Whole-genome analyses of lung function, height and smoking

Area of Science:

• Quantitative genetics
• Statistical genomics
• Pulmonary function analysis

Background:

• Understanding the genetic and environmental contributions to complex traits like height and lung function (FEV1) is crucial.
• Smoking status is a significant health behavior with potential underlying genetic components.
• Previous analyses often studied these traits independently, limiting comprehensive understanding.

Purpose of the Study:

• To jointly analyze forced expiratory volume (FEV1) and height using a novel Bayesian random regression model.
• To incorporate dense genetic marker information to disentangle genomic and environmental influences on the FEV1-height relationship.
• To estimate the genomic heritability of smoking status.

Main Methods:

• Utilized a Bayesian random regression model incorporating dense genetic markers.
• Employed a probit random regression model for the analysis of smoking status.
• Introduced a data augmentation strategy for joint posterior distribution factorization, simplifying computation.

Main Results:

• Estimated genomic heritability for height at 0.47 and for FEV1 at 0.30.
• Found a strong positive genomic correlation (0.78) and a moderate environmental correlation (0.34) between height and FEV1.
• Estimated the genomic heritability of smoking status at 0.14, indicating genetic influence.

Conclusions:

• The novel methodology effectively quantifies genetic and environmental contributions to the relationship between FEV1 and height.
• Genetic factors play a role in smoking status, as evidenced by the estimated heritability.
• The developed approach is robust, numerically stable, and extensible to multi-trait analyses.