. Genomes-project-consortium, An integrated map of genetic variation from 1,092 human genomes, Nature, vol.491, issue.7422, p.56, 2012.

G. Abraham, A. Kowalczyk, J. Zobel, and M. Inouye, Sparsnp: Fast and memory-efficient analysis of all snps for phenotype prediction, BMC Bioinformatics, vol.13, issue.1, p.88, 2012.

G. Abraham, J. Tye-din, O. Bhalala, A. Kowalczyk, J. Zobel et al., Accurate and robust genomic prediction of celiac disease using statistical learning, PLoS Genetics, vol.10, issue.2, p.1004137, 2014.

P. Achenbach, E. Bonifacio, K. Koczwara, and A. Ziegler, Natural history of type 1 diabetes, Diabetes, vol.54, issue.2, pp.25-31, 2005.

D. E. Adkins, S. L. Clark, W. E. Copeland, M. Kennedy, K. Conway et al., Genome-wide meta-analysis of longitudinal alcohol consumption across youth and early adulthood, Twin Research and Human Genetics, vol.18, issue.4, pp.335-347, 2015.

A. Agresti and M. Kateri, Categorical Data Analysis, 2011.

H. K. Åkerblom and . Trigr-study-group, The trial to reduce iddm in the genetically at risk (trigr) study: recruitment, intervention and follow-up, Diabetologia, vol.54, issue.3, pp.627-633, 2011.

P. S. Albert, D. Ratnasinghe, J. Tangrea, and S. Wacholder, Limitations of the case-only design for identifying gene-environment interactions, American Journal of Epidemiology, vol.154, issue.8, pp.687-693, 2001.

N. E. Allen, C. Sudlow, T. Peakman, and R. Collins, Uk biobank data: come and get it, Science Translational Medicine, 2014.

M. A. Atkinson, G. S. Eisenbarth, and A. W. Michels, Type 1 diabetes. The Lancet, vol.383, pp.69-82, 2014.

V. Audigier, F. Husson, and J. Josse, A principal component method to impute missing values for mixed data, Advances in Data Analysis and Classification, vol.10, pp.5-26, 2016.

P. C. Austin, An introduction to propensity score methods for reducing the effects of confounding in observational studies, Multivariate Behavioral Research, vol.46, issue.3, pp.399-424, 2011.

J. Bach, The effect of infections on susceptibility to autoimmune and allergic diseases, New England Journal of Medicine, vol.347, issue.12, pp.911-920, 2002.

F. Balazard, S. L. Fur, S. Valtat, I. Diab-collaborative-group, A. Valleron et al., Association of environmental markers with childhood type 1 diabetes mellitus revealed by a long questionnaire on early life exposures and lifestyle in a case-control study, 2016.
URL : https://hal.archives-ouvertes.fr/hal-02038642

F. Balazard, Haplotype based genetic risk estimation for complex diseases, PeerJ PrePrints, 2016.

J. M. Barker, K. J. Barriga, L. Yu, D. Miao, H. A. Erlich et al., Prediction of autoantibody positivity and progression to type 1 diabetes: Diabetes autoimmunity study in the young (daisy), The Journal of Clinical Endocrinology & Metabolism, vol.89, issue.8, pp.3896-3902, 2004.

P. J. Barter, M. Caulfield, M. Eriksson, S. M. Grundy, J. Kastelein et al., Effects of torcetrapib in patients at high risk for coronary events, New England Journal of Medicine, vol.357, issue.21, pp.2109-2122, 2007.

Y. Benjamini and Y. Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, Journal of the Royal Statistical Society. Series B (Methodological), pp.289-300, 1995.

J. Berkson, Limitations of the application of fourfold table analysis to hospital data, Biometrics Bulletin, vol.2, issue.3, pp.47-53, 1946.

A. Beyerlein, F. Wehweck, A. Ziegler, and M. Pflueger, Respiratory infections in early life and the development of islet autoimmunity in children at increased type 1 diabetes risk: evidence from the babydiet study, JAMA Pediatrics, vol.167, issue.9, pp.800-807, 2013.

E. Bonifacio, A. Beyerlein, M. Hippich, C. Winkler, K. Vehik et al., Genetic scores to stratify risk of developing multiple islet autoantibodies and type 1 diabetes: A prospective study in children, PLoS Medicine, vol.15, issue.4, pp.1-18, 2018.

V. Botta, G. Louppe, P. Geurts, and L. Wehenkel, Exploiting snp correlations within random forest for genome-wide association studies, PLOS ONE, vol.9, issue.4, 2014.

A. Boulesteix, S. Janitza, J. Kruppa, and I. R. König, Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics, Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, vol.2, issue.6, pp.493-507, 2012.

L. Breiman, Random forests, Machine Learning, vol.45, pp.5-32, 2001.

L. Breiman, Random forests, Machine Learning, vol.45, pp.5-32, 2001.

N. E. Breslow and N. E. Day, Statistical methods in cancer research. The analysis of case-control studies, International Agency for Research on Cancer, 1981.

N. E. Breslow, N. E. Day, K. T. Halvorsen, R. L. Prentice, and C. Sabai, Estimation of multiple relative risk functions in matched case-control studies, American Journal of Epidemiology, vol.108, pp.299-307, 1978.

J. Brinkley, A. A. Tsiatis, and K. J. Anstrom, A generalized estimator of the attributable benefit of an optimal treatment regime, Biometrics, vol.66, pp.512-522, 2010.

G. Broberg and N. Roll-hansen, Eugenics and the welfare state, 2005.

R. Bumgarner, Overview of dna microarrays: types, applications, and their future, Current Protocols in Molecular Biology, pp.22-23, 2013.

P. Burton, D. Clayton, L. Cardon, N. Craddock, P. Deloukas et al., Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls, Nature, vol.447, issue.7145, pp.661-678, 2007.

T. Cai, L. Tian, P. H. Wong, and L. J. Wei, Analysis of randomized comparative clinical trial data for personalized treatment selections, Biostatistics, vol.12, pp.270-282, 2011.

S. Caillat-zucman, H. Garchon, J. Timsit, R. Assan, C. Boitard et al., Age-dependent hla genetic heterogeneity of type 1 insulindependent diabetes mellitus, The Journal of Clinical Investigation, vol.90, issue.6, pp.2242-2250, 1992.

R. Capdeville, E. Buchdunger, J. Zimmermann, and A. Matter, Glivec (STI571, imatinib), a rationally developed, targeted anticancer drug, Nature Reviews Drug Discovery, vol.1, pp.493-502, 2002.

C. R. Cardwell, L. C. Stene, J. Ludvigsson, J. Rosenbauer, O. Cinek et al., Breast-feeding and childhood-onset type 1 diabetes: a pooled analysis of individual participant data from 43 observational studies, Diabetes Care, p.120438, 2012.

N. M. Chapman, K. Coppieters, M. V. Herrath, and S. Tracy, The microbiology of human hygiene and its impact on type 1 diabetes, Islets, vol.4, issue.4, pp.253-261, 2012.

P. B. Chapman, A. Hauschild, C. Robert, J. B. Haanen, P. Ascierto et al., BRIM-3 Study Group. Improved survival with vemurafenib in melanoma with BRAF V600E mutation, New England Journal of Medicine, vol.364, pp.2507-2516, 2011.

H. P. Chase, D. Boulware, H. Rodriguez, D. Donaldson, S. Chritton et al., Clare-Salzler, and Type 1 Diabetes TrialNet Nutritional Intervention to Prevent (NIP) Type 1 Diabetes Study Group. Effect of docosahexaenoic acid supplementation on inflammatory cytokine levels in infants at high genetic risk for type 1 diabetes, Pediatric Diabetes, vol.16, issue.4, pp.271-279, 2015.

N. Chatterjee, J. Shi, and M. García-closas, Developing and evaluating polygenic risk prediction models for stratified disease prevention, Nature Reviews Genetics, vol.17, issue.7, p.392, 2016.

S. Chen and G. Parmigiani, Meta-analysis of brca1 and brca2 penetrance, Journal of Clinical Oncology, vol.25, issue.11, p.1329, 2007.

, Collaborative Group on Hormonal Factors in Breast Cancer et al. Alcohol, tobacco and breast cancer-collaborative reanalysis of individual data from 53 epidemiological studies, including 58 515 women with breast cancer and 95 067 women without the disease, British Journal of Cancer, vol.87, issue.11, p.1234, 2002.

, Collaborative Group on Hormonal Factors in Breast Cancer et al. Menarche, menopause, and breast cancer risk: individual participant meta-analysis, including 118 964 women with breast cancer from 117 epidemiological studies, The Lancet Oncology, vol.13, issue.11, pp.1141-1151, 2012.

S. R. Cole, R. W. Platt, E. F. Schisterman, H. Chu, D. Westreich et al., Illustrating bias due to conditioning on a collider, International Journal of Epidemiology, vol.39, issue.2, pp.417-420, 2009.

M. Cornelis, E. Byrne, T. Esko, M. Nalls, A. Ganna et al., Genome-wide meta-analysis identifies six novel loci associated with habitual coffee consumption, Molecular Psychiatry, vol.20, issue.5, p.647, 2015.

S. S. Coughlin, Recall bias in epidemiologic studies, Journal of Clinical Epidemiology, vol.43, issue.1, pp.87-91, 1990.

D. R. Cox, Regression models and life-tables (with discussion), Journal of the Royal Statistical Society: Series B, vol.34, pp.187-220, 1972.

J. Craig, Complex diseases: research and applications, Nature Education, vol.1, issue.1, 2008.

G. Dahlquist, Can we slow the rising incidence of childhood-onset autoimmune diabetes? the overload hypothesis, Diabetologia, vol.49, issue.1, pp.20-24, 2006.

G. , D. Smith, and G. Hemani, Mendelian randomization: genetic anchors for causal inference in epidemiological studies, Human Molecular Genetics, vol.23, issue.R1, pp.89-98, 2014.

N. E. Day and D. P. Byar, Testing hypotheses in case-control studies-equivalence of mantelhaenszel statistics and logit score tests, Biometrics, vol.35, pp.623-630, 1979.

O. Delaneau, J. Zagury, and J. Marchini, Improved whole-chromosome phasing for disease and population genetic studies, Nature Methods, vol.10, issue.1, pp.5-6, 2013.

, Incidence and trends of childhood type 1 diabetes worldwide 1990-1999, Diabetic Medicine, vol.23, issue.8, pp.857-866, 2006.

J. Dong, W. Zhang, J. J. Chen, Z. Zhang, S. Han et al., Vitamin d intake and risk of type 1 diabetes: a meta-analysis of observational studies, Nutrients, vol.5, issue.9, pp.3551-3562, 2013.

M. A. , E. Merzon, L. F. Valbuena, D. Tirschwell, C. A. Paris et al., Environmental factors associated with childhood-onset type 1 diabetes mellitus: an exploration of the hygiene and overload hypotheses, Archives of Pediatrics & Adolescent Medicine, vol.164, issue.8, pp.732-738, 2010.

F. M. Egro, Why is type 1 diabetes increasing?, Journal of Molecular Endocrinology, vol.51, issue.1, pp.1-13, 2013.

R. F. Engle, Wald, likelihood ratio, and lagrange multiplier tests in econometrics, Handbook of Econometrics, vol.2, pp.775-826, 1984.

J. Evans, I. Goldfine, B. Maddux, and G. Grodsky, Are oxidative stress-activated signaling pathways mediators of insulin resistance and ?-cell dysfunction?, Diabetes, vol.52, issue.1, pp.1-8, 2003.

M. Ewertz, S. Duffy, H. Adami, G. Kvåle, E. Lund et al., Age at first birth, parity and risk of breast cancer: A meta-analysis of 8 studies from the nordic countries, International Journal of Cancer, vol.46, issue.4, pp.597-603, 1990.

, Global Alliance for Genomics and Health. A federated ecosystem for sharing genomic, clinical data, Science, vol.352, issue.6291, pp.1278-1280, 2016.

D. S. Falconer, The inheritance of liability to certain diseases, estimated from the incidence among relatives, Annals of Human Genetics, vol.29, issue.1, pp.51-76, 1965.

K. Farh, A. Marson, J. Zhu, M. Kleinewietfeld, W. Housley et al., Genetic and epigenetic fine mapping of causal autoimmune disease variants, Nature, vol.518, issue.7539, p.337, 2015.

J. A. Fernandes, F. , and B. E. Shapiro, Tay-sachs disease, Archives of Neurology, vol.61, issue.9, pp.1466-1468, 2004.

G. P. Forlenza and M. Rewers, The epidemic of type 1 diabetes: what is it telling us? Current Opinion in Endocrinology, Diabetes and Obesity, vol.18, issue.4, pp.248-251, 2011.

J. C. Foster, J. M. Taylor, and S. J. Ruberg, Subgroup identification from randomized clinical trial data, Statistics in Medicine, vol.30, pp.2867-2880, 2011.

B. Freidlin and E. L. Korn, Biomarker enrichment strategies: Matching trial design to biomarker credentials, Nature Reviews Clinical Oncology, vol.11, pp.81-90, 2014.

J. Friedman, T. Hastie, and R. Tibshirani, The elements of statistical learning, vol.1, 2001.

J. Friedman, T. Hastie, and R. Tibshirani, Regularization paths for generalized linear models via coordinate descent, Journal of Statistical Software, vol.33, issue.1, p.1, 2010.

H. Furberg, Y. Kim, J. Dackor, E. Boerwinkle, N. Franceschini et al., Genome-wide meta-analyses identify multiple loci associated with smoking behavior, Nature Genetics, vol.42, issue.5, p.441, 2010.

S. H. Gage, G. Davey-smith, J. J. Ware, J. Flint, and M. R. Munafò, G= e: What gwas can tell us about the environment, PLoS Genetics, vol.12, issue.2, p.1005765, 2016.

E. Gale, European Nicotinamide Diabetes Intervention Trial (ENDIT) Group, et al. European nicotinamide diabetes intervention trial (endit): a randomised controlled trial of intervention before the onset of type 1 diabetes, The Lancet, vol.363, issue.9413, pp.925-931, 2004.

E. A. Gale, A missing link in the hygiene hypothesis?, Diabetologia, vol.45, issue.4, pp.588-594, 2002.

F. Galton, Regression towards mediocrity in hereditary stature, The Journal of the Anthropological Institute of Great Britain and Ireland, vol.15, pp.246-263, 1886.

N. Gatto, U. Campbell, A. Rundle, and H. Ahsan, Further development of the case-only design for assessing gene-environment interaction: evaluation of and adjustment for bias, International Journal of Epidemiology, vol.33, issue.5, pp.1014-1024, 2004.

N. W. Gillham, A life of Sir Francis Galton: From African exploration to the birth of eugenics, 2001.

J. Green, D. Casabonne, and R. Newton, Coxsackie b virus serology and type 1 diabetes mellitus: a systematic review of published case-control studies, Diabetic Medicine, vol.21, issue.6, pp.507-514, 2004.

S. Greenland, Quantifying biases in causal models: classical confounding vs collider-stratification bias, Epidemiology, vol.14, issue.3, pp.300-306, 2003.

J. Greenland, J. Robins, and . Pearl, Confounding and collapsibility in causal inference, Statistical Science, pp.29-46, 1999.

N. Gujral, H. J. Freeman, and A. Thomson, Celiac disease: prevalence, diagnosis, pathogenesis and treatment, World Journal of Gastroenterology, vol.18, issue.42, p.6036, 2012.

J. Guo and Z. Geng, Collapsibility of logistic regression coefficients, Journal of the Royal Statistical Society. Series B (Methodological), pp.263-267, 1995.

N. S. Hall, Ra fisher and his advocacy of randomization, Journal of the History of Biology, vol.40, issue.2, pp.295-325, 2007.

P. Hall and C. C. Heyde, Martingale limit theory and its application, 1980.

M. A. Hamburg and F. S. Collins, The path to personalized medicine, New England Journal of Medicine, vol.363, pp.301-304, 2010.

T. Harder, K. Roepke, N. Diller, Y. Stechling, J. W. Dudenhausen et al., Birth weight, early weight gain, and subsequent risk of type 1 diabetes: systematic review and meta-analysis, American Journal of Epidemiology, vol.169, issue.12, pp.1428-1436, 2009.

V. Harjutsalo, R. Sund, M. Knip, and P. Groop, Incidence of type 1 diabetes in finland, JAMA, vol.310, issue.4, pp.427-428, 2013.

D. L. Hartl and A. G. Clark, Principles of Population Genetics, vol.116, 1997.

B. N. Howie, P. Donnelly, and J. Marchini, A flexible and accurate genotype imputation method for the next generation of genome-wide association studies, PLoS Genetics, vol.5, issue.6, p.1000529, 2009.

J. Howson, J. Cooper, D. Smyth, N. Walker, H. Stevens et al., Evidence of gene-gene interaction and age-at-diagnosis effects in type 1 diabetes, Diabetes, issue.11, pp.3012-3017, 2012.

C. Hu, H. Ding, Y. Li, J. Pearson, X. Zhang et al., Nlrp3 deficiency protects from type 1 diabetes through the regulation of chemotaxis into the pancreatic islets, Proceedings of the National Academy of Sciences, vol.112, issue.36, pp.11318-11323, 2015.

S. Hummel, M. Pflüger, M. Hummel, E. Bonifacio, and A. Ziegler, Primary dietary intervention study to reduce the risk of islet autoimmunity in children at increased risk for type 1 diabetes: the babydiet study, Diabetes Care, vol.34, issue.6, pp.1301-1305, 2011.

V. Hyttinen, J. Kaprio, L. Kinnunen, M. Koskenvuo, and J. Tuomilehto, Genetic liability of type 1 diabetes and the onset age among 22,650 young finnish twin pairs a nationwide follow-up study, Diabetes, vol.52, issue.4, pp.1052-1055, 2003.

M. Inouye, G. Abraham, C. Nelson, A. Wood, M. Sweeting et al., Genomic risk prediction of coronary artery disease in nearly 500,000 adults: implications for early screening and primary prevention. bioRxiv, 2018.

J. O. Irwin, The standard error of an estimate of expectation of life, with special reference to expectation of tumourless life in experiments with mice, Journal of Hygiene, vol.47, pp.188-189, 1949.

S. S. Jamuar and E. Tan, Clinical application of next-generation sequencing for mendelian diseases, Human Genomics, vol.9, issue.1, p.10, 2015.

H. Janes, M. D. Brown, Y. Huang, and M. S. Pepe, An approach to evaluating and comparing biomarkers for patient treatment selection, The International Journal of Biostatistics, vol.10, pp.99-121, 2014.

C. Kang, H. Janes, and Y. Huang, Combining biomarkers to optimize patient treatment recommendations, Biometrics, vol.70, pp.695-707, 2014.

J. Kang, S. Kugathasan, M. Georges, H. Zhao, and J. Cho, Improved risk prediction for crohn's disease with a multi-locus approach, Human Molecular Genetics, vol.20, issue.12, pp.2435-2442, 2011.

M. J. Khoury, Dealing with the evidence dilemma in genomics and personalized medicine, Clinical Pharmacology and Therapeutics, vol.87, pp.635-638, 2010.

M. J. Khoury and W. D. Flanders, Nontraditional epidemiologic approaches in the analysis of gene environment interaction: Case-control studies with no controls!, American Journal of Epidemiology, vol.144, issue.3, pp.207-213, 1996.

Y. Kim, W. Wang, M. Okla, I. Kang, R. Moreau et al., Suppression of nlrp3 inflammasome by ?-tocotrienol ameliorates type 2 diabetes, Journal of Lipid Research, vol.57, issue.1, pp.66-76, 2016.

M. Knip and O. Simell, Environmental triggers of type 1 diabetes. Cold Spring Harbor Perspectives in Medicine, vol.2, p.7690, 2012.

M. Knip, H. K. Åkerblom, and D. Becker, Hydrolyzed infant formula and early ?-cell autoimmunity: a randomized clinical trial, JAMA, vol.311, issue.22, pp.2279-2287, 2014.

M. Knip, H. K. Åkerblom, E. Taji, and D. Becker, Effect of hydrolyzed infant formula vs conventional formula on risk of type 1 diabetes: The trigr randomized clinical trial, JAMA, vol.319, issue.1, pp.38-48, 2018.

A. Kuhad, M. Bishnoi, V. Tiwari, and K. Chopra, Suppression of nf-?? signaling pathway by tocotrienol can prevent diabetes associated cognitive deficits, Pharmacology Biochemistry and Behavior, vol.92, issue.2, pp.251-259, 2009.

S. Künzel, J. Sekhon, P. Bickel, and B. Yu, Meta-learners for estimating heterogeneous treatment effects using machine learning, 2017.

S. L. Fur, P. Bougnères, and A. Valleron, Comparison of a french pediatric type 1 diabetes cohort's responders and non-responders to an environmental questionnaire, BMC Public Health, vol.14, issue.1, p.1241, 2014.

L. Lello, S. G. Avery, L. Tellier, A. Vazquez, G. De-los-campos et al., Accurate genomic prediction of human height, 2017.

J. Li, L. Zhao, L. Tian, T. Cai, B. Claggett et al., A predictive enrichment procedure to identify potential responders to a new therapy for randomized, comparative controlled clinical studies, Biometrics, vol.72, pp.877-887, 2016.

A. Liaw and M. Wiener, Classification and regression by randomforest, R News, vol.2, issue.3, pp.18-22, 2002.

A. Like, D. Guberski, and L. Butler, Influence of environmental viral agents on frequency and tempo of diabetes mellitus in bb/wor rats, Diabetes, vol.40, issue.2, pp.259-262, 1991.

I. Lipkovich, A. Dmitrienko, J. Denne, and G. Enas, Subgroup identification based on differential effect search-a recursive partitioning method for establishing response to treatment in patient subpopulations, Statistics in Medicine, vol.30, pp.2601-2621, 2011.

P. A. Lombardo, A century of eugenics in America: from the Indiana experiment to the human genome era, 2011.

M. H. Maathuis, D. Colombo, M. Kalisch, and P. Bühlmann, Predicting causal effects in largescale systems from observational data, Nature Methods, vol.7, issue.4, p.247, 2010.

K. E. Malone, J. R. Daling, D. R. Doody, L. Hsu, L. Bernstein et al., Prevalence and predictors of brca1 and brca2 mutations in a population-based study of breast cancer in white and black american women ages 35 to 64 years, Cancer Research, vol.66, issue.16, pp.8297-8308, 2006.

S. J. Mandrekar and D. J. Sargent, Clinical trial designs for predictive biomarker validation: Theoretical considerations and practical challenges, Journal of Clinical Oncology, vol.27, pp.4027-4034, 2009.

T. Manolio, F. Collins, N. Cox, D. Goldstein, L. Hindorff et al., Finding the missing heritability of complex diseases, Nature, vol.461, issue.7265, pp.747-753, 2009.

N. Mantel and W. Haenszel, Statistical aspects of the analysis of data from retrospective studies, Journal of the National Cancer Institute, vol.22, pp.719-748, 1959.

J. H. Mcdonald, Handbook of Biological Statistics, 2009.

Q. Mcnemar, Note on the sampling error of the difference between correlated proportions or percentages, Psychometrika, vol.12, pp.153-157, 1947.

N. E. Miller, D. S. Thelle, O. H. Forde, and O. D. Mjos, The tromso heart study: high-density lipoportein and coronary heart disease: a prospective case-control study, The Lancet, vol.309, issue.8019, pp.965-968, 1977.

B. Modan, P. Hartge, G. Hirsh-yechezkel, A. Chetrit, and F. Lubin, Parity, oral contraceptives, and the risk of ovarian cancer among carriers and noncarriers of a brca1 or brca2 mutation, New England Journal of Medicine, vol.345, issue.4, pp.235-240, 2001.

P. G. Moorman, E. S. Iversen, P. K. Marcom, J. R. Marks, F. Wang et al., Evaluation of established breast cancer risk factors as modifiers of brca1 or brca2: a multi-center case-only analysis, Breast Cancer Research and Treatment, vol.124, issue.2, pp.441-451, 2010.

J. M. Norris, X. Yin, M. M. Lamb, K. Barriga, J. Seifert et al., Omega-3 polyunsaturated fatty acid intake and islet autoimmunity in children at increased risk for type 1 diabetes, JAMA, vol.298, issue.12, pp.1420-1428, 2007.

R. A. Oram, K. Patel, A. Hill, B. Shields, T. J. Mcdonald et al., A type 1 diabetes genetic risk score can aid discrimination between type 1 and type 2 diabetes in young adults, Diabetes Care, vol.39, issue.3, pp.337-344, 2016.

C. Patel, J. Bhattacharya, and A. J. Butte, An environment-wide association study (ewas) on type 2 diabetes mellitus, PLOS ONE, vol.5, issue.5, pp.1-10, 2010.

K. A. Patel, R. A. Oram, S. E. Flanagan, E. Franco, K. Colclough et al., Type 1 diabetes genetic risk score: a novel tool to discriminate monogenic and type 1 diabetes, Diabetes, p.151690, 2016.

J. Pearl and . Causality, , 2009.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn: Machine learning in Python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

W. Piegorsch, C. Weinberg, and J. Taylor, Non-hierarchical logistic models and case-only designs for assessing susceptibility in population-based case-control studies, Statistics in Medicine, vol.13, issue.2, pp.153-162, 1994.

C. Piffaretti, S. Edorh, R. Coutant, C. Choleau, S. Guilmin-crepon et al., Incidence du diabète de type 1 chez l'enfant en France en 2013-2015, à partir du système national des données de santé (SNDS). Variations régionales. Numéro thématique, pp.571-578, 2017.

S. Purcell, B. Neale, K. Todd-brown, L. Thomas, M. Ferreira et al., Plink: a tool set for whole-genome association and populationbased linkage analyses, The American Journal of Human Genetics, vol.81, issue.3, pp.559-575, 2007.

M. Qian and S. A. Murphy, Performance guarantees for individualized treatment rules, The Annals of Statistics, vol.39, pp.1180-1210, 2011.

S. M. Rappaport, D. K. Barupal, D. Wishart, P. Vineis, and A. Scalbert, The blood exposome and its role in discovering causes of disease, Environmental Health Perspectives, vol.122, issue.8, p.769, 2014.

T. Rasmussen, E. Witsø, G. Tapia, L. C. Stene, and K. S. Rønningen, Self-reported lower respiratory tract infections and development of islet autoimmunity in children with the type 1 diabetes high-risk hla genotype: the midia study, Diabetes Metabolism Research and Reviews, vol.27, issue.8, pp.834-837, 2011.

M. J. Redondo, J. Jeffrey, P. R. Fain, G. S. Eisenbarth, and T. Orban, Concordance for islet autoimmunity among monozygotic twins, New England Journal of Medicine, vol.359, issue.26, pp.2849-2850, 2008.

P. Rosenbaum, Observational Studies, 2002.

P. Rosenbaum and D. Rubin, The central role of the propensity score in observational studies for causal effects, Biometrika, vol.70, pp.41-55, 1983.

P. R. Rosenbaum and D. Rubin, The central role of the propensity score in observational studies for causal effects, Biometrika, vol.70, issue.1, pp.41-55, 1983.

D. Rubin, Matching to remove bias in observational studies, Biometrics, vol.29, pp.159-183, 1973.

P. A. Sandercock, R. Collins, C. Counsell, B. Farrell, R. Peto et al., The International Stroke Trial (IST): A randomized trial of aspirin, subcutaneous heparin, both, or neither among 19435 patients with acute ischaemic stroke, The Lancet, vol.349, pp.1569-1581, 1997.

P. A. Sandercock, M. Niewada, and A. Cz?onkowska, The International Stroke Trial database, Trials, vol.12, p.101, 2011.

S. Schmidt and D. J. Schaid, Potential misinterpretation of the case-only study to assess geneenvironment interaction, American Journal of Epidemiology, vol.150, issue.8, pp.878-885, 1999.

D. Schneider and M. Herrath, Potential viral pathogenic mechanism in human type 1 diabetes, Diabetologia, vol.57, issue.10, pp.2009-2018, 2014.

G. G. Schwartz, A. G. Olsson, M. Abt, C. M. Ballantyne, P. J. Barter et al., Effects of dalcetrapib in patients with a recent acute coronary syndrome, New England Journal of Medicine, vol.367, issue.22, pp.2089-2099, 2012.

J. Shawe-taylor and N. Cristianini, Kernel methods for pattern analysis, 2004.

J. Shen, L. Wang, S. Daignault, D. E. Spratt, T. M. Morgan et al., Estimating the optimal personalized treatment strategy based on selected variables to prolong survival via random survival forest with weighted bootstrap, Journal of Biopharmaceutical Statistics, vol.21, pp.1-20, 2017.

Y. Shen and T. Cai, Identifying predictive markers for personalized treatment selection, Biometrics, vol.72, pp.1017-1025, 2016.

J. Shuster and J. Van-eys, Interaction between prognostic factors and treatment, Controlled Clinical Trials, vol.4, pp.209-214, 1983.

R. M. Simon, S. Paik, and D. F. Hayes, Use of archived specimens in evaluation of prognostic and predictive biomarkers, Journal of the National Cancer Institute, vol.101, pp.1446-1452, 2009.

M. Simpson, H. Brady, X. Yin, J. Seifert, K. Barriga et al., No association of vitamin d intake or 25-hydroxyvitamin d levels in childhood with risk of islet autoimmunity and type 1 diabetes: the diabetes autoimmunity study in the young (daisy), Diabetologia, vol.54, issue.11, p.2779, 2011.

D. P. Singal and M. A. Blajchman, Histocompatibility (hl-a) antigens, lymphocytotoxic antibodies and tissue antibodies in patients with diabetes mellitus, Diabetes, vol.22, issue.6, pp.429-432, 1973.

J. S. Skyler, Primary and secondary prevention of type 1 diabetes, Diabetic Medicine, vol.30, issue.2, pp.161-169, 2013.

M. Slatkin, Linkage disequilibrium-understanding the evolutionary past and mapping the medical future, Nature Reviews Genetics, vol.9, issue.6, p.477, 2008.

J. K. Bibliography, J. Snell-bergeon, F. Smith, A. Dong, K. Barón et al., Early childhood infections and the risk of islet autoimmunity: the diabetes autoimmunity study in the young (daisy), Diabetes Care, vol.35, issue.12, pp.2553-2558, 2012.

J. Snoep, A. Morabia, S. Hernández-díaz, M. A. Hernán, and J. P. Vandenbroucke, Commentary: a structural approach to berkson's fallacy and a guide to a history of opinions about it, International Journal of Epidemiology, vol.43, issue.2, pp.515-521, 2014.

D. Speed, N. Cai, M. R. Johnson, S. Nejentsev, D. J. Balding et al., Reevaluation of snp heritability in complex human traits, Nature Genetics, vol.49, issue.7, p.986, 2017.

J. Stamatoyannopoulos, Connecting the regulatory genome, Tattersall. Diabetes: the biography, vol.48, p.479, 2009.

. Teddy-study-group, The environmental determinants of diabetes in the young (teddy) study: study design, Pediatric Diabetes, vol.8, issue.5, pp.286-298, 2007.

P. I. Terasaki, A brief history of hla, Immunologic Research, vol.38, issue.1-3, pp.139-148, 2007.

R. Tewhey, V. Bansal, A. Torkamani, E. Topol, and N. Schork, The importance of phase information for human genomics, Nature Reviews Genetics, vol.12, issue.3, pp.215-223, 2011.

L. Tian, L. Zhao, and L. J. Wei, Predicting the restricted mean event time with the subject's baseline covariates in survival analysis, Biostatistics, vol.15, pp.222-233, 2014.

R. Tibshirani, Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society. Series B (Methodological), pp.267-288, 1996.

R. Tibshirani, A comparison of some error estimates for neural network models, Neural Computation, vol.8, pp.152-163, 1996.

A. Torkamani, N. E. Wineinger, and E. J. Topol, The personal and clinical utility of polygenic risk scores, Nature Reviews Genetics, p.1, 2018.

J. Tyrrell, A. R. Wood, R. M. Ames, and H. Yaghootkar, Gene-obesogenic environment interactions in the uk biobank study, International Journal of Epidemiology, vol.46, issue.2, pp.559-575, 2017.

, Paving the way for personalized medicine: FDA's role in a new era of medical product development, US Food and Drug Administration, 2013.

A. W. Van-der and . Vaart, Cambridge Series in Statistical and Probabilistic Mathematics, pp.138-152, 1998.

T. Vatanen, A. D. Kostic, E. Hennezel, and H. Siljander, Variation in microbiome lps immunogenicity contributes to autoimmunity in humans, Cell, vol.165, issue.4, pp.842-853, 2016.

K. C. Verbeeten, C. E. Elks, D. Daneman, and K. K. Ong, Association between childhood obesity and subsequent type 1 diabetes: a systematic review and meta-analysis, Diabetic Medicine, vol.28, issue.1, pp.10-18, 2011.

P. M. Visscher, W. G. Hill, and N. R. Wray, Heritability in the genomics era concepts and misconceptions, Nature Reviews Genetics, vol.9, issue.4, pp.255-266, 2008.

P. M. Visscher and J. B. Walsh, Commentary: Fisher 1918: the foundation of the genetics and analysis of complex traits, International Journal of Epidemiology, 2017.

A. Vivot, I. Boutron, P. Ravaud, and R. Porcher, Guidance for pharmacogenomic biomarker testing in labels of FDA-approved drugs, Genetics in Medicine, vol.17, pp.733-738, 2015.

A. Vivot, J. Li, J. D. Zeitoun, S. Mourah, P. Crequit et al., Pharmacogenomic biomarkers as inclusion criteria in clinical trials of oncology-targeted drugs: A mapping of ClinicalTrials.gov, Genetics in Medicine, vol.18, pp.796-805, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01867573

B. F. Voight, G. M. Peloso, M. Orho-melander, R. Frikke-schmidt, M. Barbalic et al., Plasma hdl cholesterol and risk of myocardial infarction: a mendelian randomisation study, The Lancet, vol.380, issue.9841, pp.572-580, 2012.

S. Wager, T. Hastie, and B. Efron, Confidence intervals for random forests: The jackknife and the infinitesimal jackknife, Journal of Machine Learning Research, vol.15, pp.1625-1651, 2014.

B. Wang, W. J. Canestaro, and N. K. Choudhry, Clinical evidence supporting pharmacogenomic biomarker testing provided in US Food and Drug Administration drug labels, JAMA Internal Medicine, vol.174, pp.1938-1944, 2014.

Z. Wei, K. Wang, H. Qu, H. Zhang, J. Bradfield et al., From disease association to risk assessment: an optimistic view from genome-wide association studies on type 1 diabetes, PLoS Genetics, vol.5, issue.10, p.1000678, 2009.

Z. Wei, W. Wang, J. Bradfield, J. Li, C. Cardinale et al., Large sample size, wide variant spectrum, and advanced machine-learning technique boost risk prediction for inflammatory bowel disease, The American Journal of Human Genetics, vol.92, issue.6, pp.1008-1012, 2013.

D. Welter, J. Macarthur, J. Morales, T. Burdett, P. Hall et al., The nhgri gwas catalog, a curated resource of snp-trait associations, Nucleic Acids Research, vol.42, issue.D1, pp.1001-1006, 2014.

S. Wood, mgcv: mixed gam computation vehicle with gcv/aic/reml smoothness estimation. r package version 1, pp.7-22, 2012.

N. Wray, J. Yang, M. Goddard, and P. Visscher, The genetic interpretation of area under the roc curve in genomic profiling, PLoS Genetics, vol.6, issue.2, p.1000864, 2010.

J. Yang, S. H. Lee, M. E. Goddard, P. M. Visscher, ;. Yeung et al., Enterovirus infection and type 1 diabetes mellitus: systematic review and meta-analysis of observational molecular studies, The American Journal of Human Genetics, vol.88, issue.1, p.35, 2011.

B. Zhang, A. A. Tsiatis, E. B. Laber, and M. Davidian, A robust method for estimating optimal treatment regimes, Biometrics, vol.68, pp.1010-1018, 2012.

L. Zhao, L. Tian, T. Cai, B. Claggett, and L. J. Wei, Effectively selecting a target population for a future comparative study, Journal of the American Statistical Association, vol.108, pp.527-539, 2013.

Y. Zhao, D. Zeng, A. J. Rush, and M. R. Kosorok, Estimating individualized treatment rules using outcome weighted learning, Journal of the American Statistical Association, vol.107, pp.1106-1118, 2012.

Y. Q. Zhao, D. Zeng, E. B. Laber, R. Song, M. Yuan et al., Doubly robust learning for estimating individualized treatment with censored data, Biometrika, vol.102, pp.151-168, 2015.

Y. Zheng, T. Cai, and Z. Feng, Application of the time-dependent ROC curves for prognostic accuracy with multiple biomarkers, Biometrics, vol.62, pp.279-287, 2006.

X. Zhou, N. Mayer-hamblett, U. Khan, and M. R. Kosorok, Residual weighted learning for estimating individualized treatment rules, Journal of the American Statistical Association, vol.112, pp.169-187, 2017.

R. C. Ziegelstein and . Personomics, JAMA Internal Medicine, vol.175, pp.888-889, 2015.

A. Ziegler, M. Hummel, M. Schenker, and E. Bonifacio, Autoantibody appearance and risk for development of childhood diabetes in offspring of parents with type 1 diabetes: the 2-year analysis of the german babydiab study, Diabetes, vol.48, issue.3, pp.460-468, 1999.

, Nous proposons une méthode afin d'utiliser l'information de phase pour améliorer les prédictions de risque génétique. Nous répliquons une estimation du risque génétique sur les patients d'Isis-Diab. Nous prouvons un résultat d'équivalence asymptotique entre deux méthodes d'analyse des données appariées. Nous analysons ensuite l'étude cas-témoins d'Isis-Diab basée sur des questionnaires environnementaux. Nous essayons de confirmer les résultats de cette étude en croisant le risque génétique et les facteurs environnementaux chez les patients d'Isis-Diab. Cela nous amène à proposer une nouvelle méthodologie basée sur le biais de collision et à étudier l'influence de ce dernier dans les études cas-seulement pour les interactions gène-environnement. Finalement, nous étudions la possibilité d'utiliser des essais thérapeutiques randomisés pour personnaliser les traitements. Nous proposons une nouvelle méthodologie pour estimer le bénéfice de la personnalisation et nous recommandons un choix de stratégie de personnalisation, Résumé Cette thèse porte sur l'élucidation des causes génétiques et environnementales des maladies complexes. L'application centrale est l'étude Isis-Diab sur le diabète de type 1