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.
GARFIELD classifies disease-relevant genomic features through integration of functional annotations with association signals.
Iotchkova V. et al, (2019), Nat Genet
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
Whole-Genome Sequencing Coupled to Imputation Discovers Genetic Signals for Anthropometric Traits.
Tachmazidou I. et al, (2017), Am J Hum Genet, 100, 865 - 884