MacInnis, RJ and Schmidt, DF and Makalic, E and Severi, G and FitzGerald, LM and Reumann, M and Kapuscinski, MK and Kowalczyk, A and Zhou, Z and Goudey, B and Qian, G and Bui, QM and Park, DJ and Freeman, A and Southey, MC and Amin Al Olama, A and Kote-Jarai, Z and Eeles, RA and Hopper, JL and Giles, GG, for the UK Genetic Prostate Cancer Study Collaborators, Use of a novel non-parametric version of DEPTH to identify genomic regions associated with prostate cancer risk, Cancer Epidemiology, Biomarkers and Prevention, 25, (12) pp. 1619-1624. ISSN 1055-9965 (2016) [Refereed Article]
Copyright 2016 American Association for Cancer Research
METHODS: We selected 1,854 prostate cancer cases and 1,894 controls from the UK for whom 541,129 SNPs were measured using the Illumina Infinium HumanHap550 array. Confirmation was sought using 4,152 cases and 2,874 controls, ascertained from the UK and Australia, for whom 211,155 SNPs were measured using the iCOGS Illumina Infinium array.
RESULTS: From the DEPTH analysis we identified 14 regions associated with prostate cancer risk that had been reported previously; five of which would not have been identified by conventional logistic regression. We also identified 112 novel putative susceptibility regions.
CONCLUSIONS: DEPTH can reveal new risk-associated regions that would not have been identified using a conventional logistic regression analysis of individual SNPs.
IMPACT: This study demonstrates that the DEPTH algorithm could identify additional genetic susceptibility regions that merit further investigation.
|Item Type:||Refereed Article|
|Keywords:||genome-wide association studies, machine learning algorithm, decision trees, single nucleotide polymorphism, prostate cancer|
|Research Division:||Biological Sciences|
|Research Field:||Epigenetics (incl. genome methylation and epigenomics)|
|Objective Group:||Clinical health|
|Objective Field:||Clinical health not elsewhere classified|
|UTAS Author:||FitzGerald, LM (Dr Liesel Fitzgerald)|
|Web of Science® Times Cited:||3|
|Deposited By:||Menzies Institute for Medical Research|
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