Animal science journal = Nihon chikusan Gakkaiho2026; 97(1); e70223; doi: 10.1111/asj.70223

Phenotypic Definition Influences Genetic Inference and Predictive Ability for Reining Performance in Quarter Horses Using Random Regression Models.

Santana, ML
·
Bignardi, AB

Abstract: Reining performance is influenced by subjectivity and competition structure, challenging genetic evaluation. This study estimated genetic parameters for reining performance in Brazilian Quarter Horses using random regression models (RRM) and evaluated how alternative phenotypic definitions affect genetic inference and predictive ability. A dataset comprising 21,165 longitudinal records from 1603 horses was analyzed, comparing within-group rank (RANK) and Blom-transformed rank (BLOM). Model comparison indicated that cubic RRM provided the best fit for both phenotypes. Heritability estimates were low and age-dependent, ranging from 0.01 to 0.17 for RANK and from 0.06 to 0.23 for BLOM. Permanent environmental and rider effects accounted for substantial phenotypic variance. Genetic correlations were higher between adjacent ages and declined across distant ages. RANK showed greater additive genetic stability across ages and slightly higher theoretical accuracy for total EBV among selected stallions. In contrast, BLOM showed greater consistency for the combined additive genetic + permanent environmental component, stronger favorable genetic trends, and better LR-based predictive performance across several age classes. Overall, both rank-based phenotypes were useful and complementary for genetic evaluation of reining performance. BLOM showed modest advantages for several criteria, whereas RANK retained relevant information for representing relative competitive performance on the observed scale.

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Publication Date: 2026-07-23 PubMed ID: 42489093PubMed Central: PMC13392984DOI: 10.1111/asj.70223Google Scholar: Lookup

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Conflict of Interest
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Journal Article

Summary

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Overview

This study investigates how different ways of defining reining performance in Quarter Horses affect genetic analysis and the accuracy of predicting genetic merit using random regression models (RRMs).
The researchers analyzed a large dataset of performance records and compared two phenotypic definitions—raw ranks and transformed ranks—to understand their impact on genetic parameter estimates and predictive performance.

Background and Motivation

Reining is a competitive equestrian sport where horses perform precise maneuvers; judging can be subjective, and competition format influences performance scores.
The subjectivity and structure pose challenges for genetic evaluations aiming to identify horses with superior genetic potential for reining performance.
Accurate genetic evaluations assist breeders in selecting horses with better performance capabilities, but depend on how phenotypes (performance measurements) are defined and modeled.
Longitudinal data (performance records across different ages) requires appropriate statistical models to capture genetic and environmental effects over time.

Research Objectives

Estimate genetic parameters (such as heritability and genetic correlations) of reining performance in Brazilian Quarter Horses using random regression models.
Evaluate how different phenotypic definitions—within-group rank (RANK) and Blom-transformed rank (BLOM)—influence genetic inference and predictive accuracy.
Compare model fits, genetic parameter estimates, and predictive abilities based on these phenotypic transformations.

Data and Methodology

Used a dataset with 21,165 longitudinal observations from 1,603 Quarter Horses.
Two phenotypic definitions were compared:
- RANK: Raw within-group rankings of performance in competitions.
- BLOM: RANK phenotypes transformed using Blom’s transformation to approach normality and reduce skewness.
Random Regression Models (RRMs) were applied, which allow modelling genetic effects as continuous functions over age, capturing age-dependent changes in performance.
Model selection involved testing different polynomial orders; a cubic function was best-fitting for both phenotype types.
Heritabilities, genetic correlations between ages, environmental and rider effects, and additive genetic values were estimated and analyzed.
Predictive performance was evaluated using likelihood ratio (LR)-based tests and theoretical accuracy metrics.

Key Findings

Heritability Estimates:
- Generally low and varied with horse age.
- RANK heritabilities ranged from 0.01 to 0.17.
- BLOM heritabilities were higher, ranging from 0.06 to 0.23.
Genetic Correlations:
- Higher correlations between adjacent ages, indicating that genetic control of performance changes gradually over time.
- Correlations declined when comparing ages farther apart, suggesting some changes in genetic influence across a horse’s lifespan.
Environmental and Rider Effects:
- Permanent environmental influences and rider effects contributed significantly to observed performance variation.
- Accounting for these factors is important to avoid biased genetic parameter estimates.
Predictive Ability and Genetic Trends:
- RANK phenotype showed greater additive genetic stability across ages and slightly better accuracy for estimating total estimated breeding values (EBVs) among selected stallions.
- BLOM phenotype demonstrated:
  - Greater consistency when including both additive genetic and permanent environmental effects.
  - More favorable genetic trends over time.
  - Better predictive performance based on likelihood ratio tests across multiple ages.

Conclusions and Implications

Both phenotype definitions, RANK and BLOM, are useful and complementary for genetic evaluation of reining performance given their respective strengths.
BLOM transformation provides some advantages in terms of predictive ability and detecting genetic trends, potentially due to stabilizing variance and normalizing data.
RANK preserves important relative competitive information on the observed scale, important for interpreting performance in real competitions.
Utilization of random regression models effectively captures age-dependent genetic effects, supporting more accurate and dynamic genetic evaluations.
Findings can guide breeders and geneticists in choosing suitable phenotype definitions and modeling strategies to improve genetic selection in reining horses.

Cite This Article

APA

Santana ML, Bignardi AB. (2026). Phenotypic Definition Influences Genetic Inference and Predictive Ability for Reining Performance in Quarter Horses Using Random Regression Models. Anim Sci J, 97(1), e70223. https://doi.org/10.1111/asj.70223

Publication

Animal science journal = Nihon chikusan Gakkaiho

ISSN: 1740-0929

NlmUniqueID: 100956805

Country: Australia

Language: English

Volume: 97

Issue: 1

Pages: e70223

PII: e70223

Researcher Affiliations

Santana, Mário Luiz

Grupo de Melhoramento Animal de Mato Grosso (GMAT), Instituto de Ciências Agrárias e Tecnológicas, Universidade Federal de Rondonópolis (UFR), Av. dos Estudantes, 5055, Cidade Universitária, Rondonópolis, Mato Grosso, Brazil.

Bignardi, Annaiza Braga

Grupo de Melhoramento Animal de Mato Grosso (GMAT), Instituto de Ciências Agrárias e Tecnológicas, Universidade Federal de Rondonópolis (UFR), Av. dos Estudantes, 5055, Cidade Universitária, Rondonópolis, Mato Grosso, Brazil.

MeSH Terms

Animals
Horses / genetics
Horses / physiology
Phenotype
Female
Regression Analysis
Male
Age Factors

Conflict of Interest Statement

The authors declare no conflicts of interest.

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