Impact of genomic selection for disease resistance on the spread of infection in a simulated aquaculture population
Abstract
Background In aquaculture, selection for disease resistance is typically based on mortality records from challenge tests performed on relatives of selection candidates. However, commercial success depends on limiting disease transmission, particularly the incidence and severity of outbreaks. It remains unclear whether selecting for lower mortality also reduces disease transmission. Both these outcomes are influenced by three underlying epidemiological host traits: susceptibility, infectivity, and infection-induced mortality. This simulation study evaluated the impact of genomic selection against mortality on disease transmission in a salmon population exposed to a pathogen with a fast transmission rate. Methods Mortality was assumed to be recorded on sibs of selection candidate, either as binary dead/alive status or as time to death during cohabitation/bath challenge tests. Phenotypes were simulated using a stochastic compartmental Susceptible-Infected-Removed epidemiological model, with genetic variation for the three underlying traits. Scenarios were explored by varying the genetic correlations between the three underlying traits. Challenge test designs varied in the number of groups, group sizes, and family distribution across groups. For comparison, a reference scenario with direct selection on the underlying traits was included. Genomic selection was applied over 10 discrete generations, and its impact on disease transmission was assessed using the basic reproductive ratio (R0). Results When selection was based on dead/alive status, R0 was highly sensitive to both the genetic correlations between the three underlying traits and the challenge test design. In contrast, selection based on time to death consistently reduced R0 across all scenarios (often to below 1 within four generations), regardless of trait correlations or test design. Selection on time to death primarily produced fish with reduced susceptibility to infection, while selection on dead/alive status produced fish with increased resistance and endurance to infection, delaying onset of infection or death without necessarily limiting transmission. Direct selection on the underlying epidemiological traits was the most efficient approach to reduce both mortality and transmission. Conclusions Genomic selection for disease resistance, when measured as time to death in cohabitation or bath challenge tests conducted until mortality naturally levels off, reduces both mortality and disease spread. Breeding programs may benefit from challenge test designs that enable estimation of genetic parameters for the underlying traits affecting disease transmission and survival.
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BibTeX
@article{garciaballesteros2026impact,
title = {{Impact of genomic selection for disease resistance on the spread of infection in a simulated aquaculture population}},
author = {Silvia García-Ballesteros and Jesús Fernández and Andrea B. Doeschl-Wilson and Beatriz Villanueva},
journal = {Genetics Selection Evolution},
year = {2026},
month = sep,
doi = {10.1186/s12711-026-01072-7},
url = {https://hal.science/hal-05748388v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Impact of genomic selection for disease resistance on the spread of infection in a simulated aquaculture population
AU - Silvia García-Ballesteros
AU - Jesús Fernández
AU - Andrea B. Doeschl-Wilson
AU - Beatriz Villanueva
PY - 2026
DA - 2026/09/14
JO - Genetics Selection Evolution
DO - 10.1186/s12711-026-01072-7
UR - https://hal.science/hal-05748388v1
AB - Background In aquaculture, selection for disease resistance is typically based on mortality records from challenge tests performed on relatives of selection candidates. However, commercial success depends on limiting disease transmission, particularly the incidence and severity of outbreaks. It remains unclear whether selecting for lower mortality also reduces disease transmission. Both these outcomes are influenced by three underlying epidemiological host traits: susceptibility, infectivity, and infection-induced mortality. This simulation study evaluated the impact of genomic selection against mortality on disease transmission in a salmon population exposed to a pathogen with a fast transmission rate. Methods Mortality was assumed to be recorded on sibs of selection candidate, either as binary dead/alive status or as time to death during cohabitation/bath challenge tests. Phenotypes were simulated using a stochastic compartmental Susceptible-Infected-Removed epidemiological model, with genetic variation for the three underlying traits. Scenarios were explored by varying the genetic correlations between the three underlying traits. Challenge test designs varied in the number of groups, group sizes, and family distribution across groups. For comparison, a reference scenario with direct selection on the underlying traits was included. Genomic selection was applied over 10 discrete generations, and its impact on disease transmission was assessed using the basic reproductive ratio (R0). Results When selection was based on dead/alive status, R0 was highly sensitive to both the genetic correlations between the three underlying traits and the challenge test design. In contrast, selection based on time to death consistently reduced R0 across all scenarios (often to below 1 within four generations), regardless of trait correlations or test design. Selection on time to death primarily produced fish with reduced susceptibility to infection, while selection on dead/alive status produced fish with increased resistance and endurance to infection, delaying onset of infection or death without necessarily limiting transmission. Direct selection on the underlying epidemiological traits was the most efficient approach to reduce both mortality and transmission. Conclusions Genomic selection for disease resistance, when measured as time to death in cohabitation or bath challenge tests conducted until mortality naturally levels off, reduces both mortality and disease spread. Breeding programs may benefit from challenge test designs that enable estimation of genetic parameters for the underlying traits affecting disease transmission and survival.
SN - 0999-193X
SN - 1297-9686
ER -