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. 2022 Apr 25:13:830896.
doi: 10.3389/fpls.2022.830896. eCollection 2022.

Improving Association Studies and Genomic Predictions for Climbing Beans With Data From Bush Bean Populations

Beat Keller  1 , Daniel Ariza-Suarez  1   2 , Ana Elisabeth Portilla-Benavides  2 , Hector Fabio Buendia  2 , Johan Steven Aparicio  2 , Winnyfred Amongi  3 , Julius Mbiu  4 , Susan Nchimbi Msolla  5 , Phillip Miklas  6 , Timothy G Porch  7 , James Burridge  8 , Clare Mukankusi  3 , Bruno Studer  1 , Bodo Raatz  2
Affiliations

Improving Association Studies and Genomic Predictions for Climbing Beans With Data From Bush Bean Populations

Beat Keller et al. Front Plant Sci. .

Abstract

Common bean (Phaseolus vulgaris L.) has two major origins of domestication, Andean and Mesoamerican, which contribute to the high diversity of growth type, pod and seed characteristics. The climbing growth habit is associated with increased days to flowering (DF), seed iron concentration (SdFe), nitrogen fixation, and yield. However, breeding efforts in climbing beans have been limited and independent from bush type beans. To advance climbing bean breeding, we carried out genome-wide association studies and genomic predictions using 1,869 common bean lines belonging to five breeding panels representing both gene pools and all growth types. The phenotypic data were collected from 17 field trials and were complemented with 16 previously published trials. Overall, 38 significant marker-trait associations were identified for growth habit, 14 for DF, 13 for 100 seed weight, three for SdFe, and one for yield. Except for DF, the results suggest a common genetic basis for traits across all panels and growth types. Seven QTL associated with growth habits were confirmed from earlier studies and four plausible candidate genes for SdFe and 100 seed weight were newly identified. Furthermore, the genomic prediction accuracy for SdFe and yield in climbing beans improved up to 8.8% when bush-type bean lines were included in the training population. In conclusion, a large population from different gene pools and growth types across multiple breeding panels increased the power of genomic analyses and provides a solid and diverse germplasm base for genetic improvement of common bean.

Keywords: climbing and bush type bean; common bean (Phaseolus vulgaris L.); genome-wide association studies (GWAS); genomic selection; growth habit; pleiotropy; population structure.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Phenotypes of the climbing bean panel (VEC). (A) Density diagrams for days to flowering (DF), 100 seed weight (100SdW), seed iron concentration (SdFe), and seed yield of up to 290 VEC lines in eight trials are shown. Best linear unbiased predictors (BLUEs) were calculated from each trial corrected for spatial effects in the field. (B) Pearson correlation coefficients were calculated across all trials for each trait based on the BLUEs. Trials were abbreviated based on the location Darién (Dar), Palmira (Pal), Popayán (Pop) in Colombia, Kagera in Tanzania (TzKg), or Kawanda in Uganda (UgKw), the year and the planting season (sequentially A to D). For a detailed description of each trial refer to Supplementary Table 2.
Figure 2
Figure 2
Phenotypic and genotypic characterization of five common bean breeding panels including lines with bush and climbing growth habit originating from the Andean and Mesoamerican gene pools. (A) Density diagrams of best linear unbiased estimators among five breeding bean panels are shown for days to flowering (DF), 100 seed weight (100SdW), seed iron concentration (SdFe), and seed yield. (B) Dendrogram of 1,869 lines characterized by 14,913 SNPs shows the hierarchical relationships between lines and panels [following the same color code for panels as in (A)]. (C) Principal components (PC) 1 and 2 visualize the genetic similarity across all five breeding panels. The arrows show quantitative supplementary phenotypic traits. Their cosines indicate the correlation with PC axes and their length approximate the SD of the variable. The extreme lines on the PC 1 axis are labeled. (D) Linkage disequilibrium (LD) decay is shown for all panels separately and combined. The LD was calculated in sliding windows of 100 markers and corrected for kinship in the population (rV2).
Figure 3
Figure 3
Genome-wide association studies among five breeding panels differing in growth habits. (A) The Manhattan plots show the genetic associations with climbing growth habit, days to flowering (DF), 100 seed weight (100SdW), seed iron concentration (SdFe), and seed yield. Seed yield was scaled among panels to allow comparison between them. The horizontal black lines show the Bonferroni corrected significance threshold at the 1% level. The vertical lines indicate the position of the two most significant markers for each trait. (B) Quantile distribution plots show the deviation of expected to observed p-values of SNP to trait associations for each trait.
Figure 4
Figure 4
Boxplots for allele dosage effect (0 or 2 alternative alleles) of the most significant SNPs associated with days to flowering (DF), 100 seed weight (100SdW), seed iron concentration (SdFe), and seed yield for five breeding panels. The panels included lines with bush and climbing growth habits originating from the Andean and Mesoamerican gene pools. The trait, the name of the associated marker and the QTL ID is given.
Figure 5
Figure 5
Significant marker-trait associations were analyzed for growth habit, days to flowering (DF), 100 seed weight (100SdW), seed iron concentration (SdFe), and seed yield scaled among the five breeding panels (Yield_scaled). (A) A network of the SNPs significantly (below the 1% significance level) associated with each of the four traits forming clusters according to their linkage disequilibrium is shown. Each dot represents a significant SNP and its size the associated −log10 p-value. The SNPs on Chr 1 between 43.71 and 45.47 as well as the two most significant SNPs per trait are labeled with the QTL ID. (B) Haplotypes including all SNPs between 44.60 and 45.47 Mbp on chromosome 1 were constructed using hierarchical clustering. The averaged SNP effects of the haplotypes were evaluated in all traits among all growth types, i.e., growth type I (determinate bush type), type II (indeterminate bush), type III (determinate climber), and type IV (indeterminate climber). The error bars show the SD.
Figure 6
Figure 6
Prediction abilities for different traits and models for new lines from the climbing bean panel (VEC). The first-stage best linear unbiased estimators (BLUEs) for each trial or second-stage BLUEs across trials were used. The first-stage BLUEs models were tested accounting for either genotypic effects only, i.e., for each trial separately (single trials) or all together (among trials), genotypic x environment interaction (GxE), or correlation between trials (Factor analysis). The second-stage BLUEs were used for models which take into account genotypic effects based on the VEC (TP VEC), on all five panels (TP extended), and all five panels with optimization of the training population (TP optimized). The predicted traits were days to flowering (DF), 100 seed weight (100SdW), seed iron concentration (SdFe), and seed yield. Seed yield was scaled among panels for the models based on second-stage BLUEs to allow comparison between the different growth habits. The horizontal line is the square root of heritability indicating the heritable variance of the trait in each trial. Trials were abbreviated based on the location Darién (Dar), Palmira (Pal), Popayán (Pop) in Colombia, Kagera in Tanzania (TzKg), or Kawanda in Uganda (UgKw), the year, and the planting season (sequentially A to D). For a detailed description of each trial refer to Supplementary Table 2.
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