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 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):  
Grain Yield ◽  
Insect Resistance ◽  
Genomic Prediction ◽  
Prediction Models ◽  
Hessian Fly ◽  
Wheat Breeding ◽  
Unbiased Estimation ◽  
Yield Data ◽  
Breeding Lines ◽  
Genomic Regions

Abstract Using imbalanced historical yield data to predict performance and select new lines is an arduous breeding task. 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 was used. Based on correlations among data from different environments within two adjacent years and broad-sense 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 Identification of Loci Controlling the Dwarfism Trait in the White Sailfin Molly (Poecilia latipinna) Using Genome-Wide Association Studies Based on Genotyping-By-Sequencing

Genes ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 418
Author(s):  
Fan Shao ◽  
Jing Liu ◽  
Mengyuan Ren ◽  
Junying Li ◽  
Haigang Bao ◽  
...  
Keyword(s):  
Candidate Genes ◽  
Association Studies ◽  
Genotyping By Sequencing ◽  
Genome Wide Association ◽  
Expression Level ◽  
Genome Wide Association Studies ◽  
Sailfin Molly ◽  
Genome Wide ◽  
Genomic Regions ◽  
Sailfin Mollies

Dwarfism is a condition defined by low harvest weight in fish, but also results in strange body figures which may have potential for the selective breeding of new ornamental fish strains. The objectives of this study are to reveal the physiological causes of dwarfism and identify the genetic loci controlling this trait in the white sailfin molly. Skeletons of dwarf and normal sailfin mollies were observed by X-ray radioscopy and skeletal staining. Genome-wide association studies based on genotyping-by-sequencing (n = 184) were used to map candidate genomic regions associated with the dwarfism trait. Quantitative real-time PCR was performed to determine the expression level of candidate genes in normal (n = 8) and dwarf (n = 8) sailfin mollies. We found that the dwarf sailfin molly has a short and dysplastic spine in comparison to the normal fish. Two regions, located at NW_015112742.1 and NW_015113621.1, were significantly associated with the dwarfism trait. The expression level of three candidate genes, ADAMTS like 1, Larp7 and PPP3CA, were significantly different between the dwarf and normal sailfin mollies in the hepatopancreas, with PPP3CA also showing significant differences in the vertebrae and Larp7 showing significant differences in the muscle. This study identified genomic regions and candidate genes associated with the dwarfism trait in the white sailfin molly and would provide a reference to determine dwarf-causing variations.

Statistical Methods in Genome-Wide Association Studies

2020 ◽  
Vol 3 (1) ◽  
pp. 265-288
Author(s):  
Ning Sun ◽  
Hongyu Zhao
Keyword(s):  
Genetic Architecture ◽  
Prediction Models ◽  
Association Studies ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Statistical Methodology ◽  
Risk Prediction Models ◽  
Genetic Risk Prediction ◽  
Genome Wide ◽  
Genotype Information

Since the initial success of genome-wide association studies (GWAS) in 2005, tens of thousands of genetic variants have been identified for hundreds of human diseases and traits. In a GWAS, genotype information at up to millions of genetic markers is collected from up to hundreds of thousands of individuals, together with their phenotype information. Several scientific goals can be accomplished through the analysis of GWAS data, including the identification of variants, genes, and pathways associated with diseases and traits of interest; the inference of the genetic architecture of these traits; and the development of genetic risk prediction models. In this review, we provide an overview of the statistical challenges in achieving these goals and recent progress in statistical methodology to address these challenges.

scholarly journals The Genetics of Type 2 Diabetes: A Realistic Appraisal in 2008

2008 ◽  
Vol 93 (12) ◽  
pp. 4633-4642 ◽  
Author(s):  
Jose C. Florez
Keyword(s):  
Type 2 Diabetes ◽  
Prediction Models ◽  
Association Studies ◽  
Evidence Synthesis ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Major Work ◽  
Therapeutic Implications ◽  
Genome Wide

Context: Over the last few months, genome-wide association studies have contributed significantly to our understanding of the genetic architecture of type 2 diabetes. If and how this information will impact clinical practice is not yet clear. Evidence Acquisition: Primary papers reporting genome-wide association studies in type 2 diabetes or establishing a reproducible association for specific candidate genes were compiled. Further information was obtained from background articles, authoritative reviews, and relevant meeting conferences and abstracts. Evidence Synthesis: As many as 17 genetic loci have been convincingly associated with type 2 diabetes; 14 of these were not previously known, and most of them were unsuspected. The associated polymorphisms are common in populations of European descent but have modest effects on risk. These loci highlight new areas for biological exploration and allow the initiation of experiments designed to develop prediction models and test possible pharmacogenetic and other applications. Conclusions: Although substantial progress in our knowledge of the genetic basis of type 2 diabetes is taking place, these new discoveries represent but a small proportion of the genetic variation underlying the susceptibility to this disorder. Major work is still required to identify the causal variants, test their role in disease prediction and ascertain their therapeutic implications.

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