While whole genome prediction (WGP) methods have recently demonstrated successes in the prediction of complex genetic diseases, they have not yet been applied to asthma and related phenotypes. Longitudinal patterns of lung function differ between asthmatics, but these phenotypes have not been assessed for heritability or predictive ability. Herein, we assess the heritability and genetic predictability of asthma-related phenotypes.
We applied several WGP methods to a well-phenotyped cohort of 832 children with mild-to-moderate asthma from CAMP. We assessed narrow-sense heritability and predictability for airway hyperresponsiveness, serum immunoglobulin E, blood eosinophil count, pre- and post-bronchodilator forced expiratory volume in 1 sec (FEV1), bronchodilator response, steroid responsiveness, and longitudinal patterns of lung function (normal growth, reduced growth, early decline, and their combinations). Prediction accuracy was evaluated using a training/testing set split of the cohort.
We found that longitudinal lung function phenotypes demonstrated significant narrow-sense heritability (reduced growth, 95%; normal growth with early decline, 55%). These same phenotypes also showed significant polygenic prediction (areas under the curve [AUCs] 56% to 62%). Including additional demographic covariates in the models increased prediction 4-8%, with reduced growth increasing from 62% to 66% AUC. We found that prediction with a genomic relatedness matrix was improved by filtering available SNPs based on chromatin evidence, and this result extended across cohorts.
Longitudinal reduced lung function growth displayed extremely high heritability. All phenotypes with significant heritability showed significant polygenic prediction. Using SNP-prioritization increased prediction across cohorts. WGP methods show promise in predicting asthma-related heritable traits.