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Triticum population sequencing provides insights into wheat adaptation

Nature Genetics volume 52, pages 1412–1422 (2020)Cite this article

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Abstract

Bread wheat expanded its habitat from a core area of the Fertile Crescent to global environments within ~10,000 years. The genetic mechanisms of this remarkable evolutionary success are not well understood. By whole-genome sequencing of populations from 25 subspecies within the genera Triticum and Aegilops, we identified composite introgression from wild populations contributing to a substantial portion (4–32%) of the bread wheat genome, which increased the genetic diversity of bread wheat and allowed its divergent adaptation. Meanwhile, convergent adaptation to human selection showed 2- to 16-fold enrichment relative to random expectation—a certain set of genes were repeatedly selected in Triticum species despite their drastic differences in ploidy levels and growing zones, indicating the important role of evolutionary constraints in shaping the adaptive landscape of bread wheat. These results showed the genetic necessities of wheat as a global crop and provided new perspectives on transferring adaptive success across species for crop improvement.

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Fig. 1: The worldwide collection of wheat accessions.
Fig. 2: Phylogenetic relationship of Triticum species.
Fig. 3: Genetic diversity of subspecies in the genera Triticum and Aegilops.
Fig. 4: Composite introgression of Triticum species into bread wheat.
Fig. 5: Spatiotemporal dynamics of introgression in bread wheat.
Fig. 6: The shift of genetic diversity of Triticum species.
Fig. 7: Convergent adaptation of wheats to human selection.
Fig. 8: Convergent adaptation of the gene Btr1.

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Data availability

The raw sequence data were deposited in the Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra) under accession numbers PRJNA439156 and PRJNA663409. The raw sequence data were also deposited in the Genome Sequence Archive (https://bigd.big.ac.cn/gsa) under accession number CRA001951. The genotype data from VMap 1.0 are publicly available at the Genome Variation Map (https://bigd.big.ac.cn/gvm) under accession number GVM000082.

Code availability

The genotyping software HapScanner is available at https://github.com/PlantGeneticsLab/TIGER/wiki/HapScanner. The custom code for SNP filtering is available at https://github.com/YaoZhou89/WGSc.

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Acknowledgements

We thank Y. Guo (Institute of Botany, Chinese Academy of Sciences); Z. Liu, S. Zheng, X. Zhang and X. Fu (Institute of Genetics and Developmental Biology, Chinese Academy of Sciences); Y. Zhou (Department of Ecology and Evolutionary Biology, University of California); and P. Kear (International Potato Center–China Center for Asia and the Pacific) for their valuable suggestions and comments. This research was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA24020201) and the National Natural Science Foundation of China (31970631) to F.L., the National Natural Science Foundation of China (31921005) and Strategic Priority Research Program of the Chinese Academy of Sciences (XDA24020203) to Y.J. and the China Postdoctoral Science Foundation (2018M631614) to Y.Z.

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Author notes

  1. Yao Zhou

    Present address: Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China

  2. Haofeng Chen

    Present address: National Research Institute for Family Planning, Beijing, China

  3. These authors contributed equally: Yao Zhou, Xuebo Zhao.

Authors and Affiliations

  1. State Key Laboratory of Plant Cell and Chromosome Engineering, Institute of Genetics and Developmental Biology, Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, China

    Yao Zhou, Xuebo Zhao, Yiwen Li, Jun Xu, Aoyue Bi, Lipeng Kang, Daxing Xu, Jing Wang, Changbin Yin & Fei Lu

  2. University of Chinese Academy of Sciences, Beijing, China

    Xuebo Zhao, Jun Xu, Aoyue Bi, Lipeng Kang, Daxing Xu, Ying Wang, Yuling Jiao & Fei Lu

  3. State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Innovative Academy of Seed Design, Chinese Academy of Sciences, Beijing, China

    Haofeng Chen, Yuan-ge Wang & Yuling Jiao

  4. Annoroad Gene Technology Corporation, Beijing, China

    Sanyang Liu

  5. Novogene Bioinformatics Institute, Beijing, China

    Chengzhi Jiao & Hongfeng Lu

  6. CAS-JIC Centre of Excellence for Plant and Microbial Science (CEPAMS), Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China

    Fei Lu

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Contributions

X.Z. and Y.Z. performed experiments and data analysis; X.Z. plotted manuscript figures; Y.L. and C.Y. collected plant materials; H.C., J.X. and J.W. helped with experiments; A.B., L.K., D.X., Y.W., Y.-g.W., S.L., C.J., H.L. and F.L. helped with data analysis. F.L. and Y.J. designed and supervised the research. F.L. wrote the manuscript. All authors discussed the results and commented on the manuscript.

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Correspondence to Yuling Jiao or Fei Lu.

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Extended data

Extended Data Fig. 1 The pipeline of cross-ploidy variation discovery.

a, Reference genome of genome/subgenome groups. The reference genome was divided into four taxonomic group based on genome constitution of population (AA, AABB, AABBDD and DD). b, Variation calling pipeline. We built PCR free library of all samples, which were then sequenced on Illumina machines. SNPs were called using BWA and Sentieon softwares. After filtering raw SNPs, all SNPs were merged by subgenomes.

Extended Data Fig. 2 Evaluation of variation calling.

a, Definition of syntenic regions. A site whose depth passed the depth filter was defined as syntenic. Genetic variations in syntenic regions of each lineage were retained. b, Comparison of syntenic sites identified by depth and lastz methods. The gray circle represents all sites in the chromosome, the purple circle represents the syntenic sites defined by depth-based method and the yellow circle represents the syntenic sites defined by lastz. To verify syntenic sites detected by the depth approach, we also detected the syntenic sites using whole-genome alignment to examine the consistency between the two approaches. We aligned the wild emmer genome1 to the reference with lastz and the sites with unique mapping were defined as lastz-defined syntenic sites2. For example, we detected the syntenic sites on the second part of chromosome 1 A with an overall length of 122,798,051 bp, we detected 13,061,714 and 15,383,247 syntenic sites using depth-based and lastz, respectively, and 5,445,193 sites were detected as syntenic using both methods. We found that 41.69% of depth-defined syntenic sites were also lastz-defined syntenic sites. The two approaches showed consistency, but the overlapping of syntenic sites is not high. Given the prevalent structural variations in plant genomes, the depth approach based on alignment of hundreds of genomes is likely to be more sensitive and accurate on detecting syntenic sites than aligning only two single genomes. c, IBS distance between reference genome and each accession was estimated. Chinese Spring was used as a calibration accession and had the lowest IBS distance (0.0037) as indicated by the red arrow in AABBDD taxa.

Extended Data Fig. 3 Population structure of AB lineage.

a, Phylogenetic tree of AB lineage. Wild emmer from southern Levant was used as the outgroup. Species/subspecies were represented by numbers from 1 to 22. Tip colors of the phylogeny represent sampling region of each accession, while branch colors represent ploidy levels. Branches with reliable bootstrap value (> 50) are labeled with a purple pentagram at corresponding nodes. WA: West Asia; AF: Africa; EU: Europe; EA: East Asia; SCA: South and Central Asia; AM: America. b, Ancestral coefficient of AB lineage. K varied from 2 to 7. Species are labeled with their corresponding numbers and colors. c, Nucleotide diversity and population differentiation (FST) in AB lineage. d, Nucleotide diversity and population differentiation (FST) in D lineage. Values in the circle represent nucleotide diversity and numbers next to dashed lines represent FST.

Extended Data Fig. 4 Comparison of nucleotide diversity of wheats across the genome.

a, Comparison of nucleotide diversity of bread wheat across each chromosome. b, Comparison of nucleotide diversity of landrace and Indian dwarf wheat across the genome. Green lines represent landrace, yellow lines represent cultivar and blue lines represent Yunan wheat. Grey dots are the chromosome centromeres.

Extended Data Fig. 5 The proportion of introgression in landraces estimated from different species/subspecies.

The fd statistic (left y-axis) was estimated under four-taxon topology ((P1, P2), P3, O). Landrace was used as P2, Indian dwarf wheat (a) or Yunan wheat (b) was used as P1. Each P3 group is shown in x-axis. The right y-axis shows PGI statistic from different species/subspecies.

Extended Data Fig. 6 Introgression from wild emmer, domesticated emmer, and free-threshing tetraploids to landraces across the whole genome.

Introgression from wild emmer, domesticated emmer, and free-threshing tetraploids to landraces across the whole genome. The introgression from wild emmer, domesticated emmer, and free-threshing tetraploids were represented by blue, pink, and green lines, respectively. Grey dots represent the chromosome centromeres.

Extended Data Fig. 7 Introgression from diploid ancestors to landraces across the genome.

a, The introgression from urartu to landrace. b, The introgression from strangulata to landrace. Grey dots represent the chromosome centromeres.

Extended Data Fig. 8 Correlation of PGI and geographic distance between landraces and donor populations.

The averaged distance within each donor group (wild emmer, domesticated emmer, free-threshing tetraploids, and strangulata) for each landrace was used for representing the correlation between geographic distance and PGI of individual landraces.

Extended Data Fig. 9 Distribution of introgression tract size in landrace.

The tract size distribution from wild emmer, domesticated emmer, free-threshing tetraploids was represented in a-d, respectively.

Extended Data Fig. 10 Selective sweeps comparison between negative control and the paired domestication events.

a–c, Comparison of selected genes between negative control (from wild einkorn to wild emmer) and domestication process from wild einkorn to domesticated einkorn. d-f, Comparison of selected genes between negative control and domestication process from wild emmer to domesticated emmer. (a) and (d) show the number of genes overlapped under the threshold of top 5%, (b) and (e) shows the whole genome distribution, and (c) and (f) show the enrichment of convergence.

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Zhou, Y., Zhao, X., Li, Y. et al. Triticum population sequencing provides insights into wheat adaptation. Nat Genet 52, 1412–1422 (2020). https://doi.org/10.1038/s41588-020-00722-w

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