Testing for genetic association taking into account phenotypic information of relatives

Abstract

We investigated efficient case-control association analysis using family data. The outcome of interest was coronary heart disease. We employed existing and new methods that take into account the correlations among related individuals to obtain the proper type I error rates. The methods considered for autosomal single-nucleotide polymorphisms were: 1) generalized estimating equations-based methods, 2) variance-modified Cochran-Armitage (MCA) trend test incorporating kinship coefficients, and 3) genotypic modified quasi-likelihood score test. Additionally, for X-linked single-nucleotide polymorphisms we proposed a two-degrees-of-freedom test. Performance of these methods was tested using Framingham Heart Study 500 k array data.

Association Between Interstitial Lung Abnormalities and All-Cause Mortality

“…In the FHS, all analyses accounted for familial relationships using generalized estimating equations as previously described. 25 To evaluate the association between interstitial lung abnormalities and mortality logistic regression was used (for absolute mortality) and Cox proportional hazards models (for time-to-mortality) with robust standard errors to account for familial correlation in FHS. In Cox models, all variables were assessed, and none violated the proportional hazards assumption.…”

Normal thymus in adults: appearance on CT and associations with age, sex, BMI and smoking

“…Participants’ demographics were compared between the groups with different thymic scores by linear mixed effect models for quantitative data (age, BMI, pack-year) and generalized estimating equations for categorical data (sex and smoking status) to account for familial correlations among the FHS participants [16]. The results for BMI, smoking status, and pack-year were adjusted for age and sex.…”

Pulmonary cysts identified on chest CT: are they part of aging change or of clinical significance?

“…Participant demographics were investigated using mixed effect models for quantitative variables (age, BMI, pack-years and coronary artery calcium scores) and generalised estimating equations for categorical variables (sex, smoking status and respiratory symptoms) to account for familial correlations in the cohorts 15. All the results for the demographics, including BMI, smoking status, pack-years, coronary artery calcium scores and respiratory symptoms were adjusted for age and sex.…”