Pedigree metrics: What are they good for?
Abstract
Estimating additive genetic variance (V_A) is key to understanding evolutionary potential in wild populations. V_A is commonly estimated using relatedness data from pedigrees in so-called `animal models'. The accuracy of these estimates depends strongly on information in the pedigree. Typically metrics describing pedigree structure (such as the number of individuals, maternities, pedigree depth etc.) are provided alongside V_A estimates. However, what these metrics represent is not well understood. Here, we first explored the evolution of metric reporting. Then, using 53 wild pedigrees, we demonstrated that precision of heritability (h^2; ratio of V_A to total phenotypic variance) estimates was strongly predicted by pedigree size, but other metrics provided little additional information about precision. An seldom-used analytical approximation almost exactly reproduced simulated standard errors for h^2 estimates from pedigrees and genomic relatedness matrices, offering an alternative to simulation-based power analysis for simple models. Although the utility of commonly reported metrics is limited for precision, they provide important information about risk of bias from confounding effects, which is valuable for planning future studies and assessing published ones. We demonstrated this using the example of unmodelled maternal effects. Future work on biases in h^2 estimation should explicitly take pedigree structure into account.
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BibTeX
@misc{pick2026pedigree,
title = {{Pedigree metrics: What are they good for?}},
author = {Joel L Pick and Barbara Class and Paul Acker and anne charmantier and Niels J. Dingemanse and Ned Dochtermann and Julien G. A. Martin and Denis Réale and Jane Reid and Holger Schielzeth and Céline Teplitsky and Kasha Strickland and Yimen Gerardo Araya Ajoy},
howpublished = {EcoEvoRxiv},
year = {2026},
month = oct,
doi = {10.32942/x21q39},
url = {https://ecoevorxiv.org/repository/view/15869/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Pedigree metrics: What are they good for?
AU - Joel L Pick
AU - Barbara Class
AU - Paul Acker
AU - anne charmantier
AU - Niels J. Dingemanse
AU - Ned Dochtermann
AU - Julien G. A. Martin
AU - Denis Réale
AU - Jane Reid
AU - Holger Schielzeth
AU - Céline Teplitsky
AU - Kasha Strickland
AU - Yimen Gerardo Araya Ajoy
PY - 2026
DA - 2026/10/30
PB - EcoEvoRxiv
DO - 10.32942/x21q39
UR - https://ecoevorxiv.org/repository/view/15869/
AB - Estimating additive genetic variance (V_A) is key to understanding evolutionary potential in wild populations. V_A is commonly estimated using relatedness data from pedigrees in so-called `animal models'. The accuracy of these estimates depends strongly on information in the pedigree. Typically metrics describing pedigree structure (such as the number of individuals, maternities, pedigree depth etc.) are provided alongside V_A estimates. However, what these metrics represent is not well understood. Here, we first explored the evolution of metric reporting. Then, using 53 wild pedigrees, we demonstrated that precision of heritability (h^2; ratio of V_A to total phenotypic variance) estimates was strongly predicted by pedigree size, but other metrics provided little additional information about precision. An seldom-used analytical approximation almost exactly reproduced simulated standard errors for h^2 estimates from pedigrees and genomic relatedness matrices, offering an alternative to simulation-based power analysis for simple models. Although the utility of commonly reported metrics is limited for precision, they provide important information about risk of bias from confounding effects, which is valuable for planning future studies and assessing published ones. We demonstrated this using the example of unmodelled maternal effects. Future work on biases in h^2 estimation should explicitly take pedigree structure into account.
ER -