Manifestations of Alzheimer’s disease genetic risk in the blood are evident in a multiomic analysis in healthy adults aged 18 to 90

doi:10.1038/s41598-022-09825-2

Manifestations of Alzheimer’s disease genetic risk in the blood are evident in a multiomic analysis in healthy adults aged 18 to 90

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Abstract

Genetics play an important role in late-onset Alzheimer’s Disease (AD) etiology and dozens of genetic variants have been implicated in AD risk through large-scale GWAS meta-analyses. However, the precise mechanistic effects of most of these variants have yet to be determined. Deeply phenotyped cohort data can reveal physiological changes associated with genetic risk for AD across an age spectrum that may provide clues to the biology of the disease. We utilized over 2000 high-quality quantitative measurements obtained from blood of 2831 cognitively normal adult clients of a consumer-based scientific wellness company, each with CLIA-certified whole-genome sequencing data. Measurements included: clinical laboratory blood tests, targeted chip-based proteomics, and metabolomics. We performed a phenome-wide association study utilizing this diverse blood marker data and 25 known AD genetic variants and an AD-specific polygenic risk score (PGRS), adjusting for sex, age, vendor (for clinical labs), and the first four genetic principal components; sex-SNP interactions were also assessed. We observed statistically significant SNP-analyte associations for five genetic variants after correction for multiple testing (for SNPs in or near NYAP1, ABCA7, INPP5D, and APOE), with effects detectable from early adulthood. The ABCA7 SNP and the APOE2 and APOE4 encoding alleles were associated with lipid variability, as seen in previous studies; in addition, six novel proteins were associated with the e2 allele. The most statistically significant finding was between the NYAP1 variant and PILRA and PILRB protein levels, supporting previous functional genomic studies in the identification of a putative causal variant within the PILRA gene. We did not observe associations between the PGRS and any analyte. Sex modified the effects of four genetic variants, with multiple interrelated immune-modulating effects associated with the PICALM variant. In post-hoc analysis, sex-stratified GWAS results from an independent AD case–control meta-analysis supported sex-specific disease effects of the PICALM variant, highlighting the importance of sex as a biological variable. Known AD genetic variation influenced lipid metabolism and immune response systems in a population of non-AD individuals, with associations observed from early adulthood onward. Further research is needed to determine whether and how these effects are implicated in early-stage biological pathways to AD. These analyses aim to complement ongoing work on the functional interpretation of AD-associated genetic variants.

Original language	English
Article number	6117
Journal	Scientific Reports
Volume	12
Issue number	1
DOIs	https://doi.org/10.1038/s41598-022-09825-2
State	Published - Dec 2022

Bibliographical note

Publisher Copyright:
© 2022, The Author(s).

Funding

This study was supported by the National Institutes of Health, National Institute on Aging Grants U01 AG046139 (N.E-T, T.E.G, N.D.P), R01 AG061796 (N.E-T), RF1 AG051504 (N.E-T), R01-AG062634-01 (B.W.K, E.R.M), and U19 AG023122 (N.R). ADGC. The National Institutes of Health, National Institute on Aging (NIH-NIA) supported this work through the following Grants: ADGC, U01 AG032984, RC2 AG036528; Samples from the National Cell Repository for Alzheimer’s Disease (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. We thank contributors who collected samples used in this study, as well as patients and their families, whose help and participation made this work possible; 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); NACC, U01 AG016976; NIA LOAD (Columbia University), U24 AG026395, U24 AG026390, R01AG041797; Banner Sun Health Research Institute P30 AG019610; Boston University, P30 AG013846, U01 AG10483, R01 CA129769, R01 MH080295, R01 AG017173, R01 AG025259, R01 AG048927, R01AG33193, R01 AG009029; Columbia University, P50 AG008702, R37 AG015473, R01 AG037212, R01 AG028786; Duke University, P30 AG028377, AG05128; Emory University, AG025688; Group Health Research Institute, UO1 AG006781, UO1 HG004610, UO1 HG006375, U01 HG008657; Indiana University, P30 AG10133, R01 AG009956, RC2 AG036650; Johns Hopkins University, P50 AG005146, R01 AG020688; Massachusetts General Hospital, P50 AG005134; Mayo Clinic, P50 AG016574, R01 AG032990, KL2 RR024151; Mount Sinai School of Medicine, P50 AG005138, P01 AG002219; New York University, P30 AG08051, UL1 RR029893, 5R01AG012101, 5R01AG022374, 5R01AG013616, 1RC2AG036502, 1R01AG035137; North Carolina A&T University, P20 MD000546, R01 AG28786-01A1; Northwestern University, P30 AG013854; Oregon Health & Science University, P30 AG008017, R01 AG026916; Rush University, P30 AG010161, R01 AG019085, R01 AG15819, R01 AG17917, R01 AG030146, R01 AG01101, RC2 AG036650, R01 AG22018; TGen, R01 NS059873; University of Alabama at Birmingham, P50 AG016582; University of Arizona, R01 AG031581; University of California, Davis, P30 AG010129; University of California, Irvine, P50 AG016573; University of California, Los Angeles, P50 AG016570; University of California, San Diego, P50 AG005131; University of California, San Francisco, P50 AG023501, P01 AG019724; University of Kentucky, P30 AG028383, AG05144; University of Michigan, P50 AG008671; University of Pennsylvania, P30 AG010124; University of Pittsburgh, P50 AG005133, AG030653, AG041718, AG07562, AG02365; University of Southern California, P50 AG005142; University of Texas Southwestern, P30 AG012300; University of Miami, R01 AG027944, AG010491, AG027944, AG021547, AG019757; University of Washington, P50 AG005136, R01 AG042437; University of Wisconsin, P50 AG033514; Vanderbilt University, R01 AG019085; and Washington University, P50 AG005681, P01 AG03991, P01 AG026276. The Kathleen Price Bryan Brain Bank at Duke University Medical Center is funded by NINDS Grant # NS39764, NIMH MH60451 and by Glaxo Smith Kline. Support was also from the Alzheimer’s Association (LAF, IIRG-08-89720; MP-V, IIRG-05-14147), the US Department of Veterans Affairs Administration, Office of Research and Development, Biomedical Laboratory Research Program, and BrightFocus Foundation (MP-V, A2111048). P.S.G.-H. is supported by Wellcome Trust, Howard Hughes Medical Institute, and the Canadian Institute of Health Research. Genotyping of the TGEN2 cohort was supported by Kronos Science. The TGen series was also funded by NIA Grant AG041232 to AJM and MJH, The Banner Alzheimer’s Foundation, The Johnnie B. Byrd Sr. Alzheimer’s Institute, the Medical Research Council, and the state of Arizona and also includes samples from the following sites: Newcastle Brain Tissue Resource (funding via the Medical Research Council, local NHS trusts and Newcastle University), MRC London Brain Bank for Neurodegenerative Diseases (funding via the Medical Research Council),South West Dementia Brain Bank (funding via numerous sources including the Higher Education Funding Council for England (HEFCE), Alzheimer’s Research Trust (ART), BRACE as well as North Bristol NHS Trust Research and Innovation Department and DeNDRoN), The Netherlands Brain Bank (funding via numerous sources including Stichting MS Research, Brain Net Europe, Hersenstichting Nederland Breinbrekend Werk, International Parkinson Fonds, Internationale Stiching Alzheimer Onderzoek), Institut de Neuropatologia, Servei Anatomia Patologica, Universitat de Barcelona. ADNI data collection and sharing was funded by the National Institutes of Health Grant U01 AG024904 and Department of Defense award number W81XWH-12-2-0012. ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health ( www.fnih.org ). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. We thank Drs. D. Stephen Snyder and Marilyn Miller from NIA who are ex-officio ADGC members.

Funders	Funder number
ADGC	RC2 AG036528
Araclon Biotech
Biomedical Laboratory Research Program
CereSpir, Inc.
Elan Pharmaceuticals, Inc.
Euroimmun
Glaxo Smith Kline
Hersenstichting Nederland Breinbrekend Werk
Institut de Neuropatologia
International Parkinson Fonds
Kronos Science	AG041232
MRC London Brain Bank for Neurodegenerative Diseases
NIA/NIH
National Cell Repository for Alzheimer’s Disease	U24 AG21886
Newcastle Brain Tissue Resource
North Bristol NHS Trust Research and Innovation Department and DeNDRoN
South West Dementia Brain Bank
Stichting Vanderes
US Department of Veterans Affairs Administration
National Institutes of Health (NIH)
U.S. Department of Defense	W81XWH-12-2-0012
U.S. Department of Defense
Howard Hughes Medical Institute
National Institute of Mental Health	MH60451
National Institute of Mental Health
National Institute on Aging	R01AG042437, R01-AG062634-01, R01 AG061796, U01 AG032984, U01 AG046139, RF1 AG051504, U19 AG023122
National Institute on Aging
Institute of Neurological Disorders and Stroke National Advisory Neurological Disorders and Stroke Council	NS39764
Institute of Neurological Disorders and Stroke National Advisory Neurological Disorders and Stroke Council
National Institute of Biomedical Imaging and Bioengineering
Alzheimer's Association
Lance Armstrong Foundation	IIRG-05-14147, IIRG-08-89720
Lance Armstrong Foundation
Bristol-Myers Squibb
Alzheimer's Drug Discovery Foundation
Eli Lilly and Company
Biogen IDEC
BrightFocus Foundation	A2111048
BrightFocus Foundation
Biomedical Laboratory Research and Development, VA Office of Research and Development
AbbVie
F. Hoffmann-La Roche AG
DoD Alzheimer's Disease Neuroimaging Initiative	U01 AG024904
DoD Alzheimer's Disease Neuroimaging Initiative
Astrobiology Research Trust
BioClinica Inc.
Wellcome Trust
Banner Alzheimer’s Foundation
BRACE
Higher Education Funding Council for England
Nederlandse Hersenbank
Canadian Institutes of Health Research
Medical Research Council
Newcastle University
Alzheimer’s Research United Kingdom
Eisai Incorporated
Universitat de Barcelona
Internationale Stichting Alzheimer Onderzoek

ASJC Scopus subject areas

General

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10.1038/s41598-022-09825-2

Cite this

@article{f2064b99d88347c89bf73bfe10b0960c,

title = "Manifestations of Alzheimer{\textquoteright}s disease genetic risk in the blood are evident in a multiomic analysis in healthy adults aged 18 to 90",

abstract = "Genetics play an important role in late-onset Alzheimer{\textquoteright}s Disease (AD) etiology and dozens of genetic variants have been implicated in AD risk through large-scale GWAS meta-analyses. However, the precise mechanistic effects of most of these variants have yet to be determined. Deeply phenotyped cohort data can reveal physiological changes associated with genetic risk for AD across an age spectrum that may provide clues to the biology of the disease. We utilized over 2000 high-quality quantitative measurements obtained from blood of 2831 cognitively normal adult clients of a consumer-based scientific wellness company, each with CLIA-certified whole-genome sequencing data. Measurements included: clinical laboratory blood tests, targeted chip-based proteomics, and metabolomics. We performed a phenome-wide association study utilizing this diverse blood marker data and 25 known AD genetic variants and an AD-specific polygenic risk score (PGRS), adjusting for sex, age, vendor (for clinical labs), and the first four genetic principal components; sex-SNP interactions were also assessed. We observed statistically significant SNP-analyte associations for five genetic variants after correction for multiple testing (for SNPs in or near NYAP1, ABCA7, INPP5D, and APOE), with effects detectable from early adulthood. The ABCA7 SNP and the APOE2 and APOE4 encoding alleles were associated with lipid variability, as seen in previous studies; in addition, six novel proteins were associated with the e2 allele. The most statistically significant finding was between the NYAP1 variant and PILRA and PILRB protein levels, supporting previous functional genomic studies in the identification of a putative causal variant within the PILRA gene. We did not observe associations between the PGRS and any analyte. Sex modified the effects of four genetic variants, with multiple interrelated immune-modulating effects associated with the PICALM variant. In post-hoc analysis, sex-stratified GWAS results from an independent AD case–control meta-analysis supported sex-specific disease effects of the PICALM variant, highlighting the importance of sex as a biological variable. Known AD genetic variation influenced lipid metabolism and immune response systems in a population of non-AD individuals, with associations observed from early adulthood onward. Further research is needed to determine whether and how these effects are implicated in early-stage biological pathways to AD. These analyses aim to complement ongoing work on the functional interpretation of AD-associated genetic variants.",

author = "Laura Heath and Earls, {John C.} and Magis, {Andrew T.} and Kornilov, {Sergey A.} and Lovejoy, {Jennifer C.} and Funk, {Cory C.} and Noa Rappaport and Logsdon, {Benjamin A.} and Mangravite, {Lara M.} and Kunkle, {Brian W.} and Martin, {Eden R.} and Naj, {Adam C.} and Nil{\"u}fer Ertekin-Taner and Golde, {Todd E.} and Leroy Hood and Price, {Nathan D.} and Erin Abner and Adams, {Perrie M.} and Albert, {Marilyn S.} and Albin, {Roger L.} and Mariet Allen and Alexandre Amlie-Wolf and Apostolova, {Liana G.} and Arnold, {Steven E.} and Sanjay Asthana and Atwood, {Craig S.} and Baldwin, {Clinton T.} and Barber, {Robert C.} and Barnes, {Lisa L.} and Sandra Barral and Beach, {Thomas G.} and Becker, {James T.} and Beecham, {Gary W.} and Duane Beekly and David Bennett and Bigio, {Eileen H.} and Bird, {Thomas D.} and Deborah Blacker and Boeve, {Bradley F.} and Bowen, {James D.} and Adam Boxer and Burke, {James R.} and Burns, {Jeffrey M.} and Will Bush and Mariusz Butkiewicz and Buxbaum, {Joseph D.} and Cairns, {Nigel J.} and Cantwell, {Laura B.} and Fardo, {David W.} and {Van Eldik}, {Linda J.}",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s).",

year = "2022",

month = dec,

doi = "10.1038/s41598-022-09825-2",

language = "English",

volume = "12",

number = "1",

}

TY - JOUR

T1 - Manifestations of Alzheimer’s disease genetic risk in the blood are evident in a multiomic analysis in healthy adults aged 18 to 90

AU - Heath, Laura

AU - Earls, John C.

AU - Magis, Andrew T.

AU - Kornilov, Sergey A.

AU - Lovejoy, Jennifer C.

AU - Funk, Cory C.

AU - Rappaport, Noa

AU - Logsdon, Benjamin A.

AU - Mangravite, Lara M.

AU - Kunkle, Brian W.

AU - Martin, Eden R.

AU - Naj, Adam C.

AU - Ertekin-Taner, Nilüfer

AU - Golde, Todd E.

AU - Hood, Leroy

AU - Price, Nathan D.

AU - Abner, Erin

AU - Adams, Perrie M.

AU - Albert, Marilyn S.

AU - Albin, Roger L.

AU - Allen, Mariet

AU - Amlie-Wolf, Alexandre

AU - Apostolova, Liana G.

AU - Arnold, Steven E.

AU - Asthana, Sanjay

AU - Atwood, Craig S.

AU - Baldwin, Clinton T.

AU - Barber, Robert C.

AU - Barnes, Lisa L.

AU - Barral, Sandra

AU - Beach, Thomas G.

AU - Becker, James T.

AU - Beecham, Gary W.

AU - Beekly, Duane

AU - Bennett, David

AU - Bigio, Eileen H.

AU - Bird, Thomas D.

AU - Blacker, Deborah

AU - Boeve, Bradley F.

AU - Bowen, James D.

AU - Boxer, Adam

AU - Burke, James R.

AU - Burns, Jeffrey M.

AU - Bush, Will

AU - Butkiewicz, Mariusz

AU - Buxbaum, Joseph D.

AU - Cairns, Nigel J.

AU - Cantwell, Laura B.

AU - Fardo, David W.

AU - Van Eldik, Linda J.

PY - 2022/12

Y1 - 2022/12

N2 - Genetics play an important role in late-onset Alzheimer’s Disease (AD) etiology and dozens of genetic variants have been implicated in AD risk through large-scale GWAS meta-analyses. However, the precise mechanistic effects of most of these variants have yet to be determined. Deeply phenotyped cohort data can reveal physiological changes associated with genetic risk for AD across an age spectrum that may provide clues to the biology of the disease. We utilized over 2000 high-quality quantitative measurements obtained from blood of 2831 cognitively normal adult clients of a consumer-based scientific wellness company, each with CLIA-certified whole-genome sequencing data. Measurements included: clinical laboratory blood tests, targeted chip-based proteomics, and metabolomics. We performed a phenome-wide association study utilizing this diverse blood marker data and 25 known AD genetic variants and an AD-specific polygenic risk score (PGRS), adjusting for sex, age, vendor (for clinical labs), and the first four genetic principal components; sex-SNP interactions were also assessed. We observed statistically significant SNP-analyte associations for five genetic variants after correction for multiple testing (for SNPs in or near NYAP1, ABCA7, INPP5D, and APOE), with effects detectable from early adulthood. The ABCA7 SNP and the APOE2 and APOE4 encoding alleles were associated with lipid variability, as seen in previous studies; in addition, six novel proteins were associated with the e2 allele. The most statistically significant finding was between the NYAP1 variant and PILRA and PILRB protein levels, supporting previous functional genomic studies in the identification of a putative causal variant within the PILRA gene. We did not observe associations between the PGRS and any analyte. Sex modified the effects of four genetic variants, with multiple interrelated immune-modulating effects associated with the PICALM variant. In post-hoc analysis, sex-stratified GWAS results from an independent AD case–control meta-analysis supported sex-specific disease effects of the PICALM variant, highlighting the importance of sex as a biological variable. Known AD genetic variation influenced lipid metabolism and immune response systems in a population of non-AD individuals, with associations observed from early adulthood onward. Further research is needed to determine whether and how these effects are implicated in early-stage biological pathways to AD. These analyses aim to complement ongoing work on the functional interpretation of AD-associated genetic variants.

AB - Genetics play an important role in late-onset Alzheimer’s Disease (AD) etiology and dozens of genetic variants have been implicated in AD risk through large-scale GWAS meta-analyses. However, the precise mechanistic effects of most of these variants have yet to be determined. Deeply phenotyped cohort data can reveal physiological changes associated with genetic risk for AD across an age spectrum that may provide clues to the biology of the disease. We utilized over 2000 high-quality quantitative measurements obtained from blood of 2831 cognitively normal adult clients of a consumer-based scientific wellness company, each with CLIA-certified whole-genome sequencing data. Measurements included: clinical laboratory blood tests, targeted chip-based proteomics, and metabolomics. We performed a phenome-wide association study utilizing this diverse blood marker data and 25 known AD genetic variants and an AD-specific polygenic risk score (PGRS), adjusting for sex, age, vendor (for clinical labs), and the first four genetic principal components; sex-SNP interactions were also assessed. We observed statistically significant SNP-analyte associations for five genetic variants after correction for multiple testing (for SNPs in or near NYAP1, ABCA7, INPP5D, and APOE), with effects detectable from early adulthood. The ABCA7 SNP and the APOE2 and APOE4 encoding alleles were associated with lipid variability, as seen in previous studies; in addition, six novel proteins were associated with the e2 allele. The most statistically significant finding was between the NYAP1 variant and PILRA and PILRB protein levels, supporting previous functional genomic studies in the identification of a putative causal variant within the PILRA gene. We did not observe associations between the PGRS and any analyte. Sex modified the effects of four genetic variants, with multiple interrelated immune-modulating effects associated with the PICALM variant. In post-hoc analysis, sex-stratified GWAS results from an independent AD case–control meta-analysis supported sex-specific disease effects of the PICALM variant, highlighting the importance of sex as a biological variable. Known AD genetic variation influenced lipid metabolism and immune response systems in a population of non-AD individuals, with associations observed from early adulthood onward. Further research is needed to determine whether and how these effects are implicated in early-stage biological pathways to AD. These analyses aim to complement ongoing work on the functional interpretation of AD-associated genetic variants.

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Manifestations of Alzheimer’s disease genetic risk in the blood are evident in a multiomic analysis in healthy adults aged 18 to 90

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