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Whole genome prediction and heritability of childhood asthma phenotypes

View Article: PubMed Central - PubMed

ABSTRACT

Introduction: 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.

Methods: 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.

Results: 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.

Conclusions: 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.

No MeSH data available.


Related in: MedlinePlus

Prediction on CAMP cohort using GRMs with different covariates included, and a reduced set of Non‐Zero Weighted (NZW) SNPs. GRM, genetic relatedness matrix method using Leave‐One‐Out cross validation; AHR, airway hyperresponsiveness; EOS, eosinophil count; Pre‐FEV1, pre‐bronchodilator forced expiratory volume in 1 sec; Post‐FEV1, post‐bronchodilator forced expiratory volume in 1 sec; BDR, bronchodilator response ((Post‐FEV1 − Pre‐FEV1)/Pre‐FEV1); SRE, steroid responsiveness endophenotype; NG, normal FEV1 growth (without early decline); NG‐ED, normal FEV1 growth with early decline; RG, reduced FEV1 growth (without early decline); RG‐ED, reduced FEV1 growth with early decline; ED All, early FEV1 decline (with normal growth or with reduced growth); RG All, reduced FEV1 growth (with or without early decline). GRM‐only methods for IgE, EOS, post‐FEV1, NG, NG‐ED, RG, and RG‐All meet statistical significance for greater than random performance (AUC 0.50; p < 0.05, permutation test). Additionally, all combinations of the NZW GRM with clinical/demographic covariates were significant, except AHR and BDR.
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iid3133-fig-0003: Prediction on CAMP cohort using GRMs with different covariates included, and a reduced set of Non‐Zero Weighted (NZW) SNPs. GRM, genetic relatedness matrix method using Leave‐One‐Out cross validation; AHR, airway hyperresponsiveness; EOS, eosinophil count; Pre‐FEV1, pre‐bronchodilator forced expiratory volume in 1 sec; Post‐FEV1, post‐bronchodilator forced expiratory volume in 1 sec; BDR, bronchodilator response ((Post‐FEV1 − Pre‐FEV1)/Pre‐FEV1); SRE, steroid responsiveness endophenotype; NG, normal FEV1 growth (without early decline); NG‐ED, normal FEV1 growth with early decline; RG, reduced FEV1 growth (without early decline); RG‐ED, reduced FEV1 growth with early decline; ED All, early FEV1 decline (with normal growth or with reduced growth); RG All, reduced FEV1 growth (with or without early decline). GRM‐only methods for IgE, EOS, post‐FEV1, NG, NG‐ED, RG, and RG‐All meet statistical significance for greater than random performance (AUC 0.50; p < 0.05, permutation test). Additionally, all combinations of the NZW GRM with clinical/demographic covariates were significant, except AHR and BDR.

Mentions: The GRM‐based prediction method, as described by Wheeler et al. 34, allows easy integration of covariates into the predictive model. We included covariates for age, age of asthma diagnosis, sex, CAMP treatment group, height, body mass index, self‐reported race/ethnicity, and vitamin D serum level (Table 1), as well as the top six genotype principal components. In general, including covariates in the GRM models increased their accuracy (Fig. 3), with covariates resulting in improvements in prediction of all phenotypes except airway hyperresponsiveness (Fig. 3). In order to assess the possible effect of racial confounding, we included self‐reported race as a separate covariate with the GRM, finding that race alone did not increase the GRM's predictive ability (Fig. 3).


Whole genome prediction and heritability of childhood asthma phenotypes
Prediction on CAMP cohort using GRMs with different covariates included, and a reduced set of Non‐Zero Weighted (NZW) SNPs. GRM, genetic relatedness matrix method using Leave‐One‐Out cross validation; AHR, airway hyperresponsiveness; EOS, eosinophil count; Pre‐FEV1, pre‐bronchodilator forced expiratory volume in 1 sec; Post‐FEV1, post‐bronchodilator forced expiratory volume in 1 sec; BDR, bronchodilator response ((Post‐FEV1 − Pre‐FEV1)/Pre‐FEV1); SRE, steroid responsiveness endophenotype; NG, normal FEV1 growth (without early decline); NG‐ED, normal FEV1 growth with early decline; RG, reduced FEV1 growth (without early decline); RG‐ED, reduced FEV1 growth with early decline; ED All, early FEV1 decline (with normal growth or with reduced growth); RG All, reduced FEV1 growth (with or without early decline). GRM‐only methods for IgE, EOS, post‐FEV1, NG, NG‐ED, RG, and RG‐All meet statistical significance for greater than random performance (AUC 0.50; p < 0.05, permutation test). Additionally, all combinations of the NZW GRM with clinical/demographic covariates were significant, except AHR and BDR.
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Related In: Results  -  Collection

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Show All Figures
getmorefigures.php?uid=PMC5134727&req=5

iid3133-fig-0003: Prediction on CAMP cohort using GRMs with different covariates included, and a reduced set of Non‐Zero Weighted (NZW) SNPs. GRM, genetic relatedness matrix method using Leave‐One‐Out cross validation; AHR, airway hyperresponsiveness; EOS, eosinophil count; Pre‐FEV1, pre‐bronchodilator forced expiratory volume in 1 sec; Post‐FEV1, post‐bronchodilator forced expiratory volume in 1 sec; BDR, bronchodilator response ((Post‐FEV1 − Pre‐FEV1)/Pre‐FEV1); SRE, steroid responsiveness endophenotype; NG, normal FEV1 growth (without early decline); NG‐ED, normal FEV1 growth with early decline; RG, reduced FEV1 growth (without early decline); RG‐ED, reduced FEV1 growth with early decline; ED All, early FEV1 decline (with normal growth or with reduced growth); RG All, reduced FEV1 growth (with or without early decline). GRM‐only methods for IgE, EOS, post‐FEV1, NG, NG‐ED, RG, and RG‐All meet statistical significance for greater than random performance (AUC 0.50; p < 0.05, permutation test). Additionally, all combinations of the NZW GRM with clinical/demographic covariates were significant, except AHR and BDR.
Mentions: The GRM‐based prediction method, as described by Wheeler et al. 34, allows easy integration of covariates into the predictive model. We included covariates for age, age of asthma diagnosis, sex, CAMP treatment group, height, body mass index, self‐reported race/ethnicity, and vitamin D serum level (Table 1), as well as the top six genotype principal components. In general, including covariates in the GRM models increased their accuracy (Fig. 3), with covariates resulting in improvements in prediction of all phenotypes except airway hyperresponsiveness (Fig. 3). In order to assess the possible effect of racial confounding, we included self‐reported race as a separate covariate with the GRM, finding that race alone did not increase the GRM's predictive ability (Fig. 3).

View Article: PubMed Central - PubMed

ABSTRACT

Introduction: 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&#8208;related phenotypes.

Methods: We applied several WGP methods to a well&#8208;phenotyped cohort of 832 children with mild&#8208;to&#8208;moderate asthma from CAMP. We assessed narrow&#8208;sense heritability and predictability for airway hyperresponsiveness, serum immunoglobulin E, blood eosinophil count, pre&#8208; and post&#8208;bronchodilator forced expiratory volume in 1&thinsp;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.

Results: We found that longitudinal lung function phenotypes demonstrated significant narrow&#8208;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&ndash;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.

Conclusions: Longitudinal reduced lung function growth displayed extremely high heritability. All phenotypes with significant heritability showed significant polygenic prediction. Using SNP&#8208;prioritization increased prediction across cohorts. WGP methods show promise in predicting asthma&#8208;related heritable traits.

No MeSH data available.


Related in: MedlinePlus