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. 2021 Apr 1;13(1):72.
doi: 10.1186/s13195-021-00808-5.

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

Yann Le Guen #  1 , Michael E Belloy #  2 , Valerio Napolioni  3 , Sarah J Eger  2 , Gabriel Kennedy  2 , Ran Tao  4 , Zihuai He #  2   5 , Michael D Greicius #  2 ; Alzheimer’s Disease Neuroimaging Initiative
Affiliations

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

Yann Le Guen et al. Alzheimers Res Ther. .

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.

Keywords: Age adjustment; Alzheimer’s disease; Cox regression; Exome-wide association; Genetics; KIF21B; RAB10; RIN3; TAOK2; USH2A; Whole-exome sequencing.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Proposed AD-age score visualization and its distribution in the discovery and replication samples. a Illustration of the proposed AD-age scores with a linear and a piecewise definition of the weight (age) function (see the “Methods” section). b, c Cases (AD) and controls (CN) age distribution in the discovery, composed of next generation sequencing data, and in the replication, composed of SNP-array imputed data, and d, e their respective AD-age score distributions
Fig. 2
Fig. 2
Power of different association models for two specific simulation outcomes. a–c A common variant with moderate effect size, evaluated in 1000 cases and 1000 controls at a significance level of α = 0.05, mimicking the condition of common AD cohorts genotyped on SNP arrays. d–f An uncommon variant with large effect size, evaluated in 5000 cases and 5000 controls at a significance level of α = 5 × 10−7, mimicking the condition of ADSP WES which allows exploration of uncommon and rare variant associations. Panels show power on the y-axis and age-related effect estimates on the x-axis. Outcomes for four models are shown (cf. legend) and the age-related effect estimate for AD [OR (Age-AD)] is marked by a vertical gray dotted line. From left to right, panels show simulation results for increasing mean age differences between cases and controls (cases being younger than controls where applicable)
Fig. 3
Fig. 3
Manhattan plots of exome wide associations in the four main models excluding the APOE region. The age-adjusted logistic regression has no suggestive association (dashed line, p < 1 × 10−5). The main causal variant on TREM2 is exome-wide significant (solid line, p < 5 × 10−7) in the other three models. Among suggestive associations, (i) known AD associations are in red and (ii) novel associations which replicate (p < 0.05) in an independent dataset are in blue (cf Table 3). Colored dots were bootstrapped in the AD-age score model (see the “Methods” section). The minimum p value from the adjustment models for each main model is displayed as in [7]
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