Before joining Imperial, Nicky completed her PhD titled 'Identification and characterisation of susceptibility genes for colorectal cancer' at the Institute of Cancer Research in London, working with Professor Richard Houlston. This work involved imputation and meta-analysis of genome-wide association study (GWAS) data and analysis of exome sequencing data. Prior to this, Nicky studied for a BA in Natural Sciences (Genetics) at the University of Cambridge.
Whiffin N, Ware JS, O'Donnell-Luria A, 2019, Improving the understanding of genetic variants in rare disease with large-scale reference populations, JAMA - Journal of the American Medical Association, Vol:322, ISSN:0098-7484, Pages:1305-1306
et al., 2019, Quantitative approaches to variant classification increase the yield and precision of genetic testing in Mendelian diseases: The case of hypertrophic cardiomyopathy, Genome Medicine, Vol:11, ISSN:1756-994X
et al., 2019, Using high-resolution variant frequencies empowers clinical genome interpretation and enables investigation of genetic architecture, American Journal of Human Genetics, Vol:104, ISSN:0002-9297, Pages:187-190
et al., 2018, CardioClassifier: disease- and gene-specific computational decision support for clinical genome interpretation, Genetics in Medicine, Vol:20, ISSN:1098-3600, Pages:1246-1254
Ware JS, 2018, Adaptation and validation of the ACMG/AMP variant classification framework for MYH7-associated inherited cardiomyopathies: Recommendations by ClinGen's Inherited Cardiomyopathy Expert Panel, Genetics in Medicine, Vol:20, ISSN:1098-3600, Pages:351-359
et al., 2017, Using high-resolution variant frequencies to empower clinical genome interpretation, Genetics in Medicine, Vol:19, ISSN:1530-0366, Pages:1151-1158