Phenotypic and molecular characterization of sweet sorghum accessions for bioenergy production

25Citations
Citations of this article
75Readers
Mendeley users who have this article in their library.

Abstract

Sweet sorghum [Sorghum bicolor (L.) Moench] is a type of cultivated sorghum characterized by the accumulation of high levels of sugar in the stems and high biomass accumulation, making this crop an important feedstock for bioenergy production. Sweet sorghum breeding programs that focus on bioenergy have two main goals: to improve quantity and quality of sugars in the juicy stem and to increase fresh biomass productivity. Genetic diversity studies are very important for the success of a breeding program, especially in the early stages, where understanding the genetic relationship between accessions is essential to identify superior parents for the development of improved breeding lines. The objectives of this study were: to perform phenotypic and molecular characterization of 100 sweet sorghum accessions from the germplasm bank of the Embrapa Maize and Sorghum breeding program; to examine the relationship between the phenotypic and the molecular diversity matrices; and to infer about the population structure in the sweet sorghum accessions. Morphological and agro-industrial traits related to sugar and biomass production were used for phenotypic characterization, and single nucleotide polymorphisms (SNPs) were used for molecular diversity analysis. Both phenotypic and molecular characterizations revealed the existence of considerable genetic diversity among the 100 sweet sorghum accessions. The correlation between the phenotypic and the molecular diversity matrices was low (0.35), which is in agreement with the inconsistencies observed between the clusters formed by the phenotypic and the molecular diversity analyses. Furthermore, the clusters obtained by the molecular diversity analysis were more consistent with the genealogy and the historic background of the sweet sorghum accessions than the clusters obtained through the phenotypic diversity analysis. The low correlation observed between the molecular and the phenotypic diversity matrices highlights the complementarity between the molecular and the phenotypic characterization to assist a breeding program.

Figures

  • Fig 1. Heat map of phenotypic correlations among morphological and agro-industrial traits. The color assigned to a point in the heat map grid indicates the strength of a particular correlation between two traits. The level of correlation is indicated by red for positive correlations and blue for negative correlations, as depicted in the color key. PCA: pigmentation of the coleoptile by anthocyanin; PFLA: pigmentation of the first leaf by anthocyanin; PLSA: pigmentation of the leaf sheath by anthocyanin; PC: plant color; SD: stalk diameter; SS: stalk succulence; JQ: juice quality; TC: tillering capacity; STF: synchronization of tillering and flowering; LTL: length of the third leaf; PLA: pigmentation of the leaf by anthocyanin; LMC: leaf midrib color; LA: leaf angle; PS: panicle shape; PD: panicle density; LPMR: length of the panicle main rachis; LPBP: length of the primary branch of the panicle; SEP: shape and extension of the peduncle; LPF: length of the pedicelated flower; GC: glume color; FAP: formation of the awn in the palea; SP: stigma pigmentation; OP: ovary pigmentation; GC1: grain covering; GC2: grain color; SW: 1000-seed weight; PFG: presence of the forehead on the grain; EC: endosperm composition; ET: endosperm texture; EC1: endosperm color; GL: grain lustre; PCP: purple color on the pericarp; TC1: threshing capacity; CEL: cellulose; EXT: juice extraction; FBY: fresh biomass yield; FLOW: days to flowering; HEM: hemicellulose; LIG: lignin; PH: plant height; POL: sucrose concentration in juice; RSJ: reducing sugars in the juice and TSS: total soluble solids.
  • Table 1. Fixed and random effects, heritability, average, minimum and maximum phenotypic values for the agro-industrial traits.
  • Fig 2. Neighbor-Joining tree using phenotypic data. Euclidean distances between the sweet sorghum accessions were calculated based on the standardized phenotypic data. The colors of the branches correspond to the six subpopulations defined according to the genealogy and the historic background of the sweet sorghum lines. I-P, II-P, III-P, IV-P and V-P correspond to the clusters identified through the NeighborJoining method. LIS: Landrace World Collection—ICRISAT sorghum collection; LMN: Landrace Meridian Mississippi—USDA sorghum collection; LSSM: Landrace Sorghum Seed Montpelier—CIRAD sorghum collection; ML: Modern Line; ML—EMBRAPA: Modern Line EMBRAPA; and HL: Historical Line. The scalebar (0–0.1) represents the coefficient of dissimilarity.
  • Fig 3. Neighbor-Joining tree using SNP data. Genetic distances between the sweet sorghum accessions were calculated using the identity-by-state (IBS) coefficient. The colors of the branches correspond to the six subpopulations defined according to the genealogy and the historic background of the sweet sorghum lines. I-M, II-M, III-M, IV-M, V-M and VI-M correspond to the clusters identified through the Neighbor-Joining method. LIS: Landrace World Collection—ICRISAT sorghum collection; LMN: Landrace Meridian Mississippi —USDA sorghum collection; LSSM: Landrace Sorghum Seed Montpelier—CIRAD sorghum collection; ML: Modern Line; ML—EMBRAPA: Modern Line EMBRAPA; and HL: Historical Line. The scale-bar (0–0.1) represents the coefficient of dissimilarity.
  • Fig 4. Principal component analysis using SNP data. Plotting the first two principal components (PC1 and PC2) using SNP data. The colors of the genotypes correspond to the six subpopulations of sweet sorghum according to the genealogy and the historic background. LIS: Landrace World Collection—ICRISAT sorghum collection; LMN: Landrace Meridian Mississippi—USDA sorghum collection; LSSM: Landrace Sorghum Seed Montpelier—CIRAD sorghum collection; ML: Modern Line; ML—EMBRAPA: Modern Line EMBRAPA; and HL: Historical Line.
  • Fig 5. Boxplot analysis showing the distribution of agro-industrial traits according to each cluster identified through molecular and phenotypic diversity analysis. The upper, median, and lower quartiles of gray boxes represent the 75th, 50th, and 25th percentiles of the clusters, respectively. The vertical lines represent the variation of the clusters. Dots represent outliers. CEL: cellulose; EXT: juice extraction; FBY: fresh biomass yield; FLOW: days to flowering; HEM: hemicellulose; LIG: lignin; PH: plant height; POL: sucrose concentration in juice; RSJ: reducing sugars in the juice and TSS: total soluble solids.

References Powered by Scopus

Fast and accurate long-read alignment with Burrows-Wheeler transform

8841Citations
N/AReaders
Get full text

Principal components analysis corrects for stratification in genome-wide association studies

7737Citations
N/AReaders
Get full text

TASSEL: Software for association mapping of complex traits in diverse samples

5749Citations
N/AReaders
Get full text

Cited by Powered by Scopus

Variability Assessment for Root and Drought Tolerance Traits and Genetic Diversity Analysis of Rice Germplasm using SSR Markers

68Citations
N/AReaders
Get full text

Phenotypic and molecular assessment of genetic structure and diversity in a panel of winged yam (Dioscorea alata) clones and cultivars

56Citations
N/AReaders
Get full text

Comparative assessment of genetic diversity matrices and clustering methods in white Guinea yam (Dioscorea rotundata) based on morphological and molecular markers

43Citations
N/AReaders
Get full text

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Cite

CITATION STYLE

APA

Da Silva, M. J., Pastina, M. M., De Souza, V. F., Schaffert, R. E., Carneiro, P. C. S., Noda, R. W., … Parrella, R. A. da C. (2017). Phenotypic and molecular characterization of sweet sorghum accessions for bioenergy production. PLoS ONE, 12(8). https://doi.org/10.1371/journal.pone.0183504

Readers over time

‘17‘18‘19‘20‘21‘22‘23‘2406121824

Readers' Seniority

Tooltip

PhD / Post grad / Masters / Doc 36

75%

Researcher 10

21%

Professor / Associate Prof. 2

4%

Readers' Discipline

Tooltip

Agricultural and Biological Sciences 35

74%

Biochemistry, Genetics and Molecular Bi... 9

19%

Engineering 2

4%

Energy 1

2%

Save time finding and organizing research with Mendeley

Sign up for free
0