GenRank: a R/Bioconductor package for prioritization of candidate genes

Introduction

Genetic studies employ multiple independent lines of investigation spanning pan-omics approaches to holistically understand the molecular background of complex genetic traits. This includes studying the roles of various forms of genomic variation (e.g. SNPs, InDels, and CNVs) and gene expression in multiple tissues, and the regulation of a single phenotype across single or multiple species (e.g, humans and other relevant model organisms). One of the common objectives of performing such diverse experimental assays across multiple types of cells, tissues, treatments, time-points and species is to find the causal genes underlying a specific disease or trait. Integration of data from such diverse experimental assays (hereafter referred to as evidence layers) would enable prioritization of genes that are most relevant to the phenotype. Meta-analytic approaches that integrate gene-level data from multiple evidence layers have been shown to be successful in identifying and prioritizing candidate genes for complex genetic traits (Ayalew et al., 2012). However, no implementation of candidate gene prioritization methods existed in the Bioconductor project at the time this package was written, which otherwise offers a seamless framework to perform various statistical analyses in biomedical research. The majority of the existing meta-analysis related packages in Bioconductor have been exclusively developed to integrate microarray gene expression data, but do not serve the purpose of integrating gene-level data from multiple study types. Here, we implemented three methods to rank genes by integrating gene-level data generated from multiple evidence layers.

Methods

Operation

The methods are implemented in R and available as a package in the Bioconductor repository (http://bioconductor.org/packages/GenRank/). The package requires R version 3.2.3 or later versions and runs on all operating systems. Figure 1 shows an overview of the workflow of the GenRank package.

Figure 1. Overarching theme of GeneRank Bioconductor package.

To obtain convergent evidence for the molecular basis of phenotypes, GenRank bioconductor package implements three methods to integrate gene-level data generated from multiple independent experiments. Examples of evidence layers are experiment assay-type (e.g., GWAS, RNAseq, ChIPseq), tissue-type (e.g., blood, liver, intestine), cell-type (e.g., neutrophils, lymphocytes), time-series (e.g., 0h, 2h, 6h), species-type (e.g., human, mouse, drosophila), treatment-type (e.g., control, dexamethasone, lipopolysachharide).

The convergent evidence (CE) method

The convergent evidence (CE) method aggregates ranks of genes based on a weighted vote counting method. A conceptually similar gene-level integration has been successfully used to prioritize candidate genes in neuropsychiatric diseases (Ayalew et al., 2012).

Here, to rank genes, we compute convergent evidence scores. The convergent evidence score of gene G is given by

                                                              CE(G) = CE(G_L1)/n(L₁)+....+CE(G_Ln)/n(L_n)

Here CE(G_Li) refers to the self-importance of evidence layer-i, while n(L_i) refers to the number of genes within evidence layer-i. Additionally, we propose two other ways to compute convergent evidence scores. One of them is to ignore the number of genes within each layer, thus

                                                                        CE(G) = CE(G_L1)+....+CE(G_Ln)

In this case, the convergent evidence score would be equivalent to the primitive vote counting. Another alternative method enables the researchers to determine the importance of each layer based on their own intuition. This involves assigning custom weights to each evidence layer based on their expert knowledge in the field. For example, when a researcher knows that a specific technology could yield less reproducible findings, such evidence layer could be given relatively less weight compared to the other evidence layers. Another objective way of assigning custom weights to each evidence layer could be based on the sample sizes of each evidence layer. In this case the convergent evidence score is

                                                           CE(G) = CE(G_L1) * w(L₁)+....+CE(G_Ln) * w(L_n)

where w(L_i) refers to the custom weight assigned to evidence layer-i. Figure 2 shows an illustration of how CE scores are computed.

Figure 2. An example of computing convergent evidence scores.

This illustration shows six evidence layers (Layer.1–Layer.6). The point indicates the detection of a gene in an evidence layer, while the size of the point indicates the importance of an evidence layer (custom weights assigned by the user). Here, genes A, B and D are detected twice each. However, based on a weighted vote counting method, gene D would get a better rank than genes A and B.

The rank product (RP) method

The rank product (RP) method has been used widely to perform differential expression analysis in microarray-based gene expression datasets. This biologically motivated method is simple, yet powerful and ranks genes that are consistently ranked highly in replicated experiments, based on the geometric mean (Breitling et al., 2004). This method has been implemented earlier as a Bioconductor package to perform meta-analysis of gene expression experiments (Hong et al., 2006). We adapted the rank product method to identify genes that are consistently highly ranked across evidence layers. The rank product is computed and compared to a permutation-based distribution of rank product values to estimate the proportion of false predictions (pfp; equivalent to FDR).

Combining p-values

Combining p-values has been one of the traditional methods of meta-analysis. To combine p-values of a gene from multiple evidence layers, the p-values should have been estimated from the same null hypothesis. Popular methods to combine p-values include Fisher’s and Stouffer’s methods, where the latter incorporates custom weights (e.g. sample sizes). These popular methods have already been implemented in the Bioconductor package survcomp (Schröder et al., 2011). Here, we built a wrapper around those methods to suit the overarching theme of this package (integrating gene-level data from multiple evidence layers). Missing p-values in some evidence layers could lead to a potential bias when combining p-values. To handle this issue, our implementation returns the combined p-values of only those genes, for which p-values are available at least across half of the evidence layers. However, it would be an ideal scenario to have p-values available across all evidence layers.

To avoid a potential bias owing to duplicated genes, duplicated genes are counted only once (as a single vote) within each evidence layer in all the three methods implemented in this package. When retaining duplicated genes, those with significant test statistic (e.g low p-values or high effect-size) were retained.

Use cases

The use cases are explained in detail, with example data in the package vignette available at the package webpage here:

https://www.bioconductor.org/packages/devel/bioc/vignettes/GenRank/inst/doc/GenRank_Vignette.html

Oikkonen et al. (2016) serves as an interesting use case that used convergent evidence scores to prioritize candidate genes obtained through diverse experiment types in a complex genetic trait.

bioRxiv

An earlier version of this article can be found on bioRxiv at (http://biorxiv.org/content/early/2016/04/12/048264)

Software availability

The GenRank package is hosted on Bioconductor at:

http://bioconductor.org/packages/GenRank/.

Latest source code:

https://github.com/Bioconductor-mirror/GenRank

Archived source code as at the time of publication:

http://doi.org/10.5281/zenodo.439738. (Kanduri & Järvela, 2017)

License: Artistic-2.0 license.