scholarly journals Genomic Prediction and Genome-Wide Association Studies of Flour Yield and Alveograph Quality Traits Using Advanced Winter Wheat Breeding Material

Genes ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 669 ◽  
Author(s):  
Peter S. Kristensen ◽  
Just Jensen ◽  
Jeppe R. Andersen ◽  
Carlos Guzmán ◽  
Jihad Orabi ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Genomic Prediction ◽  
Association Studies ◽  
Wheat Breeding ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Quality Traits ◽  
Breeding Programs ◽  
Genome Wide ◽  
Flour Yield

Use of genetic markers and genomic prediction might improve genetic gain for quality traits in wheat breeding programs. Here, flour yield and Alveograph quality traits were inspected in 635 F6 winter wheat breeding lines from two breeding cycles. Genome-wide association studies revealed single nucleotide polymorphisms (SNPs) on chromosome 5D significantly associated with flour yield, Alveograph P (dough tenacity), and Alveograph W (dough strength). Additionally, SNPs on chromosome 1D were associated with Alveograph P and W, SNPs on chromosome 1B were associated with Alveograph P, and SNPs on chromosome 4A were associated with Alveograph L (dough extensibility). Predictive abilities based on genomic best linear unbiased prediction (GBLUP) models ranged from 0.50 for flour yield to 0.79 for Alveograph W based on a leave-one-out cross-validation strategy. Predictive abilities were negatively affected by smaller training set sizes, lower genetic relationship between lines in training and validation sets, and by genotype–environment (G×E) interactions. Bayesian Power Lasso models and genomic feature models resulted in similar or slightly improved predictions compared to GBLUP models. SNPs with the largest effects can be used for screening large numbers of lines in early generations in breeding programs to select lines that potentially have good quality traits. In later generations, genomic predictions might be used for a more accurate selection of high quality wheat lines.

scholarly journals A New Strategy for Using Historical Imbalanced Yield Data to Conduct Genome-Wide Association Studies and Develop Genomic Prediction Models for Wheat Breeding

2021 ◽  
Author(s):  
Chenggen Chu ◽  
Shichen Wang ◽  
Jackie C. Rudd ◽  
Amir M.H. Ibrahim ◽  
Qingwu Xue ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Prediction Models ◽  
Association Studies ◽  
Wheat Breeding ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Yield Data ◽  
Breeding Lines ◽  
Genome Wide ◽  
Genomic Regions

Abstract Using imbalanced historical yield data to predict performance and select new lines is an arduous breeding task. Genome-wide association studies (GWAS) and high throughput genotyping based on sequencing techniques can increase prediction accuracy. An association mapping panel of 227 Texas elite (TXE) wheat breeding lines was used for GWAS and a training population to develop prediction models for grain yield selection. An imbalanced set of yield data collected from 102 environments (year-by-location) over ten years, through testing yield in 40–66 lines each year at 6–14 locations with 38–41 lines repeated in the test in any two consecutive years, was used. Based on correlations among data from different environments within two adjacent years and heritability estimated in each environment, yield data from 87 environments were selected and assigned to two correlation-based groups. The yield best linear unbiased estimation (BLUE) from each group, along with reaction to greenbug and Hessian fly in each line, were used for GWAS to reveal genomic regions associated with yield and insect resistance. A total of 74 genomic regions were associated with grain yield and two of them were commonly detected in both correlation-based groups. Greenbug resistance in TXE lines was mainly controlled by Gb3 on chromosome 7DL in addition to two novel regions on 3DL and 6DS, and Hessian fly resistance was conferred by the region on 1AS. Genomic prediction models developed in two correlation-based groups were validated using a set of 105 new advanced breeding lines and the model from correlation-based group G2 was more reliable for prediction. This research not only identified genomic regions associated with yield and insect resistance but also established the method of using historical imbalanced breeding data to develop a genomic prediction model for crop improvement.

scholarly journals Genome-wide association studies provide insights into the genetic determination of fruit traits of pear

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Ming-Yue Zhang ◽  
Cheng Xue ◽  
Hongju Hu ◽  
Jiaming Li ◽  
Yongsong Xue ◽  
...  
Keyword(s):  
Fruit Quality ◽  
Molecular Breeding ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Quality Traits ◽  
Fruit Traits ◽  
Stone Cell ◽  
Genome Wide ◽  
Fruit Quality Traits

AbstractPear is a major fruit tree crop distributed worldwide, yet its breeding is a very time-consuming process. To facilitate molecular breeding and gene identification, here we have performed genome-wide association studies (GWAS) on eleven fruit traits. We identify 37 loci associated with eight fruit quality traits and five loci associated with three fruit phenological traits. Scans for selective sweeps indicate that traits including fruit stone cell content, organic acid and sugar contents might have been under continuous selection during breeding improvement. One candidate gene, PbrSTONE, identified in GWAS, has been functionally verified to be involved in the regulation of stone cell formation, one of the most important fruit quality traits in pear. Our study provides insights into the complex fruit related biology and identifies genes controlling important traits in pear through GWAS, which extends the genetic resources and basis for facilitating molecular breeding in perennial trees.

P5018 Genome-wide association studies for dry-cured ham quality traits in Italian Large White and Italian Duroc pigs

2016 ◽  
Vol 94 (suppl_4) ◽  
pp. 124-124
Author(s):  
L. Fontanesi ◽  
G. Schiavo ◽  
G. Galimberti ◽  
S. Bovo ◽  
F. Bertolini ◽  
...  
Keyword(s):  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Quality Traits ◽  
Large White ◽  
Genome Wide ◽  
Dry Cured Ham

scholarly journals SeqBreed: a python tool to evaluate genomic prediction in complex scenarios

2019 ◽  
Author(s):  
M. Pérez-Enciso ◽  
L. C. Ramírez-Ayala ◽  
L.M. Zingaretti
Keyword(s):  
Genomic Prediction ◽  
Predictive Accuracy ◽  
Sequence Data ◽  
Association Studies ◽  
Single Step ◽  
Genome Wide Association ◽  
Drosophila Genome ◽  
Genome Wide Association Studies ◽  
Complex Phenotypes ◽  
Genome Wide

AbstractBackgroundGenomic Prediction (GP) is the procedure whereby molecular information is used to predict complex phenotypes. Although GP can significantly enhance predictive accuracy, it can be expensive and difficult to implement. To help in designing optimum experiments, including genome wide association studies and genomic selection experiments, we have developed SeqBreed, a generic and flexible python3 forward simulator.ResultsSeqBreed accommodates sex and mitochondrion chromosomes as well as autopolyploidy. It can simulate any number of complex phenotypes determined by any number of causal loci. SeqBreed implements several GP methods, including single step GBLUP. We demonstrate its functionality with Drosophila Genome Reference Panel (DGRP) sequence data and with tetraploid potato genotypes.ConclusionsSeqBreed is a flexible and easy to use tool appropriate for optimizing GP or genome wide association studies. It incorporates some of the most popular GP methods and includes several visualization tools. Code is open and can be freely modified. Software, documentation and examples are available at https://github.com/miguelperezenciso/SeqBreed.

Sign in / Sign up

Export Citation Format

Share Document