Identification of disease-associated loci using machine learning for genotype and network data integration.

Journal: Bioinformatics (Oxford, England)
Published Date: Dec 15, 2019

Abstract

MOTIVATION: Integration of different omics data could markedly help to identify biological signatures, understand the missing heritability of complex diseases and ultimately achieve personalized medicine. Standard regression models used in Genome-Wide Association Studies (GWAS) identify loci with a strong effect size, whereas GWAS meta-analyses are often needed to capture weak loci contributing to the missing heritability. Development of novel machine learning algorithms for merging genotype data with other omics data is highly needed as it could enhance the prioritization of weak loci.

Authors

  • Luis G Leal
    Department of Life Sciences, Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London SW7 2AZ, UK.
  • Alessia David
    Department of Life Sciences, Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London SW7 2AZ, UK.
  • Marjo-Riita Jarvelin
    Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu FI-90014, Finland.
  • Sylvain Sebert
    Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu FI-90014, Finland.
  • Minna Männikkö
    Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu FI-90014, Finland.
  • Ville Karhunen
    Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu FI-90014, Finland.
  • Eleanor Seaby
    Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Clive Hoggart
    Department of Medicine, Imperial College London, London W2 1PG, UK.
  • Michael J E Sternberg
    Department of Life Sciences, Centre for Integrative Systems Biology and Bioinformatics, Imperial College London, London SW7 2AZ, UK.

Keywords

External Resources

Popular Topics
Recent Journals