In almeidasilvaf/cageminer: Candidate Gene Miner

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Introduction

Over the past years, RNA-seq data for several species have accumulated in public repositories. Additionally, genome-wide association studies (GWAS) have identified SNPs associated with phenotypes of interest, such as agronomic traits in plants, production traits in livestock, and complex human diseases. However, although GWAS can identify SNPs, they cannot identify causative genes associated with the studied phenotype. The goal of cageminer is to integrate GWAS-derived SNPs with transcriptomic data to mine candidate genes and identify high-confidence genes associated with traits of interest.

Citation

If you use cageminer in your research, please cite us. You can obtain citation information with citation('cageminer'), as demonstrated below:

print(citation('cageminer'), bibtex = TRUE)

Installation

if(!requireNamespace('BiocManager', quietly = TRUE))
  install.packages('BiocManager')
BiocManager::install("cageminer")

# Load package after installation
library(cageminer)
set.seed(123) # for reproducibility

Data description

For this vignette, we will use transcriptomic data on pepper (Capsicum annuum) response to Phytophthora root rot [@Kim2018], and GWAS SNPs associated with resistance to Phytophthora root rot from @Siddique2019. To ensure interoperability with other Bioconductor packages, expression data are stored as SummarizedExperiment objects, and gene/SNP positions are stored as GRanges objects.

# GRanges of SNP positions
data(snp_pos)
snp_pos

# GRanges of chromosome lengths
data(chr_length) 
chr_length

# GRanges of gene coordinates
data(gene_ranges) 
gene_ranges

# SummarizedExperiment of pepper response to Phytophthora root rot (RNA-seq)
data(pepper_se)
pepper_se

Visualizing SNP distribution

Before mining high-confidence candidates, you can visualize the SNP distribution in the genome to explore possible patterns. First, let's see if SNPs are uniformly across chromosomes with plot_snp_distribution().

plot_snp_distribution(snp_pos)

As we can see, SNPs associated with resistance to Phytophthora root rot tend to co-occur in chromosome 5. Now, we can see if they are close to each other in the genome, and if they are located in gene-rich regions. We can visualize it with plot_snp_circos, which displays a circos plot of SNPs across chromosomes.

plot_snp_circos(chr_length, gene_ranges, snp_pos)

There seems to be no clustering in gene-rich regions, but we can see that SNPs in the same chromosome tend to be physically close to each other.

If you have SNP positions for multiple traits, you need to store them in GRangesList or CompressedGRangesList objects, so each element will have SNP positions for a particular trait. Then, you can visualize their distribution as you would do for a single trait. Let's simulate multiple traits to see how it works:

# Simulate multiple traits by sampling 20 SNPs 4 times
snp_list <- GenomicRanges::GRangesList(
  Trait1 = sample(snp_pos, 20),
  Trait2 = sample(snp_pos, 20),
  Trait3 = sample(snp_pos, 20),
  Trait4 = sample(snp_pos, 20)
)

# Visualize SNP distribution across chromosomes
plot_snp_distribution(snp_list)

# Visualize SNP positions in the genome as a circos plot
plot_snp_circos(chr_length, gene_ranges, snp_list)

Algorithm description

The cageminer algorithm identifies high-confidence candidate genes with 3 steps, which can be interpreted as 3 sources of evidence:

1. Select all genes in a sliding window relative to each SNP as putative candidates.
2. Find candidates from step 1 in coexpression modules enriched in guide genes (genes that are known to be associated with the trait of interest).
3. Find candidates from step 2 that are correlated with a condition of interest.

These 3 steps can be executed individually (if users want more control on what happens after each step) or all at once.

Step-by-step candidate gene mining

To run the candidate mining step by step, you will need the functions mine_step1(), mine_step2, and mine_step3.

Step 1: finding genes close to (or in linkage disequilibrium with) SNPs

The function mine_step1() identifies genes based on step 1 and returns a GRanges object with all putative candidates and their location in the genome. For that, you need to give 2 GRanges objects as input, one with the gene coordinates[^1] and another with the SNP positions.

[^1]: Tip: to create GRanges objects from genomic coordinates in GFF/GTF files, you can use the import() function from the Bioconductor package rtracklayer [@rtracklayer2009].

candidates1 <- mine_step1(gene_ranges, snp_pos)
candidates1
length(candidates1)

The first step identified r length(candidates1) putative candidate genes. By default, cageminer uses a sliding window of 2 Mb to select putative candidates[^2]. If you want to visually inspect a simulation of different sliding windows to choose a different one, you can use simulate_windows().

# Single trait
simulate_windows(gene_ranges, snp_pos)

# Multiple traits
simulate_windows(gene_ranges, snp_list)

[^2]: Note: By default, SNPs coordinates will be expanded upstream and downstream according to the input window size. However, you may have previously determined genomic intervals for each SNP (e.g., calculated based on linkage disequilibrium) for which you want to extract genes. To avoid expanding a sliding window in such cases, set expand_intervals = FALSE. This will ensure that only SNPs are expanded, but not intervals (width >1).

Step 2: finding coexpression modules enriched in guide genes

The function mine_step2() selects candidates in coexpression modules enriched in guide genes. For that, users must infer the GCN with the function exp2gcn() from the package BioNERO [@R-BioNERO]. Guide genes can be either a character vector of guide gene IDs or a data frame with gene IDs in the first column and annotation in the second column (useful if guides are divided in functional categories, for instance). Here, pepper genes associated with defense-related MapMan bins were retrieved from PLAZA 3.0 Dicots [@Proost2015] and used as guides.

The resulting object is a list of two elements:

• candidates: character vector of mined candidate gene IDs.
• enrichment: data frame of enrichment results.

# Load guide genes
data(guides)
head(guides)

# Infer GCN
sft <- BioNERO::SFT_fit(pepper_se, net_type = "signed", cor_method = "pearson")
gcn <- BioNERO::exp2gcn(pepper_se, net_type = "signed", cor_method = "pearson",
                        module_merging_threshold = 0.8, SFTpower = sft$power)

# Apply step 2
candidates2 <- mine_step2(pepper_se, gcn = gcn, guides = guides$Gene,
                          candidates = candidates1$ID)
candidates2$candidates
candidates2$enrichment

After the step 2, we got r length(candidates2$candidates) candidates.

Step 3: finding genes with altered expression in a condition of interest

The function mine_step3() identifies candidate genes whose expression levels significantly increase or decrease in a particular condition. For that, you need to specify what level from the sample metadata corresponds to this condition. The resulting object from mine_step3() is a data frame with mined candidates and their correlation to the condition of interest.

# See the levels from the sample metadata
unique(pepper_se$Condition)

# Apply step 3 using "PRR_stress" as the condition of interest
candidates3 <- mine_step3(pepper_se, candidates = candidates2$candidates,
                          sample_group = "PRR_stress")
candidates3

Finally, we got r length(unique(candidates3$gene)) high-confidence candidate genes associated with resistance to Phytophthora root rot. Genes with negative correlation coefficients to the condition can be interpreted as having significantly reduced expression in this condition, while genes with positive correlation coefficients have significantly increased expression in this condition.

Automatic candidate gene mining

Alternatively, if you are not interested in inspecting the results after each step, you can get to the same results from the previous section with a single step by using the function mine_candidates(). This function is a wrapper that calls mine_step1(), sends the results to mine_step2(), and then it sends the results from mine_step2() to mine_step3().

candidates <- mine_candidates(gene_ranges = gene_ranges, 
                              marker_ranges = snp_pos, 
                              exp = pepper_se,
                              gcn = gcn, guides = guides$Gene,
                              sample_group = "PRR_stress")
candidates

Score candidates

In some cases, you might have more high-confidence candidates than you expected, and you want to pick only the top n genes for validation, for instance. In this scenario, you need to assign scores to your mined candidates to pick the top n genes with the highest scores. The function score_genes() does that by using the formula below:

$$S_i = r_{pb} \kappa$$

where:

$\kappa$ = 2 if the gene encodes a transcription factor

$\kappa$ = 2 if the gene is a hub

$\kappa$ = 3 if the gene encodes a hub transcription factor

$\kappa$ = 1 if none of the conditions above are true

By default, score_genes picks the top 10 candidates. If there are less than 10 candidates, it will return all candidates sorted by scores. Here, TFs were obtained from PlantTFDB 4.0 [@Jin2017]. Hub genes can be identified with the function get_hubs_gcn() from the package BioNERO.

# Load TFs
data(tfs)
head(tfs)

# Get GCN hubs
hubs <- BioNERO::get_hubs_gcn(pepper_se, gcn)
head(hubs)

# Score candidates
scored <- score_genes(candidates, hubs$Gene, tfs$Gene_ID)
scored

As none of the mined candidates are hubs or encode transcription factors, their scores are simply their correlation coefficients with the condition of interest.

Session information {.unnumbered}

This document was created under the following conditions:

sessioninfo::session_info()

References {.unnumbered}

almeidasilvaf/cageminer documentation built on Sept. 9, 2023, 5:18 p.m.