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Evaluating the heritability explained by known susceptibility variants: A survey of ten complex diseases

  • Hon Cheong So
  • , Allen H.S. Gui
  • , Stacey S. Cherny
  • , Pak C. Sham*
  • *Corresponding author for this work
  • The University of Hong Kong

Research output: Contribution to journal › Article › peer-review

231 Scopus citations

Abstract

Recently, an increasing number of susceptibility variants have been identified for complex diseases. At the same time, the concern of "missing heritability" has also emerged. There is however no unified way to assess the heritability explained by individual genetic variants for binary outcomes. A systemic and quantitative assessment of the degree of "missing heritability" for complex diseases is lacking. In this study, we measure the variance in liability explained by individual variants, which can be directly interpreted as the locus-specific heritability. The method is extended to deal with haplotypes, multi-allelic markers, multi-locus genotypes, and markers in linkage disequilibrium. Methods to estimate the standard error and confidence interval are proposed. To assess our current level of understanding of the genetic basis of complex diseases, we conducted a survey of 10 diseases, evaluating the total variance explained by the known variants. The diseases under evaluation included Alzheimer's disease, bipolar disorder, breast cancer, coronary artery disease, Crohn's disease, prostate cancer, schizophrenia, systemic lupus erythematosus (SLE), type 1 diabetes and type 2 diabetes. The median total variance explained across the 10 diseases was 9.81%, while the median variance explained per associated SNP was around 0.25%. Our results suggest that a substantial proportion of heritability remains unexplained for the diseases under study. Programs to implement the methodologies described in this paper are available at.

Original languageEnglish
Pages (from-to)310-317
Number of pages8
JournalGenetic Epidemiology
Volume35
Issue number5
DOIs
StatePublished - Jul 2011
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Association study
  • Genetic architecture
  • Liability threshold model
  • Variance explained

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