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    Resources: PEPIS+PATOWAS: Tools to analyze complex trait by 2D GWAS and multi-omics data integration
    Submitted by WENCHAO ZHANG; posted on Monday, September 14, 2020

    The central dogma of biology is that the genome, transcriptome, proteome, and metabolome are cascading and connected to the end phenome. Recently, high-throughput sequencing technology, particularly the NGS (Next Generation Sequencing), made it capable to discover or measure million-scale genetic variants (such as SNPs) or expressed genes (such as transcripts). High performance mass spectrometry technology makes it possible to detect and measure a broad range of metabolites at a very high sensitivity. All these omics data are complex in nature due to the heterogeneous information and huge data size, but generally biologically correlated. Therefore, it's challenging yet necessary to develop efficient methods and tools to systematically interpret the biological insights behind multiple omics data.

    In classical GWAS (Genome Wide Association Studies), the genetic marker variants and the phenotypic traits can be connected through the canonical LMM (Linear Mixed Model). We extended the concept of genome-wide association into a broader 'ome'-wide association. Briefly, we proposed a novel LMM and developed an online tool named PATOWAS (, by which we can address not only GWAS, but also TWAS (Transcriptome wide association studies), and MWAS (Metabolome wide association studies) in one unified platform. To explain more the phenotypic variation and address the missing heritability, we also proposed another new LMM and developed another association tool named PEPIS (, by which the polygenic effect and epistasis from marker pairs can be accounted for. Using these tools, two-dimensional (2D) GWAS that accounts for epistatic genetic effects can be mapped, which are complementary to the one-dimensional (1D) GWAS mapping, and can provide more genetic information.

    In short, the combination of 2D GWAS analysis and multi-omics data integration can well address the challenging questions as GXG, GXE, which together can efficiently characterize the complex trait, such as crop yield.

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