A novel age-informed approach for genetic association analysis in Alzheimer’s disease

Abstract

Background Many Alzheimer’s disease (AD) genetic association studies disregard age or incorrectly account for it, hampering variant discovery. Methods Using simulated data, we compared the statistical power of several models: logistic regression on AD diagnosis adjusted and not adjusted for age; linear regression on a score integrating case-control status and age; and multivariate Cox regression on age-at-onset. We applied these models to real exome-wide data of 11,127 sequenced individuals (54% cases) and replicated suggestive associations in 21,631 genotype-imputed individuals (51% cases). Results Modeling variable AD risk across age results in 5–10% statistical power gain compared to logistic regression without age adjustment, while incorrect age adjustment leads to critical power loss. Applying our novel AD-age score and/or Cox regression, we discovered and replicated novel variants associated with AD on KIF21B, USH2A, RAB10, RIN3, and TAOK2 genes. Conclusion Our AD-age score provides a simple means for statistical power gain and is recommended for future AD studies.

A Fast and Robust Strategy to Remove Variant-Level Artifacts in Alzheimer Disease Sequencing Project Data

“…In the ADSP WES data, TREM2 and ABCA7-wellestablished AD risk genes 2,6 -were observed with variants, respectively, at genome-wide and suggestive significance, consistent with observations for similar models in prior studies on the prior ADSP WES discovery phase data. 7,15 Despite observing only 4 variants in ADSP WES that passed suggestive significance in model 2, our findings were overall highly consistent with prior work. 15 We also observed that certain variants identified previously were not present in our current summary statistics (eTable 10, links.lww.com/NXG/A536), reflecting differences in joint calling, QC, and the fact that currently only biallelic data were available for the new ADSP WES data.…”

“…2 In response to this observation, there has been a shift to start using exome sequencing (ES) or genome sequencing (GS) to help capture rare and/or coding variants that contribute to AD risk, which has led to several recent initial successes. [7][8][9][10][11][12][13][14][15] In the United States, the Alzheimer Disease Sequencing Project (ADSP) has taken a leading role in the sequencing of AD-related samples at scale, with resultant data being made publicly available to researchers to generate new insights into the genetic etiology of AD. The ADSP has pursued both "whole" ES (WES) and "whole" GS (WGS) approaches (although it should be noted that these for now do not actually provide whole coverage due to technical limitations), where most recently, the focus is increasingly on GS.…”

Summary statistics knockoff inference empowers identification of putative causal variants in genome-wide association studies

“…Some weaker correlations are observed for studies that use different phenotype definitions. For example, Huang et al (2017) is weakly correlated with other major AD GWAS because the authors performed a time-to-event survival analysis; the correlation between Le Guen et al (2021) and Bis et al (2019) is weaker than that between our in-house ADSP WES analysis and Bis et al (2019), because Le Guen et al (2021) used a new age-informed AD phenotype instead of clinical AD.…”

“…In-house genome-wide associations study of 15,209 cases and 14,452 controls aggregating 27 cohorts across 39 SNP array data sets, imputed using the TOPMed reference panels 47 ; 6-7. Two whole-exome sequencing analyses of data from The Alzheimer’s Disease Sequencing Project (ADSP) by Bis et al 2019 48 (5740 cases, 5096 controls), and Le Guen et al 2021 49 (6008 cases, 5119 controls); 8. In-house whole-exome sequencing analysis of ADSP (6155 cases, 5418 controls); 9.…”

Polygenic risk score analysis identifies deleterious protein-coding variants in novel immune pathway genesATP8B4, FCGR1A, andLILRB1that associate with Alzheimer’s disease

“…To evaluate the association of individual genetic variants with AD in all qualifying participants (N=9,746), we performed logistic regression in PLINK (v1.9) using an additive model while adjusting for sex, APOE ε2 and ε4 dose, sequencing centers and the first three PCs accounting for population substructure. Since the ADSP cohort was enriched for older controls (Additional File 1), we did not include age as a covariate in the regression model as correcting for age when individuals with AD are younger than controls leads to the model incorrectly inferring the age effect on AD risk, resulting in loss of statistical power [43]. Variants in the APOE region [44] (chr19: 44,495,939-45,296,742; GRCh38) were excluded from this analysis.…”