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Development of a fast and versatile method for genome‐wide association studies of longitudinal data
Author(s) -
Kleineidam Luca,
Andrade Victor,
Wagner Michael,
Lambert JeanCharles,
Ruiz Agustín,
Ramirez Alfredo
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.042441
Subject(s) - genome wide association study , genetic association , sample size determination , linear regression , statistical power , linear model , generalized linear mixed model , computer science , statistics , mathematics , genetics , biology , single nucleotide polymorphism , gene , genotype
Abstract Background Apolipoprotein E ε4 shows a strong effect on the risk for developing Alzheimer’s disease (AD) but does not modulate the speed of cognitive decline after dementia onset. Accordingly, the influence of other genetic determinants might also change during the disease course of AD. To identify those determinants, methods are needed allowing for genome‐wide analysis of genetic effects using longitudinal data. Since the disease progression of AD, measured e.g as cognitive decline, may show a non‐linear trajectory over time, we developed a fast and flexible method for conducting genome‐wide association studies (GWAS). Method We extended the previously developed generalized least square approximation of linear mixed models (Rönnegard et al., 2016) to account for potential non‐linear trajectories. As GWAS studies usually combine results from different cohorts, this approach was combined with a partial derivative‐based meta‐analysis (Roshchupkin et al., 2016) that yields identical statistical power as pooled analysis but without the need of sharing individual participant data. To ensure suitability of our method, we conducted a Monte Carlo Simulation study generating data on 8000 participants with a non‐linear decline over 6 years of follow‐up and a 70% drop‐out rate. In each condition of the simulation study, we varied the number of cohorts providing the final sample size (1/5/40 studies), the minor allele frequency (MAF; 0.33/0.03) and the effect size of the genetic predictors on the longitudinal data. Thousand replications per condition were generated. Performance was compared to standard methods (i.e., full linear mixed models and invers‐variance‐weighted meta‐analysis). Result Compared to a standard linear mixed model, our method was able to reduce, from several seconds to milliseconds, the computation time needed to calculate the association of one genetic variant with the longitudinal change. Our method producing unbiased effect estimates across all conditions. In case of less frequent variants (MAF = 0.03) and 40 studies, the standard invers‐variant weighted meta‐analysis showed higher type‐I‐error, but lower statistical power compared to the partial derivative‐based approach implemented in our method. Conclusion We developed a fast and versatile method for longitudinal GWAS that maximizes power to detect genetic determinants of a wide range of different phenotypes in research on neurodegenerative diseases.