Prediction of plant complex traits via integration of multi-omics data

文献类型： 外文期刊

第一作者： Wang, Peipei

作者： Wang, Peipei;Aba, Kenia Segura;Shiu, Shin-Han;Wang, Peipei;Wang, Peipei;Lehti-Shiu, Melissa D.;Lotreck, Serena;Shiu, Shin-Han;Lotreck, Serena;Shiu, Shin-Han;Aba, Kenia Segura;Shiu, Shin-Han;Krysan, Patrick J.

作者机构：

期刊名称：NATURE COMMUNICATIONS （ 影响因子：14.7； 五年影响因子：16.1 ）

ISSN：

年卷期： 2024 年 15 卷 1 期

页码：

收录情况： SCI

摘要： The formation of complex traits is the consequence of genotype and activities at multiple molecular levels. However, connecting genotypes and these activities to complex traits remains challenging. Here, we investigate whether integrating genomic, transcriptomic, and methylomic data can improve prediction for six Arabidopsis traits. We find that transcriptome- and methylome-based models have performances comparable to those of genome-based models. However, models built for flowering time using different omics data identify different benchmark genes. Nine additional genes identified as important for flowering time from our models are experimentally validated as regulating flowering. Gene contributions to flowering time prediction are accession-dependent and distinct genes contribute to trait prediction in different genotypes. Models integrating multi-omics data perform best and reveal known and additional gene interactions, extending knowledge about existing regulatory networks underlying flowering time determination. These results demonstrate the feasibility of revealing molecular mechanisms underlying complex traits through multi-omics data integration. Translating genotype to phenotype is a grand challenge in biology. Here, the authors investigate the utility of genome, transcriptome, and methylome data and their combinations in predicting six plant complex traits and uncovering key genes and genetic interactions in Arabidopsis.

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