Research ArticleGENOME MAPPING

Genome-environment associations in sorghum landraces predict adaptive traits

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Science Advances  03 Jul 2015:
Vol. 1, no. 6, e1400218
DOI: 10.1126/sciadv.1400218
  • Fig. 1 Diversity of sorghum landraces and environments.

    (A to C) Map of georeferenced sorghum landraces genotyped in this study (A), with neighbor-joining tree (B and C) of landraces based on genome-wide SNP distance. Several landraces fall outside the plotted map region and are not shown. The monthly climates of four landraces representing diverse agroecological zones are shown in sets (i to iv; gray lines, temperature; black lines, precipitation) in addition to our estimate of each landrace’s local rainfed growing season length (brown to green colors). At right, the neighbor-joining trees show estimated growing season length (B) and botanical race classifications (C) of landraces, demonstrating variation in climate among related accessions.

  • Fig. 2 Distribution of Tannin1 (top panels) and Maturity1 (bottom panels) alleles (represented by red/purple/blue) across space and along environmental gradients.

    Gray boxes indicate regional subsets where the locus showed the strongest association with environment: Tannin1 was most strongly associated with growing season length in South Asia, and Maturity1 was most strongly associated with absolute latitude in southern Africa. The T allele at position 61667908 on chromosome 4 corresponds to the null Tannin1 allele, whereas the T allele at position 40286721 on chromosome 6 corresponds to the null Maturity1 allele.

  • Fig. 3 Genome-wide, multivariate SNP-environment associations.

    (A) Left panel shows the first two canonical axes (RDA1 and RDA2) of an RDA of variation in 871 SNPs among 1133 accessions (chosen to minimize missing SNP calls for accessions); inset map shows the geographic distribution of the accessions. Each canonical axis represents a linear combination of environmental variables (strongly loading variables shown as arrows) that explains variation in a linear combination of SNPs among accessions (colored points representing accessions from different regions: blue, South Asia; pink, Middle East; gray, West Africa; red, East Africa; green, South Africa). (B) Proportion of total SNP variation among accessions explained in RDA by environmental variables or spatial structure within each region (excluding Middle East). (C) Enrichment of three SNP categories for environmental structure, that is, the proportion of SNP variation among accessions (in all five regions) explained by environmental conditions. Gray dots represent 1000 circular permutations of SNP categories. The right panel shows enrichment for environmental structure after removing geographic spatial structure in SNP variation via partial RDA (akin to partial regression).

  • Fig. 4 Genome-wide associations with environment (A, D, and G) used to predict change in phenotypes across treatments for breeding lines and landraces (circles in C, F, and I), and comparisons of predictions using different numbers of predictor SNPs (B, E, and H).

    The best prediction is shown for each trait in (C), (F), and (I). Note that phenotype data were not used in predictions. There were three experiments testing the effects of (i) drought treatment late in growing season in Hyderabad, India (A to C), (ii) drought treatment across growing season in Austin, United States (D to F), and (iii) aluminum toxicity in the laboratory (G to I). Horizontal dashed lines (A, D, and G) show P value thresholds delineating the nested subsets of SNPs with the strongest associations to environment tested in (B), (E), and (H). The solid horizontal line (A, D, and G) shows the set of SNPs giving the best predictions (C, F, and I; r = Pearson’s correlation coefficient). In (C) and (I), the best model combined the subset of SNPs indicated and genome-wide SNP similarity (“kinship”). SE bars (B, E, and H) were generated using nonparametric bootstraps of sampled accessions. Predictions were standardized to z scores (x axes of C, F, and I). Drought treatment data were generated here; aluminum toxicity response data are from (44). Because of skew in the data, the y axes in (F) and (I) are shown as proportions with log scaling.

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/1/6/e1400218/DC1

    Fig. S1. Landrace accessions included in the study classified into botanical races based on morphological classification (five new world accessions not shown).

    Fig. S2. Rainout shelter where plants were grown in Austin.

    Fig. S3. Seedlings planted in the Austin experiment.

    Fig. S4. Plants growing in Austin.

    Fig. S5. A representative accession (IS 25836) under irrigated (left) and imposed terminal drought (right) conditions at experimental plot in India.

    Fig. S6. Proportion of total SNP variation among accessions with known collection locations (excluding spatial outlier landraces from the Americas, China, and Southeast Asia/Oceania) explained by spatial structure or environmental variables.

    Fig. S7. Predictions of phenotypes averaged across well-watered and drought conditions from drought treatment across growing season in Austin, United States.

    Fig. S8. Predictions of phenotype change between well-watered and drought conditions from drought treatment across growing season in Austin, United States.

    Fig. S9. Predictions of phenotypes averaged across well-watered and drought conditions from drought treatment late in growing season in Hyderabad, India.

    Fig. S10. Predictions of phenotype change between well-watered and drought conditions from drought treatment late in growing season in Hyderabad, India.

    Fig. S11. GWAS for harvest index plasticity (harvest index in wet/dry) in U.S. experiment, using SNP associations with precipitation in the warmest quarter as a prior.

    Fig. S12. GWAS for panicle weight plasticity (panicle weight in wet − dry) in India experiment, using SNP associations with growing season length as a prior.

    Fig. S13. GWAS for root growth plasticity (growth in control/Al toxic) in published aluminum toxicity experiment (44), using SNP associations with topsoil pH as a prior.

    Table S1. Landraces studied and environment of origin data.

    Table S2. Accession phenotypes from experiment in Austin, United States.

    Table S3. Mean phenotypes across the 2 years of the experiment in Hyderabad, India.

    Table S4. Spearman’s rank correlation test results for two SNPs that tag known candidate genes potentially involved in local adaptation.

    Table S5. EMMA t test results for two SNPs that tag known candidate genes potentially involved in local adaptation.

    Table S6. Predictions for each accession in the Austin experiment based on SNP-environment associations in the landrace panel.

    Table S7. Predictions for each accession in the Hyderabad experiment based on SNP-environment associations in the landrace panel.

    Table S8. Predictions for each accession in the Caniato et al. (44) experiment based on SNP-environment associations in the landrace panel.

    Table S9. Predicted environments for accession in the three experiments based on kinship associations with environment of landraces (gBLUP).

    Table S10. Pearson’s correlations between predictions based on environment-genome associations and phenotypes in Austin.

    Table S11. Pearson’s correlations between predictions based on environment-genome associations and phenotypes in Hyderabad.

    Table S12. Pearson’s correlations between predictions based on environment-genome associations and phenotypes in the Caniato et al. (44) Al toxicity experiment.

    Table S13. Pearson’s correlations between phenotypes and environment of origin (where known) for landraces in the Austin experiment.

    Table S14. Pearson’s correlations between phenotypes and environment of origin (where known) for landraces in the Hyderabad experiment.

    Table S15. Pearson’s correlations between relative net root growth and environment of origin (where known) for landraces in the Caniato et al. (44) experiment.

    Table S16. Pearson’s correlation coefficients between predicted phenotypes in the Austin experiment and observed, where predictions based on genome associations with phenotypes in fivefold cross-validation.

    Table S17. Pearson’s correlation coefficients between predicted phenotypes in the Hyderabad experiment and observed, where predictions based on genome associations with phenotypes in fivefold cross-validation.

    Table S18. Pearson’s correlation coefficients between predicted phenotypes in the Caniato et al. (44) experiment and observed, where predictions based on genome associations with phenotypes in fivefold cross-validation.

    Table S19. Top 1000 SNPs associated with harvest index plasticity in Austin using SNP associations with precipitation of the warmest quarter as priors.

    Table S20. Top 1000 SNPs associated with relative net root growth (comparing control treatment with Al toxic treatment) in Caniato et al. (44) experiment, using SNP associations with topsoil pH as priors.

    Table S21. Top 1000 SNPs associated with panicle weight plasticity in Hyderabad, using SNP associations with growing season length as priors.

    References (7881)

  • Supplementary Materials

    This PDF file includes:

    • Fig. S1. Landrace accessions included in the study classified into botanical races based on morphological classification (five new world accessions not shown).
    • Fig. S2. Rainout shelter where plants were grown in Austin.
    • Fig. S3. Seedlings planted in the Austin experiment.
    • Fig. S4. Plants growing in Austin.
    • Fig. S5. A representative accession (IS 25836) under irrigated (left) and imposed terminal drought (right) conditions at experimental plot in India.
    • Fig. S6. Proportion of total SNP variation among accessions with known collection locations (excluding spatial outlier landraces from the Americas, China, and Southeast Asia/Oceania) explained by spatial structure or environmental variables.
    • Fig. S7. Predictions of phenotypes averaged across well-watered and drought conditions from drought treatment across growing season in Austin, United States.
    • Fig. S8. Predictions of phenotype change between well-watered and drought conditions from drought treatment across growing season in Austin, United States.
    • Fig. S9. Predictions of phenotypes averaged across well-watered and drought conditions from drought treatment late in growing season in Hyderabad, India.
    • Fig. S10. Predictions of phenotype change between well-watered and drought conditions from drought treatment late in growing season in Hyderabad, India.
    • Fig. S11. GWAS for harvest index plasticity (harvest index in wet/dry) in U.S. experiment, using SNP associations with precipitation in the warmest quarter as a prior.
    • Fig. S12. GWAS for panicle weight plasticity (panicle weight in wet − dry) in India experiment, using SNP associations with growing season length as a prior.
    • Fig. S13. GWAS for root growth plasticity (growth in control/Al toxic) in published aluminum toxicity experiment (44), using SNP associations with topsoil pH as a prior.
    • References (78–81)

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    Other Supplementary Material for this manuscript includes the following:

    • Table S1 (Microsoft Excel format). Landraces studied and environment of origin data.
    • Table S2 (Microsoft Excel format). Accession phenotypes from experiment in Austin, United States.
    • Table S3 (Microsoft Excel format). Mean phenotypes across the 2 years of the experiment in Hyderabad, India.
    • Table S4 (Microsoft Excel format). Spearman’s rank correlation test results for two SNPs that tag known candidate genes potentially involved in local adaptation.
    • Table S5 (Microsoft Excel format). EMMA t test results for two SNPs that tag known candidate genes potentially involved in local adaptation.
    • Table S6 (Microsoft Excel format). Predictions for each accession in the Austin experiment based on SNP-environment associations in the landrace panel.
    • Table S7 (Microsoft Excel format). Predictions for each accession in the Hyderabad experiment based on SNP-environment associations in the landrace panel.
    • Table S8 (Microsoft Excel format). Predictions for each accession in the Caniato et al. (44) experiment based on SNP-environment associations in the landrace panel.
    • Table S9 (Microsoft Excel format). Predicted environments for accession in the three experiments based on kinship associations with environment of landraces (gBLUP).
    • Table S10 (Microsoft Excel format). Pearson’s correlations between predictions based on environment-genome associations and phenotypes in Austin.
    • Table S11 (Microsoft Excel format). Pearson’s correlations between predictions based on environment-genome associations and phenotypes in Hyderabad.
    • Table S12 (Microsoft Excel format). Pearson’s correlations between predictions based on environment-genome associations and phenotypes in the Caniato et al. (44) Al toxicity experiment.
    • Table S13 (Microsoft Excel format). Pearson’s correlations between phenotypes and environment of origin (where known) for landraces in the Austin experiment.
    • Table S14 (Microsoft Excel format). Pearson’s correlations between phenotypes and environment of origin (where known) for landraces in the Hyderabad experiment.
    • Table S15 (Microsoft Excel format). Pearson’s correlations between relative net root growth and environment of origin (where known) for landraces in the Caniato et al. (44) experiment.
    • Table S16 (Microsoft Excel format). Pearson’s correlation coefficients between predicted phenotypes in the Austin experiment and observed, where predictions based on genome associations with phenotypes in fivefold cross-validation.
    • Table S17 (Microsoft Excel format). Pearson’s correlation coefficients between predicted phenotypes in the Hyderabad experiment and observed, where predictions based on genome associations with phenotypes in fivefold crossvalidation.
    • Table S18 (Microsoft Excel format). Pearson’s correlation coefficients between predicted phenotypes in the Caniato et al. (44) experiment and observed, where predictions based on genome associations with phenotypes in fivefold crossvalidation.
    • Table S19 (Microsoft Excel format). Top 1000 SNPs associated with harvest index plasticity in Austin using SNP associations with precipitation of the warmest quarter as priors.
    • Table S20 (Microsoft Excel format). Top 1000 SNPs associated with relative net root growth (comparing control treatment with Al toxic treatment) in Caniato et al. (44) experiment, using SNP associations with topsoil pH as priors.
    • Table S21 (Microsoft Excel format). Top 1000 SNPs associated with panicle weight plasticity in Hyderabad, using SNP associations with growing season length as priors.

    Download Tables S1 to S21

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