RicePilaf: a post-GWAS/QTL dashboard to integrate pangenomic, coexpression, regulatory, epigenomic, ontology, pathway, and text-mining information to provide functional insights into rice QTLs and GWAS loci

Author:

Shrestha Anish M S12ORCID,Gonzales Mark Edward M1ORCID,Ong Phoebe Clare L1ORCID,Larmande Pierre3ORCID,Lee Hyun-Sook4ORCID,Jeung Ji-Ung4ORCID,Kohli Ajay2ORCID,Chebotarov Dmytro2ORCID,Mauleon Ramil P2ORCID,Lee Jae-Sung2ORCID,McNally Kenneth L2ORCID

Affiliation:

1. Bioinformatics Lab, Advanced Research Institute for Informatics, Computing and Networking, College of Computer Studies, De La Salle University , Manila 1004 , Philippines

2. International Rice Research Institute (IRRI), Metro Manila 1301, Philippines

3. DIADE, Univ Montpellier , Cirad, IRD, 34394 Montpellier , France

4. National Institute of Crop Science , Wanju-gun 55365 , Republic of Korea

Abstract

Abstract Background As the number of genome-wide association study (GWAS) and quantitative trait locus (QTL) mappings in rice continues to grow, so does the already long list of genomic loci associated with important agronomic traits. Typically, loci implicated by GWAS/QTL analysis contain tens to hundreds to thousands of single-nucleotide polmorphisms (SNPs)/genes, not all of which are causal and many of which are in noncoding regions. Unraveling the biological mechanisms that tie the GWAS regions and QTLs to the trait of interest is challenging, especially since it requires collating functional genomics information about the loci from multiple, disparate data sources. Results We present RicePilaf, a web app for post-GWAS/QTL analysis, that performs a slew of novel bioinformatics analyses to cross-reference GWAS results and QTL mappings with a host of publicly available rice databases. In particular, it integrates (i) pangenomic information from high-quality genome builds of multiple rice varieties, (ii) coexpression information from genome-scale coexpression networks, (iii) ontology and pathway information, (iv) regulatory information from rice transcription factor databases, (v) epigenomic information from multiple high-throughput epigenetic experiments, and (vi) text-mining information extracted from scientific abstracts linking genes and traits. We demonstrate the utility of RicePilaf by applying it to analyze GWAS peaks of preharvest sprouting and genes underlying yield-under-drought QTLs. Conclusions RicePilaf enables rice scientists and breeders to shed functional light on their GWAS regions and QTLs, and it provides them with a means to prioritize SNPs/genes for further experiments. The source code, a Docker image, and a demo version of RicePilaf are publicly available at https://github.com/bioinfodlsu/rice-pilaf.

Funder

Rural Development Administration

Publisher

Oxford University Press (OUP)

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