---
_id: '7723'
abstract:
- lang: eng
  text: 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.
article_processing_charge: No
article_type: original
author:
- first_name: Luke R.
  full_name: Lloyd-Jones, Luke R.
  last_name: Lloyd-Jones
- first_name: Matthew Richard
  full_name: Robinson, Matthew Richard
  id: E5D42276-F5DA-11E9-8E24-6303E6697425
  last_name: Robinson
  orcid: 0000-0001-8982-8813
- first_name: Jian
  full_name: Yang, Jian
  last_name: Yang
- first_name: Peter M.
  full_name: Visscher, Peter M.
  last_name: Visscher
citation:
  ama: Lloyd-Jones LR, Robinson MR, Yang J, Visscher PM. Transformation of summary
    statistics from linear mixed model association on all-or-none traits to odds ratio.
    <i>Genetics</i>. 2018;208(4):1397-1408. doi:<a href="https://doi.org/10.1534/genetics.117.300360">10.1534/genetics.117.300360</a>
  apa: Lloyd-Jones, L. R., Robinson, M. R., Yang, J., &#38; Visscher, P. M. (2018).
    Transformation of summary statistics from linear mixed model association on all-or-none
    traits to odds ratio. <i>Genetics</i>. Genetics Society of America. <a href="https://doi.org/10.1534/genetics.117.300360">https://doi.org/10.1534/genetics.117.300360</a>
  chicago: Lloyd-Jones, Luke R., Matthew Richard Robinson, Jian Yang, and Peter M.
    Visscher. “Transformation of Summary Statistics from Linear Mixed Model Association
    on All-or-None Traits to Odds Ratio.” <i>Genetics</i>. Genetics Society of America,
    2018. <a href="https://doi.org/10.1534/genetics.117.300360">https://doi.org/10.1534/genetics.117.300360</a>.
  ieee: L. R. Lloyd-Jones, M. R. Robinson, J. Yang, and P. M. Visscher, “Transformation
    of summary statistics from linear mixed model association on all-or-none traits
    to odds ratio,” <i>Genetics</i>, vol. 208, no. 4. Genetics Society of America,
    pp. 1397–1408, 2018.
  ista: Lloyd-Jones LR, Robinson MR, Yang J, Visscher PM. 2018. Transformation of
    summary statistics from linear mixed model association on all-or-none traits to
    odds ratio. Genetics. 208(4), 1397–1408.
  mla: Lloyd-Jones, Luke R., et al. “Transformation of Summary Statistics from Linear
    Mixed Model Association on All-or-None Traits to Odds Ratio.” <i>Genetics</i>,
    vol. 208, no. 4, Genetics Society of America, 2018, pp. 1397–408, doi:<a href="https://doi.org/10.1534/genetics.117.300360">10.1534/genetics.117.300360</a>.
  short: L.R. Lloyd-Jones, M.R. Robinson, J. Yang, P.M. Visscher, Genetics 208 (2018)
    1397–1408.
date_created: 2020-04-30T10:45:19Z
date_published: 2018-04-01T00:00:00Z
date_updated: 2021-01-12T08:15:06Z
day: '01'
doi: 10.1534/genetics.117.300360
extern: '1'
intvolume: '       208'
issue: '4'
language:
- iso: eng
month: '04'
oa_version: None
page: 1397-1408
publication: Genetics
publication_identifier:
  issn:
  - 0016-6731
  - 1943-2631
publication_status: published
publisher: Genetics Society of America
quality_controlled: '1'
status: public
title: Transformation of summary statistics from linear mixed model association on
  all-or-none traits to odds ratio
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 208
year: '2018'
...