Smarter breeding: balancing genetic gain and diversity

A team of researchers from Wageningen University & Research has taken a deep dive into one of the central challenges in animal and plant breeding: how to maximise genetic progress without sacrificing genetic diversity. Their new study, published in Genetics Selection Evolution, systematically compares a wide range of selection strategies - and shows that combining them may be the key to long-term success.

Breeding programs traditionally select animals or plants based on their estimated breeding values (EBVs), aiming for the highest possible improvement in the next generation. But this short-term focus comes with a risk. “If we only optimise for immediate gains, we gradually lose genetic diversity, which limits future progress,” explains lead author and ABG-researcher Torsten Pook.

To address this challenge, the researchers created a “digital twin” of a breeding program using the simulation software MoBPS (Modular Breeding Program Simulator), developed in Wageningen. This allowed them to test a wide range of strategies under controlled conditions. These included giving more weight to rare beneficial genes, avoiding mating between related individuals, and optimising how much each individual contributes to the next generation.

The findings reveal clear trade-offs. Some strategies improve long-term genetic gain but reduce short-term progress, while others strike a better balance. For example, incorporating relatedness into selection decisions increased long-term genetic gain by 4% while reducing inbreeding by 18% and not sacrificing short-term performance.

However, the most striking result comes from combining multiple approaches. By integrating several diversity management strategies - optimised using an evolutionary algorithm - the researchers achieved over 5% higher genetic gain and more than 35% lower inbreeding rates compared to standard selection based on breeding values.

Co-author Tobias Niehoff emphasises: “What’s exciting is that these improvements don’t require additional costs in practice. Instead, they rely on smarter use of existing data and computational tools.”

The broader message is clear: there is no single optimal strategy for breeding. “The real gains come from combining complementary methods,” says Pook. This shift in perspective - from isolated techniques to integrated design - could help breeding companies maintain both productivity and genetic resilience in the face of growing challenges.