scholarly journals Translating insights from the seed metabolome into improved prediction for lipid-composition traits in oat (Avena sativa L.)

Genetics ◽  
2021 ◽  
Vol 217 (3) ◽  
Author(s):  
Malachy T Campbell ◽  
Haixiao Hu ◽  
Trevor H Yeats ◽  
Melanie Caffe-Treml ◽  
Lucía Gutiérrez ◽  
...  
Keyword(s):  
Prediction Model ◽  
Avena Sativa ◽  
Seed Quality ◽  
Quality Traits ◽  
Soluble Fiber ◽  
Latent Factors ◽  
Fiber Protein ◽  
Health Promoting ◽  
Best Linear Unbiased ◽  
Seed Quality Traits

Abstract Oat (Avena sativa L.) seed is a rich resource of beneficial lipids, soluble fiber, protein, and antioxidants, and is considered a healthful food for humans. Little is known regarding the genetic controllers of variation for these compounds in oat seed. We characterized natural variation in the mature seed metabolome using untargeted metabolomics on 367 diverse lines and leveraged this information to improve prediction for seed quality traits. We used a latent factor approach to define unobserved variables that may drive covariance among metabolites. One hundred latent factors were identified, of which 21% were enriched for compounds associated with lipid metabolism. Through a combination of whole-genome regression and association mapping, we show that latent factors that generate covariance for many metabolites tend to have a complex genetic architecture. Nonetheless, we recovered significant associations for 23% of the latent factors. These associations were used to inform a multi-kernel genomic prediction model, which was used to predict seed lipid and protein traits in two independent studies. Predictions for 8 of the 12 traits were significantly improved compared to genomic best linear unbiased prediction when this prediction model was informed using associations from lipid-enriched factors. This study provides new insights into variation in the oat seed metabolome and provides genomic resources for breeders to improve selection for health-promoting seed quality traits. More broadly, we outline an approach to distill high-dimensional “omics” data to a set of biologically meaningful variables and translate inferences on these data into improved breeding decisions.

scholarly journals Translating insights from the seed metabolome into improved prediction for healthful compounds in oat (Avena sativa L.)

2020 ◽  
Author(s):  
Malachy T. Campbell ◽  
Haixiao Hu ◽  
Trevor H. Yeats ◽  
Melanie Caffe-Treml ◽  
Lucía Gutiérrez ◽  
...  
Keyword(s):  
Prediction Model ◽  
Avena Sativa ◽  
Seed Quality ◽  
Quality Traits ◽  
Soluble Fiber ◽  
Latent Factors ◽  
Fiber Protein ◽  
Health Promoting ◽  
Best Linear Unbiased ◽  
Seed Quality Traits

AbstractOat (Avena sativa L.) seed is a rich resource of beneficial lipids, soluble fiber, protein, and antioxidants, and is considered a healthful food for humans. Despite these characteristics, little is known regarding the genetic controllers of variation for these compounds in oat seed. We sought to characterize natural variation in the mature seed metabolome using untargeted metabolomics on 367 diverse lines and leverage this information to improve prediction for seed quality traits. We used a latent factor approach to define unobserved variables that may drive covariance among metabolites. One hundred latent factors were identified, of which 21% were enriched for compounds associated with lipid metabolism. Through a combination of whole-genome regression and association mapping, we show that latent factors that generate covariance for many metabolites tend to have a complex genetic architecture. Nonetheless, we recovered significant associations for 23% of the latent factors. These associations were used to inform a multi-kernel genomic prediction model, which was used to predict seed lipid and protein traits in two independent studies. Predictions for eight of the 12 traits were significantly improved compared to genomic best linear unbiased prediction when this prediction model was informed using associations from lipid-enriched factors. This study provides new insights into variation in the oat seed metabolome and provides genomic resources for breeders to improve selection for health-promoting seed quality traits. More broadly, we outline an approach to distill high-dimensional ‘omics’ data to a set of biologically-meaningful variables and translate inferences on these data into improved breeding decisions.

Potential of the C Genome of Different Variants of Brassica oleracea for the Improvement of Agronomic and Seed Quality Traits of B. napus Canola

Crop Science ◽  
2019 ◽  
Vol 59 (6) ◽  
pp. 2608-2620 ◽  
Author(s):  
Azam Nikzad ◽  
Berisso Kebede ◽  
Jaime Pinzon ◽  
Jani Bhavikkumar ◽  
Rong-Cai Yang ◽  
...  
Keyword(s):  
Brassica Oleracea ◽  
Seed Quality ◽  
Quality Traits ◽  
C Genome ◽  
Seed Quality Traits

scholarly journals Genomic Prediction and Genetic Correlation of Agronomic, Blackleg Disease, and Seed Quality Traits in Canola (Brassica napus L.)

Plants ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 719
Author(s):  
Mulusew Fikere ◽  
Denise M. Barbulescu ◽  
M. Michelle Malmberg ◽  
Pankaj Maharjan ◽  
Phillip A. Salisbury ◽  
...  
Keyword(s):  
Genomic Selection ◽  
Genomic Prediction ◽  
Prediction Accuracy ◽  
Seed Quality ◽  
Agronomic Traits ◽  
Genetic Correlations ◽  
Quality Traits ◽  
Blackleg Disease ◽  
Genetic Progress ◽  
Seed Quality Traits

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.

Genotype, environment and their interaction influence seed quality traits in chickpea ( Cicer arietinum L.)

2017 ◽  
Vol 63 ◽  
pp. 21-27 ◽  
Author(s):  
Runfeng Wang ◽  
Manu P. Gangola ◽  
Sarita Jaiswal ◽  
Pooran M. Gaur ◽  
Monica Båga ◽  
...  
Keyword(s):  
Cicer Arietinum ◽  
Seed Quality ◽  
Quality Traits ◽  
Cicer Arietinum L ◽  
Seed Quality Traits

Identification of QTL/segments related to seed-quality traits in G. soja using chromosome segment substitution lines

2014 ◽  
Vol 12 (S1) ◽  
pp. S65-S69 ◽  
Author(s):  
Wubin Wang ◽  
Qingyuan He ◽  
Hongyan Yang ◽  
Shihua Xiang ◽  
Guangnan Xing ◽  
...  
Keyword(s):  
Composite Interval Mapping ◽  
Seed Quality ◽  
Interval Mapping ◽  
Chromosome Segment ◽  
Quality Traits ◽  
Negative Effects ◽  
Positive Effects ◽  
Segment Substitution ◽  
Cultivated Soybean ◽  
Seed Quality Traits

Annual wild soybean characterized by low 100-seed weight (100SW), high protein content (PRC) and low oil content (OIC) may have favourable exotic genes/alleles for broadening the genetic base of the cultivated soybean. To evaluate the wild alleles/segments, a chromosome segment substitution line population comprising 151 lines with N24852 (wild) as the donor and NN1138-2 (cultivated) as the recurrent parent was analysed using single-marker analysis, interval mapping, inclusive composite interval mapping and mixed linear composite interval mapping. On 14 segments of ten chromosomes, 17 quantitative trait loci (QTL) were identified, with two segments each containing two QTL for 100SW and OIC and one segment containing two QTL for PRC and OIC, respectively. All the seven wild alleles/segments for 100SW were associated with negative effects and three were associated with positive effects, but one was associated with a negative effect for PRC, and five were associated with negative effects, but one was associated with a positive effect for OIC. Except Satt216 and Sat_224 for 100SW, the identified QTL/segments have been reported from cultivated soybean mapping populations. The detected wild segments may provide materials for further characterization, cloning and pyramiding of the alleles conferring the seed-quality traits.

Sign in / Sign up

Export Citation Format

Share Document