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Free Content Whole‐Genome Analyses of Lung Function, Height and Smoking

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A joint analysis of FEV1 (forced expiratory volume after one second) and height is reported using novel methodology, as well as a single‐trait analysis of smoking status. A first goal of the study was to incorporate dense genetic marker information in a random regression (Bayesian) model to quantify the relative contributions of genomic and environmental factors to the relationship between FEV1 and height. Smoking status was analysed using a probit random regression model and a second goal of the study was to estimate the genomic heritability of smoking status. Estimates of genomic heritabilities for height and FEV1 are equal to 0.47 and to 0.30, respectively. The estimates of the genomic and environmental correlations between height and FEV1 are 0.78 and 0.34, respectively. The posterior mean of the genomic heritability of smoking status is equal to 0.14 and provides evidence for the presence of genetic factors associated with the trait. Under the data augmentation strategy introduced, the joint posterior distribution of FEV1 and height factorises into two independent posterior distributions. This simplifies programming and results in excellent numerical behaviour. The approach can be readily extended for the joint analysis of an arbitrary number of traits. Details are shown in an Appendix.
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Keywords: Bayesian analysis; FEV1; Lung function; height; smoking; whole‐genome analysis; whole‐genome random regression

Document Type: Research Article

Publication date: November 1, 2014

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