Genome-wide association mapping reveals epistasis and genetic interaction networks in sugar beet

2011 ◽  
Vol 123 (1) ◽  
pp. 109-118 ◽  
Author(s):  
Tobias Würschum ◽  
Hans Peter Maurer ◽  
Britta Schulz ◽  
Jens Möhring ◽  
Jochen Christoph Reif
Keyword(s):  
Sugar Beet ◽  
Association Mapping ◽  
Genetic Interaction ◽  
Interaction Networks ◽  
Genome Wide Association ◽  
Genome Wide

Genome-wide association mapping of agronomic traits in sugar beet

2011 ◽  
Vol 123 (7) ◽  
pp. 1121-1131 ◽  
Author(s):  
Tobias Würschum ◽  
Hans Peter Maurer ◽  
Thomas Kraft ◽  
Geert Janssen ◽  
Carolina Nilsson ◽  
...  
Keyword(s):  
Sugar Beet ◽  
Association Mapping ◽  
Agronomic Traits ◽  
Genome Wide Association ◽  
Genome Wide

Genome-wide association mapping in maize: status and prospects

3 Biotech ◽  
2021 ◽  
Vol 11 (5) ◽  
Author(s):  
Kumari Shikha ◽  
J. P. Shahi ◽  
M. T. Vinayan ◽  
P. H. Zaidi ◽  
A. K. Singh ◽  
...  
Keyword(s):  
Association Mapping ◽  
Genome Wide Association ◽  
Genome Wide

Genome-wide association mapping of starch granule size distribution in common wheat

2017 ◽  
Vol 77 ◽  
pp. 211-218 ◽  
Author(s):  
Jieyun Li ◽  
Awais Rasheed ◽  
Qi Guo ◽  
Yan Dong ◽  
Jindong Liu ◽  
...  
Keyword(s):  
Association Mapping ◽  
Size Distribution ◽  
Common Wheat ◽  
Starch Granule ◽  
Granule Size ◽  
Genome Wide Association ◽  
Genome Wide

scholarly journals Genome-wide association mapping identifies markers associated with cane yield components and sucrose traits in the Louisiana sugarcane core collection

Genomics ◽  
2019 ◽  
Vol 111 (6) ◽  
pp. 1794-1801 ◽  
Author(s):  
Nathanael Fickett ◽  
Andres Gutierrez ◽  
Mohit Verma ◽  
Michael Pontif ◽  
Anna Hale ◽  
...  
Keyword(s):  
Association Mapping ◽  
Yield Components ◽  
Core Collection ◽  
Genome Wide Association ◽  
Cane Yield ◽  
Genome Wide

Characterization and genome-wide association mapping of resistance to leaf rust, stem rust and stripe rust in a geographically diverse collection of spring wheat landraces

2017 ◽  
Vol 37 (9) ◽  
Author(s):  
Peace Kankwatsa ◽  
Davinder Singh ◽  
Peter C. Thomson ◽  
Ebrahiem M. Babiker ◽  
John M. Bonman ◽  
...  
Keyword(s):  
Association Mapping ◽  
Leaf Rust ◽  
Stripe Rust ◽  
Spring Wheat ◽  
Stem Rust ◽  
Genome Wide Association ◽  
Genome Wide ◽  
Wheat Landraces

scholarly journals Open access resources for genome-wide association mapping in rice

2016 ◽  
Vol 7 (1) ◽  
Author(s):  
Susan R. McCouch ◽  
Mark H. Wright ◽  
Chih-Wei Tung ◽  
Lyza G. Maron ◽  
Kenneth L. McNally ◽  
...  
Keyword(s):  
Open Access ◽  
Association Mapping ◽  
High Density ◽  
Genome Wide Association ◽  
Minor Effect ◽  
Data Set ◽  
Genome Wide ◽  
Analytical Strategies ◽  
Biological Interpretation ◽  
Rice Improvement

Abstract Increasing food production is essential to meet the demands of a growing human population, with its rising income levels and nutritional expectations. To address the demand, plant breeders seek new sources of genetic variation to enhance the productivity, sustainability and resilience of crop varieties. Here we launch a high-resolution, open-access research platform to facilitate genome-wide association mapping in rice, a staple food crop. The platform provides an immortal collection of diverse germplasm, a high-density single-nucleotide polymorphism data set tailored for gene discovery, well-documented analytical strategies, and a suite of bioinformatics resources to facilitate biological interpretation. Using grain length, we demonstrate the power and resolution of our new high-density rice array, the accompanying genotypic data set, and an expanded diversity panel for detecting major and minor effect QTLs and subpopulation-specific alleles, with immediate implications for rice improvement.

scholarly journals Genome-wide association mapping of correlated traits in cassava: dry matter and total carotenoid content

2017 ◽  
Author(s):  
Ismail Y. Rabbi ◽  
Lovina I. Udoh ◽  
Marnin Wolfe ◽  
Elizabeth Y. Parkes ◽  
Melaku A. Gedil ◽  
...  
Keyword(s):  
Association Mapping ◽  
Negative Correlation ◽  
Dry Matter ◽  
Matter Content ◽  
Genome Wide Association ◽  
Carotenoid Content ◽  
Dry Matter Content ◽  
Root Crop ◽  
Genome Wide ◽  
The Tropics

ABSTRACTCassava (Manihot esculenta (L.) Crantz) is a starchy root crop cultivated in the tropics for fresh consumption and commercial processing. Dry matter content and micronutrient density, particularly of provitamin A – traits that are negatively correlated – are among the primary selection objectives in cassava breeding. This study aimed at identifying genetic markers associated with these traits and uncovering the potential underlying cause of their negative correlation – whether linkage and/or pleiotropy. A genome-wide association mapping using 672 clones genotyped at 72,279 SNP loci was carried out. Root yellowness was used indirectly to assess variation in carotenoid content. Two major loci for root yellowness was identified on chromosome 1 at positions 24.1 and 30.5 Mbp. A single locus for dry matter content that co-located with the 24.1 Mbp peak for carotenoid content was identified. Haplotypes at these loci explained a large proportion of the phenotypic variability. Evidence of mega-base-scale linkage disequilibrium around the major loci of the two traits and detection of the major dry matter locus in independent analysis for the white- and yellow-root subpopulations suggests that physical linkage rather that pleiotropy is more likely to be the cause of the negative correlation between the target traits. Moreover, candidate genes for carotenoid (phytoene synthase) and starch biosynthesis (UDP-glucose pyrophosphorylase and sucrose synthase) occurred in the vicinity of the identified locus at 24.1 Mbp. These findings elucidate on the genetic architecture of carotenoids and dry matter in cassava and provides an opportunity to accelerate genetic improvement of these traits.CORE IDEASCassava, a starchy root crop, is a major source of dietary calories in the tropics.Most varieties consumed are poor in micronutrients, including pro-vitamin A.These two traits are governed by few major loci on chromosome one.Genetic linkage, rather than pleiotropy, is the most likely cause of their negative correlation.

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