Abstract
IntroductionWhile 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.MethodsWe 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.ResultsWe 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.ConclusionsLongitudinal 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.
Highlights
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
We selected a number of relevant asthma-related phenotypes collected at baseline in the Childhood Asthma Management Program (CAMP) study: serum total IgE, eosinophil count (EOS), pre- and post-bronchodilator forced expiratory volume in 1 sec (FEV1), bronchodilator response (BDR,/pre-FEV1), airway hyperresponsiveness (AHR, natural log of methacholine concentration needed for 20% reduction in FEV1), steroid responsiveness endophenotype (SRE, as described by Clemmer et al [44]); and longitudinal lung growth patterns [3]: Normal Growth only (NG), Normal Growth with Early Decline (NG-ED), Reduced Growth only (RG), Reduced Growth with Early Decline (RG-ED), Early Decline irrespective of normal or reduced growth (ED-All), and Reduced Growth with or without early decline (RG-All)
Our main result was that the lung function growth patterns Reduced Growth and Early Decline are both conditions with strong genetic effects
Summary
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. Asthma is a major chronic childhood disease (9% prevalence) in the USA [1, 2] It is a heterogeneous disease, with varying outcomes and clinical courses, ranging from chronic airway obstruction [3] to the remission of symptoms entirely [4]. Forecasting such diverse clinical outcomes and disease phenotypes is an important goal of personalized medicine, and one that may be achieved in part with recent advances in whole-genome prediction (WGP) [5], wherein a patient’s entire set of single nucleotide polymorphisms (SNPs) can be used to predict outcomes of interest. Genetic risk factors for low FEV1 and low FEV1 to forced vital capacity ratio (FEV1/FVC) have been shown to be associated with greater risk of COPD [13]
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