Prioritization of candidate genes in QTL regions based on associations between traits and biological processes

Abstract

BackgroundElucidation of genotype-to-phenotype relationships is a major challenge in biology. In plants, it is the basis for molecular breeding. Quantitative Trait Locus (QTL) mapping enables to link variation at the trait level to variation at the genomic level. However, QTL regions typically contain tens to hundreds of genes. In order to prioritize such candidate genes, we show that we can identify potentially causal genes for a trait based on overrepresentation of biological processes (gene functions) for the candidate genes in the QTL regions of that trait.ResultsThe prioritization method was applied to rice QTL data, using gene functions predicted on the basis of sequence- and expression-information. The average reduction of the number of genes was over ten-fold. Comparison with various types of experimental datasets (including QTL fine-mapping and Genome Wide Association Study results) indicated both statistical significance and biological relevance of the obtained connections between genes and traits. A detailed analysis of flowering time QTLs illustrates that genes with completely unknown function are likely to play a role in this important trait.ConclusionsOur approach can guide further experimentation and validation of causal genes for quantitative traits. This way it capitalizes on QTL data to uncover how individual genes influence trait variation.Electronic supplementary materialThe online version of this article (doi:10.1186/s12870-014-0330-3) contains supplementary material, which is available to authorized users.

Time-Series Analyses of Transcriptomes and Proteomes Reveal Molecular Networks Underlying Oil Accumulation in Canola

“…The results are shown in Supplementary Table S2. To avoid overly broad functional categories, we marked those GO terms higher than 1% within the genome in gray (Bargsten et al, 2014) and excluded them from further analyses (Supplementary Table S2). We found that the most significantly enriched GO biological process (FDR = 8.29E-09) in cluster 5 was fatty acid biosynthetic process ( Figure 2B ; Supplementary Table S2), which has a temporal expression pattern of “up-down-down” ( Figure 2A ).…”

QTG-Finder: a machine-learning based algorithm to prioritize causal genes of quantitative trait loci

“…Several causal variant or gene prioritization methods have been developed for human data but 223 not many in plants (Bargsten et al, 2014;Jagadeesh et al, 2016;Kircher et al, 2014;Schaefer et 224 al., 2018). Most prioritization methods have been developed for GWAS mapping in human, an 225 organism where linkage mapping cannot be performed.…”

“…A causal gene prioritization is especially helpful for the large QTLs identified by 228 linkage mapping, which can constitute hundreds to thousands of genes. One method has been 229 developed in rice to prioritize causal genes for linkage mapping (Bargsten et al, 2014). This 230 method is based on the hypothesis that causal genes from multiple QTLs of the same trait are 231 more likely to have the same biological process GO terms, and therefore genes with 232 overrepresented biological process GOs were prioritized as causal genes.…”

“…For QTLs identified by linkage mapping, finding causal genes underlying them is still a big 70 bottleneck (Bergelson and Roux, 2010). In a typical rice linkage mapping, the size of a QTL can 71 range from 200kb-3Mb, which can harbor tens to hundreds of genes depending on the mapping 72 population and gene density (Bargsten et al, 2014;Daware et al, 2017). Even in the post-73 genomic era where all the genes in the genome are uncovered, identifying QTL causal genes is 74 not straightforward since many QTLs either contain no obvious candidate genes or too many 75 genes relevant for the trait (Nuzhdin et al, 1999).…”

“…One method was developed for GWAS in maize based on 102 co-expression networks (Schaefer et al, 2018). Another method was developed for linkage 103 mapping based on biological process GOs (Bargsten et al, 2014). To date, no machine-learning 104 approaches using multiple data types have been developed to address this problem.…”