Bias and heritability of the autocorrelation based on longitudinal data used as resilience indicator

Research output: Chapter in Book/Report/Conference proceedingConference paperAcademic

Abstract

To genetically improve resilience, the autocorrelation between subsequent deviations from fitted curves of longitudinal data was proposed. Autocorrelation is related to the speed of recovery after a disturbance: a high autocorrelation means slow recovery, while a low autocorrelation means fast recovery. It is known that estimated autocorrelations are biased, while the genetic properties of the autocorrelation are largely unknown. The aims of this research were to investigate the bias and the heritability of the autocorrelation. Deterministic expressions were derived and evaluated with Monte Carlo simulation. The estimated autocorrelation was 0.2-0.3 lower than the true value when the number of records was 10. The heritability was between 0.05 and 0.10 in most situations. It is recommended to have at least 50 records per animal. This shows good opportunities for genetic improvement. This study is a first step towards better understanding of the mathematical and genetic properties of the autocorrelation.
Original languageEnglish
Title of host publicationProceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP)
Subtitle of host publicationTechnical and species orientated innovations in animal breeding, and contribution of genetics to solving societal challenges
EditorsR.F. Veerkamp, Y. de Haas
Place of PublicationWageningen
PublisherWageningen Academic Publishers
Pages680-683
ISBN (Electronic)9789086869404
DOIs
Publication statusPublished - 2022
EventWorld Congress on Genetics Applied to Livestock Production: WCGALP 2022 - Rotterdam, Netherlands
Duration: 3 Jul 20228 Jul 2022

Conference

ConferenceWorld Congress on Genetics Applied to Livestock Production: WCGALP 2022
Country/TerritoryNetherlands
CityRotterdam
Period3/07/228/07/22

Fingerprint

Dive into the research topics of 'Bias and heritability of the autocorrelation based on longitudinal data used as resilience indicator'. Together they form a unique fingerprint.

Cite this