Simple maternal effect animal models provide biased estimates of additive genetic and maternal variation

preprint OA: closed
Full text JSON View at publisher

Abstract

Maternal effects (the consistent effect of a mother on her offspring) can inflate estimates of additive genetic variation (V_A) if not properly accounted for. As they are typically assumed to cause similarities only among maternal siblings, they are often accounted for by modelling maternal identity effects. However, if maternal effects have a genetic basis, they create additional similarities among relatives with related mothers that are not captured with maternal identity effects. Unmodelled maternal genetic variance (V_{Mg}) may therefore still inflate V_A in common quantitative genetic models, which is under-appreciated in the literature. Using published data and simulations, we explore the extent of this problem. Estimates from 14 studies of eight species suggest that a large proportion of maternal variation is genetic. Both this data and simulations confirmed that unmodelled V_{Mg} can inflate V_A and underestimate total maternal variation (V_M), the bias increasing with the amount of non-sibling maternal relatives in a pedigree. Simulations show these biases are further influenced by the size and direction of any direct-maternal genetic covariance. The estimation of total V_A (i.e., the weighted sum of V_A and V_{Mg}) is additionally affected, limiting inferences about evolutionary potential from simple maternal effect models. Unbiased estimates require modelling V_{Mg} explicitly, but these models are often avoided due to perceived data limitations. We demonstrate that estimating V_{Mg} is possible even with small pedigrees, reducing bias in V_A and maintaining accuracy in estimates of V_A, V_M, and total V_A. We therefore advocate for the broader use of these models.
Full text 2,353 characters · extracted from oa-doi-fallback · 2 sections · click to expand

Abstract

Maternal effects (the consistent effect of a mother on her offspring) can inflate estimates of additive genetic variation (\Va) if not properly accounted for. As they are typically assumed to cause similarities only among maternal siblings, they are often accounted for by modelling maternal identity effects. However, if maternal effects have a genetic basis, they create additional similarities among relatives with related mothers that are not captured by maternal identity effects. Unmodelled maternal genetic variance (\Vmg) may therefore still inflate \Va estimates in common quantitative genetic models, which is underappreciated in the literature. Using published data and simulations, we explore the extent of this problem. Published estimates from eight species suggest that a large proportion of total maternal variation (\Vm) is genetic ($\sim$65\%). Both these data and simulations confirmed that unmodelled \Vmg can cause overestimation of \Va and underestimation of \Vm, the bias increasing with the proportion of non-sibling maternal relatives in a pedigree. Simulations show these biases are further influenced by the size and direction of any direct-maternal genetic covariance. The estimation of total additive genetic variation (\Vat; the weighted sum of \Va and \Vmg) is additionally affected, limiting inferences about evolutionary potential from simple maternal effect models. Unbiased estimates require modelling \Vmg explicitly, but these models are often avoided due to perceived data limitations. We demonstrate that estimating \Vmg is possible even with small pedigrees, reducing bias in \Va estimates and maintaining accuracy in estimates of \Va, \Vm, and \Vat. We therefore advocate for the broader use of these models. DOI https://doi.org/10.32942/X2V33J Subjects Ecology and Evolutionary Biology, Genetics, Zoology

Keywords

animal model, genetic variation, maternal effects, Bias, wild population Dates Published: 2024-12-16 13:33 Last Updated: 2026-03-13 16:18 Older Versions License CC BY Attribution 4.0 International Additional Metadata Conflict of interest statement: None Data and Code Availability Statement: Data and code for this project are available at https://github.com/joelpick/maternal_effects Language: English

Text is read by the "Ask this paper" AI Q&A widget below. Extraction quality varies by source — PMC NXML preserves structure cleanly, OA-HTML may include some navigation residue, and OA-PDF can have broken hyphenation. The publisher copy (via DOI) is the canonical version.

My notes (saved in your browser only)

Ask this paper AI returns verbatim quotes from the full text · source: oa-doi-fallback

Answers must be backed by verbatim quotes from this paper's full text. Hallucinated quotes are dropped automatically; if no verbatim passage answers the question, we say so. How this works

Citation neighborhood (no data yet)

We don't have any in-corpus citations linked to this paper yet. This is a recent paper (2024) — citers typically take a year or two to land, and the OpenAlex reference graph may still be filling in.

Source provenance

europepmc
last seen: 2026-05-20T01:45:00.602351+00:00