Title: | Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data |
Authors: | Romagnoni, A Jegou, S VAN STEEN, Kristel Wainrib, G Hugot, JP Peyrin-Biroulet, L Chamaillard, M Colombel, JF Cottone, M D'Amato, M D'Inca, R Halfvarson, J Henderson, P Karban, A Kennedy, NA Khan, MA Lemann, M Levine, A Massey, D Milla, M Ng, SME Oikonomou, I Peeters, H Proctor, DD Rahier, JF Rutgeerts, P Seibold, F Stronati, L Taylor, KM Torkvist, L Ublick, K Van Limbergen, J Van Gossum, A Vatn, MH Zhang, H Zhang, W Andrews, JM Bampton, PA Barclay, M Florin, TH Gearry, R Krishnaprasad, K Lawrance, IC Mahy, G Montgomery, GW Radford-Smith, G Roberts, RL Simms, LA Hanigan, K Croft, A Amininijad, L Cleynen, I Dewit, O Franchimont, D Georges, M Laukens, D Theatre, E Vermeire, S Aumais, G Baidoo, L Barrie, AM Beck, K Bernard, EJ Binion, DG Bitton, A Brant, SR Cho, JH Cohen, A Croitoru, K Daly, MJ Datta, LW Deslandres, C Duerr, RH Dutridge, D Ferguson, J Fultz, J Goyette, P Greenberg, GR Haritunians, T Jobin, G Katz, S Lahaie, RG McGovern, DP Nelson, L Ng, SM Ning, K Pare, P Regueiro, MD Rioux, JD Ruggiero, E Schumm, LP Schwartz, M Scott, R Sharma, Y Silverberg, MS Spears, D Steinhart, AH Stempak, JM Swoger, JM Tsagarelis, C Zhang, C Zhao, HY AERTS, Jan Ahmad, T Arbury, H Attwood, A Auton, A Ball, SG Balmforth, AJ Barnes, C Barrett, JC Barroso, I Barton, A Bennett, AJ Bhaskar, S Blaszczyk, K Bowes, J Brand, OJ Braund, PS Bredin, F Breen, G Brown, MJ Bruce, IN Bull, J Burren, OS Burton, J Byrnes, J Caesar, S Cardin, N Clee, CM Coffey, AJ Mc Connell, J Conrad, DF Cooper, JD Dominiczak, AF Downes, K Drummond, HE Dudakia, D Dunham, A Ebbs, B Eccles, D Edkins, S Edwards, C Elliot, A Emery, P Evans, DM Evans, G Eyre, S Farmer, A Ferrier, IN Flynn, E Forbes, A Forty, L Franklyn, JA Frayling, TM Freathy, RM Giannoulatou, E Gibbs, P Gilbert, P Gordon-Smith, K Gray, E Green, E Groves, CJ Grozeva, D Gwilliam, R Hall, A Hammond, N Hardy, M Harrison, P Hassanali, N Hebaishi, H Hines, S Hinks, A Hitman, GA Hocking, L Holmes, C Howard, E Howard, P Howson, JMM Hughes, D Hunt, S Isaacs, JD Jain, M Jewell, DP Johnson, T Jolley, JD Jones, IR Jones, LA Kirov, G Langford, CF Lango-Allen, H Lathrop, GM Lee, J Lee, KL Lees, C Lewis, K Lindgren, CM Maisuria-Armer, M Maller, J Mansfield, J Marchini, JL Martin, P Massey, DCO McArdle, WL McGuffin, P McLay, KE McVean, G Mentzer, A Mimmack, ML Morgan, AE Morris, AP Mowat, C Munroe, PB Myers, S Newman, W Nimmo, ER O'Donovan, MC Onipinla, A Ovington, NR Owen, MJ Palin, K Palotie, A Parnell, K Pearson, R Pernet, D Perry, JRB Phillips, A Plagnol, V Prescott, NJ Prokopenko, I Quail, MA Rafelt, S Rayner, NW Reid, DM Renwick, A Ring, SM Robertson, N Robson, S Russell, E St Clair, D Sambrook, JG Sanderson, JD Sawcer, SJ Schuilenburg, H Scott, CE Scott, R Seal, S Shaw-Hawkins, S Shields, BM Simmonds, MJ Smyth, DJ Somaskantharajah, E Spanova, K Steer, S Stephens, J Stevens, HE Stirrups, K Stone, MA Strachan, DP Su, Z Symmons, DPM Thompson, JR Thomson, W Tobin, MD Travers, ME Turnbull, C Vukcevic, D Wain, LV Walker, M Walker, NM Wallace, C Warren-Perry, M Watkins, NA Webster, J Weedon, MN Wilson, AG Woodburn, M Wordsworth, BP Yau, C Young, AH Zeggini, E Brown, MA Burton, PR Caulfield, MJ Compston, A Farrall, M Gough, SCL Hall, AS Hattersley, AT Hill, AVS Mathew, CG Pembrey, M Satsangi, J Stratton, MR Worthington, J Hurles, ME Duncanson, A Ouwehand, WH Parkes, M Rahman, N Todd, JA Samani, NJ Kwiatkowski, DP McCarthy, MI Craddock, N Deloukas, P Donnelly, P Blackwell, JM Bramon, E Casas, JP Corvin, A Jankowski, J Markus, HS Palmer, CNA Plomin, R Rautanen, A Trembath, RC Viswanathan, AC Wood, NW Spencer, CCA Band, G Bellenguez, C Freeman, C Hellenthal, G Pirinen, M Strange, A Blackburn, H Bumpstead, SJ Dronov, S Gillman, M Jayakumar, A McCann, OT Liddle, J Potter, SC Ravindrarajah, R Ricketts, M Waller, M Weston, P Widaa, S Whittaker, P |
Issue Date: | 2019 |
Publisher: | NATURE RESEARCH |
Source: | Scientific Reports, 9 (1) (Art N° 10351) |
Abstract: | Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers. |
Document URI: | http://hdl.handle.net/1942/33759 |
ISSN: | 2045-2322 |
e-ISSN: | 2045-2322 |
DOI: | 10.1038/s41598-019-46649-z |
ISI #: | WOS:000475832500026 |
Rights: | The Author(s) 2019
Open Access This article is licensed under a Creative Commons Attribution 4.0 International
License, which permits use, sharing, adaptation, distribution and reproduction in any medium or
format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. Te images or other third party material in this
article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the
material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the
copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
Category: | A1 |
Type: | Journal Contribution |
Appears in Collections: | Research publications
|