Genetic Heterogeneity of Clinically Defined AD. Andrew J. Saykin, PsyD Indiana ADC ADC Clinical Core Leaders Meeting April 22, 2017

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1 Genetic Heterogeneity of Clinically Defined AD Andrew J. Saykin, PsyD Indiana ADC ADC Clinical Core Leaders Meeting April 22, 2017

2 Disclosures & Acknowledgements Disclosures Eli Lilly (Collaborative Grant), Arkley BioTek (SBIR), Avid Radiopharmaceuticals, GSK Editor-in-Chief, Brain Imaging and Behavior, a Springer Nature journal Grant Support National Institute on Aging P30 AG10133 (IADC) ADNI: U01 AG & RC2 AG036535; Indiana: R01 AG19771 U01 AG032984, U24 AG21886, P30 AG010129, K01 AG National Institute of Biomedical Imaging and Bioengineering National Library of Medicine: R01 LM and K99/R00 LM National Science Foundation: IIS Foundation for the NIH (ADNI-1 GWAS) Anonymous Foundation (Challenge Grant), Gene Network Sciences, Merck, Pfizer ADNI methylation project (AbbVie, Biogen and J&J, in-kind support) Alzheimer s Association & Brin Wojcicki Foundation Whole Genome Sequencing The Indiana Neurorepository Project IUSM Strategic Research Initiative (SRI), Indiana Spinal Cord and Brain Injury Research Fund, CTSI

3 Overview Dimensions of Heterogeneity Genes and gene pathways Impact on risk/protective factors Influence on age of onset Cognitive subtypes and trajectories Signal or noise? Can we model heterogeneity? Implications

4 High Dimensionality Landscape Subjective Cognitive Decline Informant Perception Cognitive Performance Social Networks Lifestyle & environment - Cognitive stimulation, diet, exercise, sleep Biomarkers - CSF, blood, others - Multi-omics Genomics - DNA, mrna, mirna - Epigenetics Amyloid - PET/CSF Tau - PET/CSF MRI - structure MRI - function Connectome Vascular function & CVD burden Immune System, Inflammation & Oxidative Stress Metabolism & Mitochondria Saykin 2017

5 Dynamic Aspects: Temporal Heterogeneity

6 PS2 Heritability LOAD: genetic factors account for ~60-80% of risk (Gatz et al 2006); APOE accounts for up to 50% (Ashford & Mortimer 2002); so up to 30% remains to be found. APOE PS1 Chromosome 19 APP 21q21.3 Chromosome 1 Chromosome 14 Chromosome 21

7 Genetic Risk for AD: Many Pathways C. Karch & A. Goate, Biol Psychiatry 2015; 77:43 51

8 AD Cognitive Subtypes: Studies by Many Groups N. Scheltens et al Alz & Dem 4/2017

9 Cognitive Subtypes N. Scheltens et al Alz & Dem 4/2017

10 Multiple tests and cohorts N. Scheltens et al Alz & Dem 4/2017

11 Genetic architecture of memory and executive functioning in AD (P Crane R01 AG )

12 Subtypes are Heritable (0.68) (P Crane R01 AG )

13 Dynamic Aspects: Cognitive Trajectory

14 Genetics of Resilience Searching for protective factors

15 Cognitively-defined subgroups (P Crane R01 AG )) NACC Data Next Memory most common overall, followed by language Isolated substantial language deficits quite marked in MAP and especially ROS Too few with isolated substantial executive functioning deficits for genetic analyses Multiple domains more common in MAP (especially) and ROS than in ADNI or ACT

16 APOE: Proportion with 1 ε4 alleles Paul Crane et al, unpublished data

17 Genetics of Subtypes: IGAP SNP Results Gene MAF OR ACT ROS/MAP ADNI Meta P(Meta) P(Hetero) SORL1* BIN CR CLU* ABCA PICALM* FERMT CASS4* MS4A6A* EPHA1* HLA-DRB5-HLA-DRB PTK2B CD2AP ZCWPW1* SLC24A4-RIN3* INPP5D CELF NME MEF2C* CD33* Case Control Paul Crane et al, unpublished data

18 No domain with prominent deficits Gene MAF OR ACT ROS/MAP ADNI Meta P(Meta) P(Het) SORL1* BIN CR CLU* ABCA PICALM* FERMT CASS4* MS4A6A* EPHA1* HLA-DRB5-HLA-DRB PTK2B CD2AP ZCWPW1* SLC24A4-RIN3* INPP5D CELF NME MEF2C* CD33* Case Control Paul Crane et al, unpublished data

19 Isolated substantial memory deficits Gene MAF OR ACT ROS/MAP ADNI Meta P(Meta) P(Hetero) SORL1* BIN CR CLU* ABCA PICALM* FERMT CASS4* MS4A6A* EPHA1* HLA-DRB5-HLA-DRB PTK2B CD2AP ZCWPW1* SLC24A4-RIN3* INPP5D CELF NME MEF2C* CD33* Case Control Paul Crane et al, unpublished data

20 Isolated substantial language impairment Gene MAF OR ACT ROS/MAP ADNI Meta P(Meta) P(Het) SORL1* BIN CR < CLU* ABCA PICALM* FERMT CASS4* MS4A6A* EPHA1* HLA-DRB5-HLA-DRB PTK2B CD2AP ZCWPW1* SLC24A4-RIN3* INPP5D CELF NME MEF2C* CD33* Case Control Paul Crane et al, unpublished data

21 Isolated substantial visuospatial impairment Gene MAF OR ACT ROS/MAP ADNI Meta P(Meta) P(Het) SORL1* BIN CR CLU* ABCA PICALM* FERMT CASS4* MS4A6A* EPHA1* HLA-DRB5-HLA-DRB PTK2B CD2AP ZCWPW1* SLC24A4-RIN3* INPP5D CELF NME MEF2C* CD33* Case Control Paul Crane et al, unpublished data

22 Summary of genetic results (meta-analysis) Gene MAF OR No Domain Memory Visuospatial Language SORL1* BIN CR CLU* ABCA PICALM* FERMT CASS4* MS4A6A* * EPHA1* HLA-DRB5-HLA-DRB PTK2B CD2AP * ZCWPW1* SLC24A4-RIN3* INPP5D CELF NME MEF2C* CD33* Case Control Paul Crane et al, unpublished data * Het p<0.05

23 More Heterogeneity: Genetics of Quantitative Endophenotypes: Publications Using ADNI Genetic Data ( ) Year Pubs Shen et al, Brain Imaging Behav 2014; Yao et al, AAIC 2015 & AAIC 2016 & AAIC 2017; Saykin et al, Alzheimers Dement 2015 Updated: 4/2017

24 Role for Polygenic Risk Scores Mormino, E.C., et al., Polygenic risk of Alzheimer disease is associated with early- and late-life processes. Neurology, (5): p Hohman, T.J., et al., Discovery of gene-gene interactions across multiple independent data sets of late onset Alzheimer disease from the Alzheimer Disease Genetics Consortium. Neurobiol Aging, : p Gaiteri, C., et al., Genetic variants in Alzheimer disease - molecular and brain network approaches. Nat Rev Neurol, (7): p Yokoyama, J.S., et al., Decision tree analysis of genetic risk for clinically heterogeneous Alzheimer's disease. BMC Neurol, : p. 47. Martiskainen, H., et al., Effects of Alzheimer's disease-associated risk loci on cerebrospinal fluid biomarkers and disease progression: a polygenic risk score approach. J Alzheimers Dis, (2): p Escott-Price, V., et al., Common polygenic variation enhances risk prediction for Alzheimer's disease. Brain, (Pt 12): p Desikan, R.S., et al., Genetic assessment of age-associated Alzheimer disease risk: Development and validation of a polygenic hazard score. PLoS Med, (3): p. e

25 Working toward a Systems Biology of AD Saykin et al, Alzheimer s & Dementia 11 (2015)

26 From Noise to Signal: Modeling Heterogeneity Interacting Domains Biomarkers Moderators Outcomes Baseline Cognitive Status Neural Activity & Connectivity Amyloid Burden Tau Burden Microvascular Pathology Neurodegeneration Inflammation & Related Processes Report & Testing Task & RS fmri PET & CSF PET & CSF WMHI, MH, CBF MRI & DTI Fluid Biomarkers - Age - Education - Sex - Genetics - APOE, other - Family Hx - Reserve - Brain - Cognitive - Lifestyle - Exercise - Cognitive - Diet - Environment - Medications Longitudinal Progression: Cognitive Changes Clinical Status MRI & Biomarker Changes * WMHI: white matter hyperintensities; MH: microhemorrhages; CBF: cerebral blood flow

27 IDEAS CMS coverage for amyloid imaging with Evidence Development Trial sponsored by the Alzheimer s Association Managed by the American College of Radiology (ACR) and American College of Radiology Imaging Network (ACRIN) 18,500 patients with cognitive impairment & unclear diagnosis IU site (Apostolova) Genetics add-on study led by IU (Foroud & Saykin) Remote consent and saliva kits for DNA (NCRAD) & Analyses (Imaging Genomics Lab)

28 Relevance for Interventions Cummings et al, Alzheimer s & Dementia: Translational Research & Clinical Interventions 2 (2016)

29 Summary & Discussion Heterogeneity is multidimensional with several axes clinical, neuropathological & genetic Memory prominent APOE+ canonical AD pathology Heterogeneity is dynamic time is another dimension Phenotypes; gene expression, epigenetic modifications, etc. Hopefully we can turn noise into signal -> modeling Understand fundamental disease mechanisms and Tailor therapeutics with real precision How can the ADCs best contribute to resolving issues of heterogeneity?