Principal components methods for narrow-sense heritability in the analysis of multidimensional longitudinal cognitive phenotypes

Background: Genetic association studies of longitudinal cognitive phenotypes are an alternate approach to discovering genetic risk factors for Alzheimer's disease (AD). However, the standard linear mixed model approach is limited in the face of multidimensional longitudinal data and multiple genotypes. In this setting, the principal components of heritability (PCH) approach may increase efficiency by deriving a linear combination of phenotypes to maximize the heritability attributable to a particular genetic locus. The current study investigated the performance of two PCH methods, the Principal Components of Heritability Association Test (PCHAT) and C2BAT, in detecting association of the known AD susceptibility allele APOE-ϵ4 with cognitive function at baseline and decline in cognition over time.

Methods: PCHAT, C2BAT, and standard linear mixed models were used to test for association between APOE-ϵ4 allele and performance on 19 neuropsychological tests using subjects without dementia at baseline from the Religious Orders Study (ROS) (n = 693) and Memory and Aging Project (MAP) (n = 778). Analyses were conducted across the three methods for three nested phenotype definitions (all 19 measures, executive function and episodic memory measures, and episodic memory only), and for baseline data only versus longitudinal change.

Results: In all cases, APOE-ϵ4 was significantly associated with baseline level of and change over time in cognitive function, and PCHAT and C2BAT yielded evidence of association comparable to or stronger than conventional methods.

Conclusion: PCHAT, C2BAT, and other PCH methods may have utility for genetic association studies of multidimensional cognitive and other phenotypes by maximizing genetic information while limiting multiple comparisons.