Importance of genetic architecture in marker selection decisions for genomic prediction

被引:3
作者
Della Coletta, Rafael [1 ]
Fernandes, Samuel B. [2 ]
Monnahan, Patrick J. [1 ]
Mikel, Mark A. [3 ,4 ]
Bohn, Martin O. [3 ]
Lipka, Alexander E. [3 ]
Hirsch, Candice N. [1 ]
机构
[1] Univ Minnesota, Dept Agron & Plant Genet, St Paul, MN 55108 USA
[2] Univ Arkansas, Dept Crop Soil & Environm Sci, Fayetteville, AR 72701 USA
[3] Univ Illinois, Dept Crop Sci, Urbana, IL 61801 USA
[4] Univ Illinois, Roy J Carver Biotechnol Ctr, Urbana, IL 61801 USA
基金
美国国家科学基金会; 美国农业部;
关键词
Simulation; Structural variation; Plant breeding; Genotype-by-environment; Maize; COPY NUMBER VARIATION; HYBRID PERFORMANCE; MAIZE; TOLERANCE; ASSOCIATION; RESISTANCE; GENOTYPE; IMPACT;
D O I
10.1007/s00122-023-04469-w
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
Key messageWe demonstrate potential for improved multi-environment genomic prediction accuracy using structural variant markers. However, the degree of observed improvement is highly dependent on the genetic architecture of the trait.AbstractBreeders commonly use genetic markers to predict the performance of untested individuals as a way to improve the efficiency of breeding programs. These genomic prediction models have almost exclusively used single nucleotide polymorphisms (SNPs) as their source of genetic information, even though other types of markers exist, such as structural variants (SVs). Given that SVs are associated with environmental adaptation and not all of them are in linkage disequilibrium to SNPs, SVs have the potential to bring additional information to multi-environment prediction models that are not captured by SNPs alone. Here, we evaluated different marker types (SNPs and/or SVs) on prediction accuracy across a range of genetic architectures for simulated traits across multiple environments. Our results show that SVs can improve prediction accuracy, but it is highly dependent on the genetic architecture of the trait and the relative gain in accuracy is minimal. When SVs are the only causative variant type, 70% of the time SV predictors outperform SNP predictors. However, the improvement in accuracy in these instances is only 1.5% on average. Further simulations with predictors in varying degrees of LD with causative variants of different types (e.g., SNPs, SVs, SNPs and SVs) showed that prediction accuracy increased as linkage disequilibrium between causative variants and predictors increased regardless of the marker type. This study demonstrates that knowing the genetic architecture of a trait in deciding what markers to use in large-scale genomic prediction modeling in a breeding program is more important than what types of markers to use.
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页数:14
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