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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

Asthma phenotypes predicted by four methods, using a reduced set of SNPs predicted to be of greater functional relevance. SVM, supportā€vector machine; NB, naĆÆve Bayes; GRM, genetic relatedness matrix; LASSO, least absolute shrinkage and selection operator regression; AHR, airway hyperresponsiveness; EOS, eosinophil count; Preā€FEV1, preā€bronchodilator forced expiratory volume in 1ā€‰sec; Postā€FEV1, postā€bronchodilator forced expiratory volume in 1ā€‰sec; BDR, bronchodilator response ((Postā€FEV1ā€‰āˆ’ā€‰Preā€FEV1)/Preā€FEV1); SRE, steroid responsiveness endophenotype; NG, normal FEV1 growth (without early decline); NGā€ED, normal FEV1 growth with early decline; RG, reduced FEV1 growth (without early decline); RGā€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). *Indicate prediction meeting statistical significance for greater than random performance (AUC 0.50; pā€‰<ā€‰0.05, permutation test).
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iid3133-fig-0002: Asthma phenotypes predicted by four methods, using a reduced set of SNPs predicted to be of greater functional relevance. SVM, supportā€vector machine; NB, naĆÆve Bayes; GRM, genetic relatedness matrix; LASSO, least absolute shrinkage and selection operator regression; AHR, airway hyperresponsiveness; EOS, eosinophil count; Preā€FEV1, preā€bronchodilator forced expiratory volume in 1ā€‰sec; Postā€FEV1, postā€bronchodilator forced expiratory volume in 1ā€‰sec; BDR, bronchodilator response ((Postā€FEV1ā€‰āˆ’ā€‰Preā€FEV1)/Preā€FEV1); SRE, steroid responsiveness endophenotype; NG, normal FEV1 growth (without early decline); NGā€ED, normal FEV1 growth with early decline; RG, reduced FEV1 growth (without early decline); RGā€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). *Indicate prediction meeting statistical significance for greater than random performance (AUC 0.50; pā€‰<ā€‰0.05, permutation test).

Mentions: We additionally performed Whole Genome Prediction with genomic relatedness matrixā€based methods using several SNPā€reweighting schemes. We found improvements in prediction using the Nonā€Zero Weight (NZW) SNP set (Supplemental Fig. S1). This set was composed of all SNPs given nonā€zero weights by the procedure proposed by Croteauā€Chonka et al. 56 (see Methods), although retained SNPsā€™ weights were not changed from the inverseā€variance weights used by Yang et al. 46 These results are shown in Figure 2, where the GRMā€based method was able to significantly predict bronchodilator response.


Whole genome prediction and heritability of childhood asthma phenotypes
Asthma phenotypes predicted by four methods, using a reduced set of SNPs predicted to be of greater functional relevance. SVM, supportā€vector machine; NB, naĆÆve Bayes; GRM, genetic relatedness matrix; LASSO, least absolute shrinkage and selection operator regression; AHR, airway hyperresponsiveness; EOS, eosinophil count; Preā€FEV1, preā€bronchodilator forced expiratory volume in 1ā€‰sec; Postā€FEV1, postā€bronchodilator forced expiratory volume in 1ā€‰sec; BDR, bronchodilator response ((Postā€FEV1ā€‰āˆ’ā€‰Preā€FEV1)/Preā€FEV1); SRE, steroid responsiveness endophenotype; NG, normal FEV1 growth (without early decline); NGā€ED, normal FEV1 growth with early decline; RG, reduced FEV1 growth (without early decline); RGā€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). *Indicate prediction meeting statistical significance for greater than random performance (AUC 0.50; pā€‰<ā€‰0.05, permutation test).
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC5134727&req=5

iid3133-fig-0002: Asthma phenotypes predicted by four methods, using a reduced set of SNPs predicted to be of greater functional relevance. SVM, supportā€vector machine; NB, naĆÆve Bayes; GRM, genetic relatedness matrix; LASSO, least absolute shrinkage and selection operator regression; AHR, airway hyperresponsiveness; EOS, eosinophil count; Preā€FEV1, preā€bronchodilator forced expiratory volume in 1ā€‰sec; Postā€FEV1, postā€bronchodilator forced expiratory volume in 1ā€‰sec; BDR, bronchodilator response ((Postā€FEV1ā€‰āˆ’ā€‰Preā€FEV1)/Preā€FEV1); SRE, steroid responsiveness endophenotype; NG, normal FEV1 growth (without early decline); NGā€ED, normal FEV1 growth with early decline; RG, reduced FEV1 growth (without early decline); RGā€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). *Indicate prediction meeting statistical significance for greater than random performance (AUC 0.50; pā€‰<ā€‰0.05, permutation test).
Mentions: We additionally performed Whole Genome Prediction with genomic relatedness matrixā€based methods using several SNPā€reweighting schemes. We found improvements in prediction using the Nonā€Zero Weight (NZW) SNP set (Supplemental Fig. S1). This set was composed of all SNPs given nonā€zero weights by the procedure proposed by Croteauā€Chonka et al. 56 (see Methods), although retained SNPsā€™ weights were not changed from the inverseā€variance weights used by Yang et al. 46 These results are shown in Figure 2, where the GRMā€based method was able to significantly predict bronchodilator response.

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