MRC WIMM Group Leader
Valentina is a mathematician/statistician by training with experience in large-scale analyses of quantitative genetic variation and relevant statistical method development (e.g. multivariate association analysis, missing value imputation, functional enrichment analysis).
Valentina’s current interests lie in the development and application of new computational and statistical approaches aimed at utilizing high-dimensional datasets in biology and medicine to their full potential (e.g. by integration across different layers of information). In particular, she is keen to develop methodological advancements to accelerate the discovery and interpretation of multidimensional phenotypic consequences of common and rare genetic variation as well as to use genetic information to infer direction of causality between different layers of phenotypic information.
Variation in PU.1 binding and chromatin looping at neutrophil enhancers influences autoimmune disease susceptibility
Watt S. et al, (2019)
GARFIELD classifies disease-relevant genomic features through integration of functional annotations with association signals.
Iotchkova V. et al, (2019), Nat Genet, 51, 343 - 353
Low-frequency variation in TP53 has large effects on head circumference and intracranial volume.
Haworth S. et al, (2019), Nat Commun, 10
Author Correction: Discovery and refinement of genetic loci associated with cardiometabolic risk using dense imputation maps.
Iotchkova V. et al, (2018), Nat Genet, 50
Low frequency genetic variation in the TP53 locus has large effects on head circumference and intracranial volume
St Pourcain B. et al, (2018), BEHAVIOR GENETICS, 48, 514 - 515