Genome-wide association mapping reveals new loci associated with light-colored seed coat at harvest and slow darkening in carioca beans

Abstract Background Common bean (Phaseolus vulgaris L.) is a legume whose grain can be stored for months, a common practice among Brazilian growers. Over time, seed coats become darker and harder to cook, traits that are undesirable to consumers, who associate darker-colored beans with greater age. Like commercial pinto and cranberry bean varieties, carioca beans that have darker seeds at harvest time and after storage are subject to decreased market values. Results The goal of our study was to identify the genetic control associated with lightness of seed coat color at harvest (HL) and with tolerance to post-harvest seed coat darkening (PHD) by a genome-wide association study. For that purpose, a carioca diversity panel previously validated for association mapping studies was used with 138 genotypes and 1,516 high-quality SNPs. The panel was evaluated in two environments using a colorimeter and the CIELAB scale. Shelf storage for 30 days had the most expressive results and the L* (luminosity) parameter led to the greatest discrimination of genotypes. Three QTL were identified for HL, two on chromosome Pv04 and one on Pv10. Regarding PHD, results showed that genetic control differs for L* after 30 days and for the ΔL* (final L*—initial L*); only ΔL* was able to properly express the PHD trait. Four phenotypic classes were proposed, and five QTL were identified through six significant SNPs. Conclusions Lightness of seed coat color at harvest showed an oligogenic inheritance corroborated by moderate broad-sense heritability and high genotypic correlation among the experiments. Only three QTL were significant for this trait – two were mapped on Pv04 and one on Pv10. Considering the ΔL, six QTL were mapped on four different chromosomes for PHD. The same HL QTL at the beginning of Pv10 was also associated with ΔL* and could be used as a tool in marker-assisted selection. Several candidate genes were identified and may be useful to accelerate the genetic breeding process.

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Genome-Wide Association Mapping of Seed Coat Color in Brassica napus

Journal of Agricultural and Food Chemistry ◽

10.1021/acs.jafc.7b01226 ◽

2017 ◽

Vol 65 (26) ◽

pp. 5229-5237 ◽

Cited By ~ 6

Author(s):

Jia Wang ◽

Xiaohua Xian ◽

Xinfu Xu ◽

Cunmin Qu ◽

Kun Lu ◽

...

Keyword(s):

Brassica Napus ◽

Association Mapping ◽

Seed Coat ◽

Coat Color ◽

Genome Wide Association ◽

Seed Coat Color ◽

Genome Wide

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Genome-wide association study of seed coat color in sesame (Sesamum indicum L.)

10.21203/rs.2.18296/v2 ◽

2020 ◽

Author(s):

Hongxian Mei ◽

Chengqi Cui ◽

Yanyang Liu ◽

Yan Liu ◽

Xianghua Cui ◽

...

Keyword(s):

Association Study ◽

Seed Coat ◽

Genetic Basis ◽

Genome Wide Association Study ◽

Coat Color ◽

Sesame Seed ◽

Significant Snps ◽

Genome Wide Association ◽

Seed Coat Color ◽

Genome Wide

Abstract Background: Sesame is an important and ancient oilseed crop. Sesame seed coat color is an extremely important agronomic trait, and is related to biochemical functions involved in protein and oil metabolism, and antioxidant content. Because of its complication, the genetic basis of sesame seed coat color remains poorly understood.Results: Genome-wide association study (GWAS) using 42,781 single-nucleotide polymorphisms (SNPs) was performed with a diverse association-mapping panel comprising 366 sesame germplasm lines in 12 environments. In total, 224 significant SNPs (P < 2.34×10−7) explaining approximately 13.34% of the phenotypic variation on average were identified, and 35 significant SNPs were detected in more than 6 environments. Out of 224 significant SNPs, 22 were located in the confidence intervals of previous reported quantitative trait loci. A total of 92 candidate genes were identified in the vicinity of the 4 SNPs that were most significantly associated with sesame seed coat color. Conclusions: The results in this paper will provide new insights into the genetic basis of sesame seed coat color, and should be useful for molecular breeding in sesame.

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Genome-wide association study of seed coat color in sesame (Sesamum indicum L.)

10.21203/rs.2.18296/v1 ◽

2019 ◽

Author(s):

Hongxian Mei ◽

Chengqi Cui ◽

Yanyang Liu ◽

Yan Liu ◽

Xianghua Cui ◽

...

Keyword(s):

Seed Coat ◽

Genetic Basis ◽

Genome Wide Association Study ◽

Coat Color ◽

Sesame Seed ◽

Significant Snps ◽

Sesamum Indicum ◽

Genome Wide Association ◽

Seed Coat Color ◽

Genome Wide

Abstract Background: Sesame (Sesamum indicum L.) is an important and ancient oilseed crop. Sesame seed coat color is an extremely important agronomic trait, and is related to biochemical functions involved in protein and oil metabolism, and antioxidant content. Because of its complication, the genetic basis of sesame seed coat color remains poorly understood.Results: Genome-wide association study (GWAS) using 42,781 SNPs was performed with a diverse association-mapping panel comprising 366 sesame germplasm lines in 12 environments. In total, 224 significant SNPs (P < 2.34×10−7) explaining approximately 13.34% of the phenotypic variation on average were identified, and 35 significant SNPs were detected in more than 6 environments. Out of 224 significant SNPs, 22 were located in the confidence intervals of previous reported quantitative trait loci (QTLs). A total of 92 candidate genes were identified in the vicinity of the 4 SNPs that were most significantly associated with sesame seed coat color. Conclusions: The results in this paper will provide new insights into the genetic basis of sesame seed coat color, and should be useful for molecular breeding in sesame.

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Genome-Wide Association Mapping for Heat and Drought Adaptive Traits in Pea

Genes ◽

10.3390/genes12121897 ◽

2021 ◽

Vol 12 (12) ◽

pp. 1897

Author(s):

Endale G. Tafesse ◽

Krishna K. Gali ◽

V. B. Reddy Lachagari ◽

Rosalind Bueckert ◽

Thomas D. Warkentin

Keyword(s):

Association Mapping ◽

Candidate Genes ◽

Vegetation Index ◽

Genome Wide Association Study ◽

Genome Wide Association ◽

Nucleotide Polymorphisms ◽

Adaptive Traits ◽

Flowering Duration ◽

Genome Wide ◽

A Genome

Heat and drought, individually or in combination, limit pea productivity. Fortunately, substantial genetic diversity exists in pea germplasm for traits related to abiotic stress resistance. Understanding the genetic basis of resistance could accelerate the development of stress-adaptive cultivars. We conducted a genome-wide association study (GWAS) in pea on six stress-adaptive traits with the aim to detect the genetic regions controlling these traits. One hundred and thirty-five genetically diverse pea accessions were phenotyped in field studies across three or five environments under stress and control conditions. To determine marker trait associations (MTAs), a total of 16,877 valuable single nucleotide polymorphisms (SNPs) were used in association analysis. Association mapping detected 15 MTAs that were significantly (p ≤ 0.0005) associated with the six stress-adaptive traits averaged across all environments and consistent in multiple individual environments. The identified MTAs were four for lamina wax, three for petiole wax, three for stem thickness, two for the flowering duration, one for the normalized difference vegetation index (NDVI), and two for the normalized pigment and chlorophyll index (NPCI). Sixteen candidate genes were identified within a 15 kb distance from either side of the markers. The detected MTAs and candidate genes have prospective use towards selecting stress-hardy pea cultivars in marker-assisted selection.

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Defining the genetic control of human blood plasma N-glycome using genome-wide association study

10.1101/365486 ◽

2018 ◽

Cited By ~ 1

Author(s):

Sodbo Zh. Sharapov ◽

Yakov A. Tsepilov ◽

Lucija Klaric ◽

Massimo Mangino ◽

Gaurav Thareja ◽

...

Keyword(s):

Blood Plasma ◽

Association Study ◽

Genetic Control ◽

Human Blood ◽

Genome Wide Association Study ◽

Genome Wide Association ◽

Human Blood Plasma ◽

Genome Wide ◽

A Genome

AbstractGlycosylation is a common post-translational modification of proteins. It is known, that glycans are directly involved in the pathophysiology of every major disease. Defining genetic factors altering glycosylation may provide a basis for novel approaches to diagnostic and pharmaceutical applications. Here, we report a genome-wide association study of the human blood plasma N-glycome composition in up to 3811 people. We discovered and replicated twelve loci. This allowed us to demonstrate a clear overlap in genetic control between total plasma and IgG glycosylation. Majority of loci contained genes that encode enzymes directly involved in glycosylation (FUT3/FUT6, FUT8, B3GAT1, ST6GAL1, B4GALT1, ST3GAL4, MGAT3, and MGAT5). We, however, also found loci that are likely to reflect other, more complex, aspects of plasma glycosylation process. Functional genomic annotation suggested the role of DERL3, which potentially highlights the role of glycoprotein degradation pathway, and such transcription factor as IKZF1.

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A Genome-wide Combinatorial Strategy Dissects Complex Genetic Architecture of Seed Coat Color in Chickpea

Frontiers in Plant Science ◽

10.3389/fpls.2015.00979 ◽

2015 ◽

Vol 6 ◽

Cited By ~ 16

Author(s):

Deepak Bajaj ◽

Shouvik Das ◽

Hari D. Upadhyaya ◽

Rajeev Ranjan ◽

Saurabh Badoni ◽

...

Keyword(s):

Seed Coat ◽

Genetic Architecture ◽

Coat Color ◽

Seed Coat Color ◽

Genome Wide ◽

A Genome ◽

Combinatorial Strategy

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Eagle for better genome-wide association mapping

G3 Genes|Genome|Genetics ◽

10.1093/g3journal/jkab204 ◽

2021 ◽

Author(s):

Andrew W George ◽

Arunas Verbyla ◽

Joshua Bowden

Keyword(s):

Association Mapping ◽

Genome Wide Association Study ◽

R Package ◽

Study Data ◽

Single Locus ◽

Genome Wide Association ◽

Genome Wide ◽

A Genome ◽

Wide Scale ◽

Trait Associations

Abstract Eagle is an R package for multi-locus association mapping on a genome-wide scale. It is unlike other multi-locus packages in that it is easy-to-use for R users and non-users alike. It has two modes of use, command line and GUI. Eagle is fully documented and has its own supporting website, http://eagle.r-forge.r-project.org/index.html. Eagle is a significant improvement over the method-of-choice, single-locus association mapping. It has greater power to detect SNP-trait associations. It is based on model selection, linear mixed models, and a clever idea on how random effects can be used to identify SNP-trait associations. Through an example with real mouse data, we demonstrate Eagle’s ability to bring clarity and increased insight to single-locus findings. Initially, we see Eagle complementing single-locus analyses. However, over time, we hope the community will make, increasingly, multi-locus association mapping their method-of-choice for the analysis of genome-wide association study data.

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