TY - JOUR
T1 - The prediction of Alzheimer’s disease through multi-trait genetic modeling
AU - Clark, Kaylyn
AU - Fu, Wei
AU - Liu, Chia Lun
AU - Ho, Pei Chuan
AU - Wang, Hui
AU - Lee, Wan Ping
AU - Chou, Shin Yi
AU - Wang, Li San
AU - Tzeng, Jung Ying
N1 - Funding Information:
The Alzheimer’s Disease Genetics Consortium (ADGC) supported collection and genotyping of samples used in this study through National Institute on Aging (NIA) grants U01AG032984 and RC2AG036528. Data for this study were prepared, archived, and distributed by the National Institute on Aging Alzheimer’s Disease Data Storage Site (NIAGADS) at the University of Pennsylvania (U24-AG041689-01). The Center for Applied Genomics at the Children’s Hospital of Philadelphia Research Institute performed genotyping of samples. The NACC database is funded by NIA/NIH grant U24 AG072122. NACC data are contributed by the NIA-funded ADRCs: P30 AG062429 (PI James Brewer, MD, Ph.D.), P30 AG066468 (PI Oscar Lopez, MD), P30 AG062421 (PI Bradley Hyman, MD, Ph.D.), P30 AG066509 (PI Thomas Grabowski, MD), P30 AG066514 (PI Mary Sano, Ph.D.), P30 AG066530 (PI Helena Chui, MD), P30 AG066507 (PI Marilyn Albert, Ph.D.), P30 AG066444 (PI John Morris, MD), P30 AG066518 (PI Jeffrey Kaye, MD), P30 AG066512 (PI Thomas Wisniewski, MD), P30 AG066462 (PI Scott Small, MD), P30 AG072979 (PI David Wolk, MD), P30 AG072972 (PI Charles DeCarli, MD), P30 AG072976 (PI Andrew Saykin, PsyD), P30 AG072975 (PI David Bennett, MD), P30 AG072978 (PI Neil Kowall, MD), P30 AG072977 (PI Robert Vassar, Ph.D.), P30 AG066519 (PI Frank LaFerla, Ph.D.), P30 AG062677 (PI Ronald Petersen, MD, Ph.D.), P30 AG079280 (PI Eric Reiman, MD), P30 AG062422 (PI Gil Rabinovici, MD), P30 AG066511 (PI Allan Levey, MD, Ph.D.), P30 AG072946 (PI Linda Van Eldik, Ph.D.), P30 AG062715 (PI Sanjay Asthana, MD, FRCP), P30 AG072973 (PI Russell Swerdlow, MD), P30 AG066506 (PI Todd Golde, MD, Ph.D.), P30 AG066508 (PI Stephen Strittmatter, MD, Ph.D.), P30 AG066515 (PI Victor Henderson, MD, MS), P30 AG072947 (PI Suzanne Craft, Ph.D.), P30 AG072931 (PI Henry Paulson, MD, Ph.D.), P30 AG066546 (PI Sudha Seshadri, MD), P20 AG068024 (PI Erik Roberson, MD, Ph.D.), P20 AG068053 (PI Justin Miller, Ph.D.), P20 AG068077 (PI Gary Rosenberg, MD), P20 AG068082 (PI Angela Jefferson, Ph.D.), P30 AG072958 (PI Heather Whitson, MD), and P30 AG072959 (PI James Leverenz, MD). Samples from the National Centralized Repository for Alzheimer’s Disease and Related Dementias (NCRAD), which receives government support under a cooperative agreement grant (U24 AG21886) awarded by the National Institute on Aging (NIA), were used in this study. Additional funding from the National Institute on Aging (RF1AG074328) and University of Pennsylvania Predoctoral Training Grant in Computational Genomics (5T32HG000046-22).
Funding Information:
The Alzheimer’s Disease Genetics Consortium (ADGC) supported collection and genotyping of samples used in this study through National Institute on Aging (NIA) grants U01AG032984 and RC2AG036528. Data for this study were prepared, archived, and distributed by the National Institute on Aging Alzheimer’s Disease Data Storage Site (NIAGADS) at the University of Pennsylvania (U24-AG041689-01). The Center for Applied Genomics at the Children’s Hospital of Philadelphia Research Institute performed genotyping of samples. The NACC database is funded by NIA/NIH grant U24 AG072122. NACC data are contributed by the NIA-funded ADRCs: P30 AG062429 (PI James Brewer, MD, Ph.D.), P30 AG066468 (PI Oscar Lopez, MD), P30 AG062421 (PI Bradley Hyman, MD, Ph.D.), P30 AG066509 (PI Thomas Grabowski, MD), P30 AG066514 (PI Mary Sano, Ph.D.), P30 AG066530 (PI Helena Chui, MD), P30 AG066507 (PI Marilyn Albert, Ph.D.), P30 AG066444 (PI John Morris, MD), P30 AG066518 (PI Jeffrey Kaye, MD), P30 AG066512 (PI Thomas Wisniewski, MD), P30 AG066462 (PI Scott Small, MD), P30 AG072979 (PI David Wolk, MD), P30 AG072972 (PI Charles DeCarli, MD), P30 AG072976 (PI Andrew Saykin, PsyD), P30 AG072975 (PI David Bennett, MD), P30 AG072978 (PI Neil Kowall, MD), P30 AG072977 (PI Robert Vassar, Ph.D.), P30 AG066519 (PI Frank LaFerla, Ph.D.), P30 AG062677 (PI Ronald Petersen, MD, Ph.D.), P30 AG079280 (PI Eric Reiman, MD), P30 AG062422 (PI Gil Rabinovici, MD), P30 AG066511 (PI Allan Levey, MD, Ph.D.), P30 AG072946 (PI Linda Van Eldik, Ph.D.), P30 AG062715 (PI Sanjay Asthana, MD, FRCP), P30 AG072973 (PI Russell Swerdlow, MD), P30 AG066506 (PI Todd Golde, MD, Ph.D.), P30 AG066508 (PI Stephen Strittmatter, MD, Ph.D.), P30 AG066515 (PI Victor Henderson, MD, MS), P30 AG072947 (PI Suzanne Craft, Ph.D.), P30 AG072931 (PI Henry Paulson, MD, Ph.D.), P30 AG066546 (PI Sudha Seshadri, MD), P20 AG068024 (PI Erik Roberson, MD, Ph.D.), P20 AG068053 (PI Justin Miller, Ph.D.), P20 AG068077 (PI Gary Rosenberg, MD), P20 AG068082 (PI Angela Jefferson, Ph.D.), P30 AG072958 (PI Heather Whitson, MD), and P30 AG072959 (PI James Leverenz, MD). Samples from the National Centralized Repository for Alzheimer’s Disease and Related Dementias (NCRAD), which receives government support under a cooperative agreement grant (U24 AG21886) awarded by the National Institute on Aging (NIA), were used in this study. Additional funding from the National Institute on Aging (RF1AG074328) and University of Pennsylvania Predoctoral Training Grant in Computational Genomics (5T32HG000046-22).
Publisher Copyright:
Copyright © 2023 Clark, Fu, Liu, Ho, Wang, Lee, Chou, Wang and Tzeng.
PY - 2023
Y1 - 2023
N2 - To better capture the polygenic architecture of Alzheimer’s disease (AD), we developed a joint genetic score, MetaGRS. We incorporated genetic variants for AD and 24 other traits from two independent cohorts, NACC (n = 3,174, training set) and UPitt (n = 2,053, validation set). One standard deviation increase in the MetaGRS is associated with about 57% increase in the AD risk [hazard ratio (HR) = 1.577, p = 7.17 E-56], showing little difference from the HR for AD GRS alone (HR = 1.579, p = 1.20E-56), suggesting similar utility of both models. We also conducted APOE-stratified analyses to assess the role of the e4 allele on risk prediction. Similar to that of the combined model, our stratified results did not show a considerable improvement of the MetaGRS. Our study showed that the prediction power of the MetaGRS significantly outperformed that of the reference model without any genetic information, but was effectively equivalent to the prediction power of the AD GRS.
AB - To better capture the polygenic architecture of Alzheimer’s disease (AD), we developed a joint genetic score, MetaGRS. We incorporated genetic variants for AD and 24 other traits from two independent cohorts, NACC (n = 3,174, training set) and UPitt (n = 2,053, validation set). One standard deviation increase in the MetaGRS is associated with about 57% increase in the AD risk [hazard ratio (HR) = 1.577, p = 7.17 E-56], showing little difference from the HR for AD GRS alone (HR = 1.579, p = 1.20E-56), suggesting similar utility of both models. We also conducted APOE-stratified analyses to assess the role of the e4 allele on risk prediction. Similar to that of the combined model, our stratified results did not show a considerable improvement of the MetaGRS. Our study showed that the prediction power of the MetaGRS significantly outperformed that of the reference model without any genetic information, but was effectively equivalent to the prediction power of the AD GRS.
UR - http://www.scopus.com/inward/record.url?scp=85167514062&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85167514062&partnerID=8YFLogxK
U2 - 10.3389/fnagi.2023.1168638
DO - 10.3389/fnagi.2023.1168638
M3 - Article
AN - SCOPUS:85167514062
SN - 1663-4365
VL - 15
JO - Frontiers in Aging Neuroscience
JF - Frontiers in Aging Neuroscience
M1 - 1168638
ER -