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A two-step approach combining the Gompertz growth model with genomic selection for longitudinal data
Ricardo Pong-Wong1, Georgia Hadjipavlou
1The Roslin Institute and R(D)SVS, University of Edinburgh, Roslin, Midlothian, EH25 9PS, UK. ricardo.pong-wong@roslin.ed.ac.uk.
BMC Proceedings
|April 13, 2010
Summary
Genomic selection accurately estimated breeding values for longitudinal data using Gompertz curves, even predicting values beyond observed data. This two-step approach effectively combined curve fitting and genomic evaluation without compromising accuracy.
Area of Science:
- Quantitative genetics
- Animal breeding
- Statistical modeling
Background:
- Utilized Gompertz growth curve to model simulated longitudinal data.
- Applied genomic evaluation to derived model parameters and predicted trait values.
Purpose of the Study:
- To assess the efficacy of combining curve fitting with genomic selection for longitudinal data.
- To estimate breeding values at time points with no phenotypic records.
Main Methods:
- Modeled simulated longitudinal data using the Gompertz growth curve.
- Performed genomic evaluation on model parameters and predicted phenotypes.
- Estimated the proportion of SNPs affecting trait parameters.
Main Results:
- Gompertz model showed good data fit despite logistic model simulation.
- Genomic breeding values from predicted phenotypes highly correlated with observed values (accuracy > 0.93).
- Identified QTL for asymptotic value but less successfully for other logistic curve parameters.
Conclusions:
- A two-step approach combining curve fitting and genomic selection is effective for longitudinal data.
- This method simplifies complex tasks without negatively impacting breeding value estimation.
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