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Pathway based factor analysis of gene expression data produces highly heritable phenotypes that associate with age

Andrew Brown, Zhihao Ding, Ana Viñuela, Dan Glass, Leopold Parts, Tim Spector, John Winn, Richard Durbin
doi: https://doi.org/10.1101/016154
Andrew Brown
*Wellcome Trust Sanger Institute, Hinxton, Cambridge, CB10 1HH, UK
†NORMENT, KG Jebsen Centre for Psychosis Research, Division of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway
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Zhihao Ding
*Wellcome Trust Sanger Institute, Hinxton, Cambridge, CB10 1HH, UK
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Ana Viñuela
§Department of Twin Research and Genetic Epidemiology, King’s College London, St Thomas’ Campus, Westminster Bridge Road, London SE1 7EH, UK
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Dan Glass
§Department of Twin Research and Genetic Epidemiology, King’s College London, St Thomas’ Campus, Westminster Bridge Road, London SE1 7EH, UK
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Leopold Parts
**Donnelly Centre for Cellular and Biomolecular Research, University of Toronto, Toronto, M5S 3E1, Canada
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Tim Spector
§Department of Twin Research and Genetic Epidemiology, King’s College London, St Thomas’ Campus, Westminster Bridge Road, London SE1 7EH, UK
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John Winn
††Microsoft Research Cambridge, 21 Station Road, Cambridge CB1 2FB, UK
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Richard Durbin
*Wellcome Trust Sanger Institute, Hinxton, Cambridge, CB10 1HH, UK
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  • For correspondence: rd@sanger.ac.uk
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Abstract

Statistical factor analysis methods have previously been used to remove noise components from high dimensional data prior to genetic association mapping, and in a guided fashion to summarise biologically relevant sources of variation. Here we show how the derived factors summarising pathway expression can be used to analyse the relationships between expression, heritability and ageing. We used skin gene expression data from 647 twins from the MuTHER Consortium and applied factor analysis to concisely summarise patterns of gene expression, both to remove broad confounding influences and to produce concise pathway-level phenotypes. We derived 930 “pathway phe-notypes” which summarised patterns of variation across 186 KEGG pathways (five phenotypes per pathway). We identified 69 significant associations of age with phenotype from 57 distinct KEGG pathways at a stringent Bon-ferroni threshold (P < 5.38 × 10−5). These phenotypes are more heritable (h2 = 0.32) than gene expression levels. On average, expression levels of 16% of genes within these pathways are associated with age. Several significant pathways relate to metabolising sugars and fatty acids, others with insulin signalling. We have demonstrated that factor analysis methods combined with biological knowledge can produce more reliable phenotypes with less stochastic noise than the individual gene expression levels, which increases our power to discover biologically relevant associations. These phenotypes could also be applied to discover associations with other environmental factors.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted March 06, 2015.
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Pathway based factor analysis of gene expression data produces highly heritable phenotypes that associate with age
Andrew Brown, Zhihao Ding, Ana Viñuela, Dan Glass, Leopold Parts, Tim Spector, John Winn, Richard Durbin
bioRxiv 016154; doi: https://doi.org/10.1101/016154
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Pathway based factor analysis of gene expression data produces highly heritable phenotypes that associate with age
Andrew Brown, Zhihao Ding, Ana Viñuela, Dan Glass, Leopold Parts, Tim Spector, John Winn, Richard Durbin
bioRxiv 016154; doi: https://doi.org/10.1101/016154

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