Package 'martini'

Title: GWAS Incorporating Networks
Description: martini deals with the low power inherent to GWAS studies by using prior knowledge represented as a network. SNPs are the vertices of the network, and the edges represent biological relationships between them (genomic adjacency, belonging to the same gene, physical interaction between protein products). The network is scanned using SConES, which looks for groups of SNPs maximally associated with the phenotype, that form a close subnetwork.
Authors: Hector Climente-Gonzalez [aut, cre] , Chloe-Agathe Azencott [aut]
Maintainer: Hector Climente-Gonzalez <[email protected]>
License: GPL-3
Version: 1.27.0
Built: 2024-11-04 06:06:09 UTC
Source: https://github.com/bioc/martini

Help Index

Get gene-interaction network.

Description

Creates a network of SNPs where each SNP is connected as in the GM network and, in addition, to all the other SNPs pertaining to any interactor of the gene it is mapped to. Corresponds to the gene-interaction (GI) network described by Azencott et al.

Usage

get_GI_network(
  gwas,
  organism = 9606,
  snpMapping = snp2ensembl(gwas, organism),
  ppi = get_gxg("biogrid", organism, flush),
  col_ppi = c("gene1", "gene2"),
  col_genes = c("snp", "gene"),
  flush = FALSE
)

Arguments

gwas

A SnpMatrix object with the GWAS information.
organism

Tax ID of the studied organism. The default is 9606 (human).
snpMapping

A data.frame informing how SNPs map to genes. It contains minimum two columns: SNP id and a gene it maps to. Each row corresponds to one gene-SNP mapping. Unless column names are specified using col_genes, involved columns must be named 'snp' and 'gene'.
ppi

A data.frame describing protein-protein interactions with at least two colums. Gene ids must be the contained in snpMapping. Unless column names are specified using col_ppi, involved columns must be named gene1 and gene2.
col_ppi

Optional, length-2 character vector with the names of the two columns involving the protein-protein interactions.
col_genes

Optional, length-2 character vector with the names of the two columns involving the SNP-gene mapping. The first element is the column of the SNP, and the second is the column of the gene.
flush

Remove cached results? Boolean value.

Value

An igraph network of the GI network of the SNPs.

References

Azencott, C. A., Grimm, D., Sugiyama, M., Kawahara, Y., & Borgwardt, K. M. (2013). Efficient network-guided multi-locus association mapping with graph cuts. Bioinformatics, 29(13), 171-179. https://doi.org/10.1093/bioinformatics/btt238

Examples

get_GI_network(minigwas, snpMapping = minisnpMapping, ppi = minippi)

Get gene membership network.

Description

Creates a network of SNPs where each SNP is connected as in the GS network and, in addition, to all the other SNPs pertaining to the same gene. Corresponds to the gene membership (GM) network described by Azencott et al.

Usage

get_GM_network(
  gwas,
  organism = 9606,
  snpMapping = snp2ensembl(gwas, organism),
  col_genes = c("snp", "gene")
)

Arguments

gwas

A SnpMatrix object with the GWAS information.
organism

Tax ID of the studied organism. The default is 9606 (human).
snpMapping

A data.frame informing how SNPs map to genes. It contains minimum two columns: SNP id and a gene it maps to. Each row corresponds to one gene-SNP mapping. Unless column names are specified using col_genes, involved columns must be named 'snp' and 'gene'.
col_genes

Optional, length-2 character vector with the names of the two columns involving the SNP-gene mapping. The first element is the column of the SNP, and the second is the column of the gene.

Value

An igraph network of the GM network of the SNPs.

References

Azencott, C. A., Grimm, D., Sugiyama, M., Kawahara, Y., & Borgwardt, K. M. (2013). Efficient network-guided multi-locus association mapping with graph cuts. Bioinformatics, 29(13), 171-179. https://doi.org/10.1093/bioinformatics/btt238

Examples

get_GM_network(minigwas, snpMapping = minisnpMapping)

Get genomic sequence network

Description

Creates a network of SNPs where each SNP is connected to its adjacent SNPs in the genome sequence. Corresponds to the genomic sequence (GS) network described by Azencott et al.

Usage

get_GS_network(gwas)

Arguments

gwas

A SnpMatrix object with the GWAS information.

Value

An igraph network of the GS network of the SNPs.

References

Azencott, C. A., Grimm, D., Sugiyama, M., Kawahara, Y., & Borgwardt, K. M. (2013). Efficient network-guided multi-locus association mapping with graph cuts. Bioinformatics, 29(13), 171-179. https://doi.org/10.1093/bioinformatics/btt238

Examples

get_GS_network(minigwas)

Converts a MAP data.frame to a BED data.frame

Description

Takes a map file and:

  • column 1: Used as the chromosome column in the BED file.

  • column 4: Used as start and end in the BED data.frame (as we work with SNPs).

Usage

gwas2bed(gwas)

Arguments

gwas

A SnpMatrix object with the GWAS information.

Value

A BED data.frame.

Include linkage disequilibrium information in the network.

Description

Include linkage disequilibrium information in the SNP network. The weight of the edges will be lower the higher the linkage is.

Usage

ldweight_edges(net, ld, method = "inverse")

Arguments

net

A SNP network.
ld

A dsCMatrix or dgCMatrix containing linkage disequilibrium measures, like the output of ld.
method

How to incorporate linkage-disequilibrium values into the network.

Value

An copy of net where the edges weighted according to linkage disequilibrium.

Examples

ld <- snpStats::ld(minigwas[['genotypes']], depth = 2, stats = "R.squared")
# don't weight edges for which LD cannot be calculated
ld[is.na(ld)] <- 0
gi <- get_GI_network(minigwas, snpMapping = minisnpMapping, ppi = minippi)
ldGi <- ldweight_edges(gi, ld)

Maxflow algorithm

Description

Run the maxflow algorithm.

Usage

maxflow(A, As, At)

Arguments

A

A sparse matrix with the connectivity.
As

A vector containing the edges to the source.
At

A vector containing the edges to the sink.

Value

A list with vector indicating if the feature was selected and the objective score.

Min-cut algorithm

Description

Run the mincut algorithm.

Usage

mincut_c(c, eta, lambda, W)

Arguments

c

A vector with the association of each SNP with the phenotype.
eta

A numeric with the value of the eta parameter.
lambda

A numeric with the value of the eta parameter.
W

A sparse matrix with the connectivity.

Value

A list with vector indicating if the feature was selected and the objective score.

Description of the minigwas dataset.

Description

Small GWAS example.

Format

A list with 3 items:

genotypes

Genotype and phenotype information.

fam

Simulated network.

map

Result of runing find_cones with gwas and net.

Examples

data(minigwas)

# access different elements
minigwas[["genotypes"]]
minigwas[["map"]]
minigwas[["fam"]]

PPIs for the minigwas dataset.

Description

data.frame describing pairs of proteins that interact for minigwas.

Examples

data(minippi)

head(minippi)

Genes for the minigwas dataset.

Description

data.frame that maps SNPs from minigwas to their gene.

Examples

data(minisnpMapping)

head(minisnpMapping)

Ideogram of SConES results.

Description

Create a circular ideogram of the a network results using the circlize package (Gu et al., 2014).

Usage

plot_ideogram(gwas, net, covars = data.frame(), genome = "hg19")

Arguments

gwas

A SnpMatrix object with the GWAS information.
net

An igraph network that connects the SNPs.
covars

A data frame with the covariates. It must contain a column 'sample' containing the sample IDs, and an additional columns for each covariate.
genome

Abbreviations of the genome to use: hg19 for human (default), mm10 for mouse, etc.

Value

A circular ideogram, including the manhattan plot, and the interactions between the selected SNPs.

References

Gu, Z., Gu, L., Eils, R., Schlesner, M., & Brors, B. (2014). circlize Implements and enhances circular visualization in R. Bioinformatics (Oxford, England), 30(19), 2811-2. https://doi.org/10.1093/bioinformatics/btu393

Find connected explanatory SNPs

Description

Finds the SNPs maximally associated with a phenotype while being connected in an underlying network.

Usage

scones(
  gwas,
  net,
  eta,
  lambda,
  covars = data.frame(),
  score = c("chi2", "glm", "r2"),
  family = c("binomial", "poisson", "gaussian", "gamma"),
  link = c("logit", "log", "identity", "inverse")
)

Arguments

gwas

A SnpMatrix object with the GWAS information.
net

An igraph network that connects the SNPs.
eta

Value of the eta parameter.
lambda

Value of the lambda parameter.
covars

A data frame with the covariates. It must contain a column 'sample' containing the sample IDs, and an additional columns for each covariate.
score

Association score to measure association between genotype and phenotype. Possible values: chi2 (default), glm.
family

A string defining the generalized linear model family. This should match one of "binomial", "poisson", "gaussian" or "gamma". See snp.rhs.tests for details.
link

A string defining the link function for the GLM. This should match one of "logit", "log", "identity" or "inverse". See snp.rhs.tests for details.

Value

A copy of the SnpMatrix$map data.frame, with the following additions:

  • c: contains the univariate association score for every single SNP.

  • selected: logical vector indicating if the SNP was selected by SConES or not.

  • module: integer with the number of the module the SNP belongs to.

References

Azencott, C. A., Grimm, D., Sugiyama, M., Kawahara, Y., & Borgwardt, K. M. (2013). Efficient network-guided multi-locus association mapping with graph cuts. Bioinformatics, 29(13), 171-179. https://doi.org/10.1093/bioinformatics/btt238

Examples

gi <- get_GI_network(minigwas, snpMapping = minisnpMapping, ppi = minippi)
scones(minigwas, gi, 10, 1)

Find connected explanatory features

Description

Finds the features maximally associated with a phenotype while being connected in an underlying network.

Usage

scones_(X, y, featnames, net, eta, lambda)

Arguments

X

n x d design matrix
y

Vector of length n with the outcomes
featnames

Vector of length d with the feature names
net

An igraph network that connects the SNPs.
eta

Value of the eta parameter.
lambda

Value of the lambda parameter.

Value

A copy of the SnpMatrix$map data.frame, with the following additions:

  • c: contains the univariate association score for every single SNP.

  • selected: logical vector indicating if the SNP was selected by SConES or not.

  • module: integer with the number of the module the SNP belongs to.

Examples

X <- as(minigwas[['genotypes']], 'numeric')
X <- X + matrix(rnorm(2500, sd = 0.1), nrow(X), ncol(X))
gi <- get_GI_network(minigwas, snpMapping = minisnpMapping, ppi = minippi)
scones_(X, minigwas[['fam']]$affected, minigwas[['map']]$snp, gi, 10, 1)

Find connected explanatory SNPs.

Description

Finds the SNPs maximally associated with a phenotype while being connected in an underlying network. Select the hyperparameters by cross-validation.

Usage

scones.cv(
  gwas,
  net,
  covars = data.frame(),
  score = c("chi2", "glm", "r2"),
  criterion = c("stability", "bic", "aic", "aicc", "global_clustering",
    "local_clustering"),
  etas = numeric(),
  lambdas = numeric(),
  family = c("binomial", "poisson", "gaussian", "gamma"),
  link = c("logit", "log", "identity", "inverse"),
  max_prop_snp = 0.5
)

Arguments

gwas

A SnpMatrix object with the GWAS information.
net

An igraph network that connects the SNPs.
covars

A data frame with the covariates. It must contain a column 'sample' containing the sample IDs, and an additional columns for each covariate.
score

Association score to measure association between genotype and phenotype. Possible values: chi2 (default), glm.
criterion

String with the function to measure the quality of a split.
etas

Numeric vector with the etas to explore in the grid search. If ommited, it's automatically created based on the association scores.
lambdas

Numeric vector with the lambdas to explore in the grid search. If ommited, it's automatically created based on the association scores.
family

A string defining the generalized linear model family. This should match one of "binomial", "poisson", "gaussian" or "gamma". See snp.rhs.tests for details.
link

A string defining the link function for the GLM. This should match one of "logit", "log", "identity" or "inverse". See snp.rhs.tests for details.
max_prop_snp

Maximum proportion of SNPs accepted in the model (between 0 and 1). Larger solutions will be discarded.

Value

A copy of the SnpMatrix$map data.frame, with the following additions:

  • c: contains the univariate association score for every single SNP.

  • selected: logical vector indicating if the SNP was selected by SConES or not.

  • module: integer with the number of the module the SNP belongs to.

References

Azencott, C. A., Grimm, D., Sugiyama, M., Kawahara, Y., & Borgwardt, K. M. (2013). Efficient network-guided multi-locus association mapping with graph cuts. Bioinformatics, 29(13), 171-179. https://doi.org/10.1093/bioinformatics/btt238

Examples

gi <- get_GI_network(minigwas, snpMapping = minisnpMapping, ppi = minippi)
scones.cv(minigwas, gi)
scones.cv(minigwas, gi, score = "glm")

Find connected explanatory features

Description

Finds the features maximally associated with a phenotype while being connected in an underlying network. Select the hyperparameters by cross-validation.

Usage

scones.cv_(X, y, featnames, net)

Arguments

X

n x d design matrix
y

Vector of length n with the outcomes
featnames

Vector of length d with the feature names
net

An igraph network that connects the SNPs.

Value

A copy of the SnpMatrix$map data.frame, with the following additions:

  • c: contains the univariate association score for every single SNP.

  • selected: logical vector indicating if the SNP was selected by SConES or not.

  • module: integer with the number of the module the SNP belongs to.

Examples

X <- as(minigwas[['genotypes']], 'numeric')
X <- X + matrix(rnorm(2500, sd = 0.1), nrow(X), ncol(X))
gi <- get_GI_network(minigwas, snpMapping = minisnpMapping, ppi = minippi)
scones.cv_(X, minigwas[['fam']]$affected, minigwas[['map']]$snp, gi)

Find connected explanatory SNPs.

Description

Finds the SNPs maximally associated with a phenotype while being connected in an underlying network (Azencott et al., 2013).

Usage

search_cones(
  gwas,
  net,
  encoding = "additive",
  sigmod = FALSE,
  covars = data.frame(),
  associationScore = c("chi2", "glm"),
  modelScore = c("stability", "bic", "aic", "aicc", "global_clustering",
    "local_clustering"),
  etas = numeric(),
  lambdas = numeric()
)

Arguments

gwas

A SnpMatrix object with the GWAS information.
net

An igraph network that connects the SNPs.
encoding

SNP encoding (unused argument).
sigmod

Boolean. If TRUE, use the Sigmod variant of SConES, meant to prioritize tightly connected clusters of SNPs.
covars

A data frame with the covariates. It must contain a column 'sample' containing the sample IDs, and an additional columns for each covariate.
associationScore

Association score to measure association between genotype and phenotype.
modelScore

String with the function to measure the quality of a split.
etas

Numeric vector with the etas to explore in the grid search. If ommited, it's automatically created based on the association scores.
lambdas

Numeric vector with the lambdas to explore in the grid search. If ommited, it's automatically created based on the association scores.

Value

A copy of the SnpMatrix$map data.frame, with the following additions:

  • c: contains the univariate association score for every single SNP.

  • selected: logical vector indicating if the SNP was selected by SConES or not.

  • module: integer with the number of the module the SNP belongs to.

References

Azencott, C. A., Grimm, D., Sugiyama, M., Kawahara, Y., & Borgwardt, K. M. (2013). Efficient network-guided multi-locus association mapping with graph cuts. Bioinformatics, 29(13), 171-179. https://doi.org/10.1093/bioinformatics/btt238

Examples

## Not run: gi <- get_GI_network(minigwas, snpMapping = minisnpMapping, ppi = minippi)
search_cones(minigwas, gi)
search_cones(minigwas, gi, encoding = "recessive")
search_cones(minigwas, gi, associationScore = "skat")
## End(Not run)

Find connected explanatory SNPs

Description

Finds the SNPs maximally associated with a phenotype while being connected in an underlying network.

Usage

sigmod(
  gwas,
  net,
  eta,
  lambda,
  covars = data.frame(),
  score = c("chi2", "glm", "r2"),
  family = c("binomial", "poisson", "gaussian", "gamma"),
  link = c("logit", "log", "identity", "inverse")
)

Arguments

gwas

A SnpMatrix object with the GWAS information.
net

An igraph network that connects the SNPs.
eta

Value of the eta parameter.
lambda

Value of the lambda parameter.
covars

A data frame with the covariates. It must contain a column 'sample' containing the sample IDs, and an additional columns for each covariate.
score

Association score to measure association between genotype and phenotype. Possible values: chi2 (default), glm.
family

A string defining the generalized linear model family. This should match one of "binomial", "poisson", "gaussian" or "gamma". See snp.rhs.tests for details.
link

A string defining the link function for the GLM. This should match one of "logit", "log", "identity" or "inverse". See snp.rhs.tests for details.

Value

A copy of the SnpMatrix$map data.frame, with the following additions:

  • c: contains the univariate association score for every single SNP.

  • selected: logical vector indicating if the SNP was selected by SConES or not.

  • module: integer with the number of the module the SNP belongs to.

References

Liu, Y., Brossard, M., Roqueiro, D., Margaritte-Jeannin, P., Sarnowski, C., Bouzigon, E., Demenais, F. (2017). SigMod: an exact and efficient method to identify a strongly interconnected disease-associated module in a gene network. Bioinformatics, 33(10), 1536–1544. https://doi.org/10.1093/bioinformatics/btx004

Examples

gi <- get_GI_network(minigwas, snpMapping = minisnpMapping, ppi = minippi)
sigmod(minigwas, gi, 10, 1)

Find connected explanatory features

Description

Finds the features maximally associated with a phenotype while being connected in an underlying network.

Usage

sigmod_(X, y, featnames, net, eta, lambda)

Arguments

X

n x d design matrix
y

Vector of length n with the outcomes
featnames

Vector of length d with the feature names
net

An igraph network that connects the SNPs.
eta

Value of the eta parameter.
lambda

Value of the lambda parameter.

Value

A copy of the SnpMatrix$map data.frame, with the following additions:

  • c: contains the univariate association score for every single SNP.

  • selected: logical vector indicating if the SNP was selected by SConES or not.

  • module: integer with the number of the module the SNP belongs to.

Examples

X <- as(minigwas[['genotypes']], 'numeric')
X <- X + matrix(rnorm(2500, sd = 0.1), nrow(X), ncol(X))
gi <- get_GI_network(minigwas, snpMapping = minisnpMapping, ppi = minippi)
sigmod_(X, minigwas[['fam']]$affected, minigwas[['map']]$snp, gi, 10, 1)

Find connected explanatory SNPs.

Description

Finds the SNPs maximally associated with a phenotype while being connected in an underlying network. Select the hyperparameters by cross-validation.

Usage

sigmod.cv(
  gwas,
  net,
  covars = data.frame(),
  score = c("chi2", "glm", "r2"),
  criterion = c("stability", "bic", "aic", "aicc", "global_clustering",
    "local_clustering"),
  etas = numeric(),
  lambdas = numeric(),
  family = c("binomial", "poisson", "gaussian", "gamma"),
  link = c("logit", "log", "identity", "inverse"),
  max_prop_snp = 0.5
)

Arguments

gwas

A SnpMatrix object with the GWAS information.
net

An igraph network that connects the SNPs.
covars

A data frame with the covariates. It must contain a column 'sample' containing the sample IDs, and an additional columns for each covariate.
score

Association score to measure association between genotype and phenotype. Possible values: chi2 (default), glm.
criterion

String with the function to measure the quality of a split.
etas

Numeric vector with the etas to explore in the grid search. If ommited, it's automatically created based on the association scores.
lambdas

Numeric vector with the lambdas to explore in the grid search. If ommited, it's automatically created based on the association scores.
family

A string defining the generalized linear model family. This should match one of "binomial", "poisson", "gaussian" or "gamma". See snp.rhs.tests for details.
link

A string defining the link function for the GLM. This should match one of "logit", "log", "identity" or "inverse". See snp.rhs.tests for details.
max_prop_snp

Maximum proportion of SNPs accepted in the model (between 0 and 1). Larger solutions will be discarded.

Value

A copy of the SnpMatrix$map data.frame, with the following additions:

  • c: contains the univariate association score for every single SNP.

  • selected: logical vector indicating if the SNP was selected by SConES or not.

  • module: integer with the number of the module the SNP belongs to.

References

Liu, Y., Brossard, M., Roqueiro, D., Margaritte-Jeannin, P., Sarnowski, C., Bouzigon, E., Demenais, F. (2017). SigMod: an exact and efficient method to identify a strongly interconnected disease-associated module in a gene network. Bioinformatics, 33(10), 1536–1544. https://doi.org/10.1093/bioinformatics/btx004

Examples

gi <- get_GI_network(minigwas, snpMapping = minisnpMapping, ppi = minippi)
sigmod.cv(minigwas, gi)
sigmod.cv(minigwas, gi, score = "glm")

Find connected explanatory features

Description

Finds the features maximally associated with a phenotype while being connected in an underlying network. Select the hyperparameters by cross-validation.

Usage

sigmod.cv_(X, y, featnames, net)

Arguments

X

n x d design matrix
y

Vector of length n with the outcomes
featnames

Vector of length d with the feature names
net

An igraph network that connects the SNPs.

Value

A copy of the SnpMatrix$map data.frame, with the following additions:

  • c: contains the univariate association score for every single SNP.

  • selected: logical vector indicating if the SNP was selected by SConES or not.

  • module: integer with the number of the module the SNP belongs to.

Examples

X <- as(minigwas[['genotypes']], 'numeric')
X <- X + matrix(rnorm(2500, sd = 0.1), nrow(X), ncol(X))
gi <- get_GI_network(minigwas, snpMapping = minisnpMapping, ppi = minippi)
sigmod.cv_(X, minigwas[['fam']]$affected, minigwas[['map']]$snp, gi)

Simulate causal SNPs

Description

Selects randomly interconnected genes as causal, then selects a proportion of them as causal.

Usage

simulate_causal_snps(net, ngenes = 20, pcausal = 1)

Arguments

net

An igraph gene-interaction (GI) network that connects the SNPs.
ngenes

Number of causal genes.
pcausal

Number between 0 and 1, proportion of the SNPs in causal genes that are causal themselves.

Value

A vector with the ids of the simulated causal SNPs.

Examples

gi <- get_GI_network(minigwas, snpMapping = minisnpMapping, ppi = minippi)
simulate_causal_snps(gi, ngenes=2)

Simulate phenotype

Description

Simulates a phenotype from a GWAS experiment and a specified set of causal SNPs. If the data is qualitative, only controls are used.

Usage

simulate_phenotype(
  gwas,
  snps,
  h2,
  model = "additive",
  effectSize = rnorm(length(snps)),
  qualitative = FALSE,
  ncases,
  ncontrols,
  prevalence
)

Arguments

gwas

A SnpMatrix object with the GWAS information.
snps

Character vector with the SNP ids of the causal SNPs. Must match SNPs in gwas[["map"]][["snp.name"]].
h2

Heritability of the phenotype (between 0 and 1).
model

String specifying the genetic model under the phenotype. Accepted values: "additive".
effectSize

Numeric vector with the same lenght as the number of causal SNPs. It indicates the effect size of each of the SNPs; if absent, they are sampled fron a normal distribution.
qualitative

Bool indicating if the phenotype is qualitative or not (quantitative).
ncases

Integer specifying the number of cases to simulate in a qualitative phenotype. Required if qualitative = TRUE.
ncontrols

Integer specifying the number of controls to simulate in a qualitative phenotype. Required if qualitative = TRUE.
prevalence

Value between 0 and 1 specifying the population prevalence of the disease. Note that ncases cannot be greater than prevalence * number of samples. Required if qualitative = TRUE.

Value

A copy of the GWAS experiment with the new phenotypes in gwas[["fam"]][["affected"]].

References

Inspired from GCTA simulation tool: http://cnsgenomics.com/software/gcta/Simu.html.

Examples

gi <- get_GI_network(minigwas, snpMapping = minisnpMapping, ppi = minippi)
causal <- simulate_causal_snps(gi, ngenes = 2)
simulate_phenotype(minigwas, causal, h2 = 1)

Vertices with an attribute

Description

Returns the nodes matching some condition.

Usage

subvert(net, attr, values, affirmative = TRUE)

Arguments

net

An igraph network.
attr

An attribute of the vertices.
values

Possible values of attr
affirmative

Logical. States if a condition must be its affirmation (e.g. all nodes with gene name "X"), or its negation (all nodes not with gene name "X").

Value

The vertices with attribute equal to any of the values in values.

Examples

gi <- get_GI_network(minigwas, snpMapping = minisnpMapping, ppi = minippi)
martini:::subvert(gi, "gene", "A")
martini:::subvert(gi, "name", c("1A1", "1A3"))

Make pseudo SnpMatrix object

Description

Wrap design matrix and outcome vector into a pseudo SnpMatrix object.

Usage

wrap_Xy(X, y, featnames, net)

Arguments

X

n x d design matrix
y

Vector of length n with the outcomes
featnames

Vector of length d with the feature names
net

An igraph network that connects the SNPs.