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Genetic characterization of the wheat association mapping initiative (WAMI) panel for dissection of complex traits in spring wheat

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The wheat association mapping initiative is appropriate for gene discovery without the confounding effects of phenology and plant height.

Abstract

The wheat association mapping initiative (WAMI) population is a set of 287 diverse advanced wheat lines with a narrow range of variation for days to heading (DH) and plant height (PH). This study aimed to characterize the WAMI and showed that this diverse panel has a favorable genetic background in which stress adaptive traits and their alleles contributing to final yield can be identified with reduced confounding major gene effects through genome-wide association studies (GWAS). Using single nucleotide polymorphism (SNP) markers, we observed lower gene diversity on the D genome, compared with the other genomes. Population structure was primarily related to the distribution of the 1B.1R rye translocation. The narrow range of variation for DH and PH in the WAMI population still entailed segregation for a few markers associated with the former traits, while Rht genes were associated with grain yield (GY). Genotype by environment (G × E) interaction for GY was primarily explained by Rht-B1, Vrn-A1 and markers on chromosomes 2D and 3A when running GWAS with genotype scores from the G × E biplot. The use of PC scores from the G × E biplot seems a promising tool to determine genes and markers associated with complex interactions across environments. The WAMI panel lends itself to GWAS for complex trait dissection by avoiding the confounding effects of DH and PH which were reduced to a minimum (using Rht-B1 and Vrn-A1 scores as covariables), with significant associations with GY on chromosomes 2D, 3A and 3B.

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Acknowledgments

Authors would like to thank Mayra Barcelo, Araceli Torres and Eugenio Perez for assistance with data and trial management. Editing assistance was received from Emma Quilligan. The German Federal Ministry for Economy Cooperation and Development (BMZ) and the Australian Grains Research and Development Corporation (GRDC) are acknowledged for their financial support.

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standards

This experiment complies with the current laws of Mexican authorities.

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Authors and Affiliations

  1. CIMMYT, PO Box 39, Emek, Ankara, 06511, Turkey

    M. S. Lopes

  2. CIMMYT, Int. Apdo Postal 6-641, 06600, Mexico, DF, Mexico

    S. Dreisigacker, R. J. Peña, S. Sukumaran & M. P. Reynolds

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  1. M. S. Lopes
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  2. S. Dreisigacker
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  3. R. J. Peña
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  4. S. Sukumaran
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  5. M. P. Reynolds
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Corresponding author

Correspondence to M. P. Reynolds.

Additional information

Communicated by Peter Langridge.

Electronic supplementary material

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122_2014_2444_MOESM1_ESM.pptx

Supplementary Fig. 1- Q–Q plots (cumulative distribution of observed association p values versus expected association p values against a theoretical cumulative distribution function of no association represented by a red straight line, X = Y all log transformed) for grain yield (GY), days to heading (DH) and plant height (PH) measured under IRRIGATED, DROUGHT, HEAT and heat combined with drought (HD) conditions. QQ plots are also shown for average of all traits across all environments (AVG), average across stress environments (AVG STRESS), and PCs resulting from the PC scores of the genotype by environment interaction biplot. Blue circles indicate observed association p values and gray bar corresponds to 95 % confidence interval for the Q–Q plot under the null hypothesis of no association between the SNP and the trait. Q–Q plots are only shown for the traits showing significant associated markers. (PPTX 447 kb)

122_2014_2444_MOESM2_ESM.xlsx

Supplementary Table 1- SNP markers associated with the Rht-B1, Rht-D1, Vrn-A1, Vrn-B1, and Vrn-D1 functional genes. SNP markers shown had the highest–log q association values for the former functional genes. SNP marker, chromosome (Chr) and position (Pos) where these were located are shown for each functional gene. GWAS was conducted with scores obtained for each functional marker. (XLSX 10 kb)

122_2014_2444_MOESM3_ESM.xlsx

Supplementary Table 2- Evaluation GWAS models for grain yield (GY), days to heading (DH) and plant height (PH) measured in four environments under IRRIGATED, DROUGHT, HEAT, heat combined with drought (HD), average across stress environments (AVG STRESS with averages of DROUGHT, HEAT and HD environments), average across all environments (AVG), and scores of genotype by environment interaction biplot (PC1 and PC2 from site regression). Two sets of models were tested for each trait: 1) no correction was applied in the GWAS besides the kinship and PCs; 2) scores for Rht and Vrn functional genes included as covariates for correction of their effects in GWAS plus kinship and PCs resulting from population structure analysis; The best model is highlighted in yellow and corresponds to the highest BIC (Schwarz, 1978). (XLSX 14 kb)

122_2014_2444_MOESM4_ESM.xlsx

Supplementary Table 3- SNP markers associated with grain yield (GY) by GWAS in the WAMI grown under 4 environments (IRRIGATED, DROUGHT, HEAT and heat combined with drought, HD), calculated as an average of all environments (AVG), calculated as an average of stress environments (AVG STRESS) and PC scores obtained in the genotype by environment biplot. GWAS results are shown without and with correction by Vrn and Rht as covariables (“NO CORRECTION” and “CORRECTION”, respectively). SNP marker associated with trait, chromosome (Chr), position, -log(p value), major allele frequency (maf), R2 with and without the marker, false discovery rate (FDR) and estimates of allelic effects for each marker (Allelic Eff). *, position determined by marker–marker association. (XLSX 14 kb)

122_2014_2444_MOESM5_ESM.xlsx

Supplementary Table 4- SNP markers associated with days to heading (DH) by GWAS in the WAMI grown under 4 environments (IRRIGATED, DROUGHT, HEAT and heat combined with drought, HD), calculated as an average of all environments (AVG), calculated as an average of stress environments (AVG STRESS) and PC scores obtained in the genotype by environment biplot. GWAS results are shown without and with correction by Vrn and Rht as covariables (“NO CORRECTION” and “CORRECTION”, respectively). SNP marker associated with trait, chromosome (Chr), position, -log(p value), major allele frequency (maf), R2 with and without the marker, false discovery rate (FDR) and estimates of allelic effects for each marker (Allelic Eff). *, position determined by marker–marker association. (XLSX 14 kb)

122_2014_2444_MOESM6_ESM.xlsx

Supplementary Table 5- SNP markers associated with plant height (PH) by GWAS in the WAMI grown under 4 environments (IRRIGATED, DROUGHT, HEAT and heat combined with drought, HD), calculated as an average of all environments (AVG), calculated as an average of stress environments (AVG STRESS) and PC scores obtained in the genotype by environment biplot. GWAS results are shown without and with correction by Vrn and Rht as covariables (“NO CORRECTION” and “CORRECTION”, respectively). SNP marker associated with trait, chromosome (Chr), position, -log(p value), major allele frequency (maf), R2 with and without the marker, false discovery rate (FDR) and estimates of allelic effects for each marker (Allelic Eff). *, position determined by marker–marker association. (XLSX 13 kb)

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Lopes, M.S., Dreisigacker, S., Peña, R.J. et al. Genetic characterization of the wheat association mapping initiative (WAMI) panel for dissection of complex traits in spring wheat. Theor Appl Genet 128, 453–464 (2015). https://doi.org/10.1007/s00122-014-2444-2

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