A meta-analysis on the heritability of vertebrate telomere length

Heung Ying Janet Chik*, Alexandra M Sparks, Julia Schroeder, Hannah L Dugdale

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

10 Citations (Scopus)
51 Downloads (Pure)

Abstract

Telomere dynamics are linked with both cellular and organismal senescence, and life history, individual quality and health. Telomere dynamics, particularly telomere length, have therefore garnered much research interest in evolutionary biology. To examine the evolution of telomere length, it is important to quantify its heritability, the proportion of total variation explained by additive genetic effects. Many studies have quantified telomere length heritability, but estimates are varied, and no general conclusion has been drawn. Additionally, it is unclear whether biological and methodological factors influence telomere length heritability estimates. We present the first meta-analysis of telomere length heritability, using 104 estimates from 43 studies over 18 vertebrate species. We calculated an overall mean heritability and examined how estimates varied by study, phylogeny, species-specific ecology, environmental setting, age at sampling, laboratory methods, statistical methods, sex and repeated measurements. Overall heritability was moderate (44.9%, 95% CI: 25.2-64.7%), and there was considerable heterogeneity in heritability estimates, in particular among studies and estimates. Laboratory method influenced heritability estimates, with in-gel hybridization TRF yielding higher heritabilities than qPCR and Southern blot TRF. There was also an effect from statistical method, with twin-based and SNP-based estimates lower than correlation-based or pedigree-based estimates. Our results highlight an overall heritable basis of telomere length, and we recommend future research on a wider range of taxa, and the use of variance-partitioning methods with relatedness or SNP data over correlation methods to minimize heritability estimation bias.

Original languageEnglish
Pages (from-to)1283-1295
Number of pages13
JournalJournal of Evolutionary Biology
Volume35
Issue number10
Early online date6-Aug-2022
DOIs
Publication statusPublished - Oct-2022

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