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dc.contributor.authorJalali-Najafabadi, Farideh
dc.contributor.authorStadler, Michael
dc.contributor.authorDand, Nick
dc.contributor.authorJadon, Deepak
dc.contributor.authorSoomro, Mehreen
dc.contributor.authorHo, Pauline
dc.contributor.authorMarzo-Ortega, Helen
dc.contributor.authorHelliwell, Philip
dc.contributor.authorKorendowych, Eleanor
dc.contributor.authorSimpson, Michael A
dc.contributor.authorPackham, Jonathan
dc.contributor.authorSmith, Catherine H
dc.contributor.authorBarker, Jonathan N
dc.contributor.authorMcHugh, Neil
dc.contributor.authorWarren, Richard B
dc.contributor.authorBarton, Anne
dc.contributor.authorBowes, John
dc.contributor.authorBADBIR Study Group
dc.contributor.authorBSTOP Study Group
dc.date.accessioned2022-01-28T14:44:06Z
dc.date.available2022-01-28T14:44:06Z
dc.date.issued2021-12-02
dc.identifier.issn2045-2322
dc.identifier.other34857774
dc.identifier.otherPMC8640070
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/333070
dc.description.abstractIn view of the growth of clinical risk prediction models using genetic data, there is an increasing need for studies that use appropriate methods to select the optimum number of features from a large number of genetic variants with a high degree of redundancy between features due to linkage disequilibrium (LD). Filter feature selection methods based on information theoretic criteria, are well suited to this challenge and will identify a subset of the original variables that should result in more accurate prediction. However, data collected from cohort studies are often high-dimensional genetic data with potential confounders presenting challenges to feature selection and risk prediction machine learning models. Patients with psoriasis are at high risk of developing a chronic arthritis known as psoriatic arthritis (PsA). The prevalence of PsA in this patient group can be up to 30% and the identification of high risk patients represents an important clinical research which would allow early intervention and a reduction of disability. This also provides us with an ideal scenario for the development of clinical risk prediction models and an opportunity to explore the application of information theoretic criteria methods. In this study, we developed the feature selection and psoriatic arthritis (PsA) risk prediction models that were applied to a cross-sectional genetic dataset of 1462 PsA cases and 1132 cutaneous-only psoriasis (PsC) cases using 2-digit HLA alleles imputed using the SNP2HLA algorithm. We also developed stratification method to mitigate the impact of potential confounder features and illustrate that confounding features impact the feature selection. The mitigated dataset was used in training of seven supervised algorithms. 80% of data was randomly used for training of seven supervised machine learning methods using stratified nested cross validation and 20% was selected randomly as a holdout set for internal validation. The risk prediction models were then further validated in UK Biobank dataset containing data on 1187 participants and a set of features overlapping with the training dataset.Performance of these methods has been evaluated using the area under the curve (AUC), accuracy, precision, recall, F1 score and decision curve analysis(net benefit). The best model is selected based on three criteria: the 'lowest number of feature subset' with the 'maximal average AUC over the nested cross validation' and good generalisability to the UK Biobank dataset. In the original dataset, with over 100 different bootstraps and seven feature selection (FS) methods, HLA_C_*06 was selected as the most informative genetic variant. When the dataset is mitigated the single most important genetic features based on rank was identified as HLA_B_*27 by the seven different feature selection methods, consistent with previous analyses of this data using regression based methods. However, the predictive accuracy of these single features in post mitigation was found to be moderate (AUC= 0.54 (internal cross validation), AUC=0.53 (internal hold out set), AUC=0.55(external data set)). Sequentially adding additional HLA features based on rank improved the performance of the Random Forest classification model where 20 2-digit features selected by Interaction Capping (ICAP) demonstrated (AUC= 0.61 (internal cross validation), AUC=0.57 (internal hold out set), AUC=0.58 (external dataset)). The stratification method for mitigation of confounding features and filter information theoretic feature selection can be applied to a high dimensional dataset with the potential confounders.
dc.languageeng
dc.publisherSpringer Science and Business Media LLC
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourcenlmid: 101563288
dc.sourceessn: 2045-2322
dc.titleApplication of information theoretic feature selection and machine learning methods for the development of genetic risk prediction models.
dc.typeArticle
dc.date.updated2022-01-28T14:44:05Z
prism.issueIdentifier1
prism.publicationNameSci Rep
prism.volume11
dc.identifier.doi10.17863/CAM.80494
dcterms.dateAccepted2021-09-27
rioxxterms.versionofrecord10.1038/s41598-021-00854-x
rioxxterms.versionVoR
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/
dc.contributor.orcidJadon, Deepak [0000-0003-0600-4566]
dc.identifier.eissn2045-2322
pubs.funder-project-idVersus Arthritis (21754 , 20380, 21173, 20385, 21755, 21754)
pubs.funder-project-idMedical Research Council (MR/R016615, MR/S003126/1, MR/L011808/1, MR/R001839/1)
cam.issuedOnline2021-12-02


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Attribution 4.0 International
Except where otherwise noted, this item's licence is described as Attribution 4.0 International