Run a short EM on p-values to fit a 2-component Beta mixture

Description

Fits a two-component mixture model to gene-level p-values where the null component is typically Uniform(0,1) (i.e., Beta(1,1)) and the alternative component is Beta(alpha1, 1) with alpha1 < 1 enriching small p-values. Returns updated mixture parameters and posterior probabilities of belonging to the signal (alternative) component.

Usage

EMinfer(pvalues, pi0, pi1, alpha0, alpha1, max.it)

Arguments

Value

A list with elements:

alpha0

Estimated null shape parameter.

alpha1

Estimated alternative shape parameter.

pi

Estimated mixing proportion for the signal component (pi1).

post

Posterior probabilities P(Z=1 \mid p) for each p-value.

Function to estimate the posterior probability of association (PPA) based on the integration of -OMICs and functional data via representation learning of candidate genes.

Description

Function to estimate the posterior probability of association (PPA) based on the integration of -OMICs and functional data via representation learning of candidate genes.

Usage

Gene_PPA(
  gene.pval,
  Gene_ID,
  pval,
  embeddings,
  lambda = 0,
  alpha0 = 1,
  alpha1 = 0.2,
  pi0 = 0.6,
  pi1 = 0.4,
  max.it = 10,
  model,
  verbose = F
)

Arguments

Details

The function Gene_PPA allows the user to select different options that define how the EM algorithm models the distribution of v (the auxiliary information matrix (feature/annotation matrix) attached to each p-value). The EM algorithms employed here are adapted from Wu et al. (2018):

• proj: Fits a two-component mixture model that combines p-values with an LDA-based one-dimensional projection of the feature matrix v. The projection is obtained by soft LDA using current posterior weights, and class-conditional densities of the projected score are estimated nonparametrically via weighted KDE.

• M: Fits a two-component mixture model that combines p-values with the feature matrix v by estimating a separate weighted KDE for each feature and each component. The joint density is approximated by the product of marginal densities (naive independence across features).

• LR: Fits a two-component mixture model where p-values follow a Beta-mixture and the prior probability of belonging to the signal component is modeled as a logistic function of v. Logistic regression parameters are estimated within the EM iterations, with optional ridge regularization controlled by lambda.

• NB: Fits a two-component mixture model that combines p-values with v assuming a Gaussian naive Bayes model for the features. Component-specific feature means are estimated, with feature-wise variances shared across components, and features are treated as conditionally independent given component membership.

• MVN: Fits a two-component mixture model that combines p-values with v using a multivariate normal model with shared covariance (parametric, correlation-aware).

Value

Returns the posterior probability of association for each gene.

References

Wu et al. (2018) Methods, 145, doi:10.1016/j.ymeth.2018.06.002.

Global variables

Description

Global variables

Function to estimate gene-level p-value using Davies algorithm

Description

Function to estimate gene-level p-value using Davies algorithm

Usage

Liu_ld(matrix.ld, marker_pvalues)

Arguments

Value

A vector of p-values for each gene annotated within the defined coordinates

Compute the centrality metrics for the nodes composing the network generated by the NetVis function

Description

Compute the centrality metrics for the nodes composing the network generated by the NetVis function

Usage

NetCen(data, g1, g2)

Arguments

Details

This function returns the following centrality metrics for each node that composed the network: Degree (The number of edges incident to the node), Betweenness (The fraction of shortest paths between pairs of nodes that pass through the node), Closeness (The inverse of the sum of the shortest path distances from the node to all other nodes), and Eigenvector Centrality (The centrality measure based on the eigenvector of the adjacency matrix).

Value

A data frame with the centrality metrics for each node in the network.

Create a dynamic network representing the relationship between two groups of variables

Description

Create a dynamic network representing the relationship between two groups of variables

Usage

NetVis(
  data,
  g1,
  g2,
  col1 = "aquamarine",
  col2 = "red",
  edge_col = "gray",
  remove_label = NULL,
  node_size = c(15, 40),
  font_size = 45,
  edge_width = 1
)

Arguments

Details

This function returns a dynamic network, using visNetwork, representing the connection between two groups. For example, the output from the find_genes_qtls_around_markers() function can be used here to represent the connections between markers and QTLs. Another option is to combine the data frames with both gene and QTL annotation around markers to reprsent the connections between genes and QTLs.

Value

A dynamic network representing the connection between two groups.

Network Embedding using biased random walk and Word2Vec.

Description

Network Embedding using biased random walk and Word2Vec.

Usage

Net_embedding(
  net_list,
  p = 0.25,
  q = 1,
  num = 10,
  l = 80,
  vector_size = 32,
  window = 10,
  min_count = 1,
  sg_model = 1,
  workers = 4,
  epochs = 20
)

Arguments

Details

This function performs a network embedding using the python libraries stellargraph and gensim through the BiasedRandomWalk and Word2Vec functions, respectively.

Value

A list of data frames with node embeddings. Each data frame contains nxm dimensions, where n is the number of unique nodes in the network and m is the number of reduced dimensions defined in the function.

Estimate the number of effective markers in a chromosome based on an adapted version of the simpleM methodology

Description

Estimate the number of effective markers in a chromosome based on an adapted version of the simpleM methodology

Usage

Nmarkers_SimpleM(ld.file, PCA_cutoff = 0.995)

Arguments

Details

This function estimate the effective number of markers in a chromosome using adapted version of the simpleM methodology described in Gao et al. (2008). The function use as input a data frame composed by three mandatory columns (SNP_A, SNP_B, and R). This data frame can be obtained using PLINK or any other software to compute LD between markers. Additionally, a threshold for percentage of the sum of the variances explained by the markers must be provided. The number of effective markers identified by this approach can be used in multiple testing corrections, such as Bonferroni.

Value

The effective number of markers identified by the SimpleM approach

References

Gao et al. (2008) Genet Epidemiol, Volume 32, Issue 4, Pages 361-369. (doi:10.1002/gepi.20310)

Estimate the number of independent segments in a chromosome based on the effective population size

Description

Estimate the number of independent segments in a chromosome based on the effective population size

Usage

Nseg_chr(chr.table, chr_length, Ne)

Arguments

Details

This function uses a adapted version of the formula proposed by Goddard et al. (2011) to estimate the independent number of segments in a chromosome based on the effective population size.

Value

A data frame with the effective number of segments in each chromosome.

References

Goddard et al. (2011) Journal of animal breeding and genetics, Volume 128, Issue 6, Pages 409-421. (doi:10.1111/j.1439-0388.2011.00964.x)

Compute Meff statistic based on PCA to determine the number of effective markers

Description

Compute Meff statistic based on PCA to determine the number of effective markers

Usage

PCA_Meff(eigenV, cut.off)

Arguments

Value

The effective number of markers identified by the SimpleM approach

Compute a multi-trait test statistic for pleiotropic effects using summary statistics from association tests

Description

Compute a multi-trait test statistic for pleiotropic effects using summary statistics from association tests

Usage

PleioChiTest(data)

Arguments

Details

This function tests a null hypothesis stating that each SNP does not affect any of the traits included in the input file. The method applied here is an implementation of the statistic proposed at Bolormaa et al. (2014) and is approximately distributed as a chi-squared with n degrees of freedom, where n is equal the number of traits included in the input file.

Value

A data frame with the multi-trait chi-squared statistics and the correspondent p-value obtained for each SNP.

References

Bolormaa et al. (2014) Plos Genetics, Volume 10, Issue 3, e1004198. (doi:10.1371/journal.pgen.1004198)

Plot enrichment results for QTL enrichment analysis

Description

Takes the output from qtl_enrich function and creates a bubble plot with enrichment results

Usage

QTLenrich_plot(qtl_enrich, x, pval)

Arguments

Value

A plot with the QTL enrichment results

Candidate markers identified by GWAS associated with fertility traits in cattle

Description

Data from a systematic review which evaluated 18 articles regarding genome-wide association studies for male fertility traits in beef and dairy cattle

Usage

data(QTLmarkers)

Format

A data frame with 141 rows and 7 variables:

• Associated.marker: Significantly associated marker

• SNP.reference: The rs ID when available

• Trait: Trait associated

• CHR: Chromosome

• BP: Chromosomal position in base pairs (bovine reference assembly UMD3.1)

• Breed: Breed used in the study

• Reference: Study which the markers were retrieved

References

Fonseca et al. (2018) Journal of Animal Science, Volume 96, Issue 12, December 2018, Pages 4978-4999. (doi:10.1093/jas/sky382)

Examples

data(QTLmarkers)

Candidate windows identified by GWAS associated with fertility traits in cattle

Description

Data from a systematic review which evaluated 18 articles regarding genomw-wide association studies for male fertility traits in beef and dairy cattle

Usage

data(QTLwindows)

Format

A data frame with 50 rows and 8 variables:

• First.marker.in.the.window: First marker mapped in the candidate window

• Last.marker.in.the.window: Last marker mapped in the candidate window

• Trait: Trait associated

• CHR: Chromosome

• BP1: Chromosomal position in base pairs for the first marker mapped in the candidate window(bovine reference assembly UMD3.1)

• BP1: Chromosomal position in base pairs for the last marker mapped in the candidate window (bovine reference assembly UMD3.1)

• Breed: Breed used in the study

• Reference: Study which the markers were retrieved

References

Fonseca et al. (2018) Journal of Animal Science, Volume 96, Issue 12, December 2018, Pages 4978-4999. (doi:10.1093/jas/sky382)

Examples

data(QTLwindows)

Function to perform Weighted Z-score Approach with LD Information

Description

Function to perform Weighted Z-score Approach with LD Information

Usage

WZ_ld(marker_ld, marker_pvalues)

Arguments

Value

A vector of p-values for each gene annotated within the defined coordinates

Sub-function to auto-stop the clusters created for parallel processes

Description

Sub-function to auto-stop the clusters created for parallel processes

Usage

autoStopCluster(cl)

Arguments

Search genes and QTLs around candidate regions

Description

Takes a list of candidate markers and or regions (haplotypes, CNVs, windows, etc.) and search for genes or QTLs in a determined interval

Usage

find_genes_qtls_around_markers(
  db_file,
  marker_file,
  method = c("gene", "qtl"),
  marker = c("snp", "haplotype"),
  interval = 0,
  nThreads = NULL,
  verbose = TRUE
)

Arguments

Value

A dataframe with the genes or QTLs mapped within the specified intervals

Examples

data(QTLmarkers)
data(gffQTLs)
out.qtls<-find_genes_qtls_around_markers(db_file=gffQTLs, marker_file=QTLmarkers,
method = "qtl", marker = "snp",
interval = 500000, nThreads = 1)

Function to annotate markers and the respective p-values within genes

Description

Function to annotate markers and the respective p-values within genes

Usage

find_markers_genes(db_file, marker_file, int = 0)

Arguments

Value

A data frame containing the markers mapped within the selected interval for each gene in the annotation file

Estimate a gene-level p-value using Weighted Z-score approach, Meta-analysis with LD correlation coefficients approach, and Davies algorithm

Description

Estimate a gene-level p-value using Weighted Z-score approach, Meta-analysis with LD correlation coefficients approach, and Davies algorithm

Usage

gene_pval(data, db_file, marker_ld, interval, p)

Arguments

Details

Requires a table with p-values from a association test, a gtf file file the gene coordinates in the same assembly used to map the variants used in the association study, and a data frame with pairwise linkage disequilibrium (LD) values between markers. This analysis must be performed for each chromosome individually. The data frame with the results of the association study must have three mandatory columns names as CHR, BP and SNP containing the chromosome, base pair position and marker name, respectively. The gtf file must be imported by the import_gff_gtf() function from GALLO or can be customized by the user, since it has the same columns names. The LD table must contain three mandatory columns, SNP_A, SNP_B and R. where, the first two columns must contain the marker names and the third column, the LD value between these markers. This data frame can be obtained using PLINK or any other software which computes pairwise LD between markers in the same chromosome. In the absence of LD values between any two SNPs in the data frame, a LD equal zero is assumed

Value

A data frame with the gene level p-values obtained using the Weighted Z-score approach (P_WZ_ld), Meta-analysis with LD correlation coefficients approach (P_meta_LD), and Liu algorithm (P_Liu, Liu et al. (2009))

References

Liu et al. (2009) Computational Statistics & Data Analysis, Volume 53, (doi:10.1016/j.csda.2008.11.025)

A gff example for QTL annotation

Description

Data from the Animal QTLdb comprasing the bovine QTL annotation

Usage

data(gffQTLs)

Format

A data frame with 111742 rows and 6 variables:

• chr: Chromosome

• database: The database which the QTL information was retrieved

• QTL_type: The class of each QTL annotated in the database

• start_pos: Start position in the genome for each QTL

• end_pos: End position in the genome for each QTL

• extra_info: Additional information about the QTLs, such as QTL ID, Name, PUBMED ID, mapping type, among others

Examples

data(gffQTLs)

A gtf example for gene annotation

Description

Data from the Ensembl comprasing the gene annotation for the bovine genome

Usage

data(gtfGenes)

Format

A data frame with 24616 rows and 8 variables:

• chr: Chromosome

• start_pos: Start position in the genome for each geme

• end_pos: End position in the genome for each gene

• width Gene length

• strand Strand which the gene is mapped (+ or -)

• gene_id Ensemble gene ID

• gene_name Gene symbol

• gene_biotype Gene biotype

Examples

data(gtfGenes)

Import .gtf and .gff files to be used during gene and QTL annotation, respectively

Description

Takes a .gft or .gff file and import into a dataframe

Usage

import_gff_gtf(db_file, file_type)

Arguments

Value

A dataframe with the gtf or gtf content

Examples

gffpath <- system.file("extdata", "example.gff", package="GALLO")

qtl.inp <- import_gff_gtf(db_file=gffpath,file_type="gff")

Compute the eigenvalues for the linkage disequilibrium (LD) matrix and use as input for PCA_Meff function to compute the effective number of markers

Description

Compute the eigenvalues for the linkage disequilibrium (LD) matrix and use as input for PCA_Meff function to compute the effective number of markers

Usage

inferCut(mat.r, cut.off)

Arguments

Value

The effective number of markers identified by the SimpleM approach

Function to compute the weighted 1-dimensional kernel density estimator

Description

Function to compute the weighted 1-dimensional kernel density estimator

Usage

kde(x, w = NA)

Arguments

Value

Returns an estimate of the probability density at each x[i]

Function to perform Meta-Analysis with LD Correlation Coefficients

Description

Function to perform Meta-Analysis with LD Correlation Coefficients

Usage

meta_LD(marker_ld, marker_pvalues)

Arguments

Value

A vector of p-values for each gene annotated within the defined coordinates

Function to perform an EM algorithm (logistic prior P(Z = 1 \mid v) + beta p-values (discriminative prior)) for a 2-component mixture where each observation i has a p-value p_i, and an auxiliary feature vector v_i.

Description

Function to perform an EM algorithm (logistic prior P(Z = 1 \mid v) + beta p-values (discriminative prior)) for a 2-component mixture where each observation i has a p-value p_i, and an auxiliary feature vector v_i.

Usage

model.LR(
  pvalues,
  v,
  lambda,
  alpha0,
  alpha1,
  pi0,
  pi1,
  max.it,
  verbose = verbose
)

Arguments

Value

Returns the posterior probability of association for each gene.

Function to perform an EM algorithm (“Multiple” univariate KDEs (nonparametric naive Bayes)) for a 2-component mixture where each observation i has a p-value pi, and an auxiliary feature vector vi

Description

Function to perform an EM algorithm (“Multiple” univariate KDEs (nonparametric naive Bayes)) for a 2-component mixture where each observation i has a p-value pi, and an auxiliary feature vector vi

Usage

model.M(pvalues, v, alpha0, alpha1, pi0, pi1, max.it, verbose = verbose)

Arguments

Value

Returns the posterior probability of association for each gene.

Function to perform an EM algorithm (multivariate normal with shared covariance (parametric, correlation-aware)) for a 2-component mixture where each observation i has a p-value p_i, and an auxiliary feature vector v_i.

Description

Function to perform an EM algorithm (multivariate normal with shared covariance (parametric, correlation-aware)) for a 2-component mixture where each observation i has a p-value p_i, and an auxiliary feature vector v_i.

Usage

model.MVN(pvalues, v, alpha0, alpha1, pi0, pi1, max.it, verbose = verbose)

Arguments

Value

Returns the posterior probability of association for each gene.

Function to perform an EM algorithm (Gaussian naive Bayes (parametric, independent features)) for a 2-component mixture where each observation i has a p-value p_i, and an auxiliary feature vector v_i.

Description

Function to perform an EM algorithm (Gaussian naive Bayes (parametric, independent features)) for a 2-component mixture where each observation i has a p-value p_i, and an auxiliary feature vector v_i.

Usage

model.NB(pvalues, v, alpha0, alpha1, pi0, pi1, max.it, verbose = verbose)

Arguments

Value

Returns the posterior probability of association for each gene.

Function to perform an EM algorithm (LDA projection + 1D KDE (nonparametric, correlation-aware)) for a 2-component mixture where each observation i has a p-value p_i, and an auxiliary feature vector v_i.

Description

Function to perform an EM algorithm (LDA projection + 1D KDE (nonparametric, correlation-aware)) for a 2-component mixture where each observation i has a p-value p_i, and an auxiliary feature vector v_i.

Usage

model.proj(pvalues, v, pi0, pi1, alpha0, alpha1, max.it, verbose = verbose)

Arguments

Value

Returns the posterior probability of association for each gene.

Overlapping between grouping factors

Description

Takes a dataframe with a column of genes, QTLs (or other data) and a grouping column and create some matrices with the overlapping information

Usage

overlapping_among_groups(file, x, y)

Arguments

Value

A list with three matrices: 1) A matrix with the number of overlapping data; 2) A matrix with the percentage of overlapping; 3) A matrix with the combination of the two previous one

Examples

data(QTLmarkers)
data(gtfGenes)
genes.out <- find_genes_qtls_around_markers(db_file=gtfGenes,
marker_file=QTLmarkers,method="gene",
marker="snp",interval=100000, nThreads=1)
overlapping.out<-overlapping_among_groups(
file=genes.out,x="Reference",y="gene_id")

Plot overlapping between data and grouping factors

Description

Takes the output from overlapping_among_groups function and creates a heatmap with the overlapping between groups

Usage

plot_overlapping(overlapping_matrix, nmatrix, ntext, group, labelcex = 1)

Arguments

Value

A heatmap with the overlapping between groups

Examples

data(QTLmarkers)
data(gtfGenes)
genes.out <- find_genes_qtls_around_markers(
db_file=gtfGenes, marker_file=QTLmarkers,
method="gene", marker="snp",interval=100000,
nThreads=1)

overlapping.out<-overlapping_among_groups(
file=genes.out,x="Reference",y="gene_id")
plot_overlapping(overlapping.out,
nmatrix=2,ntext=2,
group=unique(genes.out$Reference))

Plot QTLs information from the find_genes_qtls_around_markers output

Description

Takes the output from find_genes_qtls_around_markers and create plots for the frequency of each QTL type and trait

Usage

plot_qtl_info(
  qtl_file,
  qtl_plot = c("qtl_type", "qtl_name"),
  n = "all",
  qtl_class = NULL,
  horiz = FALSE,
  ...
)

Arguments

Value

A plot with the requested information

Examples

data(QTLmarkers)
data(gffQTLs)

out.qtls<-find_genes_qtls_around_markers(db_file=gffQTLs,
marker_file=QTLmarkers, method = "qtl",
marker = "snp", interval = 500000,
nThreads = 1)

plot_qtl_info(out.qtls, qtl_plot = "qtl_type", cex=2)

Performs a QTL enrichment analysis based on a hypergeometric test for each QTL class

Description

Takes the output from find_genes_qtls_around_markers and run a QTL enrichment analysis

Usage

qtl_enrich(
  qtl_db,
  qtl_file,
  qtl_type = c("QTL_type", "Name"),
  enrich_type = c("genome", "chromosome"),
  chr.subset = NULL,
  nThreads = NULL,
  padj = c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none"),
  verbose = TRUE
)

Arguments

Details

The simple bias of investigation for some traits (such as milk production related traits in the QTL database for cattle) may result in a larger proportion of records in the database. Consequently, the simple investigation of the proportion of each QTL type might not be totally useful. In order to reduce the impact of this bias, a QTL enrichment analysis can be performed. The QTL enrichment analysis performed by GALLO package is based in a hypergeometric test using the number of annoatted QTLs within the candidate regions and the total number of the same QTL in the QTL database.

Value

A data frame with the p-value for the enrichment result

Examples

data(QTLmarkers)
data(gffQTLs)
out.qtls<-find_genes_qtls_around_markers(
db_file=gffQTLs,marker_file=QTLmarkers,
method = "qtl",marker = "snp",
interval = 500000, nThreads = 1)

out.enrich<-qtl_enrich(qtl_db=gffQTLs,
qtl_file=out.qtls, qtl_type = "Name",
enrich_type = "chromosome",chr.subset = NULL,
padj = "fdr",nThreads = 1)

Plot relationship between data and grouping factors

Description

Takes the output from find_genes_qtls_around_markers function and creates a chord plot with the relationship between groups

Usage

relationship_plot(
  qtl_file,
  x,
  y,
  grid.col = "gray60",
  degree = 90,
  canvas.xlim = c(-2, 2),
  canvas.ylim = c(-2, 2),
  cex,
  gap
)

Arguments

Value

A chords relating x and y

Examples

data(QTLmarkers)
data(gffQTLs)
out.qtls<-find_genes_qtls_around_markers(
db_file=gffQTLs, marker_file=QTLmarkers,
method = "qtl", marker = "snp",
interval = 500000, nThreads = 1)

out.enrich<-qtl_enrich(qtl_db=gffQTLs,
qtl_file=out.qtls, qtl_type = "Name",
enrich_type = "chromosome",
chr.subset = NULL, padj = "fdr",nThreads = 1)

out.enrich$ID<-paste(out.enrich$QTL," - ",
"CHR",out.enrich$CHR,sep="")

out.enrich.filtered<-out.enrich[which(out.enrich$adj.pval<0.05),]

out.qtls$ID<-paste(out.qtls$Name," - ",
"CHR",out.qtls$CHR,sep="")

out.enrich.filtered<-out.enrich.filtered[order(out.enrich.filtered$adj.pval),]

out.qtls.filtered<-out.qtls[which(out.qtls$ID%in%out.enrich.filtered$ID[1:10]),]

out.qtls.filtered[which(out.qtls.filtered$Reference==
"Feugang et al. (2010)"), "color_ref"]<-"purple"

out.qtls.filtered[which(out.qtls.filtered$Reference==
"Buzanskas et al. (2017)"),"color_ref"]<-"pink"

color.grid<-c(rep("black",length(unique(out.qtls.filtered$Abbrev))),
unique(out.qtls.filtered$color_ref))

names(color.grid)<-c(unique(out.qtls.filtered$Abbrev),
unique(out.qtls.filtered$Reference))

relationship_plot(qtl_file=out.qtls.filtered,
x="Abbrev", y="Reference",cex=1,gap=5,
degree = 90, canvas.xlim = c(-5, 5),
canvas.ylim = c(-3, 3), grid.col = color.grid)

Function to performs a linear discriminant analysis (LDA) for a multivariate set of features

Description

Function to performs a linear discriminant analysis (LDA) for a multivariate set of features

Usage

sLDA(z, v)

Arguments

Value

Returns a one-dimensional discriminant score y for each observation, computed by projecting the feature matrix v onto the first LDA direction estimated using the soft class-membership weights z.

Sub-function to split comment column from QTL output

Description

Takes a list of candidate markers and search for genes a determined interval

Usage

splitQTL_comment(output.final)

Arguments

Value

A data frame with the extra_info column content, from the gff file, broken in several additional columns

Sub-function to search genes around candidate markers

Description

Takes a list of candidate markers and search for genes a determined interval

Usage

sub_genes_markers(chr_list, db_file, marker_file, nThreads = NULL, int = 0)

Arguments

Value

A dataframe with the genes or QTLs mapped within the specified intervals

Sub-function to search genes around candidate markers

Description

Takes a list of candidate markers and search for genes a determined interval

Usage

sub_genes_windows(chr_list, db_file, marker_file, nThreads = NULL, int = 0)

Arguments

Value

A dataframe with the genes or QTLs mapped within the specified intervals

Performs a QTL enrichment analysis based in a Bootstrap simulation for each QTL class using the QTL information per chromosome

Description

Takes the output from find_genes_qtls_around_markers and run a QTL enrichment analysis

Usage

sub_qtlEnrich_chom(
  qtl_file,
  qtl_type,
  qtl.file.types,
  table.qtl.class,
  padj,
  qtl_db,
  search_qtl,
  nThreads
)

Arguments

Value

A data frame with the p-value for th enrichment result

Performs a QTL enrichment analysis based in a Bootstrap simulation for each QTL class using the QTL information across the whole genome

Description

Takes the output from find_genes_qtls_around_markers and run a QTL enrichment analysis

Usage

sub_qtlEnrich_geno(
  qtl_file,
  qtl_type,
  qtl.file.types,
  table.qtl.class,
  padj,
  qtl_db,
  search_qtl,
  nThreads
)

Arguments

Value

A data frame with the p-value for th enrichment result

Sub-function to search QTLs around candidate markers

Description

Takes a list of candidate markers and search for genes a determined interval

Usage

sub_qtl_markers(chr_list, db_file, marker_file, nThreads = NULL, int = 0)

Arguments

Value

A dataframe with the QTLs mapped within the specified intervals

Sub-function to search QTLs around candidate markers

Description

Takes a list of candidate markers and search for genes a determined interval

Usage

sub_qtl_windows(chr_list, db_file, marker_file, nThreads = NULL, int = 0)

Arguments

Value

A dataframe with the QTLs mapped within the specified intervals