Uncovering ghost introgression through genomic analysis of a distinct eastern Asian hickory species

Author:

Zhang Wei‐Ping12ORCID,Ding Ya‐Mei12,Cao Yu1,Li Pan3,Yang Yang1,Pang Xiao‐Xu1,Bai Wei‐Ning1,Zhang Da‐Yong1

Affiliation:

1. Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, College of Life Sciences Beijing Normal University Beijing 100875 China

2. State Key Laboratory of Plant Diversity and Specialty Crops, South China Botanical Garden Chinese Academy of Sciences Guangzhou 510650 China

3. Systematic and Evolutionary Botany and Biodiversity Group, MOE Laboratory of Biosystem Homeostasis and Protection, College of Life Sciences Zhejiang University Hangzhou 310058 China

Abstract

SUMMARYGhost introgression, or the transfer of genetic material from extinct or unsampled lineages to sampled species, has attracted much attention. However, conclusive evidence for ghost introgression, especially in plant species, remains scarce. Here, we newly assembled chromosome‐level genomes for both Carya sinensis and Carya cathayensis, and additionally re‐sequenced the whole genomes of 43 C. sinensis individuals as well as 11 individuals representing 11 diploid hickory species. These genomic datasets were used to investigate the reticulation and bifurcation patterns within the genus Carya (Juglandaceae), with a particular focus on the beaked hickory C. sinensis. By combining the D‐statistic and BPP methods, we obtained compelling evidence that supports the occurrence of ghost introgression in C. sinensis from an extinct ancestral hickory lineage. This conclusion was reinforced through the phylogenetic network analysis and a genome scan method VolcanoFinder, the latter of which can detect signatures of adaptive introgression from unknown donors. Our results not only dispel certain misconceptions about the phylogenetic history of C. sinensis but also further refine our understanding of Carya's biogeography via divergence estimates. Moreover, the successful integration of the D‐statistic and BPP methods demonstrates their efficacy in facilitating a more precise identification of introgression types.

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

Wiley

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