Deep learning-based polygenic risk analysis for Alzheimer’s disease prediction
Authors: Zhou Xiaopu , Chen Yu , Ip Fanny C. F. , Jiang Yuanbing , Cao Han , Lv Ge , Zhong Huan , Chen Jiahang , Ye Tao , Chen Yuewen , Zhang Yulin , Ma Shuangshuang , Lo Ronnie M. N. , Tong Estella P. S. ,   Alzheimer’s Disease Neuroimaging Initiative, Weiner Michael W. , Aisen Paul , Petersen Ronald , Jack Clifford R. , Jagust William , Trojanowski John Q. , Toga Arthur W. , Beckett Laurel , Green Robert C. , Saykin Andrew J. , Morris John , Shaw Leslie M. , Khachaturian Zaven , Sorensen Greg , Kuller Lew , Raichle Marcus , Paul Steven , Davies Peter , Fillit Howard , Hefti Franz , Holtzman David , Mesulam Marek M. , Potter William , Snyder Peter , Schwartz Adam , Montine Tom , Thomas Ronald G. , Donohue Michael , Walter Sarah , Gessert Devon , Sather Tamie , Jiminez Gus , Harvey Danielle , Bernstein Matthew , Thompson Paul , Schuff Norbert , Borowski Bret , Gunter Jeff , Senjem Matt , Vemuri Prashanthi , Jones David , Kantarci Kejal , Ward Chad , Koeppe Robert A. , Foster Norm , Reiman Eric M. , Chen Kewei , Mathis Chet , Landau Susan , Cairns Nigel J. , Householder Erin , Taylor-Reinwald Lisa , Lee Virginia , Korecka Magdalena , Figurski Michal , Crawford Karen , Neu Scott , Foroud Tatiana M. , Potkin Steven G. , Shen Li , Faber Kelley , Kim Sungeun , Nho Kwangsik , Thal Leon , Buckholtz Neil , Albert Marylyn , Frank Richard , Hsiao John , Kaye Jeffrey , Quinn Joseph , Lind Betty , Carter Raina , Dolen Sara , Schneider Lon S. , Pawluczyk Sonia , Beccera Mauricio , Teodoro Liberty , Spann Bryan M. , Brewer James , Vanderswag Helen , Fleisher Adam , Heidebrink Judith L. , Lord Joanne L. , Mason Sara S. , Albers Colleen S. , Knopman David , Johnson Kris , Doody Rachelle S. , Villanueva-Meyer Javier , Chowdhury Munir , Rountree Susan , Dang Mimi , Stern Yaakov , Honig Lawrence S. , Bell Karen L. , Ances Beau , Carroll Maria , Leon Sue , Mintun Mark A. , Schneider Stacy , Oliver Angela , Marson Daniel , Griffith Randall , Clark David , Geldmacher David , Brockington John , Roberson Erik , Grossman Hillel , Mitsis Effie , de Toledo-Morrell Leyla , Shah Raj C. , Duara Ranjan , Varon Daniel , Greig Maria T. , Roberts Peggy , Onyike Chiadi , D’Agostino Daniel , Kielb Stephanie , Galvin James E. , Cerbone Brittany , Michel Christina A. , Rusinek Henry , de Leon Mony J. , Glodzik Lidia , De Santi Susan , Doraiswamy P. Murali , Petrella Jeffrey R. , Wong Terence Z. , Arnold Steven E. , Karlawish Jason H. , Wolk David , Smith Charles D. , Jicha Greg , Hardy Peter , Sinha Partha , Oates Elizabeth , Conrad Gary , Lopez Oscar L. , Oakley MaryAnn , Simpson Donna M. , Porsteinsson Anton P. , Goldstein Bonnie S. , Martin Kim , Makino Kelly M. , Ismail M. Saleem , Brand Connie , Mulnard Ruth A. , Thai Gaby , McAdams-Ortiz Catherine , Womack Kyle , Mathews Dana , Quiceno Mary , Diaz-Arrastia Ramon , King Richard , Weiner Myron , Martin-Cook Kristen , DeVous Michael , Levey Allan I. , Lah James J. , Cellar Janet S. , Burns Jeffrey M. , Anderson Heather S. , Swerdlow Russell H. , Apostolova Liana , Tingus Kathleen , Woo Ellen , Silverman Daniel H. S. , Lu Po H. , Bartzokis George , Graff-Radford Neill R. , Parfitt Francine , Kendall Tracy , Johnson Heather , Farlow Martin R. , Hake Ann Marie , Matthews Brandy R. , Herring Scott , Hunt Cynthia , van Dyck Christopher H. , Carson Richard E. , MacAvoy Martha G. , Chertkow Howard , Bergman Howard , Hosein Chris , Hsiung Ging-Yuek Robin , Feldman Howard , Mudge Benita , Assaly Michele , Bernick Charles , Munic Donna , Kertesz Andrew , Rogers John , Trost Dick , Kerwin Diana , Lipowski Kristine , Wu Chuang-Kuo , Johnson Nancy , Sadowsky Carl , Martinez Walter , Villena Teresa , Turner Raymond Scott , Johnson Kathleen , Reynolds Brigid , Sperling Reisa A. , Johnson Keith A. , Marshall Gad , Frey Meghan , Lane Barton , Rosen Allyson , Tinklenberg Jared , Sabbagh Marwan N. , Belden Christine M. , Jacobson Sandra A. , Sirrel Sherye A. , Kowall Neil , Killiany Ronald , Budson Andrew E. , Norbash Alexander , Johnson Patricia Lynn , Allard Joanne , Lerner Alan , Ogrocki Paula , Hudson Leon , Fletcher Evan , Carmichael Owen , Olichney John , DeCarli Charles , Kittur Smita , Borrie Michael , Lee T-Y. , Bartha Rob , Johnson Sterling , Asthana Sanjay , Carlsson Cynthia M. , Preda Adrian , Nguyen Dana , Tariot Pierre , Reeder Stephanie , Bates Vernice , Capote Horacio , Rainka Michelle , Scharre Douglas W. , Kataki Maria , Adeli Anahita , Zimmerman Earl A. , Celmins Dzintra , Brown Alice D. , Pearlson Godfrey D. , Blank Karen , Anderson Karen , Santulli Robert B. , Kitzmiller Tamar J. , Schwartz Eben S. , Sink Kaycee M. , Williamson Jeff D. , Garg Pradeep , Watkins Franklin , Ott Brian R. , Querfurth Henry , Tremont Geoffrey , Salloway Stephen , Malloy Paul , Correia Stephen , Rosen Howard J. , Miller Bruce L. , Mintzer Jacobo , Spicer Kenneth , Bachman David , Pasternak Stephen , Rachinsky Irina , Drost Dick , Pomara Nunzio , Hernando Raymundo , Sarrael Antero , Schultz Susan K. , Boles Ponto Laura L. , Shim Hyungsub , Smith Karen Elizabeth , Relkin Norman , Chaing Gloria , Raudin Lisa , Smith Amanda , Fargher Kristin , Raj Balebail Ashok , Neylan Thomas , Grafman Jordan , Davis Melissa , Morrison Rosemary , Hayes Jacqueline , Finley Shannon , Friedl Karl , Fleischman Debra , Arfanakis Konstantinos , James Olga , Massoglia Dino , Fruehling J. Jay , Harding Sandra , Peskind Elaine R. , Petrie Eric C. , Li Gail , Yesavage Jerome A. , Taylor Joy L. , Furst Ansgar J. , Mok Vincent C. T. , Kwok Timothy C. Y. , Guo Qihao , Mok Kin Y. , Shoai Maryam , Hardy John , Chen Lei , Fu Amy K. Y. , Ip Nancy Y. 
Published: April 2023

Abstract
Background
The polygenic nature of Alzheimer’s disease (AD) suggests that multiple variants jointly contribute to disease susceptibility. As an individual’s genetic variants are constant throughout life, evaluating the combined effects of multiple disease-associated genetic risks enables reliable AD risk prediction. Because of the complexity of genomic data, current statistical analyses cannot comprehensively capture the polygenic risk of AD, resulting in unsatisfactory disease risk prediction. However, deep learning methods, which capture nonlinearity within high-dimensional genomic data, may enable more accurate disease risk prediction and improve our understanding of AD etiology. Accordingly, we developed deep learning neural network models for modeling AD polygenic risk.

Methods
We constructed neural network models to model AD polygenic risk and compared them with the widely used weighted polygenic risk score and lasso models. We conducted robust linear regression analysis to investigate the relationship between the AD polygenic risk derived from deep learning methods and AD endophenotypes (i.e., plasma biomarkers and individual cognitive performance). We stratified individuals by applying unsupervised clustering to the outputs from the hidden layers of the neural network model.

Results
The deep learning models outperform other statistical models for modeling AD risk. Moreover, the polygenic risk derived from the deep learning models enables the identification of disease-associated biological pathways and the stratification of individuals according to distinct pathological mechanisms.

Conclusion
Our results suggest that deep learning methods are effective for modeling the genetic risks of AD and other diseases, classifying disease risks, and uncovering disease mechanisms.