Adaptive and maladaptive introgression in grapevine domestication

Domesticated grapevines spread to Europe around 3,000 years ago. Previous studies have revealed genomic signals of introgression from wild to cultivated grapes in Europe, but the time, mode, genomic pattern, and biological effects of these introgression events have not been investigated. Here, we studied resequencing data from 345 samples spanning the distributional range of wild (Vitis vinifera ssp. sylvestris) and cultivated (V. vinifera ssp. vinifera) grapes. Based on machine learning–based population genetic analyses, we detected evidence for a single domestication of grapevine, followed by continuous gene flow between European wild grapes (EU) and cultivated grapes over the past ~2,000 y, especially from EU to wine grapes. We also inferred that soft-selective sweeps were the dominant signals of artificial selection. Gene pathways associated with the synthesis of aromatic compounds were enriched in regions that were both selected and introgressed, suggesting EU wild grapes were an important resource for improving the flavor of cultivated grapes. Despite the potential benefits of introgression in grape improvement, the introgressed fragments introduced a higher deleterious burden, with most deleterious SNPs and structural variants hidden in a heterozygous state. Cultivated wine grapes have benefited from adaptive introgression with wild grapes, but introgression has also increased the genetic load. In general, our study of beneficial and harmful effects of introgression is critical for genomic breeding of grapevine to take advantage of wild resources.

Key words: population genetics; grape breeding; machine learning; viticulture; structural variation

Analysis Code for Introgression in Grapes

SuperSFS

Citation

Hua, Xiao, et al. "Adaptive and maladaptive introgression in grapevine domestication." Proceedings of the National Academy of Sciences 120.24 (2023): e2222041120.

If you have any questions, please feel free to contact Yongfeng Zhou: [email protected] or Hua Xiao [email protected]