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Population genomics unravels the Holocene history of bread wheat and its relatives

Matters Arising to this article was published on 24 July 2023

Abstract

Deep knowledge of crop biodiversity is essential to improving global food security. Despite bread wheat serving as a keystone crop worldwide, the population history of bread wheat and its relatives, both cultivated and wild, remains elusive. By analysing whole-genome sequences of 795 wheat accessions, we found that bread wheat originated from the southwest coast of the Caspian Sea and underwent a slow speciation process, lasting ~3,300 yr owing to persistent gene flow from its relatives. Soon after, bread wheat spread across Eurasia and reached Europe, South Asia and East Asia ~7,000 to ~5,000 yr ago, shaping a diversified but occasionally convergent adaptive landscape in novel environments. By contrast, the cultivated relatives of bread wheat experienced a population decline by ~82% over the past ~2,000 yr due to the food choice shift of humans. Further biogeographical modelling predicted a continued population shrinking of many bread wheat relatives in the coming decades because of their vulnerability to the changing climate. These findings will guide future efforts in protecting and utilizing wheat biodiversity to enhance global wheat production.

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Fig. 1: The representative collection of wheat in this study.
Fig. 2: Demographic models of bread wheat speciation.
Fig. 3: Trans-Eurasian expansion of bread wheat.
Fig. 4: Geographic expansion reshaped the adaptive genetic diversity of bread wheat.
Fig. 5: The population size fluctuation of wheat from the past to the future.

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Data availability

The raw sequence data of 50 newly sequenced accessions were deposited in the Genome Sequence Archive (https://ngdc.cncb.ac.cn/gsa/) under accession number PRJCA005979. The genotype data from VMap 1.1 are publicly available at the Genome Variation Map (https://bigd.big.ac.cn/gvm) under accession number GVM000272. The sequence data for the remaining 745 accessions were downloaded from Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra) under accession numbers PRJNA663409, PRJNA439156, PRJNA476679 and PRJNA596843. The data on environmental variables and altitude are from WorldClim (https://www.worldclim.org/).

Code availability

The custom code for demographic history is available at https://github.com/xuebozhao16/VMap1.1-Population_history_of_wheats.

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Acknowledgements

We thank Y. Guo and S. Ge (Institute of Botany, Chinese Academy of Sciences) for suggestions on phylogenetic relationship inference and comments on the manuscript; J. Terhorst (University of Michigan) for suggestions on effective population size inference; M. K. Jones (University of Cambridge), X. Liu (Washington University in St Louis) and J. d’Alpoim Guedes (University of California San Diego) for suggestions on manuscript revision. This work was supported by the National Natural Science Foundation of China (31921005, 32225038 and 31970631), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA24020201 and XDA24040102), the National Key Research and Development Program (2022YFF1002904 and 2021YFF1000203-01), the National Major Agricultural Program (NK2022060101), the Hainan Yazhou Bay Seed Lab (B21HJ0001 and B21HJ0111) and the Informatization Plan of Chinese Academy of Sciences (CAS-WX2021SF-0109-02) to F.L.

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X.Z. and Y.G. performed data analysis, plotted manuscript figures and drafted the manuscript. Y.L., C.Y. and J.W. collected plant materials. L.K., C.Y., A.B., D.X., Z.Z., J.Z., X.Y., J.X., S.X., X.S., M.Z. and P.K. helped with data analysis. X.F. and Z.L. contributed to project coordination. F.L. conceived the idea, coordinated the project and finalized the manuscript. All authors discussed the results and commented on the manuscript.

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Correspondence to Fei Lu.

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Nature Plants thanks Alexandra Przewieslik-Allen, Hugo Oliveira and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended Data Fig. 1 FST between bread wheat and other subspecies.

a, FST between bread wheat and other subspecies in A lineage (AA, AABB and AABBDD taxa). b, FST between bread wheat and other subspecies in B lineage (BB/SS, AABB and AABBDD taxa). c, FST between bread wheat and other subspecies in D lineage (DD and AABBDD taxa).

Extended Data Fig. 2 The local phylogenies of TtBtr1-A and TtBtr1-B using all the SNPs within the 1 Mb region encompassing domestication loci.

a, The tree topology of the gene TtBtr1-A in the A subgenome. b, The tree topology of the gene TtBtr1-B in the B subgenome.

Extended Data Fig. 3 Domesticated emmer received substantial introgressed genomic segments from southern Levant wild emmer.

a, The geographic distribution of emmer accessions. b, Nucleotide diversity of in the southern wild emmer (n = 38 accessions), northern wild emmer (n = 11 accessions), and domesticated emmer (n = 29 accessions). The box edges represent the interquartile range, the horizontal lines represent median values and the whiskers extend to 1.5 the interquartile range in boxplots. The nucleotide diversity of domesticated emmer is higher than wild emmer in northern Levant (one-sided Student’s t-test). c, Phylogenetic topology used for inferring introgression to domesticated emmer (upper) and fd distribution between two populations above (bottom). d, Haplotype distribution in different emmer populations on genomic position 200-350 M of chromosome 4B.

Extended Data Fig. 4 Comparison of different gene flow scenarios of bread wheat subpopulation using fastsimcoal2.

By assessing gene flow among ten regions, only the subpopulations in Iberian Peninsula, the Indus Valley, Yunnan Province in China, and East China showed early and tiny gene flow, compared to frequent and magnitude gene flow in other regions (n = 20 random pairs). The box edges represent the interquartile range, the horizontal lines represent median values and the whiskers extend to 1.5 the interquartile range in boxplots.

Extended Data Fig. 5 Species phylogenetic network using 9,612 gene trees of AB lineages inferred by phyloNet software.

a-e, The first five maximum pseudo-likelihood trees with reticulation. Blue lines indicate hybridization events that connect a hybrid species and its two parents.

Extended Data Fig. 6 The cloned genes of environmental variables associated with.

a, All 22 environmental factors, including 11 temperature-related variables, 8 precipitation-related variables, altitude, latitude and longitude. b, Temperature-related variables. c, Precipitation-related variables. d, Altitude. e, Latitude and longitude. Each column is a gene, and each row is selective sweeps from XP-CLR results between paired regions, including WA vs. EU, WA vs. IA, WA vs. EA, WA vs. SH, EU vs. IA, EU vs. EA, EU vs. SH, IA vs. EA, IA vs. SH and EA vs. SH. The colors in the panels represent the number of environmental variables associated with the gene. ‘0’ represents the gene located in the XP-CLR region but does not have significant Bayenv sites. A number greater than 0 represents the number of environmental variables that are related to the gene.

Extended Data Fig. 7 Linear regression analysis and haplotype analysis for all 63 SNPs between the two neighboring genes encompassing Ppd-D1.

a, Linear regression analysis (Pearson’s r2) on the altitude of 57 SH landrace accessions with all 63 SNPs between the two adjacent genes containing Ppd-D1. The dot where the arrow points to is novel discovered stop-gain mutation in Ppd-D1. The redder the color of the dots is, the more significant the P-value is (one-sided Student’s t-test). b, Haplotypes distribution using all 63 SNPs in the SH subpopulation. Samples are arranged in reverse order from top to bottom according to altitude.

Extended Data Fig. 8 Effective population size history of wheat populations assessed by the coalescent approach SMC + + .

a, The effective population size over time of wild diploids, wild tetraploids and bread wheat since Holocene. The thick line represents the mean and the grey-shaded area represents the quartiles (n = 20 random pairs). The lines above plotted the global temperature for the past 100,000 years. b, The Ne of diploids in Aegilops over time. c, The Ne of diploids in Triticum over time. d, The Ne of tetraploids in Triticum over time. e, The Ne of hexaploids in Triticum over time. The thick line represents the mean and the grey-shaded area represents the quartiles of random individuals (n = 20 random pairs).

Extended Data Fig. 9 The inferred weighted importance between SNPs and environmental factors by GF analysis with different SNPs data sets.

a, Schematic diagram of genetic offset. b, The bioclimatic variable Prec7 shows the most significant effect among the randomly chosen 30k SNPs. c, The bioclimatic variable Temp8 shows the most significant effect among the randomly chosen 30k adaptation-associated SNPs. d, Cumulative importance of allelic frequency turnover relative to Temp8 in random SNPs. e, Cumulative importance of allelic frequency turnover relative to Temp8 in adaptation-associated SNPs. Thin light purple lines represented cumulative turnover for individual candidate SNPs. The solid red line indicated the turnover across all candidate SNPs.

Extended Data Fig. 10 Genetic offset (GO) map based on adaptation-associated SNPs projections.

RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5 (https://worldclim.org/) were used to predict the genetic offset in 2040-2060 (left panel) and 2080-2100 (right panel).

Supplementary information

Supplementary Information

Supplementary Notes 1–4 and Figs. 1–39.

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Supplementary Tables 1–30.

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Zhao, X., Guo, Y., Kang, L. et al. Population genomics unravels the Holocene history of bread wheat and its relatives. Nat. Plants 9, 403–419 (2023). https://doi.org/10.1038/s41477-023-01367-3

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