Heritability, selection, and the response to selection in the presence of phenotypic measurement error: Effects, cures, and the role of repeated measurements

Abstract

Quantitative genetic analyses require extensive measurements of phenotypic traits, a task that is often not trivial, especially in wild populations. On top of instrumental measurement error, some traits may undergo transient (i.e., nonpersistent) fluctuations that are biologically irrelevant for selection processes. These two sources of variability, which we denote here as measurement error in a broad sense, are possible causes for bias in the estimation of quantitative genetic parameters. We illustrate how in a continuous trait transient effects with a classical measurement error structure may bias estimates of heritability, selection gradients, and the predicted response to selection. We propose strategies to obtain unbiased estimates with the help of repeated measurements taken at an appropriate temporal scale. However, the fact that in quantitative genetic analyses repeated measurements are also used to isolate permanent environmental instead of transient effects requires that the information content of repeated measurements is carefully assessed. To this end, we propose to distinguish "short-term" from "long-term" repeats, where the former capture transient variability and the latter help isolate permanent effects. We show how the inclusion of the corresponding variance components in quantitative genetic models yields unbiased estimates of all quantities of interest, and we illustrate the application of the method to data from a Swiss snow vole population.

Polygenic Architecture of Human Neuroanatomical Diversity

“…). Heritability estimates unbiased for measurement errors can be obtained with repeated measurements (Ponzi et al 2018) , however, the datasets we used to measure brain volumes provide only one MRI per subject. We used datasets from CoRR, the Consortium for Reliability and Reproducibility (Zuo et al 2014) , which includes multiple MRIs per individual to estimate intraclass correlation coefficients (ICC) (Fisher 1970) for each regional volume.…”

Measuring Complex Morphological Traits with 3D Photogrammetry: A Case Study with Deer Antlers

“…The resulting estimates of measurement variance can then be incorporated into statistical analyses with a variety of techniques (e.g. Fuller 1987;Buonaccorsi 2010;Hansen and Bartoszek 2012;Morrissey 2016;Ponzi et al 2018). Assessment of bias requires separate treatments, but again can be accounted for if it is estimated.…”

“…The measurement variance σ 2 m is the entity that is normally used to correct for measurement imprecision in statistical models (e.g. Fuller 1987;Buonaccorsi 2010;Hansen and Bartoszek 2012;Morrissey 2016;Ponzi et al 2018).…”

Limited mass‐independent individual variation in resting metabolic rate in a wild population of snow voles (Chionomys nivalis)

“…Repeated measurements taken over different time scales (i.e., short-term and long-term repeated measurements) can be used to separate transient (e.g. measurement error) from permanent environmental effects and hence to obtain unbiased repeatability estimates (Ponzi et al, 2018).…”