Nicky is an Imperial College Research Fellow using bioinformatics and large genomic datasets to study the contribution of rare variants to human disease.
Nicky's current research harnesses sequence constraint in large reference datasets, such as the genome aggregation database (gnomAD), to identify deleterious variants in non-coding regions with effects on translation efficiency and a role in disease.
Nicky is a member of the Cardiovascular Genetics and Genomics research team.
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 cardiac conditions (ICCs; cardioclassifier.org)
Prior to her current role, Nicky led development of bioinformatics infrastructure to establish a new clinical diagnostic service for ICCs within the Royal Brompton Hospital, using next-generation sequencing (NGS).
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