Prioritising Candidate Genes Causing QTL Using Hierarchical Orthologous Groups

Overview

Journal Bioinformatics

Publisher Oxford University Press

Specialty Biology

Date 2018 Nov 14

PMID 30423067

Citations 2

Authors

Alex Warwick Vesztrocy

Christophe Dessimoz

Henning Redestig

Affiliations

Soon will be listed here.

Abstract

Motivation: A key goal in plant biotechnology applications is the identification of genes associated to particular phenotypic traits (for example: yield, fruit size, root length). Quantitative Trait Loci (QTL) studies identify genomic regions associated with a trait of interest. However, to infer potential causal genes in these regions, each of which can contain hundreds of genes, these data are usually intersected with prior functional knowledge of the genes. This process is however laborious, particularly if the experiment is performed in a non-model species, and the statistical significance of the inferred candidates is typically unknown.

Results: This paper introduces QTLSearch, a method and software tool to search for candidate causal genes in QTL studies by combining Gene Ontology annotations across many species, leveraging hierarchical orthologous groups. The usefulness of this approach is demonstrated by re-analysing two metabolic QTL studies: one in Arabidopsis thaliana, the other in Oryza sativa subsp. indica. Even after controlling for statistical significance, QTLSearch inferred potential causal genes for more QTL than BLAST-based functional propagation against UniProtKB/Swiss-Prot, and for more QTL than in the original studies.

Availability And Implementation: QTLSearch is distributed under the LGPLv3 license. It is available to install from the Python Package Index (as qtlsearch), with the source available from https://bitbucket.org/alex-warwickvesztrocy/qtlsearch.

Supplementary Information: Supplementary data are available at Bioinformatics online.

Citing Articles

OMA orthology in 2024: improved prokaryote coverage, ancestral and extant GO enrichment, a revamped synteny viewer and more in the OMA Ecosystem.

Altenhoff A, Warwick Vesztrocy A, Bernard C, Train C, Nicheperovich A, Prieto Banos S Nucleic Acids Res. 2023; 52(D1):D513-D521.

PMID: 37962356 PMC: 10767875. DOI: 10.1093/nar/gkad1020.

Benchmarking gene ontology function predictions using negative annotations.

Warwick Vesztrocy A, Dessimoz C Bioinformatics. 2020; 36(Suppl_1):i210-i218.

PMID: 32657372 PMC: 7355306. DOI: 10.1093/bioinformatics/btaa466.

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