Abstract
Most existing genome-wide association analyses are cross-sectional, utilizing only phenotypic data at a single time point, e.g. baseline. On the other hand, longitudinal studies, such as Alzheimer's Disease Neuroimaging Initiative (ADNI), collect phenotypic information at multiple time points. In this article, as a case study, we conducted both longitudinal and cross-sectional analyses of the ADNI data with several brain imaging (not clinical diagnosis) phenotypes, demonstrating the power gains of longitudinal analysis over cross-sectional analysis. Specifically, we scanned genome-wide single nucleotide polymorphisms (SNPs) with 56 brain-wide imaging phenotypes processed by FreeSurfer on 638 subjects. At the genome-wide significance level () or a less stringent level (e.g. ), longitudinal analysis of the phenotypic data from the baseline to month 48 identified more SNP-phenotype associations than cross-sectional analysis of only the baseline data. In particular, at the genome-wide significance level, both SNP rs429358 in gene APOE and SNP rs2075650 in gene TOMM40 were confirmed to be associated with various imaging phenotypes in multiple regions of interests (ROIs) by both analyses, though longitudinal analysis detected more regional phenotypes associated with the two SNPs and indicated another significant SNP rs439401 in gene APOE. In light of the power advantage of longitudinal analysis, we advocate its use in current and future longitudinal neuroimaging studies.
Highlights
There has been increasing interest in genome-wide association studies (GWASs) with neuroimaging phenotypes
Instead of using only the baseline structural magnetic resonance imaging (MRI) scans as phenotypes, we took advantage of the longitudinal imaging phenotypes measured at multiple time points from the baseline to 48 months, demonstrating the application of a linear mixed-effects model and its associated power gains
The advantage of longitudinal analysis is not surprising: assuming no single nucleotide polymorphisms (SNPs)-age interactions, a cross-sectional study based on the baseline can only capture the mean differences of a phenotype across the subgroups of subjects; in contrast, a longitudinal study offers the opportunity to estimate the mean values of the phenotype at the baseline, and the rates of the changes of the phenotype in the genotypic groups
Summary
There has been increasing interest in genome-wide association studies (GWASs) with neuroimaging phenotypes. Alzheimer’s Disease Neuroimaging Initiative (ADNI) provides a rich source of brain imaging, neuropsychological and genetic data, including genome-wide single nucleotide polymorphisms (SNPs) [1,2]. Instead of using only the baseline structural MRI scans as phenotypes, we took advantage of the longitudinal imaging phenotypes measured at multiple time points from the baseline to 48 months, demonstrating the application of a linear mixed-effects model and its associated power gains. The advantage of longitudinal analysis is not surprising: assuming no SNP-age interactions, a cross-sectional study based on the baseline can only capture the mean differences of a phenotype across the (genetic) subgroups of subjects; in contrast, a longitudinal study offers the opportunity to estimate the mean values of the phenotype at the baseline, and the rates of the changes of the phenotype in the genotypic groups. Some alternative but popular and simpler models would fail for the longitudinal data here
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