---
res:
  bibo_abstract:
  - Genome-wide association studies (GWAS) have identified thousands of loci that
    are robustly associated with complex diseases. The use of linear mixed model (LMM)
    methodology for GWAS is becoming more prevalent due to its ability to control
    for population structure and cryptic relatedness and to increase power. The odds
    ratio (OR) is a common measure of the association of a disease with an exposure
    (e.g., a genetic variant) and is readably available from logistic regression.
    However, when the LMM is applied to all-or-none traits it provides estimates of
    genetic effects on the observed 0–1 scale, a different scale to that in logistic
    regression. This limits the comparability of results across studies, for example
    in a meta-analysis, and makes the interpretation of the magnitude of an effect
    from an LMM GWAS difficult. In this study, we derived transformations from the
    genetic effects estimated under the LMM to the OR that only rely on summary statistics.
    To test the proposed transformations, we used real genotypes from two large, publicly
    available data sets to simulate all-or-none phenotypes for a set of scenarios
    that differ in underlying model, disease prevalence, and heritability. Furthermore,
    we applied these transformations to GWAS summary statistics for type 2 diabetes
    generated from 108,042 individuals in the UK Biobank. In both simulation and real-data
    application, we observed very high concordance between the transformed OR from
    the LMM and either the simulated truth or estimates from logistic regression.
    The transformations derived and validated in this study improve the comparability
    of results from prospective and already performed LMM GWAS on complex diseases
    by providing a reliable transformation to a common comparative scale for the genetic
    effects.@eng
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Luke R.
      foaf_name: Lloyd-Jones, Luke R.
      foaf_surname: Lloyd-Jones
  - foaf_Person:
      foaf_givenName: Matthew Richard
      foaf_name: Robinson, Matthew Richard
      foaf_surname: Robinson
      foaf_workInfoHomepage: http://www.librecat.org/personId=E5D42276-F5DA-11E9-8E24-6303E6697425
    orcid: 0000-0001-8982-8813
  - foaf_Person:
      foaf_givenName: Jian
      foaf_name: Yang, Jian
      foaf_surname: Yang
  - foaf_Person:
      foaf_givenName: Peter M.
      foaf_name: Visscher, Peter M.
      foaf_surname: Visscher
  bibo_doi: 10.1534/genetics.117.300360
  bibo_issue: '4'
  bibo_volume: 208
  dct_date: 2018^xs_gYear
  dct_isPartOf:
  - http://id.crossref.org/issn/0016-6731
  - http://id.crossref.org/issn/1943-2631
  dct_language: eng
  dct_publisher: Genetics Society of America@
  dct_title: Transformation of summary statistics from linear mixed model association
    on all-or-none traits to odds ratio@
...