Longitudinal Analysis Is More Powerful than Cross-Sectional Analysis in Detecting Genetic Association with Neuroimaging Phenotypes

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.

Genome-wide association analysis of secondary imaging phenotypes from the Alzheimer's disease neuroimaging initiative study

“…The SNP marker rs2075650, identified by LReg and SPREG, is associated with the right hippocampus and the right amygdala. The association between rs2075650 and the right hippocampus was also detected by previous works (Shen et al (2010); Xu et al (2014)). …”

“…Although we have designed the simulation studies by assuming a single quantitative secondary trait and the additive mode of inheritance, the studies can be easily extended to consider other kind of secondary traits such as binary traits (Wang and Shete (2011); Chen et al (2013)), longitudinal traits (Skup et al (2012); Xu et al (2014)), multiple traits (Lin et al (2012); Zhang et al (2014); Zhu et al (2014)) as well as other modes of inheritance. We only include AD patients and CN subjects in the GWAS of ADNI data in the paper, but we may include the MCI subjects in our GWAS and treat them as controls by following the analysis in Kim and Pan (2015).…”

A cautionary note on using secondary phenotypes in neuroimaging genetic studies

“…Although we have only considered single quantitative secondary phenotype–single SNP associations, we anticipate that our conclusions will be likely to hold for other cases, such as for binary secondary phenotypes (Wang and Shete 2010; Chen et al 2013), multiple secondary phenotypes (Lin et al 2012; Zhang et al 2014; Zhu et al 2014), longitudinal secondary phenotypes (Skup et al 2012; Xu et al 2014), or for gene-gene or gene-environment interactions (Ge et al 2015; Hibar et al 2015b), though further studies are needed.…”

The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities

“…[61][62][63] Recent works propose phenotyping strategies to overcome hurdles using multiple data sources to more accurately ascertain disease status. [64][65][66][67][68][69][70][71][72] However, future work is needed to provide statistical methods for incorporating data of different types for phenome generation. For a detailed review of phenotyping procedures, see Bush et al 7 Figure S8 provides some examples of the types of structured and unstructured EHR information that can be used to construct phenotypes.…”