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For each neuropsychological raw score, we performed univariate analysis of variance, with age, years of education, disease duration and treatment (LED) as covariates (ANCOVA), as these variables might affect cognition.
We also checked for heteroscedasticity (nonconstant variance with age) and found that higher ages were associated with higher variance in ARQ (Levene's test: F = 8.17, P = 0.006), but given the large sample size, this should not affect the estimate of the regression line [33].
Parameters reflecting biological age are expected to show an increased variance with age.
All traits showed a similar pattern in the change of the among-individual variance with age.
This is suggested by an increase of total phenotypic variance with age.
The factors were only able to predict only 7% of variance, with age contributing for most of the variation.
Similar(42)
(a ) Drift-plots show that mianserin attenuates increasing drift-variance with age.
Each subsample showed identical increases in drift-variance with age, confirming a transcriptome-wide effect.
(g ) Drift-plots show daf-2 RNAi attenuates increasing drift-variance with age in a manner dependent on daf-16.
We constructed drift-plots for all 19,196 genes in the data of cohort #1, which revealed a dramatic increase in drift-variance with age, showing a progressive loss of mRNA stoichiometries and co-expression patterns observed in young-adults.
Sanderson et al. [7] evaluated ulnar variance in over 1,000 cohorts and found that ulnar variance decreases with age.
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