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Mol Genet Genomic Med. 2015 May;3(3):182-8. doi: 10.1002/mgg3.129. Epub 2015 Jan 14.

Quantifying the cumulative effect of low-penetrance genetic variants on breast cancer risk.

Author information

1
Mathematical Sciences, University of Southampton Southampton, SO17 1BJ, United Kingdom.
2
Cancer Sciences Academic Unit and University of Southampton Clinical Trials Unit, Faculty of Medicine, University of Southampton and University Hospital Southampton Foundation Trust Tremona Road, Southampton, SO16 6YA, United Kingdom.
3
Human Genetics, Faculty of Medicine, University of Southampton Tremona Road, Southampton, SO16 6YA, United Kingdom.
4
Centre for Molecular, Environmental, Genetic and Analytic Epidemiology, School of Population and Global Health, The University of Melbourne Carlton, Victoria, Australia.
5
Mathematical Sciences, University of Southampton Southampton, SO17 1BJ, United Kingdom ; Human Development and Health, Faculty of Medicine, University of Southampton Tremona Road, Southampton, SO16 6YA, United Kingdom ; Institute for Life Sciences, University of Southampton Southampton, SO17 1BJ, United Kingdom.

Abstract

Many common diseases have a complex genetic basis in which large numbers of genetic variations combine with environmental factors to determine risk. However, quantifying such polygenic effects has been challenging. In order to address these difficulties we developed a global measure of the information content of an individual's genome relative to a reference population, which may be used to assess differences in global genome structure between cases and appropriate controls. Informally this measure, which we call relative genome information (RGI), quantifies the relative "disorder" of an individual's genome. In order to test its ability to predict disease risk we used RGI to compare single-nucleotide polymorphism genotypes from two independent samples of women with early-onset breast cancer with three independent sets of controls. We found that RGI was significantly elevated in both sets of breast cancer cases in comparison with all three sets of controls, with disease risk rising sharply with RGI. Furthermore, these differences are not due to associations with common variants at a small number of disease-associated loci, but rather are due to the combined associations of thousands of markers distributed throughout the genome. Our results indicate that the information content of an individual's genome may be used to measure the risk of a complex disease, and suggest that early-onset breast cancer has a strongly polygenic component.

KEYWORDS:

breast cancer; information theory; polygenic disorder

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