Personalized Genetic Assessment of Age Associated Alzheimer's Disease

bioRxiv preprint doi: https://doi.org/10.1101/074864; this version posted September 13, 2016. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license. 1 Personalized genetic assessment of age-associated Alzheimer’s disease risk 2 3 Rahul S. Desikan, MD, PhD1*#; Chun Chieh Fan, MD2*; Yunpeng Wang, PhD3,4; Andrew 4 J. Schork, MS2; Howard J. Cabral, PhD5; L. Adrienne Cupples, PhD5; Wesley K. 5 Thompson, PhD6; Lilah Besser, MSPH7; Walter A. Kukull, PhD7; Dominic Holland, 6 PhD3; Chi-Hua Chen, PhD8; James B. Brewer, MD, PhD3,8,19; David S. Karow, MD, 7 PhD8; Karolina Kauppi, PhD8; Aree Witoelar, PhD4; Celeste M. Karch, PhD9; Luke W. 8 Bonham, BS10; Jennifer S. Yokoyama, PhD10; Howard J. Rosen, MD10; Bruce L. Miller, 9 MD10; William P. Dillon, MD1; David M. Wilson, MD, PhD1; Christopher P. Hess, MD, 10 PhD1; Margaret Pericak-Vance, PhD11; Jonathan L. Haines, PhD12; Lindsay A. Farrer, 11 PhD13; Richard Mayeux, MD14; John Hardy, PhD15; Alison M. Goate, PhD16; Bradley T. 12 Hyman, MD, PhD17; Gerard D. Schellenberg, PhD18; Linda K. McEvoy, PhD8; Ole A. 13 Andreassen, MD, PhD4#; Anders M. Dale, PhD2,3,8# for the ADNI and ADGC 14 investigators 15 16 *Contributed equally 17 1Neuroradiology Section, Department of Radiology and Biomedical Imaging, University 18 of California, San Francisco, San Francisco, CA USA 19 Departments of 2Cognitive Sciences and 3Neurosciences, University of California, San 20 Diego, La Jolla, CA, USA 21 4NORMENT; Institute of Clinical Medicine, University of Oslo and Division of Mental 22 Health and Addiction, Oslo University Hospital, Oslo, Norway 23 5Department of Biostatistics, Boston University School of Public Health, Boston, MA, 24 USA 25 6Institute for Biological Psychiatry, Sankt Hans Psychiatric Hospital, Roskilde, Denmark 26 7National Alzheimer's Coordinating Center, Department of Epidemiology, University of 27 Washington, Seattle, WA, USA 28 8Department of Radiology, University of California, San Diego, La Jolla, CA, USA 29 9Department of Psychiatry, Washington University, St. Louis, MO, USA 30 10Department of Neurology, University of California, San Francisco, San Francisco, CA, 31 USA 32 11The John P. Hussman Institute for Human Genomics, University of Miami, Miami, 33 Florida, USA 34 12Department of Epidemiology and Biostatistics and Institute for Computational 35 Biology, Case Western University, Cleveland, Ohio, USA 36 13Departments of Medicine (Biomedical Genetics), Neurology, Ophthalmology, 37 Biostatistics, and Epidemiology, Boston University Schools of Medicine and Public 38 Health, Boston, Massachusetts, USA 39 14Department of Neurology, Taub Institute on Alzheimer's Disease and the Aging Brain, 40 and Gertrude H. Sergievsky Center, Columbia University, New York, New York, USA 41 15Department of Molecular Neuroscience, UCL Institute of Neurology, London, UK 42 16Departments of Neuroscience, Genetics and Genomic Sciences, Icahn School of 43 Medicine at Mount Sinai, New York, NY, USA 1 bioRxiv preprint doi: https://doi.org/10.1101/074864; this version posted September 13, 2016. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license. 44 17Department of Neurology, Massachusetts General Hospital, Boston, MA, USA 45 18Department of Pathology and Laboratory Medicine, University of Pennsylvania 46 Perelman School of Medicine, Philadelphia, PA, USA 47 19Shiley-Marcos Alzheimer’s Disease Research Center, University of California, San 48 Diego, La Jolla, CA, USA 49 50 51 #Correspondence should be addressed to: 52 53 Dr. Rahul S. Desikan 54 Neuroradiology Section, L-352 55 University of California, San Francisco 56 505 Parnassus Avenue 57 San Francisco, CA, USA 94143 58 Email: [email protected] 59 Phone: (415)-353-1079 60 61 Dr. Ole A. Andreassen 62 KG Jebsen Centre for Psychosis Research 63 Building 49, Oslo University Hospital, Ullevål 64 Kirkeveien 166, PO Box 4956 Nydalen 65 0424 Oslo, Norway 66 Email: [email protected] 67 Ph: +47 23 02 73 50 (22 11 78 43 dir) 68 Fax: +47 23 02 73 33 69 70 71 Dr. Anders M. Dale 72 Department of Radiology 73 University of California, San Diego 74 8950 Villa La Jolla Drive, Suite C101 75 La Jolla, CA, USA 92037-0841 76 Emails: [email protected] 77 Phone: (858)-822-6671 78 Fax: (858)-534-1078 79 80 81 *Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging 82 Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to 83 the design and implementation of ADNI and/or provided data but did not participate in analysis or writing 84 of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp- 85 content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf 86 87 88 Manuscript = 3,095 words 89 90 91 ABSTRACT 2 bioRxiv preprint doi: https://doi.org/10.1101/074864; this version posted September 13, 2016. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license. 92 Importance: Identifying individuals at risk for developing Alzheimer’s disease (AD) is 93 of utmost importance. Although genetic studies have identified APOE and other AD 94 associated single nucleotide polymorphisms (SNPs), genetic information has not been 95 integrated into an epidemiological framework for personalized risk prediction. 96 Objective: To develop, replicate and validate a novel polygenic hazard score for 97 predicting age-specific risk for AD. 98 Setting: Multi-center, multi-cohort genetic and clinical data. 99 Participants: We assessed genetic data from 17,008 AD patients and 37,154 controls 100 from the International Genetics of Alzheimer’s Project (IGAP), and 6,409 AD patients 101 and 9,386 older controls from Phase 1 Alzheimer’s Disease Genetics Consortium 102 (ADGC). As independent replication and validation cohorts, we also evaluated genetic, 103 neuroimaging, neuropathologic, CSF and clinical data from ADGC Phase 2, National 104 Institute of Aging Alzheimer’s Disease Center (NIA ADC) and Alzheimer’s Disease 105 Neuroimaging Initiative (ADNI) (total n = 20,680) 106 Main Outcome(s) and Measure(s): Use the IGAP cohort to first identify AD associated 107 SNPs (at p < 10-5). Next, integrate these AD associated SNPs into a Cox proportional 108 hazards model using ADGC phase 1 genetic data, providing a polygenic hazard score 109 (PHS) for each participant. Combine population based incidence rates, and genotype- 110 derived PHS for each individual to derive estimates of instantaneous risk for developing 111 AD, based on genotype and age. Finally, assess replication and validation of PHS in 112 independent cohorts. 113 Results: Individuals in the highest PHS quantiles developed AD at a considerably lower 114 age and had the highest yearly AD incidence rate. Among APOE ε3/3 individuals, PHS 3 bioRxiv preprint doi: https://doi.org/10.1101/074864; this version posted September 13, 2016. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license. 115 modified expected age of AD onset by more than 10 years between the lowest and 116 highest deciles. In independent cohorts, PHS strongly predicted empirical age of AD 117 onset (p = 1.1 x 10-26), longitudinal progression from normal aging to AD (p = 1.54 x 10- 118 10) and associated with markers of AD neurodegeneration. 119 Conclusions: We developed, replicated and validated a clinically usable PHS for 120 quantifying individual differences in age-specific risk of AD. Beyond APOE, polygenic 121 architecture plays an important role in modifying AD risk. Precise quantification of AD 122 genetic risk will be useful for early diagnosis and therapeutic strategies. 123 4 bioRxiv preprint doi: https://doi.org/10.1101/074864; this version posted September 13, 2016. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license. 124 INTRODUCTION 125 126 Late onset Alzheimer’s disease (AD), the most common form of dementia, places a large 127 emotional and economic burden on patients and society. With increasing health care 128 expenditures among cognitively impaired elderly1, identifying individuals at risk for 129 developing AD is of utmost importance for potential preventative and therapeutic 130 strategies. Inheritance of the ε4 allele of apolipoprotein E (APOE) on chromosome 19q13 131 is the most significant risk factor for developing late-onset AD.2 APOE ε4 has a dose 132 dependent effect on age of onset, increases AD risk three-fold in heterozygotes and 133 fifteen-fold in homozygotes, and is implicated in 20-25% of patients with AD.3 134 In addition to APOE, recent genome-wide association studies (GWAS) have 135 identified numerous AD associated single nucleotide polymorphisms (SNPs), most of 136 which have a small effect on disease risk.4-5 Although no single polymorphism may be 137 informative clinically, a combination of APOE and non-APOE SNPs may help identify 138 older individuals at increased risk for AD. Despite the detection of novel AD associated 139 genes, GWAS findings have not yet been incorporated into a genetic epidemiology 140 framework for individualized risk prediction.

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