Multi‐Ancestry Genome‐wide Association Analysis of Late‐Onset Alzheimer’s Disease (LOAD) in 60,941 Individuals Identifies Multiple Novel Cross‐Ancestry Associations and Implicates Amyloid and Complement Pathways
Authors: Naj Adam C. , Rajabli Farid , Reitz Christiane , Jun Gyungah R , Benchek Penelope , Tosto Giuseppe , Sha Jin , Zhu Congcong , Kushch Nicholas A. , Lee Wan‐Ping , Haut Jacob , Hamilton‐Nelson Kara L. , Wheeler Nicholas R. , Zhao Yi , Farrell John , Grunin Michelle , Leung Yuk Yee , Li Donghe , da Fonseca Eder Lucio , Mez Jesse B. , Palmer Ellen L , Pillai Jagan A. , Sherva Richard , Song Yeunjoo E. , Zhang Xiaoling , Iqbal Taha , Pathak Omkar , Valladares Otto , Kuzma Amanda B , Kunkle Brian W. , Bush William S. , Wang Li‐San , Farrer Lindsay A. , Haines Jonathan L. , Mayeux Richard , Pericak‐Vance Margaret A , Schellenberg Gerald D. ,   Alzheimer’s Disease Genetics Consortium (ADGC)
Published: December 2023

Background

Lack of diversity in expanding genomic studies has limited the identification of LOAD risk variants that may be more prevalent in non‐European ancestry groups with more modest power requirements for detection. To help reverse this trend, we constructed and analyzed a multi‐ancestry collection of GWAS datasets in the Alzheimer’s Disease Genetics Consortium (ADGC) to identify novel shared and ancestry‐unique LOAD susceptibility loci and related biological pathways.

Method

The multi‐ancestry data collection of the ADGC includes GWAS genotype and phenotype data on 38,774 non‐Hispanic White (NHW), 7,454 African American (AA), 11,436 Hispanic (HI), and 3,277 East Asian (EAS) subjects imputed to the NHLBI TOPMed v5 reference panel. We performed a two‐stage analysis: (1) single‐variant association using score‐based logistic regression for population‐based datasets and generalized linear mix‐models for family studies with covariate adjustment for onset/exam age, sex, principal components for population substructure, and APOE ε2/ε3/ε4 genotype, followed by within‐ancestry fixed‐effects meta‐analysis using METAL; and (2) cross‐ancestry meta‐analysis of within‐ancestry associations using random‐effects modeling (RE2) in METASOFT. Secondary analyses include pathway analysis using EnrichR and gene‐based association testing using SKAT‐O (on‐going).

Result

In addition to APOE region associations, we identified 13 loci with genome‐wide significant (GWS; P≤5×10−8) cross‐ancestry associations including chromosomes 1q32.2 (CR1), 2q14 (BIN1), 6p21.1 (TREM2), 6p12.3 (CD2AP), 8p21.2 (PTK2B), 8p21.1 (CLU), 8q24.3 (SHARPIN), 11q12 (MS4A6A), 11q14 (PICALM), 19p13.3 (ABCA7), and novel loci at 11p2 (LRRC4C) and 12q24.13 (LHX5‐AS1). An additional 13 loci reached suggestive significance (P<10−5), including loci with ancestry‐specific associations attaining GWS such as PALM2AKAP2 (P = 2.5×10−8 in EAS), GRB14 (P = 1.7×10−8 in HI), and KIAA0825 (P = 2.9×10−8 in NHW). Follow‐up pathway analysis implicated multiple amyloid regulation pathways (strongest: negative regulation of APP catabolism, adjusted P = 1.6×10−4) and the classical complement pathway (adjusted P = 1.3×10−3). Follow‐up analyses including fine‐mapping and gene‐based analyses are on‐going.

Conclusion

Cross‐ancestry GWAS meta‐analyses identified novel LOAD susceptibility loci in/near LRRC4C and LHX5‐AS1, both with known roles in neuronal development, as well as several novel ancestry‐unique loci, showing their tremendous value for gene discovery with smaller sample sizes than current European ancestry LOAD GWAS. Even larger multi‐ancestry studies will provide even more power for further elucidating the genomics of LOAD.