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Refining fine-mapping: effect sizes and regional heritability

View ORCID ProfileChristian Benner, Aki S. Havulinna, Veikko Salomaa, Samuli Ripatti, Matti Pirinen
doi: https://doi.org/10.1101/318618
Christian Benner
1Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00014 Helsinki, Finland
2Department of Public Health, University of Helsinki, 00014 Helsinki, Finland
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  • ORCID record for Christian Benner
  • For correspondence: christian.benner@helsinki.fi matti.pirinen@helsinki.fi
Aki S. Havulinna
1Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00014 Helsinki, Finland
3National Institute for Health and Welfare, 00271 Helsinki, Finland
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Veikko Salomaa
3National Institute for Health and Welfare, 00271 Helsinki, Finland
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Samuli Ripatti
1Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00014 Helsinki, Finland
2Department of Public Health, University of Helsinki, 00014 Helsinki, Finland
4Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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Matti Pirinen
1Institute for Molecular Medicine Finland (FIMM), University of Helsinki, 00014 Helsinki, Finland
2Department of Public Health, University of Helsinki, 00014 Helsinki, Finland
5Helsinki Institute for Information Technology HIIT and Department of Mathematics and Statistics, University of Helsinki, 00014 Helsinki, Finland
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  • For correspondence: christian.benner@helsinki.fi matti.pirinen@helsinki.fi
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Abstract

Recent statistical approaches have shown that the set of all available genetic variants explains considerably more phenotypic variance of complex traits and diseases than the individual variants that are robustly associated with these phenotypes. However, rapidly increasing sample sizes constantly improve detection and prioritization of individual variants driving the associations between genomic regions and phenotypes. Therefore, it is useful to routinely estimate how much phenotypic variance the detected variants explain for each region by taking into account the correlation structure of variants and the uncertainty in their causal status. Here we extend the FINEMAP software to estimate the effect sizes and regional heritability under the probabilistic model that assumes a handful of causal variants per each region. Using the UK Biobank data to simulate GWAS regions with only a few causal variants, we demonstrate that FINEMAP provides higher precision and enables more detailed decomposition of regional heritability into individual variants than the variance component model implemented in BOLT or the fixed-effect model implemented in HESS. Using data from 51 serum biomarkers and four lipid traits from the FINRISK study, we estimate that FINEMAP captures on average 24% more regional heritability than the variant with the lowest P-value alone and 20% less than BOLT. Our simulations suggest how an upward bias of BOLT and a downward bias of FINEMAP could together explain the observed difference between the methods. We conclude that FINEMAP enables computationally efficient estimation of effect sizes and regional heritability in the era of biobank scale data.

Footnotes

  • Software availability FINEMAP v1.2 is freely available for Mac OS X and Unix at www.finemap.me

Copyright 
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-NC-ND 4.0 International license.
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Posted May 10, 2018.
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Refining fine-mapping: effect sizes and regional heritability
Christian Benner, Aki S. Havulinna, Veikko Salomaa, Samuli Ripatti, Matti Pirinen
bioRxiv 318618; doi: https://doi.org/10.1101/318618
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Refining fine-mapping: effect sizes and regional heritability
Christian Benner, Aki S. Havulinna, Veikko Salomaa, Samuli Ripatti, Matti Pirinen
bioRxiv 318618; doi: https://doi.org/10.1101/318618

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