Premium
Alzheimer's disease GWAS weighted by multi‐omics and endophenotypes identifies novel risk loci
Author(s) -
Ma Yiyi,
Vardarajan Badri N.,
Bennett David A.,
Fornage Myriam,
Seshadri Sudha,
Destefano Anita L.,
De Jager Philip L.
Publication year - 2020
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1002/alz.043977
Subject(s) - genome wide association study , endophenotype , expression quantitative trait loci , biology , snp , quantitative trait locus , single nucleotide polymorphism , genetics , genetic association , computational biology , cognitive decline , genetic architecture , gene , disease , dementia , neuroscience , cognition , medicine , genotype
Abstract Background We have increasing information on the role of certain genetic variants in a wide variety of molecular traits and endophenotypes of Alzheimer’s disease (AD), but their impacts on the prioritization of AD risk gene are unknown. Method Based on the summary statistics of the genome‐wide association study (GWAS) of AD published by the International Genomics of Alzheimer’s Project (IGAP) consortium (Kunkle et al., 2019), we have weighted each SNP by their associations with traits of eQTL (RNA expression), mQTL (DNA methylation), haQTL (histone acetylation), brain phosphorylated tangles (TAU), β‐amyloid protein (Aβ), and cognitive decline derived from the ROS‐MAP samples of postmortem dorsal‐lateral prefrontal cortex of AMP‐AD. Then, we collapsed all of the weighted SNPs found in each gene using the SKATO test, based on the 1000 reference genome. Result 38∼43% of genes (n = 12,899) have an increase in their statistical significance level after incorporating the weights of their genetic effects on omic molecular traits and AD endophenotypes. A subset of these gene‐level assessments reach the trait‐specific Bonferroni corrected genome‐wide significance threshold ( P < 0.05/number of genes for each trait). The burden of TAU pathology has the strongest effect with RASGRF2, FCF1, ZCWPW1, C6orf10, VAC14, ADGRF2, ACP2, DDB2 , and CR1 L being prioritized. Similarly, we have results prioritized by weighting by (1) Amyloid β: FCF1, NRXN1, PLCB1 , and DLG2 ; (2) cognitive decline: AKAP13, CR1L, KIAA1671 , and CR2 ; and (3) eQTL: TRIT1, RAPSN , and C6orf10 . The latter also emerges from the haQTL analysis, and no results emerge from the mQTL analysis. Conclusion Weighting by omic molecular traits and endophenotypes leading to AD is able to prioritize additional genes that could contribute to AD susceptibility. Functional validation is ongoing.