Genetic analysis of swimming performance in rainbow trout (Oncorhynchus mykiss) using image traits derived from deep learning

The physical and physiological condition of fish directly influences their swimming performance, which is crucial for their health and survival. This study explored how physical characteristics affect swimming performance in rainbow trout. 3D images were used to capture the morphology of fish and assess its impact on critical swimming speed ( U crit ), measured via individual swim tests. A convolutional neural network (CNN) was utilized to predict U crit from the images. Using Gradient-weighted Class Activation Maps (GradCAM), image regions that contributed to U crit predictions were visualized. These regions were further refined into areas that are biologically relevant to U crit , leading to the definition of four swim traits: head volume, caudal fin volume, epaxial muscle volume, and shape. Our findings indicated that U crit is moderately heritable. Genetically, heavier fish demonstrated poorer swimming performance; among fish of the same weight, those with larger and broader epaxial muscles, larger heads, and smaller caudal fins performed worse. Although genetic improvement of U crit is feasible, caution is advised because of potential correlated responses that reduce the body volume and epaxial muscle volume. The interdisciplinary workflow (data collection, model construction, visualization, interpretation, definition, and evaluation) in this study demonstrated how image-based deep learning can be used as a hypothesis-free approach to deepen the understanding of the genetic background of complex traits. Additionally, it highlights the value of genetic analysis to validate the physiological interpretation of Explainable AI, broadening the opportunities to discover novel phenotypes in aquaculture. • Image-based deep learning is an effective data-driven approach to identify relevant morphological traits that explain complex trait variance. • Genetic analysis can be applied to validate the interpretation of Explainable AI result. • Morphology explains part of the variance in critical swimming speed in rainbow trout. • Finer scale morphological traits showed higher genetic correlation than body volume with critical swimming speed. • Positive selection on critical swimming speed may decrease both body volume and epaxial muscle volume.

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Publication Details

Journal
Aquaculture
Published
2025-04-21
DOI
https://doi.org/10.1016/j.aquaculture.2025.742607
Citations
2
Primary Topic
Fish Ecology and Management Studies
Type
article
Field-Weighted Citation Impact
4.63
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article

Genetic analysis of swimming performance in rainbow trout (Oncorhynchus mykiss) using image traits derived from deep learning

R.J.W. Blonk, Hans Komen, Haris Ahmad Khan, Robert Mussgnug et al.
2 citations
Aquaculture
Fish Ecology and Management Studies
4.63
article

Genetic analysis of swimming performance in rainbow trout (Oncorhynchus mykiss) using image traits derived from deep learning

R.J.W. Blonk, Hans Komen, Haris Ahmad Khan, Robert Mussgnug, Arjan P. Palstra, J.W.M. Bastiaansen, Yuuko Xue
article en
2 citations

Abstract

The physical and physiological condition of fish directly influences their swimming performance, which is crucial for their health and survival. This study explored how physical characteristics affect swimming performance in rainbow trout. 3D images were used to capture the morphology of fish and assess its impact on critical swimming speed ( U crit ), measured via individual swim tests. A convolutional neural network (CNN) was utilized to predict U crit from the images. Using Gradient-weighted Class Activation Maps (GradCAM), image regions that contributed to U crit predictions were visualized. These regions were further refined into areas that are biologically relevant to U crit , leading to the definition of four swim traits: head volume, caudal fin volume, epaxial muscle volume, and shape. Our findings indicated that U crit is moderately heritable. Genetically, heavier fish demonstrated poorer swimming performance; among fish of the same weight, those with larger and broader epaxial muscles, larger heads, and smaller caudal fins performed worse. Although genetic improvement of U crit is feasible, caution is advised because of potential correlated responses that reduce the body volume and epaxial muscle volume. The interdisciplinary workflow (data collection, model construction, visualization, interpretation, definition, and evaluation) in this study demonstrated how image-based deep learning can be used as a hypothesis-free approach to deepen the understanding of the genetic background of complex traits. Additionally, it highlights the value of genetic analysis to validate the physiological interpretation of Explainable AI, broadening the opportunities to discover novel phenotypes in aquaculture. • Image-based deep learning is an effective data-driven approach to identify relevant morphological traits that explain complex trait variance. • Genetic analysis can be applied to validate the interpretation of Explainable AI result. • Morphology explains part of the variance in critical swimming speed in rainbow trout. • Finer scale morphological traits showed higher genetic correlation than body volume with critical swimming speed. • Positive selection on critical swimming speed may decrease both body volume and epaxial muscle volume.

AquacultureVol. 606
Hendrix Genetics (Netherlands) (NL), Centre for BioSystems Genomics (NL), Wageningen University & Research (NL)
Life below water
Openalex Percentile: Top 7%
Fish Ecology and Management Studies
4.63
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