Optimizing genomic prediction for complex traits via investigating multiple factors in switchgrass

文献类型： 外文期刊

第一作者： Wang, Peipei

作者： Wang, Peipei;Meng, Fanrui;Aba, Kenia Estefania Segura;Shiu, Shin-Han;Wang, Peipei;Wang, Peipei;Meng, Fanrui;Del Azodi, Christina Brady;Shiu, Shin-Han;Aba, Kenia Estefania Segura;Shiu, Shin-Han;Casler, Michael D.;Shiu, Shin-Han

作者机构：

期刊名称：PLANT PHYSIOLOGY （ 影响因子：6.9； 五年影响因子：7.7 ）

ISSN： 0032-0889

年卷期： 2025 年 198 卷 3 期

页码：

收录情况： SCI

摘要： Genomic prediction has accelerated breeding processes and provided mechanistic insights into the genetic bases of complex traits. To further optimize genomic prediction, we assess the impact of genome assemblies, genotyping approaches, variant types, allelic complexities, polyploidy levels, and population structures on the prediction of 20 complex traits in switchgrass (Panicum virgatum L.), a perennial biofuel feedstock. Surprisingly, short read-based genome assembly performs comparably to or even better than long read-based assembly. Due to higher gene coverage, exome capture and multi-allelic variants outperform genotyping-by-sequencing and bi-allelic variants, respectively. Tetraploid models show higher prediction accuracy than octoploid models for most traits, likely due to the greater genetic distances among tetraploids. Depending on the trait in question, different types of variants need to be integrated for optimal predictions. Our study provides insights into the factors influencing genomic prediction outcomes, guiding best practices for future studies and for improving agronomic traits in switchgrass and other species through selective breeding. Exome capture and multi-allelic variants lead to better prediction for complex traits than genotyping-by-sequencing and bi-allelic variants due to higher gene coverage in switchgrass, respectively.

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