Genetic diversity, population structure and marker trait associations for seed quality traits in cotton (Gossypium hirsutum)

2015 ◽  
Vol 94 (1) ◽  
pp. 87-94 ◽  
Author(s):  
ASHOK BADIGANNAVAR ◽  
GERALD O. MYERS
2006 ◽  
Vol 86 (4) ◽  
pp. 1015-1025 ◽  
Author(s):  
F. Katepa-Mupondwa ◽  
R. K. Gugel ◽  
J. P. Raney

The objective of this research was to study the amount and pattern of phenotypic diversity among 179 Sinapis alba accessions maintained in germplasm collections at Plant Gene Resources of Canada (PGRC) and the Saskatoon Research Centre of Agriculture and Agri-Food Canada (SRC-AAFC). Accessions were evaluated in five field trials at Saskatoon from 1994 to 1998. Observations were recorded on number of days to flower and to mature, plant height, 1000-seed weight, oil and protein content and selected fatty acids and glucosinolates. Analysis of variance and mean comparisons were used to characterize variation in the germplasm. There was significant variation among accessions for all traits except some minor fatty acids and glucosinolates. Principal component analysis indicated that five or six principal components provided a good summary of the data, accounting for 75–80% of the variation. In all trials, the first principal component axis separated accessions predominantly on the basis of erucic acid (C22:1) and oleic acid (C18:1), with associated C22 and C18 fatty acids. The relative importance of agronomic, morphological and other seed quality traits varied among the trials, but they were always less important than C22:1 and C18:1. Cluster analysis generated 10–13 groups of accessions in each trial except in 1997 (five clusters). Distinct clusters were identified that possessed high or low values for C22:1, C18:1, oil and protein content, maturity, plant height and seed weight. Seed colour was not used as a classification variable; however, brown-seeded accessions were grouped into one distinct cluster due to a significantly higher level of C22:1 in these accessions. This study demonstrates that the S. alba accessions maintained at PGRC and SRC-AAFC are a source of genetic diversity for breeding both condiment (high glucosinolate and C22:1 content) and vegetable (low glucosinolate and C22:1 content) oilseed yellow mustard and for conducting genetic studies. Key words: Sinapis alba, genetic diversity, cluster analysis, principal component analysis


Author(s):  
Ines Jlassi ◽  
Fethi Bnejdi ◽  
Mourad Saadoun ◽  
Abdelhamid Hajji ◽  
Dhouha Mansouri ◽  
...  

Crop Science ◽  
2019 ◽  
Vol 59 (6) ◽  
pp. 2608-2620 ◽  
Author(s):  
Azam Nikzad ◽  
Berisso Kebede ◽  
Jaime Pinzon ◽  
Jani Bhavikkumar ◽  
Rong-Cai Yang ◽  
...  

Plants ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 719
Author(s):  
Mulusew Fikere ◽  
Denise M. Barbulescu ◽  
M. Michelle Malmberg ◽  
Pankaj Maharjan ◽  
Phillip A. Salisbury ◽  
...  

Genomic selection accelerates genetic progress in crop breeding through the prediction of future phenotypes of selection candidates based on only their genomic information. Here we report genetic correlations and genomic prediction accuracies in 22 agronomic, disease, and seed quality traits measured across multiple years (2015–2017) in replicated trials under rain-fed and irrigated conditions in Victoria, Australia. Two hundred and two spring canola lines were genotyped for 62,082 Single Nucleotide Polymorphisms (SNPs) using transcriptomic genotype-by-sequencing (GBSt). Traits were evaluated in single trait and bivariate genomic best linear unbiased prediction (GBLUP) models and cross-validation. GBLUP were also expanded to include genotype-by-environment G × E interactions. Genomic heritability varied from 0.31to 0.66. Genetic correlations were highly positive within traits across locations and years. Oil content was positively correlated with most agronomic traits. Strong, not previously documented, negative correlations were observed between average internal infection (a measure of blackleg disease) and arachidic and stearic acids. The genetic correlations between fatty acid traits followed the expected patterns based on oil biosynthesis pathways. Genomic prediction accuracy ranged from 0.29 for emergence count to 0.69 for seed yield. The incorporation of G × E translates into improved prediction accuracy by up to 6%. The genomic prediction accuracies achieved indicate that genomic selection is ready for application in canola breeding.


Sign in / Sign up

Export Citation Format

Share Document