Genome-wide association mapping for seed protein content in finger millet (Eleusine coracana) global collection through genotyping by sequencing

AbstractGrain legumes are highly valuable plant species, as they produce seeds with high protein content. Increasing seed protein production and improving seed nutritional quality represent an agronomical challenge in order to promote plant protein consumption of a growing population. In this study, we used the genetic diversity, naturally present in Medicago truncatula, a model plant for legumes, to identify genes/loci regulating seed traits. Indeed, using sequencing data of 162 accessions from the Medicago HAPMAP collection, we performed genome-wide association study for 32 seed traits related to seed size and seed composition such as seed protein content/concentration, sulfur content/concentration. Using different GWAS and postGWAS methods, we identified 79 quantitative trait nucleotides (QTNs) as regulating seed size, 41 QTNs for seed composition related to nitrogen (i.e. storage protein) and sulfur (i.e. sulfur-containing amino acid) concentrations/contents. Furthermore, a strong positive correlation between seed size and protein content was revealed within the selected Medicago HAPMAP collection. In addition, several QTNs showed highly significant associations in different seed phenotypes for further functional validation studies, including one near an RNA-Binding Domain protein, which represents a valuable candidate as central regulator determining both seed size and composition. Finally, our findings in M. truncatula represent valuable resources to be exploitable in many legume crop species such as pea, common bean, and soybean due to its high synteny, which enable rapid transfer of these results into breeding programs and eventually help the improvement of legume grain production.

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Genome‐Wide Association Mapping of Fusarium Head Blight Resistance in Wheat using Genotyping‐by‐Sequencing

The Plant Genome ◽

10.3835/plantgenome2015.04.0028 ◽

2016 ◽

Vol 9 (1) ◽

Cited By ~ 58

Author(s):

Marcio P. Arruda ◽

Patrick Brown ◽

Gina Brown‐Guedira ◽

Allison M. Krill ◽

Carrie Thurber ◽

...

Keyword(s):

Association Mapping ◽

Fusarium Head Blight ◽

Fusarium Head Blight Resistance ◽

Genotyping By Sequencing ◽

Genome Wide Association ◽

Blight Resistance ◽

Head Blight ◽

Genome Wide

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Genome-Wide Association Studies and Genomic Selection for Grain Protein Content Stability in a Nested Association Mapping Population of Spring Wheat

10.1101/2021.04.15.440064 ◽

2021 ◽

Author(s):

Karansher S. Sandhu ◽

Paul D. Mihalyov ◽

Megan J. Lewien ◽

Michael O. Pumphrey ◽

Arron H Carter

Keyword(s):

Association Mapping ◽

Protein Content ◽

Genomic Selection ◽

Genetic Control ◽

Spring Wheat ◽

Grain Protein Content ◽

Genome Wide Association ◽

Grain Protein ◽

Nested Association Mapping ◽

Genome Wide

Grain protein content (GPC) is controlled by complex genetic systems and their interactions, and is an important quality determinant for hard spring wheat as it has a positive effect on bread and pasta quality. GPC is variable among genotypes and strongly influenced by environment. Thus, understanding the genetic control of wheat GPC and identifying genotypes with improved stability is an important breeding goal. The objectives of this research were to identify genetic backgrounds with less variation for GPC across environments and identify quantitative trait loci (QTLs) controlling the stability of GPC. A spring wheat nested association mapping (NAM) population of 650 recombinant inbred lines (RIL) derived from 26 diverse founder parents crossed to one common parent, 'Berkut', was phenotyped over three years of field trials (2014-2016). Genomic selection models were developed and compared based on prediction of GPC and GPC stability. After observing variable genetic control of GPC within the NAM population, seven RIL families displaying reduced marker-by-environment interaction were selected based on a stability index derived from Finlay-Wilkinson regression. A genome-wide association study identified seven significant QTLs for GPC stability with a Bonferroni-adjusted P value <0.05. This study also demonstrated that genome-wide prediction of GPC with ridge regression best linear unbiased estimates reached up to r = 0.69. Genomic selection can be used to apply selection pressure for GPC and improve genetic gain for GPC.

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Genome-wide association study (GWAS) for morphological and yield-related traits in an oil palm hybrid (Elaeis oleifera x Elaeis guineensis) population

10.21203/rs.2.13122/v3 ◽

2019 ◽

Author(s):

Jaime Osorio ◽

Gina Garzón ◽

Paola Delgadillo ◽

Silvio Bastidas ◽

Leidy Moreno ◽

...

Keyword(s):

Association Mapping ◽

Candidate Genes ◽

Oil Palm ◽

Plant Architecture ◽

Elaeis Guineensis ◽

Genotyping By Sequencing ◽

Genome Wide Association ◽

Elaeis Oleifera ◽

Genome Wide ◽

Genomic Regions

Abstract Background The genus Elaeis has two species of economic importance for the oil palm agroindustry: Elaeis oleifera (O), native to the Americas, and Elaeis guineensis (G), native to Africa. This work provides to our knowledge, the first association mapping study in an interspecific OxG oil palm population, which shows tolerance to pests and diseases, high oil quality, and acceptable fruit bunch production. Results Using genotyping-by-sequencing (GBS), we identified a total of 3,776 single nucleotide polymorphisms (SNPs) that were used to perform a genome-wide association analysis (GWAS) in 378 OxG hybrid population for 10 agronomic traits. Twelve genomic regions (SNPs) were located near candidate genes implicated in multiple functional categories, such as tissue growth, cellular trafficking, and physiological processes. Conclusions We provide new insights on genomic regions that mapped on candidate genes involved in plant architecture and yield. These potential candidate genes need to be confirmed for future targeted functional analyses. Associated markers to the traits of interest may be valuable resources for the development of marker-assisted selection in oil palm breeding. Keywords: Association mapping, Elaeis guineensis , Elaeis oleifera , genotyping-by-sequencing, plant architecture, yield.

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Genomic Selection and Genome-Wide Association Studies for Grain Protein Content Stability in a Nested Association Mapping Population of Wheat

Agronomy ◽

10.3390/agronomy11122528 ◽

2021 ◽

Vol 11 (12) ◽

pp. 2528

Author(s):

Karansher S. Sandhu ◽

Paul D. Mihalyov ◽

Megan J. Lewien ◽

Michael O. Pumphrey ◽

Arron H. Carter

Keyword(s):

Association Mapping ◽

Protein Content ◽

Genomic Selection ◽

Genetic Control ◽

Spring Wheat ◽

Grain Protein Content ◽

Genome Wide Association ◽

Grain Protein ◽

Nested Association Mapping ◽

Genome Wide

Grain protein content (GPC) is controlled by complex genetic systems and their interactions and is an important quality determinant for hard spring wheat as it has a positive effect on bread and pasta quality. GPC is variable among genotypes and strongly influenced by the environment. Thus, understanding the genetic control of wheat GPC and identifying genotypes with improved stability is an important breeding goal. The objectives of this research were to identify genetic backgrounds with less variation for GPC across environments and identify quantitative trait loci (QTLs) controlling the stability of GPC. A spring wheat nested association mapping (NAM) population of 650 recombinant inbred lines (RIL) derived from 26 diverse founder parents crossed to one common parent, ‘Berkut’, was phenotyped over three years of field trials (2014–2016). Genomic selection models were developed and compared based on predictions of GPC and GPC stability. After observing variable genetic control of GPC within the NAM population, seven RIL families displaying reduced marker-by-environment interaction were selected based on a stability index derived from a Finlay–Wilkinson regression. A genome-wide association study identified eighteen significant QTLs for GPC stability with a Bonferroni-adjusted p-value < 0.05 using four different models and out of these eighteen QTLs eight were identified by two or more GWAS models simultaneously. This study also demonstrated that genome-wide prediction of GPC with ridge regression best linear unbiased estimates reached up to r = 0.69. Genomic selection can be used to apply selection pressure for GPC and improve genetic gain for GPC.

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