Regularized regression can improve estimates of multivariate selection in the face of multicollinearity and limited data.

ABSTRACT: The breeder's equation, Δz¯=Gβ  , allows us to understand how genetics (the genetic covariance matrix, G) and the vector of linear selection gradients β interact to generate evolutionary trajectories. Estimation of β using multiple regression of trait values on relative fitness revolutionized the way we study selection in laboratory and wild populations. However, multicollinearity, or correlation of predictors, can lead to very high variances of and covariances between elements of β, posing a challenge for the interpretation of the parameter estimates. This is particularly relevant in the era of big data, where the number of predictors may approach or exceed the number of observations. A common approach to multicollinear predictors is to discard some of them, th

SUBMITTER: Sztepanacz JL 

PROVIDER: S-EPMC11358252 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature