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Dr Olivia Fletcher

Team Leader

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Dr Olivia Fletcher leads a team of genetic epidemiologists and molecular biologists working on the Generations Study, the British Breast Cancer Study and other population-based studies. She also took over leadership of the Complex Trait Genetics team from Dr Nick Orr in July 2017. Team: Complex Trait Genetics
Team: Functional Genetic Epidemiology

T 020 7153 5177

Biography

Dr Olivia Fletcher leads a team of genetic epidemiologists and molecular biologists working on the Generations Study, the British Breast Cancer Study and other population-based studies. She is based within the Breast Cancer Now Toby Robins Research Centre at the ICR. She obtained her first degree in Biochemistry at the University of Oxford, her PhD in the laboratory of Gene Structure and Expression at The National Institute of Medical Research, Mill Hill, and an MSc in Medical Statistics at the London School of Hygiene and Tropical Medicine.

Her research interests focus on the identification and characterisation of genetic variants that are associated with breast cancer risk – specifically, low penetrance variants that map to non-coding DNA. To facilitate these studies, Dr Fletcher uses quantitative intermediate phenotypes – for example, female sex hormones, peptide growth factors and their binding proteins.

In a study of premenopausal hormone levels Dr Fletcher identified a CYP3A7allele (CYP3A7*1C) that is associated with a dramatic reduction in circulating hormone levels and a modest reduction in breast cancer risk. This allele may also impact on the way in which carriers metabolise certain clinically prescribed drugs, including cytotoxic agents used in the treatment of breast cancer.

Dr Fletcher has also contributed to developing a more systematic approach to the functional characterisation of genetic risk loci identified, primarily, through genome-wide association studies (GWAS). In collaboration with scientists at the Babraham Institute, Dr Fletcher developed a modified chromosome conformation capture-based method that allows high-resolution analysis of regulatory interactions between risk loci and their target genes. She used this methodology recently to identify putative target genes at 33 breast cancer risk loci. 

Her work is funded by Breast Cancer Now.


Research highlight: 11p15.5 Risk Locus

Image below: Interaction peaks, shown in a looping format, are aligned with raw data. Raw data is shown as one virtual 4C library per row (numbered T47D 1 -14 and MDA 1 – 14) such that each row shows the density of reads at each (non-captured) cis fragment forming di-tags with the single captured fragment. Interaction peaks between two captured fragments are red, interaction peaks between one captured fragment and one non-captured fragmentare blue.

Intensity of individual interactions are proportional to -log2(PFDR). Capture regions are shown as black bars; data are aligned with genomic coordinates (hg19) and RefSeq genes. Target genes (ie the subset at which an interaction peak co-localises with the TSS) are shown in red. At this locus, the predominant interaction peaks target uncaptured HindIII fragments colocalising with IGF2 and captured fragments colocalizing withLSP1, TNNT3 andMRPL23 in MDA-MB-231.

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