Incorporating genetic load contributes to predicting Arabidopsis thaliana’s response to climate change

Juan Jiang, Jia-Fu Chen, Xin-Tong Li, Li Wang, Jian-Feng Mao, Bao-Sheng Wang and Ya-Long Guo ()
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Juan Jiang: Chinese Academy of Sciences
Jia-Fu Chen: Chinese Academy of Sciences
Xin-Tong Li: Chinese Academy of Sciences
Li Wang: Chinese Academy of Agricultural Sciences
Jian-Feng Mao: Umeå University
Bao-Sheng Wang: Chinese Academy of Sciences
Ya-Long Guo: Chinese Academy of Sciences

Nature Communications, 2025, vol. 16, issue 1, 1-12

Abstract: Abstract Understanding how species respond to climate change can facilitate species conservation and crop breeding. Current prediction frameworks about population vulnerability focused on predicting range shifts or local adaptation but ignored genetic load, which is also crucial for adaptation. By analyzing 1115 globally distributed Arabidopsis thaliana natural accessions, we find that effective population size (Ne) is the major contributor of genetic load variation, both along genome and among populations, and can explain 74-94% genetic load variation in natural populations. Intriguingly, Ne affects genetic load by changing both effectiveness of purifying selection and GC biased gene conversion strength. In particular, by incorporating genetic load, genetic offset and species distribution models (SDM), we predict that, the populations at species’ range edge are generally at higher risk. The populations at the eastern range perform poorer in all aspects, southern range have higher genetic offset and lower SDM suitability, while northern range have higher genetic load. Among the diverse natural populations, the Yangtze River basin population is the most vulnerable population under future climate change. Overall, here we deciphered the driving forces of genetic load in A. thaliana, and incorporated SDM, local adaptation and genetic load to predict the fate of populations under future climate change.

Date: 2025
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DOI: 10.1038/s41467-025-58021-z

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