Informed conditioning on clinical covariates increases power in case-control association studies.

PLoS Genet
Authors
Keywords
Abstract

Genetic case-control association studies often include data on clinical covariates, such as body mass index (BMI), smoking status, or age, that may modify the underlying genetic risk of case or control samples. For example, in type 2 diabetes, odds ratios for established variants estimated from low-BMI cases are larger than those estimated from high-BMI cases. An unanswered question is how to use this information to maximize statistical power in case-control studies that ascertain individuals on the basis of phenotype (case-control ascertainment) or phenotype and clinical covariates (case-control-covariate ascertainment). While current approaches improve power in studies with random ascertainment, they often lose power under case-control ascertainment and fail to capture available power increases under case-control-covariate ascertainment. We show that an informed conditioning approach, based on the liability threshold model with parameters informed by external epidemiological information, fully accounts for disease prevalence and non-random ascertainment of phenotype as well as covariates and provides a substantial increase in power while maintaining a properly controlled false-positive rate. Our method outperforms standard case-control association tests with or without covariates, tests of gene x covariate interaction, and previously proposed tests for dealing with covariates in ascertained data, with especially large improvements in the case of case-control-covariate ascertainment. We investigate empirical case-control studies of type 2 diabetes, prostate cancer, lung cancer, breast cancer, rheumatoid arthritis, age-related macular degeneration, and end-stage kidney disease over a total of 89,726 samples. In these datasets, informed conditioning outperforms logistic regression for 115 of the 157 known associated variants investigated (P-value = 1 × 10(-9)). The improvement varied across diseases with a 16% median increase in χ(2) test statistics and a commensurate increase in power. This suggests that applying our method to existing and future association studies of these diseases may identify novel disease loci.

Year of Publication
2012
Journal
PLoS Genet
Volume
8
Issue
11
Pages
e1003032
Date Published
2012
ISSN
1553-7404
URL
DOI
10.1371/journal.pgen.1003032
PubMed ID
23144628
PubMed Central ID
PMC3493452
Links
Grant list
U19 CA148127 / CA / NCI NIH HHS / United States
R01 ES020337 / ES / NIEHS NIH HHS / United States
R01 CA092824 / CA / NCI NIH HHS / United States
HL043851 / HL / NHLBI NIH HHS / United States
HL69757 / HL / NHLBI NIH HHS / United States
5T32ES007142-27 / ES / NIEHS NIH HHS / United States
U01-CA98233-07 / CA / NCI NIH HHS / United States
R01 HG006399 / HG / NHGRI NIH HHS / United States
17552 / Arthritis Research UK / United Kingdom
U01-CA98710-06 / CA / NCI NIH HHS / United States
U01-CA98216-06 / CA / NCI NIH HHS / United States
CA 047988 / CA / NCI NIH HHS / United States
Intramural NIH HHS / United States
Arthritis Research UK / United Kingdom
U19 HL069757 / HL / NHLBI NIH HHS / United States
U01-CA98758-07 / CA / NCI NIH HHS / United States
R21 ES020754 / ES / NIEHS NIH HHS / United States