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Genome-Wide Analysis of Tar Spot Complex Resistance in Maize Using Genotyping-by-Sequencing SNPs and Whole-Genome Prediction

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TLDR
Major QTL on bin 8.03 confirmed by association and linkage mapping indicates TSC resistance in tropical maize could be improved by MAS and GS individually or stepwise.
Abstract
Tar spot complex (TSC) is one of the most destructive foliar diseases of maize ( L) in tropical and subtropical areas of Central and South America, causing significant grain yield losses when weather conditions are conducive To dissect the genetic architecture of TSC resistance in maize, association mapping, in conjunction with linkage mapping, was conducted on an association-mapping panel and three biparental doubled-haploid (DH) populations using genotyping-by-sequencing (GBS) single-nucleotide polymorphisms (SNPs) Association mapping revealed four quantitative trait loci (QTL) on chromosome 2, 3, 7, and 8 All the QTL, except for the one on chromosome 3, were further validated by linkage mapping in different genetic backgrounds Additional QTL were identified by linkage mapping alone A major QTL located on bin 803 was consistently detected with the largest phenotypic explained variation: 13% in association-mapping analysis and 1318 to 4331% in linkage-mapping analysis These results indicated that TSC resistance in maize was controlled by a major QTL located on bin 803 and several minor QTL with smaller effects on other chromosomes Genomic prediction results showed moderate-to-high prediction accuracies in different populations using various training population sizes and marker densities Prediction accuracy of TSC resistance was >050 when half of the population was included into the training set and 500 to 1,000 SNPs were used for prediction Information obtained from this study can be used for developing functional molecular markers for marker-assisted selection (MAS) and for implementing genomic selection (GS) to improve TSC resistance in tropical maize

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Effect of Trait Heritability, Training Population Size and Marker Density on Genomic Prediction Accuracy Estimation in 22 bi-parental Tropical Maize Populations.

TL;DR: This study provides useful information to maize breeders to design genomic selection workflow in their breeding programs by evaluating the genomic prediction accuracy (rMG) of the six trait-environment combinations under various levels of training population size (TPS) and marker density (MD).
Journal ArticleDOI

Enhancing Genetic Gain through Genomic Selection: From Livestock to Plants.

TL;DR: Large-scale application of genomic selection in plants can be achieved by refining field management to improve heritability estimation and prediction accuracy and developing optimum GS models with the consideration of genotype-by-environment interaction and non-additive effects, along with significant cost reduction.
Journal ArticleDOI

Molecular Breeding for Nutritionally Enriched Maize: Status and Prospects

TL;DR: The status and prospects of developing nutritionally enriched maize by successfully harnessing conventional and molecular marker-assisted breeding are outlined, highlighting the need for intensification of efforts to create greater impacts on malnutrition in maize-consuming populations, especially in the low- and middle-income countries.
Journal ArticleDOI

Phenotypic and genetic analysis of spike and kernel characteristics in wheat reveals long-term genetic trends of grain yield components.

TL;DR: Some of the examined traits were already the basis of grain yield progress in wheat in the past decades, and a more targeted exploitation of the available variation, potentially coupled with genomic approaches, may assist wheat breeding in continuing to increase yield levels globally.
References
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Journal Article

R: A language and environment for statistical computing.

R Core Team
- 01 Jan 2014 - 
TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
Journal ArticleDOI

Prediction of Total Genetic Value Using Genome-Wide Dense Marker Maps

TL;DR: It was concluded that selection on genetic values predicted from markers could substantially increase the rate of genetic gain in animals and plants, especially if combined with reproductive techniques to shorten the generation interval.
Journal ArticleDOI

TASSEL: software for association mapping of complex traits in diverse samples

TL;DR: TASSEL (Trait Analysis by aSSociation, Evolution and Linkage) implements general linear model and mixed linear model approaches for controlling population and family structure and allows for linkage disequilibrium statistics to be calculated and visualized graphically.
Journal ArticleDOI

A Robust, Simple Genotyping-by-Sequencing (GBS) Approach for High Diversity Species

TL;DR: A procedure for constructing GBS libraries based on reducing genome complexity with restriction enzymes (REs) is reported, which is simple, quick, extremely specific, highly reproducible, and may reach important regions of the genome that are inaccessible to sequence capture approaches.
Journal ArticleDOI

Efficient Methods to Compute Genomic Predictions

TL;DR: Efficient methods for processing genomic data were developed to increase reliability of estimated breeding values and to estimate thousands of marker effects simultaneously, and a blend of first- and second-order Jacobi iteration using 2 separate relaxation factors converged well for allele frequencies and effects.
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