A pangenome analysis pipeline (PSVCP) provides insights into rice functional gene identification

Wang, J.; Wu, Y.; Zhang, S.; Hu, H.; Yuan, Y.; Dong, J.; Chen, L.; Ma, Y.; Yang, T.; Zhou, L.; Chen, J.; Liu, B.; Li, C.; Edwards, D.; Zhao, J.

doi:10.1101/2022.06.15.496234

bioRxiv · 10.1101/2022.06.15.496234

A pangenome analysis pipeline (PSVCP) provides insights into rice functional gene identification

Wang, J.·Wu, Y.·Zhang, S.·Hu, H.·Yuan, Y.·Dong, J.·Chen, L.·Ma, Y.·Yang, T.·Zhou, L.·Chen, J.·Liu, B.·Li, C.·Edwards, D.·Zhao, J.

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Abstract

BackgroundA pangenome aims to capture the complete genetic diversity within a species and reduce bias in genetic analysis inherent in using a single reference genome. However, the current linear format of most plant pangenomes limits the presentation of position information for novel sequences. Graph pangenomes have been developed to overcome this limitation. However, there is a lack of bioinformatics analysis tools for graph format genomes. ResultsTo overcome this problem, we have developed a novel pangenome construction strategy and a downstream pangenome analysis pipeline that captures position information while maintaining a linearized layout. We applied this strategy to construct a high-quality rice pangenome using 12 representative rice genomes and analyze an international rice panel with 413 diverse accessions using the pangenome reference. Our results provide insights into rice population structure and genomic diversity. Applying the pangenome for PAV-based GWAS analysis can identify causal structural variations for rice grain weight and plant height, while SNP-based GWAS can only identify approximate genomic locations. Additionally, a new locus (qPH8-1) was found to be associated with plant height on chromosome 8 that could not be detected using the SNP-based GWAS. ConclusionsOur results demonstrate that the pangenome constructed by our pipeline combined with PAV-based GWAS can provide additional power for genomic and genetic analysis. The pangenome constructed in this study and associated genome sequence data provide valuable genomic resources for future rice crop improvement.

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Wang, J., Wu, Y., Zhang, S., Hu, H., Yuan, Y., Dong, J., Chen, L., Ma, Y., Yang, T., Zhou, L., Chen, J., Liu, B., Li, C., Edwards, D., Zhao, J.. 2022-06-17. A pangenome analysis pipeline (PSVCP) provides insights into rice functional gene identification. https://doi.org/10.1101/2022.06.15.496234

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