A new strategy for using historical imbalanced yield data to conduct genome-wide association studies and develop genomic prediction models for wheat breeding

被引:0
|
作者
Chenggen Chu
Shichen Wang
Jackie C. Rudd
Amir M. H. Ibrahim
Qingwu Xue
Ravindra N. Devkota
Jason A. Baker
Shannon Baker
Bryan Simoneaux
Geraldine Opena
Haixiao Dong
Xiaoxiao Liu
Kirk E. Jessup
Ming-Shun Chen
Kele Hui
Richard Metz
Charles D. Johnson
Zhiwu S. Zhang
Shuyu Liu
机构
[1] Texas A&M AgriLife Research Center,Sugarbeet & Potato Research Unit
[2] Edward T. Schafer Agricultural Research Center,Genomics and Bioinformatics Service Center
[3] USDA-ARS,Soil and Crop Sciences Department
[4] Texas A&M AgriLife Research,Soil and Crop Sciences Department
[5] Texas A&M University,undefined
[6] Washington State University,undefined
[7] Hard Winter Wheat Genetics Research Unit,undefined
[8] USDA-ARS,undefined
来源
Molecular Breeding | 2022年 / 42卷
关键词
Genetic correlation; Wheat breeding; Imbalanced data; Insect resistance; Genomic prediction accuracy;
D O I
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学科分类号
摘要
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 10 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, was 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.
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