Whole genome prediction and heritability of childhood asthma phenotypes

Michael J McGeachie, George L Clemmer, Damien C Croteau-Chonka, Peter J Castaldi, Michael H Cho, Joanne E Sordillo, Jessica A Lasky-Su, Benjamin A Raby, Kelan G Tantisira, Scott T Weiss, Michael J McGeachie, George L Clemmer, Damien C Croteau-Chonka, Peter J Castaldi, Michael H Cho, Joanne E Sordillo, Jessica A Lasky-Su, Benjamin A Raby, Kelan G Tantisira, Scott T Weiss

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.

Trial registration: ClinicalTrials.gov NCT00000575.

Keywords: Childhood asthma; heritability; longitudinal lung function patterns; polygenic prediction; whole‐genome prediction.

Figures

Figure 1
Figure 1
Asthma phenotypes predicted by four WGP methods. A number of WGP methods were used to predict 13 phenotypes in CAMP asthmatics. SVM, support‐vector machine; NB, naïve Bayes; GRM, genetic relatedness matrix; LASSO, least absolute shrinkage and selection operator regression; 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).
Figure 2
Figure 2
Asthma phenotypes predicted by four methods, using a reduced set of SNPs predicted to be of greater functional relevance. SVM, support‐vector machine; NB, naïve Bayes; GRM, genetic relatedness matrix; LASSO, least absolute shrinkage and selection operator regression; 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). *Indicate prediction meeting statistical significance for greater than random performance (AUC 0.50; p < 0.05, permutation test).
Figure 3
Figure 3
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.
Figure 4
Figure 4
Prediction using WGP methods in CAMP non‐Hispanic white subjects only, with a reduced set of NZW SNPs. SVM, support‐vector machine; NB, naïve Bayes; GRM, genetic relatedness matrix; LASSO, least absolute shrinkage and selection operator regression; 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). *Indicate prediction meeting statistical significance for greater than random performance (AUC 0.50; p < 0.05, permutation test).

References

    1. Akinbami, L. 2006. The state of childhood asthma, United States, 1980–2005. Adv. Data. Dec 12:1–24.
    1. Eder, W. , Ege M. J., and von Mutius E.. 2006. The asthma epidemic. N. Engl. J. Med. 355:2226–2235.
    1. McGeachie, M. J. , Yates K. P., Zhou X., Guo F., Sternberg A. L., Van Natta M. L., Wise R. A., Szefler S. J., Sharma S., Kho A. T., et al. 2016. Patterns of growth and decline in lung function in persistent childhood asthma. N. Engl. J. Med. 374:1842–1852.
    1. Tantisira, K. G. , Fuhlbrigge A. L., Tonascia J., Van Natta M., Zeiger R. S., Strunk R. C., Szefler S. J., Weiss S. T.; and Childhood Asthma Management Program Research Group. 2006. Bronchodilation and bronchoconstriction: predictors of future lung function in childhood asthma. J. Allergy Clin. Immunol. 117:1264–1271.
    1. de Los Campos, G. , Hickey J. M., Pong‐Wong R., Daetwyler H. D., and Calus M. P.. 2013. Whole‐genome regression and prediction methods applied to plant and animal breeding. Genetics 193:327–345.
    1. Apostol, G. G. , D. R. Jacobs, Jr. , Tsai A. W., Crow R. S., Williams O. D., Townsend M. C., and Beckett W. S.. 2002. Early life factors contribute to the decrease in lung function between ages 18 and 40: the Coronary Artery Risk Development in Young Adults study. Am. J. Respir. Crit. Care Med. 166:166–172.
    1. Lange, P. , Parner J., Vestbo J., Schnohr P., and Jensen G.. 1998. A 15‐year follow‐up study of ventilatory function in adults with asthma. N. Engl. J. Med. 339:1194–1200.
    1. Strunk, R. C. , Weiss S. T., Yates K. P., Tonascia J., Zeiger R. S., and Szefler S. J.. 2006. Mild to moderate asthma affects lung growth in children and adolescents. J. Allergy Clin. Immunol. 118:1040–1047.
    1. Irvin, C. G. 2000. Interaction between the growing lung and asthma: role of early intervention. J. Allergy Clin. Immunol. 105:S540–S546.
    1. Jamrozik, E. , Knuiman M. W., James A., Divitini M., and Musk A. W.. 2009. Risk factors for adult‐onset asthma: a 14‐year longitudinal study. Respirology 14:814–821.
    1. Ulrik, C. S. 1999. Outcome of asthma: longitudinal changes in lung function. Eur. Respir. J. 13:904–918.
    1. Svanes, C. , Sunyer J., Plana E., Dharmage S., Heinrich J., Jarvis D., de Marco R., Norbäck D., Raherison C., Villani S., et al. 2010. Early life origins of chronic obstructive pulmonary disease. Thorax 65:14–20.
    1. Soler Artigas, M. , Loth D. W., Wain L. V., Gharib S. A., Obeidat M., Tang W., Zhai G., Zhao J. H., Smith A. V., Huffman J. E., et al. 2011. Genome‐wide association and large‐scale follow up identifies 16 new loci influencing lung function. Nat. Genet. 43:1082–1090.
    1. Chatterjee, N. , Wheeler B., Sampson J., Hartge P., Chanock S. J., and Park J. H.. 2013. Projecting the performance of risk prediction based on polygenic analyses of genome‐wide association studies. Nat. Genet. 45:400–405, 5e1–5e3.
    1. Skadhauge, L. R. , Christensen K., Kyvik K. O., and Sigsgaard T.. 1999. Genetic and environmental influence on asthma: a population‐based study of 11,688 Danish twin pairs. Eur. Respir. J. 13:8–14.
    1. Polderman, T. J. C. , Benyamin B., de Leeuw C. A., Sullivan P. F., van Bochoven A., Visscher P. M., and Posthuma D.. 2015. Meta‐analysis of the heritability of human traits based on fifty years of twin studies. Nat. Genet. 47:702–709.
    1. Givelber, R. J. , Couropmitree N. N., Gottlieb D. J., Evans J. C., Levy D., Myers R. H., and O'Connor G. T.. 1998. Segregation analysis of pulmonary function among families in the Framingham Study. Am. J. Respir. Crit. Care Med. 157:1445–1451.
    1. Gottlieb, D. J. , Wilk J. B., Harmon M., Evans J. C., Joost O., Levy D., O'Connor G. T., and Myers R. H.. 2001. Heritability of longitudinal change in lung function. The Framingham study. Am. J. Respir. Crit. Care Med. 164:1655–1659.
    1. Hukkinen, M. , Kaprio J., Broms U., Viljanen A., Kotz D., Rantanen T., and Korhonen T.. 2011. Heritability of lung function: a twin study among never‐smoking elderly women. Twin Res. Hum. Genet. 14:401–407.
    1. Palmer, L. J. , Knuiman M. W., Divitini M. L., Burton P. R., James A. L., Bartholomew H. C., Ryan G., and Musk A. W.. 2001. Familial aggregation and heritability of adult lung function: results from the Busselton Health Study. Eur. Respir. J. 17:696–702.
    1. de Los Campos, G. , Vazquez A. I., Fernando R., Klimentidis Y. C., and Sorensen D.. 2013. Prediction of complex human traits using the genomic best linear unbiased predictor. PLoS Genet. 9:e1003608.
    1. Abraham, G. , Tye‐Din J. A., Bhalala O. G., Kowalczyk A., Zobel J., and Inouye M.. 2014. Accurate and robust genomic prediction of celiac disease using statistical learning. PLoS Genet. 10:e1004137.
    1. Wellcome Trust Case Control Consortium. 2007. Genome‐wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature 447:661–678.
    1. Dudbridge, F. 2013. Power and predictive accuracy of polygenic risk scores. PLoS Genet. 9:e1003348.
    1. Manolio, T. A. , Collins F. S., Cox N. J., Goldstein D. B., Hindorff L. A., Hunter D. J., McCarthy M. I., Ramos E. M., Cardon L. R., Chakravarti A., et al. 2009. Finding the missing heritability of complex diseases. Nature 461:747–753.
    1. Lee, S. H. , Wray N. R., Goddard M. E., and Visscher P. M.. 2011. Estimating missing heritability for disease from genome‐wide association studies. Am. J. Hum. Genet. 88:294–305.
    1. Wood, A. R. , Esko T., Yang J., Vedantam S., Pers T. H., Gustafsson S., Chu A. Y., Estrada K., Luan J., Kutalik Z., et al. 2014. Defining the role of common variation in the genomic and biological architecture of adult human height. Nat. Genet. 46:1173–1186.
    1. Makowsky, R. , Pajewski N. M., Klimentidis Y. C., Vazquez A. I., Duarte C. W., Allison D. B., and de los Campos G.. 2011. Beyond missing heritability: prediction of complex traits. PLoS Genet. 7:e1002051.
    1. Li, S. , Zhao J. H., Luan J., Luben R. N., Rodwell S. A., Khaw K. T., Ong K. K., Wareham N. J., and Loos R. J.. 2010. Cumulative effects and predictive value of common obesity‐susceptibility variants identified by genome‐wide association studies. Am. J. Clin. Nutr. 91:184–190.
    1. van Hoek, M. , Dehghan A., Witteman J. C., van Duijn C. M., Uitterlinden A. G., Oostra B. A., Hofman A., Sijbrands E. J., and Janssens A. C.. 2008. Predicting type 2 diabetes based on polymorphisms from genome‐wide association studies: a population‐based study. Diabetes 57:3122–3128.
    1. Kundu, S. , Mihaescu R., Meijer C. M., Bakker R., and Janssens A. C.. 2014. Estimating the predictive ability of genetic risk models in simulated data based on published results from genome‐wide association studies. Front. Genet. 5:179.
    1. Sonis, S. , Antin J., Tedaldi M., and Alterovitz G.. 2013. SNP‐based Bayesian networks can predict oral mucositis risk in autologous stem cell transplant recipients. Oral Dis. 19:721–727.
    1. Vazquez, A. I. , de los Campos G., Klimentidis Y. C., Rosa G. J., Gianola D., Yi N., and Allison D. B.. 2012. A comprehensive genetic approach for improving prediction of skin cancer risk in humans. Genetics 192:1493–1502.
    1. Wheeler, H. E. , Aquino‐Michaels K., Gamazon E. R., Trubetskoy V. V., Dolan M. E., Huang R. S., Cox N. J., and Im H. K.. 2014. Poly‐omic prediction of complex traits: OmicKriging. Genet. Epidemiol. 38:402–415.
    1. Abraham, G. , Kowalczyk A., Zobel J., and Inouye M.. 2013. Performance and robustness of penalized and unpenalized methods for genetic prediction of complex human disease. Genet. Epidemiol. 37:184–195.
    1. Evans, D. M. , Visscher P. M., and Wray N. R.. 2009. Harnessing the information contained within genome‐wide association studies to improve individual prediction of complex disease risk. Hum. Mol. Genet. 18:3525–3531.
    1. Prosperi, M. C. , Marinho S., Simpson A., Custovic A., and Buchan I. E.. 2014. Predicting phenotypes of asthma and eczema with machine learning. BMC Med Genomics 7 Suppl 1:S7.
    1. Spycher, B. D. , Henderson J., Granell R., Evans D. M., Smith G. D., Timpson N. J., and Sterne J. A.. 2012. Genome‐wide prediction of childhood asthma and related phenotypes in a longitudinal birth cohort. J. Allergy Clin. Immunol. 130:503–9.e7.
    1. McGeachie, M. J. , Wu A. C., Chang H. H., Lima J. J., Peters S. P., and Tantisira K. G.. 2013. Predicting inhaled corticosteroid response in asthma with two associated SNPs. Pharmacogenomics J. 13:306–311.
    1. Xu, M. , Tantisira K. G., Wu A., Litonjua A. A., Chu J. H., Himes B. E., Damask A., and Weiss S. T.. 2011. Genome Wide Association Study to predict severe asthma exacerbations in children using random forests classifiers. BMC Med. Genet. 12:90.
    1. The Childhood Asthma Management Project Research Group. 1999. The Childhood Asthma Management Program (CAMP): design, rationale, and methods. Childhood Asthma Management Program Research Group. Control. Clin. Trials 20:91–120.
    1. The Childhood Asthma Management Project Research Group. 2000. Long‐term effects of budesonide or nedocromil in children with asthma. The Childhood Asthma Management Program Research Group. N. Engl. J. Med. 343:1054–1063.
    1. Chang, C. C. , Chow C. C., Tellier L. C., Vattikuti S., Purcell S. M., and Lee J. J.. 2015. Second‐generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4:7.
    1. Clemmer, G. L. , Wu A. C., Rosner B., McGeachie M. J., Litonjua A. A., Tantisira K. G., and Weiss S. T.. 2015. Measuring the corticosteroid responsiveness endophenotype in asthmatic patients. J. Allergy Clin. Immunol. 136:274–281.
    1. Brehm, J. M. , Schuemann B., Fuhlbrigge A. L., Hollis B. W., Strunk R. C., Zeiger R. S., Weiss S. T., and Litonjua A. A., for the Childhood Asthma Management Program Research Group. 2010. Serum vitamin D levels and severe asthma exacerbations in the Childhood Asthma Management Program study. J. Allergy Clin. Immunol. 126:52–8.e5.
    1. Yang, J. , Lee S. H., Goddard M. E., and Visscher P. M.. 2011. GCTA: a tool for genome‐wide complex trait analysis. Am. J. Hum. Genet. 88:76–82.
    1. Tibshirani, R. 1996. Regression shrinkage and selection via the lasso. J. Royal Statistical Soc. B 58:267–288.
    1. Cortes, C. , and Vapnik V.. 1995. Support‐vector networks. Mach. Learn. 20:273–297.
    1. Domingos, P. , and Pazzani M.. 1997. On the optimality of the simple Bayesian classifier under zero‐one loss. Mach. Learn. 29:103–130.
    1. Yao, C. , Leng N., Weigel K. A., Lee K. E., Engelman C. D., and Meyers K. J.. 2014. Prediction of genetic contributions to complex traits using whole genome sequencing data. BMC Proc. 8:S68.
    1. Friedman, J. , Hastie T., and Tibshirani R.. 2010. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33:1–22.
    1. Wu, J. , Pfeiffer R. M., and Gail M. H.. 2013. Strategies for developing prediction models from genome‐wide association studies. Genet. Epidemiol. 37:768–777.
    1. Lasko, T. A. , Bhagwat J. G., Zou K. H., and Ohno‐Machado L.. 2005. The use of receiver operating characteristic curves in biomedical informatics. J. Biomed. Inform. 38:404–415.
    1. Provost, F. , and Fawcett T.. 2001. Robust Classification for imprecise environments. Mach. Learn. 44:203–231.
    1. Yang, J. , Benyamin B., McEvoy B. P., Gordon S., Henders A. K., Nyholt D. R., Madden P. A., Heath A. C., Martin N. G., Montgomery G. W., et al. 2010. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42:565–569.
    1. Croteau‐Chonka, D. C. , Rogers A. J., Raj T., McGeachie M. J., Qiu W., Ziniti J. P., Stubbs B. J., Liang L., Martinez F. D., Strunk R. C., et al. 2015. Expression quantitative trait loci information improves predictive modeling of disease relevance of non‐coding genetic variation. PLoS ONE 10:e0140758.
    1. McGeachie, M. J. , Clemmer G. L., Lasky‐Su J., Dahlin A., Raby B. A., and Weiss S. T.. 2014. Joint GWAS analysis: comparing similar GWAS at different genomic resolutions identifies novel pathway associations with six complex diseases. Genom. Data 2:202–211.
    1. Stern, D. A. , Morgan W. J., Wright A. L., Guerra S., and Martinez F. D.. 2007. Poor airway function in early infancy and lung function by age 22 years: a non‐selective longitudinal cohort study. Lancet 370:758–764.
    1. Wang, X. , Mensinga T. T., Schouten J. P., Rijcken B., and Weiss S. T.. 2004. Determinants of maximally attained level of pulmonary function. Am. J. Respir. Crit. Care Med. 169:941–949.
    1. Tager, I. B. , Segal M. R., Speizer F. E., and Weiss S. T.. 1988. The natural history of forced expiratory volumes. Effect of cigarette smoking and respiratory symptoms. Am. Rev. Respir. Dis. 138:837–849.
    1. Tang, W. , Kowgier M., Loth D. W., Soler Artigas M., Joubert B. R., Hodge E., Gharib S. A., Smith A. V., Ruczinski I., Gudnason V., et al. 2014. Large‐scale genome‐wide association studies and meta‐analyses of longitudinal change in adult lung function. PLoS ONE 9:e100776.
    1. Shigemizu, D. , Abe T., Morizono T., Johnson T. A., Boroevich K. A., Hirakawa Y., Ninomiya T., Kiyohara Y., Kubo M., Nakamura Y., et al. 2014. The construction of risk prediction models using GWAS data and its application to a type 2 diabetes prospective cohort. PLoS ONE 9:e92549.
    1. de Maturana, E. L. , Chanok S. J., Picornell A. C., Rothman N., Herranz J., Calle M. L., García‐Closas M., Marenne G., Brand A., Tardón A., et al. 2014. Whole genome prediction of bladder cancer risk with the Bayesian LASSO. Genet. Epidemiol. 38:467–476.

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