Imperial College London

ProfessorAnneBowcock

Faculty of MedicineNational Heart & Lung Institute

Visiting Professor
 
 
 
//

Contact

 

+44 (0)20 7594 1511a.bowcock

 
 
//

Location

 

Guy Scadding BuildingRoyal Brompton Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Anbunathan:2017:10.1016/j.jid.2016.03.047,
author = {Anbunathan, H and Bowcock, AM},
doi = {10.1016/j.jid.2016.03.047},
journal = {J Invest Dermatol},
pages = {e113--e118},
title = {The Molecular Revolution in Cutaneous Biology: The Era of Genome-Wide Association Studies and Statistical, Big Data, and Computational Topics.},
url = {http://dx.doi.org/10.1016/j.jid.2016.03.047},
volume = {137},
year = {2017}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - The investigation of biological systems involving all organs of the body including the skin is in era of big data. This requires heavy-duty computational tools, and novel statistical methods. Microarrays have allowed the interrogation of thousands of common genetic markers in thousands of individuals from the same population (termed genome wide association studies or GWAS) to reveal common variation associated with disease or phenotype. These markers are usually single nucleotide polymorphisms (SNPs) that are relatively common in the population. In the case of dermatological diseases such as alopecia areata, vitiligo, psoriasis and atopic dermatitis, common variants have been identified that are associated with disease, and these provide insights into biological pathways and reveal possible novel drug targets. Other skin phenotypes such as acne, color and skin cancers are also being investigated with GWAS. Analyses of such large GWAS datasets require a consideration of a number of statistical issues including the testing of multiple markers, population substructure, and ultimately a requirement for replication. There are also issues regarding the missing heritability of disease that cannot be entirely explained with current GWAS approaches. Next generation sequencing technologies such as exome and genome sequencing of similar patient cohorts will reveal additional variants contributing to disease susceptibility. However, the data generated with these approaches will be orders of magnitude greater than that those generated with arrays, with concomitant challenges in the identification of disease causing variants.
AU - Anbunathan,H
AU - Bowcock,AM
DO - 10.1016/j.jid.2016.03.047
EP - 118
PY - 2017///
SP - 113
TI - The Molecular Revolution in Cutaneous Biology: The Era of Genome-Wide Association Studies and Statistical, Big Data, and Computational Topics.
T2 - J Invest Dermatol
UR - http://dx.doi.org/10.1016/j.jid.2016.03.047
UR - https://www.ncbi.nlm.nih.gov/pubmed/28411841
VL - 137
ER -