  A gene's rate of sequence evolution is among the most fundamental
evolutionary quantities in common use, but what determines evolutionary rates
has remained unclear. Here, we show that the two most commonly used methods to
disentangle the determinants of evolutionary rate, partial correlation analysis
and ordinary multivariate regression, produce misleading or spurious results
when applied to noisy biological data. To overcome these difficulties, we
employ an alternative method, principal component regression, which is a
multivariate regression of evolutionary rate against the principal components
of the predictor variables. We carry out the first combined analysis of seven
predictors (gene expression level, dispensability, protein abundance, codon
adaptation index, gene length, number of protein-protein interactions, and the
gene's centrality in the interaction network). Strikingly, our analysis reveals
a single dominant component which explains 40-fold more variation in
evolutionary rate than any other, suggesting that protein evolutionary rate has
a single determinant among the seven predictors. The dominant component
explains nearly half the variation in the rate of synonymous and protein
evolution. Our results support the hypothesis that selection against the cost
of translation-error-induced protein misfolding governs the rate of synonymous
and protein sequence evolution in yeast.
