Principal components methods for narrow‐sense heritability in the analysis of multidimensional longitudinal cognitive phenotypes
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.061 | 0.170 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".