def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if argv is None:
argv = sys.argv
# setup command line parser
parser = E.OptionParser(version="%prog version: $Id$",
usage=globals()["__doc__"])
parser.add_option("--score-method", dest="method", type="choice",
choices=["PICS", "LDscore", "ABF", "R2_rank",
"get_eigen", "calc_prior", "credible_set",
"summarise"],
help="SNP scoring/prioritisation method to apply.")
parser.add_option("--database", dest="database", type="string",
help="SQL database containing LD information "
"in table format. Expects columns SNP_A, "
"SNP_B, R2, BP_A and BP_B (Plink --r2 output)")
parser.add_option("--ld-directory", dest="ld_dir", type="string",
help="directory containing tabix-index BGZIP "
"LD files. Assumes Plink used to calculate LD")
parser.add_option("--table-name", dest="table", type="string",
help="name of the SQL table containing the LD"
"values")
parser.add_option("--chromosome", dest="chromosome", type="string",
help="chromosome to subset the association results "
"file on")
parser.add_option("--ld-threshold", dest="ld_threshold", type="float",
help="the threshold of LD above which variants will "
"be taken forward.")
parser.add_option("--rank-threshold", dest="rank_threshold", type="float",
help="the threshold in terms of the top n% SNPs to "
"output based on the ranking metric. e.g. "
"--rank-threshold=0.01 is the top 1% SNPs")
parser.add_option("--credible-interval", dest="interval", type="float",
help="The credible set interval size to generate the "
"credible set of SNPs")
parser.add_option("--prior-variance", dest="prior_var", type="float",
help="the prior variance used to weight the SNP "
"variance")
parser.add_option("--fine-map-window", dest="map_window", type="int",
help="the region size to included around the index "
"SNP as the fine-mapping region.")
parser.add_option("--eigen-score-directory", dest="eigen_dir", type="string",
help="PATH to directory containing tabix indexed "
"eigen score files")
parser.add_option("--flat-prior", dest="flat_prior", action="store_true",
help="Ignore functional annotation information and "
"use an uninformative prior on each SNP")
parser.add_option("--snp-set", dest="snp_set", type="string",
help="Pre-defined SNP set as a list of SNP IDs."
"If used to calculate priors contains column of scores.")
parser.add_option("--distribution", dest="dist", type="choice",
choices=["normal", "t", "gamma", "lognormal",
"exponential"],
help="distribution from which to draw prior "
"probabilities")
parser.add_option("--distribution-parameters", dest="dist_params", type="string",
help="distribution parameters as a comma-separated list")
parser.add_option("--lead-snp-id", dest="lead_snp", type="int",
help="0-based item number in filename")
parser.add_option("--filename-separator", dest="separator", type="string",
help="filename separator to extract information")
parser.add_option("--snp-column", dest="snp_col", type="int",
help="0-based index of SNP ID column number")
parser.add_option("--probability-column", dest="prob_col", type="int",
help="0-based index of posterior probabilities column"
" number")
parser.set_defaults(ld_dir=None,
dist="normal",
dist_params=None,
snp_set=None,
prior_var=0.04,
interval=0.99,
eigen_dir=None,
map_window=100000,
ld_threshold=0.5,
database=None,
table=None,
flat_prior=False,
lead_snp=2,
separator="_",
snp_col=0,
prob_col=1,
)
# add common options (-h/--help, ...) and parse command line
(options, args) = E.Start(parser, argv=argv)
infile = argv[-1]
if len(infile.split(",")) > 1:
pass
else:
peek = pd.read_table(infile, nrows=5, sep="\s*", header=0)
try:
if len(peek["TEST"] != "ADD"):
clean = False
else:
clean = True
except KeyError:
clean = True
if options.method == "LDscore":
snpscores = gwas.snpPriorityScore(gwas_results=infile,
database=options.database,
table_name=options.table,
chromosome=options.chromosome,
ld_dir=options.ld_dir,
clean=clean)
# take top 1%, all SNPs doesn't achieve anything useful
ranks = int(len(snpscores.index) * 0.01)
snpscores = snpscores.iloc[:ranks]
elif options.method == "PICS":
snp_list = {}
if options.snp_set and not options.flat_prior:
with IOTools.openFile(options.snp_set, "r") as sfile:
for line in sfile.readlines():
snp = line.split("\t")[0]
try:
score = float(line.split("\t")[-1].rstrip("\n"))
except ValueError:
score = 0
snp_list[snp] = float(score)
# get the parameter estimates for the distribution
# if they have not been provided
if not options.dist_params:
dist_params = gwas.estimateDistributionParameters(data=snp_list.values(),
distribution=options.dist)
else:
dist_params = tuple([float(fx) for fx in options.dist_params.split(",")])
E.info("Calculating priors on SNPs")
priors = gwas.calcPriorsOnSnps(snp_list=snp_list,
distribution=options.dist,
params=dist_params)
elif options.snp_set and options.flat_prior:
with IOTools.openFile(options.snp_set, "r") as sfile:
for line in sfile.readlines():
snp = line.split("\t")[0]
snp_list[snp] = 1.0
priors = snp_list
else:
# allow for no priors or scores to be set,
# use of priors will be ignored,
# i.e. when prior and likelihood are not from
# conjugate distributions
priors = None
# PICS scores expects the gwas results file to
# only contain the region of interest, which
# represents an independent association signal
snpscores = gwas.PICSscore(gwas_results=infile,
database=options.database,
table_name=options.table,
chromosome=options.chromosome,
priors=priors,
clean=clean,
ld_dir=options.ld_dir,
ld_threshold=options.ld_threshold)
snpscores.columns = ["SNP", "PICS"]
posterior_sum = 0
snpscores.sort_values(ascending=False,
inplace=True)
post_snps = []
for snp in snpscores.index:
if posterior_sum < 99.0:
posterior_sum += snpscores.loc[snp]
post_snps.append(snp)
else:
break
snpscores = snpscores.loc[post_snps]
snpscores.drop_duplicates(inplace=True)
elif options.method == "R2_rank":
# rank SNPs based on their LD with the lead
# SNP, take the top n% SNPs
snpscores = gwas.LdRank(gwas_results=infile,
database=options.database,
table_name=options.table,
ld_dir=options.ld_dir,
chromosome=options.chromosome,
ld_threshold=options.ld_threshold,
top_snps=options.rank_threshold,
clean=clean)
elif options.method == "ABF":
snpscores = gwas.ABFScore(gwas_results=infile,
region_size=options.map_window,
chromosome=options.chromosome,
prior_variance=options.prior_var,
clean=clean)
elif options.method == "get_eigen":
E.info("Fetching Eigen scores")
snpscores = gwas.getEigenScores(eigen_dir=options.eigen_dir,
bim_file=infile,
snp_file=options.snp_set)
snpscores = pd.DataFrame(snpscores).T
elif options.method == "credible_set":
E.info("Creating credible set")
snpscores = gwas.makeCredibleSet(probs_file=infile,
credible_set=options.interval,
lead_snp_indx=options.lead_snp,
filename_sep=options.separator,
snp_column=options.snp_col,
probs_column=options.prob_col)
elif options.method == "summarise":
E.info("Collating SNP prioritisation resuslts")
file_list = infile.split(",")
snpscores = gwas.summariseResults(file_list=file_list)
snpscores.to_csv(options.stdout, index_label="SNP",
sep="\t")
# write footer and output benchmark information.
E.Stop()
def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if argv is None:
argv = sys.argv
# setup command line parser
parser = E.OptionParser(version="%prog version: $Id$",
usage=globals()["__doc__"])
parser.add_option("--program",
dest="program",
type="choice",
choices=["plink2", "gcta", "plinkdev"],
help="program to execute genome-wide analysis")
parser.add_option("--input-file-pattern",
dest="infile_pattern",
type="string",
help="file prefix that identifies a group of files")
parser.add_option("--input-file-format",
dest="file_format",
type="choice",
choices=[
"plink", "plink_binary", "oxford", "oxford_binary",
"vcf", "GRM_binary", "GRM_gz"
],
help="format of input files")
parser.add_option("--phenotypes-file",
dest="pheno_file",
type="string",
help="text file of additional phenotypes")
parser.add_option("--pheno",
dest="pheno",
type="string",
help="either phenotype file column header or number")
parser.add_option("--covariates-file",
dest="covariate_file",
type="string",
help="file containing covariates")
parser.add_option("--covariate-column",
dest="covar_col",
type="string",
help="column number(s) or header(s) to include in "
"association model")
parser.add_option("--method",
dest="method",
type="choice",
choices=[
"ld_prune", "summary", "flag_hets",
"remove_relations", "check_gender", "IBD"
],
help="method to apply to genome-wide data")
parser.add_option("--IBD-parameter",
dest="ibd_param",
type="choice",
choices=["norm", "relatives", "full"],
help="param "
"to pass to IBD calculations")
parser.add_option("--principal-components",
dest="num_pcs",
type="int",
help="the number of principal components to output")
parser.add_option("--matrix-shape",
dest="matrix_shape",
type="choice",
choices=["triangle", "square", "square0"],
help="output matrix shape.",
default="triangle")
parser.add_option("--matrix-compression",
dest="matrix_compress",
type="choice",
choices=["gz", "bin", "bin4"],
help="compression to apply to output matrix file",
default="gz")
parser.add_option("--matrix-form",
dest="matrix_form",
type="choice",
choices=["distance", "grm"],
help="type of relationship matrix to calculate")
parser.add_option(
"--matrix-metric",
dest="matrix_metric",
type="choice",
choices=["fhat", "cov", "ibc2", "ibc3", "ibs", "genomic", "hamming"],
help="value to calculate for diagonal elements of the "
"grm. Default is fhat for grm and hamming for distance.")
parser.add_option(
"--matrix-options",
dest="matrix_options",
type="string",
help="modifiers of matrix output, see plink documentation "
"for details")
parser.add_option("--strand-flip-subset",
dest="flip_subset",
action="store_true",
help="apply strand flipping to a subset of samples")
parser.add_option("--flip-scan-type",
dest="scan_param",
type="choice",
choices=["default", "window", "threshold"],
help="strand flipping scan to apply to SNPs")
parser.add_option("--sort-type",
dest="sort_type",
type="choice",
choices=["none", "natural", "ascii", "file"],
help="sort type to input files")
parser.add_option("--merge-file-format",
dest="merge_format",
type="choice",
choices=["plink", "binary_plink"],
help="format of input files to be merged")
parser.add_option(
"--merge-mode",
dest="merge_mode",
type="choice",
choices=[
"default", "original_missing", "new_nonmissing", "no_overwrite",
"force", "report_all", "report_nonmissing"
],
help="merge mode to apply to dealing with merge conflicts")
parser.add_option("--duplicates-method",
dest="dup_method",
type="choice",
choices=["same_ref", "id_match", "suppress_first"],
help="method for identifying and dealing with duplicate "
"variants")
parser.add_option("--summary-method",
dest="summary_method",
type="choice",
choices=[
"allele_frequency", "missing_data", "hardy_weinberg",
"mendel_errors", "inbreeding", "inbreeding_coef",
"gender_checker", "wrights_fst"
],
help="summary statistics to calculate")
parser.add_option("--summary-parameter",
dest="sum_param",
type="string",
help="optional parameters that can be passed to summary "
"statistics methods")
parser.add_option(
"--genotype-rate",
dest="filt_genotype_rate",
type="string",
help="genotyping rate threshold. SNPs below this threshold "
"will be excluded from analysis")
parser.add_option("--indiv-missing",
dest="filt_missingness",
type="string",
help="individual missingness rate. Individuals below "
"this threshold will be excluded from analysis")
parser.add_option("--hardy-weinberg",
dest="filt_hwe",
type="string",
help="hardy-weinberg p-value threshold for SNPs. SNPs "
"with a 2df chisquared p-value below this will be "
"filtered out")
parser.add_option(
"--min-allele-frequency",
dest="filt_min_allele_frequency",
type="string",
help="only include SNPs with an allele frequency equal to "
"or above this threshold")
parser.add_option(
"--max-allele-frequency",
dest="filt_max_allele_frequency",
type="string",
help="only include SNPs with an allele frequency equal to "
"or below this threshold")
parser.add_option(
"--mendelian-error",
dest="filt_mendelian_error",
type="string",
help="exclude individuals/trios with mendelian errors that "
"exceed this value")
parser.add_option("--min-quality-score",
dest="filt_min_qaul_score",
type="string",
help="reset the minimum low bound of quality scores for "
"variants in a VCF file. Default is 0")
parser.add_option(
"--max-quality-score",
dest="filt_max_qual_score",
type="string",
help="reset the maximum upper bound of quality scores for "
"a VCCF file. Default is Inf")
parser.add_option("--allow-no-gender",
dest="filt_allow_no_sex",
type="string",
help="allow individuals with gender missing")
parser.add_option("--enforce-gender",
dest="filt_enforce_sex",
type="string",
help="only include individuals with non-missing gender "
"information")
parser.add_option("--keep-individuals",
dest="filt_keep",
type="string",
help="a file containing individuals IDs to keep, "
"one per row")
parser.add_option("--remove-individuals",
dest="filt_remove",
type="string",
help="a file of individual IDs to remove, one per row")
parser.add_option("--subset-filter",
dest="filt_subset_filter",
type="choice",
choices=[
"cases", "controls", "males", "females", "founders",
"nonfounders"
],
help="only apply filters to the specific subset of "
"individuals supplied")
parser.add_option(
"--extract-snps",
dest="filt_extract",
type="string",
help="text file of variant IDs to include in the analysis, "
"ignoring all others")
parser.add_option("--exclude-snps",
dest="filt_exclude",
type="string",
help="a file of variant IDs to exclude from analysis")
parser.add_option("--restrict-chromosome",
dest="filt_chromosome",
type="string",
help="restict analysis to either a single chromosome, "
"or a comma-separated list of chromosomes")
parser.add_option("--exclude-chromosomes",
dest="filt_exclude_chromosome",
type="string",
help="exclude all variants on these "
"chromosome(s)")
parser.add_option(
"--autosome-only",
dest="filt_autosome",
action="store_true",
help="if present only autosomal variants will be analysed")
parser.add_option(
"--pseudo-autosome",
dest="filt_pseudo_autosome",
action="store_true",
help="include on the pseudo-autosomal region of chromosome X")
parser.add_option("--ignore-indels",
dest="filt_ignore_indels",
action="store_true",
help="only include bi-allelic single nucleotide "
"variants in analysis")
parser.add_option(
"--snp-range",
dest="filt_snp_bp_range",
type="string",
help="comma separated list of from, to genome co-ordinates "
"within which to include variants for analysis")
parser.add_option("--snp-id-range",
dest="filt_snp_id_range",
type="string",
help="comma separate list of IDs from, to within which "
"to include variants for analysis.")
parser.add_option("--snp-id",
dest="filt_specific_snp",
type="string",
help="include a single snp in the analysis given by "
"it's variant ID.")
parser.add_option("--exclude-variant",
dest="filt_exclude_snp",
type="string",
help="exclude a single variant from the analysis, "
"given by it's variant ID")
parser.add_option(
"--covariate-filter",
dest="filt_covariate_filter",
type="string",
help="covariate column headers or column numbers on which "
"to filter on. Requries --covariate-file")
parser.add_option(
"--filter-parameter",
dest="param",
type="string",
help="parameter values to be passed to filtering function")
parser.add_option("--window-size",
dest="window_size",
type="string",
help="alters the behaviour of the --snp-range and "
"--include/exclude snp options. variants within +/- "
"half * window_size (kb) are included")
parser.add_option(
"--range-resolution",
dest="filt_range_resolution",
type="choice",
choices=["bp", "kb", "mb"],
help="alters the (from, to) range resolution to either bp, "
"kb or mb")
parser.add_option(
"--output-file-pattern",
dest="out_pattern",
type="string",
help="output file pattern prefix. file suffixes are dependent "
"on the task executed")
parser.add_option("--threads",
dest="threads",
type="int",
help="the number of threads to use for multi-threaded "
"processes")
parser.add_option("--use-kb",
dest="kb",
action="store_true",
help="if present uses a kb sized window for LD pruning")
parser.add_option("--prune-method",
dest="prune_method",
type="choice",
choices=["R2", "VIF"],
help="type of LD pruning to "
"perform, pair-wise LD or variance inflation factor")
parser.add_option("--step-size",
dest="step",
type="string",
help="step size to advance window by")
parser.add_option("--threshold",
dest="threshold",
type="string",
help="threshold on which to filter results")
parser.add_option("--parallel",
dest="parallel",
type="int",
help="number of jobs to split task into")
parser.add_option("--memory",
dest="memory",
type="string",
help="amount of memory to reserve for the task")
# add common options (-h/--help, ...) and parse command line
(options, args) = E.Start(parser, argv=argv)
parser.set_defaults(sum_param=None,
dup_method="same_ref",
matrix_shape="triangle",
matrix_options=None,
matrix_compress="gz",
kb=False,
random_seed=random.randint(0, 19999),
memory="60G",
parallel=None)
if not options.infile_pattern:
infiles = (argv[-1]).split(",")
else:
infiles = options.infile_pattern
# create a new filegroup object
geno_files = gwas.FileGroup(files=infiles,
file_format=options.file_format,
genotype_format="imputed")
if options.pheno_file:
geno_files.set_phenotype(pheno_file=options.pheno_file,
pheno=options.pheno)
else:
pass
# add FileGroup object to the gwas program object
if options.program == "plink2":
gwas_object = gwas.Plink2(files=geno_files)
gwas_object.program_call(infiles=geno_files,
outfile=options.out_pattern)
elif options.program == "plinkdev":
gwas_object = gwas.PlinkDev(files=geno_files)
gwas_object.program_call(infiles=geno_files,
outfile=options.out_pattern)
elif options.program == "gcta":
gwas_object = gwas.GCTA(files=geno_files)
gwas_object.program_call(infiles=geno_files,
outfile=options.out_pattern)
else:
pass
# collect filtering options from options
opt_dict = options.__dict__
filter_keys = [fx for fx in opt_dict.keys() if re.search("filt", fx)]
filter_dict = {k: options.__dict__[k] for k in filter_keys if opt_dict[k]}
# iteratively add all filters to GWASProgram object
for fkey in filter_dict:
filt_key = fkey.replace("filt_", "")
filter_value = filter_dict[fkey]
gwas_object.apply_filters(filter_type=filt_key,
filter_value=filter_value)
# handle summary statistics
if options.method == "ld_prune":
gwas_object._qc_methods(ld_prune=options.prune_method,
kb=True,
window=options.window_size,
step=options.step,
threshold=options.threshold)
elif options.method == "IBD":
# use sum param to pass arguments to ibd estiamte
# these are norm, full or relatitves
gwas_object._qc_methods(ibd=options.ibd_param)
elif options.method == "summary":
if options.summary_method == "allele_frequency":
gwas_object._output_statistics(allele_frequency=options.sum_param)
elif options.summary_method == "hardy_weinberg":
gwas_object._output_statistics(hardy_weinberg=options.sum_param)
elif options.summary_method == "missing_data":
gwas_object._output_statistics(missing_data=options.sum_param)
elif options.summary_method == "mendel_errors":
gwas_object._output_statistics(mendel_errors=options.sum_param)
elif options.summary_method == "inbreeding":
gwas_object._output_statistics(inbreeding=options.sum_param)
elif options.summary_method == "inbreeding_coef":
gwas_object._output_statistics(inbreeding_coef=options.sum_param)
elif options.summary_method == "gender_checker":
gwas_object._output_statistics(gender_checker=options.sum_param)
elif options.summary_method == "wrights_fst":
gwas_object._output_statistics(wrights_fst=options.sum_param)
else:
pass
elif options.method == "remove_relations":
gwas_object._run_tasks(remove_relations="cutoff",
parameter=options.threshold)
elif options.method == "check_gender":
gwas_object._run_tasks(check_gender="")
else:
pass
gwas_object.build_statement(infiles=geno_files,
outfile=options.out_pattern,
threads=options.threads,
memory=options.memory,
parallel=options.parallel)
# write footer and output benchmark information.
E.Stop()
def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if argv is None:
argv = sys.argv
# setup command line parser
parser = E.OptionParser(version="%prog version: $Id$",
usage=globals()["__doc__"])
parser.add_option("-t", "--test", dest="test", type="string",
help="supply help")
parser.add_option("-p", "--plot-type", dest="plot_type", type="choice",
choices=["histogram", "barplot", "density",
"boxplot", "scatter", "map",
"pca"],
help="the plot type to generate")
parser.add_option("--plot-n-pc", dest="n_pcs", type="int",
help="The number of principal components to "
"plot")
parser.add_option("-g", "--group-by", dest="group_by", type="string",
help="column header to group observations by")
parser.add_option("-x", "--x-column", dest="x_col", type="string",
help="column to plot on X axis")
parser.add_option("-y", "--y-column", dest="y_col", type="string",
help="column to plot on y axis")
parser.add_option("-i", "--index_column", dest="indx", type="string",
help="column number that refers to the row index")
parser.add_option("--output-file", dest="outfile", type="string",
help="path and filename to save plot to")
parser.add_option("--labels", dest="labels", type="string",
help="a comma-separated list of axis labels. "
"The first 2 correspond to the X and Y-axis, "
"respectively, and the third is the plot title")
parser.add_option("--metadata-file", dest="meta_file", type="string",
help="file containing metadata for annotating "
"plots with. Use `--group-labels` to define table "
"columns to use")
parser.add_option("--fam-file", dest="fam_file", type="string",
help="Plink .fam file containing file IDs")
parser.add_option("--xvar-labels", dest="xvar_labs", type="string",
help="a comma-separated list of variable X labels"
"only applies when X is a discrete or categorical "
"variable. The labels must be in the correct order")
parser.add_option("--group-labels", dest="group_labs", type="string",
help="a comma-separated list of grouping variable "
"labels. Can only apply when the grouping variable "
"is discrete or categorical. The labels must be "
"input in the order of the data")
parser.add_option("--yvar-labels", dest="yvar_labs", type="string",
help="a comma-separated list of variable Y labels"
"only applies when Y is a discrete or categorical "
"variable")
parser.add_option("--var-type", dest="var_type", type="choice",
choices=["continuous", "categorical", "integer"],
help="The data type of the variables to be plotted."
"The default is continuous")
parser.add_option("--coordinate-file", dest="coordinates", type="string",
help="file containing co-ordinates data")
parser.add_option("--coords-id-col", dest="coord_ids", type="string",
help="column header containing individual IDs")
parser.add_option("--lattitude-column", dest="lat_col", type="string",
help="column header containing lattitude co-ordinates")
parser.add_option("--longitude-column", dest="long_col", type="string",
help="column header containing longitude co-ordinates")
parser.add_option("--reference-value", dest="ref_val", type="string",
help="categorical variable level to dichotomise on")
# add common options (-h/--help, ...) and parse command line
(options, args) = E.Start(parser, argv=argv)
parser.set_defaults(y_col=None,
group_by=None,
indx=None,
labels="X,Y,title",
xvar_labs=None,
yvar_labs=None,
var_type="continuous")
infile = argv[-1]
df = pd.read_table(infile, sep="\t", index_col=options.indx,
header=0)
if options.plot_type == "map":
df = pd.read_table(infile, sep="\t", index_col=options.indx,
header=0)
coords_df = pd.read_table(options.coordinates, sep="\t",
header=0, index_col=options.indx)
gwas.plotMapPhenotype(data=df,
coords=coords_df,
coord_id_col=options.coord_ids,
lat_col=options.lat_col,
long_col=options.long_col,
save_path=options.outfile,
xvar=options.x_col,
var_type=options.var_type,
xlabels=options.xvar_labs,
level=options.ref_val)
elif options.plot_type == "pca":
data = gwas.parseFlashPCA(pcs_file=infile,
fam_file=options.fam_file)
gwas.plotPCA(data=data,
nPCs=options.n_pcs,
point_labels=options.group_labs,
save_path=options.outfile,
headers=False,
metadata=options.meta_file,
multiplot=True)
else:
df = pd.read_table(infile, sep="\t", index_col=options.indx,
header=0)
gwas.plotPhenotype(data=df,
plot_type=options.plot_type,
x=options.x_col,
y=options.y_col,
group=options.group_by,
save_path=options.outfile,
labels=options.labels,
xlabels=options.xvar_labs,
ylabels=options.yvar_labs,
glabels=options.group_labs,
var_type=options.var_type)
# write footer and output benchmark information.
E.Stop()
def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if argv is None:
argv = sys.argv
# setup command line parser
parser = E.OptionParser(version="%prog version: $Id$",
usage=globals()["__doc__"])
parser.add_option("--task",
dest="task",
type="choice",
choices=["get_hits", "extract_results", "merge_freq"],
help="task to perform")
parser.add_option("--p-threshold",
dest="p_threshold",
type="float",
help="threshold for association p-value, below "
"which results will be output")
parser.add_option("--output-directory",
dest="outdir",
type="string",
help="output file directory")
parser.add_option("--snp-set",
dest="snpset",
type="string",
help="file containing list of SNP per row to "
"extract from GWAS results")
parser.add_option(
"--frequency-directory",
dest="freq_dir",
type="string",
help="Directory containing plink .frq files corresponding"
" to all chromosomes")
# add common options (-h/--help, ...) and parse command line
(options, args) = E.Start(parser, argv=argv)
# if the input is a list of files, split them
infile = argv[-1]
infiles = infile.split(",")
if len(infiles) > 1:
results = gwas.GWASResults(assoc_file=infiles)
elif len(infiles) == 1:
results = gwas.GWASResults(assoc_file=infile)
else:
raise IOError("no input files detected, please specifiy association "
"results files as the last command line argument")
if options.task == "get_hits":
hits = results.getHits(float(options.p_threshold))
for name, region in hits:
try:
try:
top_reg = region.sort_values(by="CHISQ", ascending=False)
top_bp = top_reg.iloc[0]["BP"]
top_snp = top_reg.iloc[0]["SNP"]
except KeyError:
top_reg = region
top_reg.loc[:, "STAT"] = abs(top_reg["STAT"])
top_reg = top_reg.sort_values(by="STAT", ascending=False)
top_bp = top_reg.iloc[0]["BP"]
top_snp = top_reg.iloc[0]["SNP"]
except KeyError:
top_reg = region
top_reg.loc[:, "STAT"] = abs(top_reg["T"])
top_reg = top_reg.sort_values(by="T", ascending=False)
top_bp = top_reg.iloc[0]["BP"]
top_snp = top_reg.iloc[0]["SNP"]
outname = "_".join(
["chr%s" % str(name),
str(top_bp), top_snp, "significant"])
outfile = outname + ".tsv"
out_file = "/".join([options.outdir, outfile])
E.info("output association results from Chr%s to %s" %
(str(name), out_file))
# this keeps outputing the first column as unamed: 0,
# need to remove this
try:
region.drop([region.columns[0]], inplace=True, axis=1)
except:
pass
region.to_csv(out_file, sep="\t", index=None)
elif options.task == "extract_results":
with IOTools.openFile(options.snpset, "r") as sfile:
snpset = sfile.readlines()
snpset = [snp.rstrip("\n") for snp in snpset]
snp_df = results.extractSNPs(snpset)
snp_df.dropna(axis=0, how='all', inplace=True)
snp_df.drop_duplicates(subset=["SNP"], inplace=True)
snp_df.to_csv(options.stdout, sep="\t", index=None)
elif options.task == "merge_freq":
# sequentially merge GWAS result with frequency data
# to make file for GCTA joint analysis
regex = re.compile("(\S+).frq$")
cojo_df = results.mergeFrequencyResults(options.freq_dir,
file_regex=regex)
cojo_df.to_csv(options.stdout, sep="\t", index=None)
else:
pass
# write footer and output benchmark information.
E.Stop()
def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if argv is None:
argv = sys.argv
# setup command line parser
parser = E.OptionParser(version="%prog version: $Id$",
usage=globals()["__doc__"])
parser.add_option("-t",
"--test",
dest="test",
type="string",
help="supply help")
parser.add_option(
"--task",
dest="task",
type="choice",
choices=["mafs", "penetrance", "detect_duplicates", "allele_diff"],
help="task to perform")
parser.add_option("--ped-file",
dest="ped_file",
type="string",
help="plink format .ped file")
parser.add_option("--map-file",
dest="map_file",
type="string",
help="plink format .map file")
parser.add_option("--freq-file",
dest="mafs",
type="string",
help="text file containing populations minor "
"allele frequencies of variants. One row per "
"variant with ID MAF")
parser.add_option("--groups-file",
dest="group_file",
type="string",
help="file containing group labels for individuals "
"in the provided ped file")
parser.add_option("--ref-label",
dest="ref_label",
type="string",
help="group label to be used as the reference case")
parser.add_option("--test-label",
dest="test_label",
type="string",
help="group label to be used as the test case")
parser.add_option("--subset",
dest="subset",
type="choice",
choices=["cases", "gender"],
help="subset the "
"data by either case/control or gender")
parser.add_option("--take-last",
dest="take",
action="store_true",
help="if use duplicates will take the last variant, "
"default behaviour is to take the first")
parser.add_option("--outfile-pattern",
dest="out_pattern",
type="string",
help="outfile pattern to use for finding duplicates "
"and triallelic variants")
parser.add_option("--snp-set",
dest="snp_subset",
type="string",
help="list of SNPs to include")
# add common options (-h/--help, ...) and parse command line
(options, args) = E.Start(parser, argv=argv)
parser.set_defaults(mafs=None, subset=None, take_last=False)
if options.task == "mafs":
mafs = gwas.countByVariantAllele(options.ped_file, options.map_file)
mafs.to_csv(options.stdout, index_col=None, sep="\t")
elif options.task == "penetrance":
summary, pens = gwas.calcPenetrance(options.ped_file,
options.map_file,
subset=options.subset,
mafs=options.mafs,
snpset=options.snp_subset)
pens.to_csv(options.stdout, sep="\t", index_label="SNP")
summary.to_csv("/".join([os.getcwd(), "penetrance_summary.txt"]),
sep="\t",
index_label="SNP")
elif options.task == "allele_diff":
allele_diffs = gwas.calcMaxAlleleFreqDiff(
ped_file=options.ped_file,
map_file=options.map_file,
group_file=options.group_file,
test=options.test_label,
ref=options.ref_label)
allele_diffs.to_csv(options.stdout, sep="\t")
elif options.task == "detect_duplicates":
# find variants with duplicated position and shared reference
# allele indicative of triallelic variants - also same ID
# ouput to a filter list
infile = argv[-1]
dups, tris, oves = gwas.findDuplicateVariants(bim_file=infile,
take_last=options.take)
if os.path.isabs(options.out_pattern):
with open(options.out_pattern + ".triallelic", "w") as otfile:
for tvar in tris:
otfile.write("%s\n" % tvar)
with open(options.out_pattern + ".duplicates", "w") as odfile:
for dvar in dups:
odfile.write("%s\n" % dvar)
with open(options.out_pattern + ".overlapping", "w") as ovfile:
for ovar in oves:
ovfile.write("%s\n" % ovar)
else:
outpattern = os.path.abspath(options.out_pattern)
with open(outpattern + ".triallelic", "w") as otfile:
for tvar in tris:
otfile.write("%s\n" % tvar)
with open(outpattern + ".duplicates", "w") as odfile:
for dvar in dups:
odfile.write("%s\n" % dvar)
with open(outpattern + ".overlapping", "w") as ovfile:
for ovar in oves:
ovfile.write("%s\n" % ovar)
# write footer and output benchmark information.
E.Stop()
def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if argv is None:
argv = sys.argv
# setup command line parser
parser = E.OptionParser(version="%prog version: $Id$",
usage=globals()["__doc__"])
parser.add_option("-t", "--test", dest="test", type="string",
help="supply help")
parser.add_option("--task", dest="task", type="choice",
choices=["merge_exclusions", "flag_hets",
"find_inbreds", "flag_relations",
"discordant_gender"],
help="task to execute on phenotype file(s)")
parser.add_option("--gender-check-file", dest="gender_check",
type="string", help="output from gender checking "
"by Plink, suffix should be .sexcheck")
parser.add_option("--relationship-file", dest="relations",
type="string", help="output file from IBS "
"calculation. Should contain all pairwise "
"relationships.")
parser.add_option("--inbreeding-coef-file", dest="inbreed_file",
type="string", help="file containing either Plink "
"or GCTA estimates of F, inbreeding coefficient")
parser.add_option("--inbreeding-coefficient", dest="inbred_coeff", type="choice",
choices=["Fhat1", "Fhat2", "Fhat3", "F",
"ibc"], help="inbreeding coefficient "
"to use to identify highly inbred individuals")
parser.add_option("--inbred-cutoff", dest="inbred_cutoff", type="float",
help="threshold above which individuals are classed "
"as inbred.")
parser.add_option("--ibs-cutoff", dest="ibs_cutoff", type="float",
help="IBS threshold to flag individuals as being "
"closely related")
parser.add_option("--trimmed-relationships", dest="rel_cutoff",
type="string", help="output file from Plink "
"--rel-cutoff with trimmed data set of unrelated "
"individuals.")
parser.add_option("--heterozygotes-file", dest="hets_file", type="string",
help="file from heterozygote analysis containing observed "
"homozygosity and F coefficients")
parser.add_option("--auxillary-file", dest="aux_file", type="string",
help="a file of IIDs and FIDs for individuals that are "
"to be removed from analysis, unrelated to QC")
parser.add_option("--plotting-path", dest="plot_path", type="string",
help="PATH to save any plots to")
# add common options (-h/--help, ...) and parse command line
(options, args) = E.Start(parser, argv=argv)
if options.task == "flag_hets":
# calculate heterozygosity rates, find and flag
# individuals > 3 s.d. away from mean value
# rate = (nonissing - homs) / nonmissing
# i.e. non-homozygote rate
flags = gwas.flagExcessHets(options.hets_file,
plot=True,
plot_path=options.plot_path)
flags.to_csv(options.stdout, index=None, sep="\t")
elif options.task == "merge_exclusions":
exclusions = gwas.mergeQcExclusions(hets_file=options.hets_file,
inbred_file=options.inbreed_file,
related_file=options.relations,
gender_file=options.gender_check,
mask_file=options.aux_file)
exclusions.to_csv(options.stdout, index=None, sep="\t")
elif options.task == "find_inbreds":
inbreds = gwas.flagInbred(inbred_file=options.inbreed_file,
inbreeding_coefficient=options.inbred_coeff,
ibc_threshold=options.inbred_cutoff,
plot=True,
plot_path=options.plot_path)
inbreds.to_csv(options.stdout, sep="\t", index=None)
elif options.task == "flag_relations":
# the input file is likely to be huge! Ergo, read the file in chunks
# calculate any related individuals and store them, store
# an array of IBD values for plotting, drop the rest
relate = gwas.flagRelated(ibd_file=options.relations,
chunk_size=500000,
threshold=options.ibs_cutoff,
plot=True,
plotting_path=options.plot_path)
elif options.task == "discordant_gender":
sex_discord = gwas.flagGender(gender_file=options.gender_check,
plot=True,
plot_path=options.plot_path)
sex_discord.to_csv(options.stdout, index=None, sep="\t")
else:
pass
# write footer and output benchmark information.
E.Stop()
def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if argv is None:
argv = sys.argv
# setup command line parser
parser = E.OptionParser(version="%prog version: $Id$",
usage=globals()["__doc__"])
parser.add_option("-t",
"--test",
dest="test",
type="string",
help="supply help")
parser.add_option("-p",
"--plot-type",
dest="plot_type",
type="choice",
choices=[
"histogram", "barplot", "density", "boxplot",
"scatter", "map", "pca"
],
help="the plot type to generate")
parser.add_option("--plot-n-pc",
dest="n_pcs",
type="int",
help="The number of principal components to "
"plot")
parser.add_option("-g",
"--group-by",
dest="group_by",
type="string",
help="column header to group observations by")
parser.add_option("-x",
"--x-column",
dest="x_col",
type="string",
help="column to plot on X axis")
parser.add_option("-y",
"--y-column",
dest="y_col",
type="string",
help="column to plot on y axis")
parser.add_option("-i",
"--index_column",
dest="indx",
type="string",
help="column number that refers to the row index")
parser.add_option("--output-file",
dest="outfile",
type="string",
help="path and filename to save plot to")
parser.add_option("--labels",
dest="labels",
type="string",
help="a comma-separated list of axis labels. "
"The first 2 correspond to the X and Y-axis, "
"respectively, and the third is the plot title")
parser.add_option("--metadata-file",
dest="meta_file",
type="string",
help="file containing metadata for annotating "
"plots with. Use `--group-labels` to define table "
"columns to use")
parser.add_option("--fam-file",
dest="fam_file",
type="string",
help="Plink .fam file containing file IDs")
parser.add_option("--xvar-labels",
dest="xvar_labs",
type="string",
help="a comma-separated list of variable X labels"
"only applies when X is a discrete or categorical "
"variable. The labels must be in the correct order")
parser.add_option("--group-labels",
dest="group_labs",
type="string",
help="a comma-separated list of grouping variable "
"labels. Can only apply when the grouping variable "
"is discrete or categorical. The labels must be "
"input in the order of the data")
parser.add_option("--yvar-labels",
dest="yvar_labs",
type="string",
help="a comma-separated list of variable Y labels"
"only applies when Y is a discrete or categorical "
"variable")
parser.add_option("--var-type",
dest="var_type",
type="choice",
choices=["continuous", "categorical", "integer"],
help="The data type of the variables to be plotted."
"The default is continuous")
parser.add_option("--coordinate-file",
dest="coordinates",
type="string",
help="file containing co-ordinates data")
parser.add_option("--coords-id-col",
dest="coord_ids",
type="string",
help="column header containing individual IDs")
parser.add_option("--lattitude-column",
dest="lat_col",
type="string",
help="column header containing lattitude co-ordinates")
parser.add_option("--longitude-column",
dest="long_col",
type="string",
help="column header containing longitude co-ordinates")
parser.add_option("--reference-value",
dest="ref_val",
type="string",
help="categorical variable level to dichotomise on")
# add common options (-h/--help, ...) and parse command line
(options, args) = E.Start(parser, argv=argv)
parser.set_defaults(y_col=None,
group_by=None,
indx=None,
labels="X,Y,title",
xvar_labs=None,
yvar_labs=None,
var_type="continuous")
infile = argv[-1]
df = pd.read_table(infile, sep="\t", index_col=options.indx, header=0)
if options.plot_type == "map":
df = pd.read_table(infile, sep="\t", index_col=options.indx, header=0)
coords_df = pd.read_table(options.coordinates,
sep="\t",
header=0,
index_col=options.indx)
gwas.plotMapPhenotype(data=df,
coords=coords_df,
coord_id_col=options.coord_ids,
lat_col=options.lat_col,
long_col=options.long_col,
save_path=options.outfile,
xvar=options.x_col,
var_type=options.var_type,
xlabels=options.xvar_labs,
level=options.ref_val)
elif options.plot_type == "pca":
data = gwas.parseFlashPCA(pcs_file=infile, fam_file=options.fam_file)
gwas.plotPCA(data=data,
nPCs=options.n_pcs,
point_labels=options.group_labs,
save_path=options.outfile,
headers=False,
metadata=options.meta_file,
multiplot=True)
else:
df = pd.read_table(infile, sep="\t", index_col=options.indx, header=0)
gwas.plotPhenotype(data=df,
plot_type=options.plot_type,
x=options.x_col,
y=options.y_col,
group=options.group_by,
save_path=options.outfile,
labels=options.labels,
xlabels=options.xvar_labs,
ylabels=options.yvar_labs,
glabels=options.group_labs,
var_type=options.var_type)
# write footer and output benchmark information.
E.Stop()
def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if argv is None:
argv = sys.argv
# setup command line parser
parser = E.OptionParser(version="%prog version: $Id$",
usage=globals()["__doc__"])
parser.add_option("--program",
dest="program",
type="choice",
choices=["plink2", "gcta", "plinkdev"],
help="program to execute genome-wide analysis")
parser.add_option("--input-file-pattern",
dest="infile_pattern",
type="string",
help="file prefix that identifies a group of files")
parser.add_option("--input-file-format",
dest="file_format",
type="choice",
choices=[
"plink", "plink_binary", "oxford", "oxford_binary",
"vcf", "GRM_binary", "GRM_gz", "GRM_plink"
],
help="format of input files")
parser.add_option("--phenotypes-file",
dest="pheno_file",
type="string",
help="text file of additional phenotypes")
parser.add_option("--pheno",
dest="pheno",
type="string",
help="either phenotype file column header or number")
parser.add_option("--covariates-file",
dest="covariate_file",
type="string",
help="file containing covariates. Used as the "
"continuous covariates in GCTA-based analyses")
parser.add_option("--covariate-column",
dest="covar_col",
type="string",
help="column number(s) or header(s) to include in "
"association model")
parser.add_option("--discrete-covariates-file",
dest="covariate_discrete",
type="string",
help="file containing discrete covariates "
"to adjust for in GCTA-based analyses")
parser.add_option("--association-model",
dest="assoc_model",
type="choice",
choices=["recessive", "dominant", "genotype"],
help="model to report from association analysis")
parser.add_option("--method",
dest="method",
type="choice",
choices=[
"association", "summary", "format", "matrix", "reml",
"bivariate_reml", "pca", "lmm", "simulation",
"epistasis", "ld", "estimate_haplotypes"
],
help="method to apply to genome-wide data")
parser.add_option("--reml-method",
dest="reml_method",
type="choice",
choices=[
"standard", "priors", "reml_algorithm",
"unconstrained", "GxE", "LRT", "BLUP_EBV", "snpBLUP",
"no_residual", "fixed_cor"
],
help="method for REML estimate of heritability method "
"including either single or dual phenotypes")
parser.add_option("--reml-parameters",
dest="reml_param",
type="string",
help="comma separated list of parameters to pass to "
"REML variance components analysis")
parser.add_option("--prevalence",
dest="prevalence",
type="float",
help="binary trait prevalence in a cohort study. "
"Used to estimate h2 on the liability threshold "
"scale.")
parser.add_option("--lmm-method",
dest="lmm_method",
type="choice",
choices=["standard", "loco", "no_covar"],
help="type of linear mixed model analysis to run")
parser.add_option("--grm-prefix",
dest="grm_prefix",
type="string",
help="prefix of the pre-computed GRM files to use "
"in the linear mixed model analysis")
parser.add_option(
"--epistasis-method",
dest="epi_method",
type="choice",
choices=["fast_epistasis", "epistasis", "two_locus", "adjusted"],
help="epistasis method to use")
parser.add_option("--epistasis-parameter",
dest="epi_param",
type="string",
help="modifiers of epistasis functions")
parser.add_option("--epistasis-threshold",
dest="epi_sig",
type="string",
help="statistical significance threshold for counting "
"interactions")
parser.add_option("--epistasis-report-threshold",
dest="epi_report",
type="string",
help="threshold used to count the "
"proportion of statistically significant interactions")
parser.add_option("--set-file",
dest="set_file",
type="string",
help="file containing variant sets as per Plink "
".set file specification")
parser.add_option("--set-method",
dest="set_method",
type="choice",
choices=["set-by-all", "set-by-set"],
help="set method to use when `set_file` provided")
parser.add_option("--principal-components",
dest="num_pcs",
type="int",
help="the number of principal components to output")
parser.add_option("--matrix-shape",
dest="matrix_shape",
type="choice",
choices=["triangle", "square", "square0"],
help="output matrix shape.",
default="triangle")
parser.add_option("--matrix-compression",
dest="matrix_compress",
type="choice",
choices=["gz", "bin", "bin4"],
help="compression to apply to output matrix")
parser.add_option("--matrix-form",
dest="matrix_form",
type="choice",
choices=["distance", "grm"],
help="type of relationship matrix to calculate")
parser.add_option(
"--matrix-metric",
dest="matrix_metric",
type="choice",
choices=["fhat", "cov", "ibc2", "ibc3", "ibs", "genomic", "hamming"],
help="value to calculate for diagonal elements of the "
"grm. Default is fhat for grm and hamming for distance.")
parser.add_option(
"--matrix-options",
dest="matrix_options",
type="string",
help="modifiers of matrix output, see plink documentation "
"for details")
parser.add_option("--association-method",
dest="assoc_method",
type="choice",
choices=["linear", "logistic", "assoc", "qassoc"],
help="association analysis to run")
parser.add_option(
"--permutation",
dest="permutation",
action="store_true",
help="perform association testing by permutation analysis")
parser.add_option("--repeats",
dest="n_perms",
type="int",
help="number of repetitions for permutation analysis")
parser.add_option("--association-options",
dest="assoc_option",
type="string",
help="association analysis modifiers")
parser.add_option("--format-method",
dest="format_method",
type="choice",
choices=[
"change_format", "change_missing_values",
"update_variants", "update_samples", "flip_strands",
"flip_scan", "sort", "merge", "find_duplicates"
],
help="file formatting to apply to input files")
parser.add_option("--format-parameter",
dest="format_param",
type="string",
help="formatting parameter, where appropriate")
parser.add_option(
"--reformat-type",
dest="reformat",
type="choice",
choices=["plink", "plink_binary", "oxford", "oxford_binary", "raw"],
help="new format of input files to be reformatted to")
parser.add_option("--apply-missing",
dest="apply_missing",
type="choice",
choices=["genotype", "phenotype"],
help="genotype or phenotype missing values to alter")
parser.add_option("--update-variant-attribute",
dest="variant_update",
type="choice",
choices=[
"variant_ids", "missing_id", "chromosome",
"centimorgan", "name", "alleles", "map"
],
help="update variant attributes")
parser.add_option("--update-sample-attribute",
dest="sample_update",
type="choice",
choices=["sample_ids", "parents", "gender"],
help="sample attributes to be updated")
parser.add_option("--strand-flip-subset",
dest="flip_subset",
action="store_true",
help="apply strand flipping to a subset of samples")
parser.add_option("--flip-scan-type",
dest="scan_param",
type="choice",
choices=["default", "window", "threshold"],
help="strand flipping scan to apply to SNPs")
parser.add_option("--sort-type",
dest="sort_type",
type="choice",
choices=["none", "natural", "ascii", "file"],
help="sort type to input files")
parser.add_option("--merge-file-format",
dest="merge_format",
type="choice",
choices=["plink", "plink_binary"],
help="format of input files to be merged")
parser.add_option(
"--merge-mode",
dest="merge_mode",
type="choice",
choices=[
"default", "original_missing", "new_nonmissing", "no_overwrite",
"force", "report_all", "report_nonmissing"
],
help="merge mode to apply to dealing with merge conflicts")
parser.add_option("--duplicates-method",
dest="dup_method",
type="choice",
choices=["same_ref", "id_match", "suppress_first"],
help="method for identifying and dealing with duplicate "
"variants")
parser.add_option("--summary-method",
dest="summary_method",
type="choice",
choices=[
"allele_frequency", "missing_data", "hardy_weinberg",
"mendel_errors", "inbreeding", "gender_checker",
"wrights_fst", "case_control_fst"
],
help="summary statistics to calculate")
parser.add_option("--summary-parameter",
dest="sum_param",
type="string",
help="optional parameters that can be passed to summary "
"statistics methods")
parser.add_option("--haplotype-frequency",
dest="filt_haplotype_frequency",
type="string",
help="min allele frequency for SNPs to be "
"considered for a haplotype")
parser.add_option("--haplotype-size",
dest="filt_haplotype_size",
type="string",
help="maximum genomic size of "
"haplotypes")
parser.add_option("--ld-statistic",
dest="ld_stat",
type="choice",
choices=["r", "r2"],
help="compute either the raw "
"inter variant allele count correlation, R, or the "
"squared correlation, R^2")
parser.add_option("--ld-min",
dest="ld_min",
type="string",
help="minimum value to report for pair-wise LD "
"calculations. Beware output files may be very "
"large if `ld_min` is very small.")
parser.add_option("--ld-window",
dest="ld_window",
type="string",
help="distance between SNPs, beyond which LD will "
"not be calculated")
parser.add_option("--ld-format-output",
dest="ld_shape",
type="choice",
choices=["square", "table", "triangle", "square0"],
help="output either as table, or matrix format with a "
"specific shape.")
parser.add_option(
"--genotype-rate",
dest="filt_genotype_rate",
type="string",
help="genotyping rate threshold. SNPs below this threshold "
"will be excluded from analysis")
parser.add_option("--indiv-missing",
dest="filt_missingness",
type="string",
help="individual missingness rate. Individuals below "
"this threshold will be excluded from analysis")
parser.add_option("--hardy-weinberg",
dest="filt_hwe",
type="string",
help="hardy-weinberg p-value threshold for SNPs. SNPs "
"with a 2df chisquared p-value below this will be "
"filtered out")
parser.add_option(
"--min-allele-frequency",
dest="filt_min_allele_frequency",
type="string",
help="only include SNPs with an allele frequency equal to "
"or above this threshold")
parser.add_option(
"--max-allele-frequency",
dest="filt_max_allele_frequency",
type="string",
help="only include SNPs with an allele frequency equal to "
"or below this threshold")
parser.add_option(
"--mendelian-error",
dest="filt_mendelian_error",
type="string",
help="exclude individuals/trios with mendelian errors that "
"exceed this value")
parser.add_option("--keep-individuals",
dest="filt_keep",
type="string",
help="a file containing individuals IDs to keep, "
"one per row")
parser.add_option("--remove-individuals",
dest="filt_remove",
type="string",
help="a file of individual IDs to remove, one per row")
parser.add_option("--min-quality-score",
dest="filt_min_qaul_score",
type="string",
help="reset the minimum low bound of quality scores for "
"variants in a VCF file. Default is 0")
parser.add_option(
"--max-quality-score",
dest="filt_max_qual_score",
type="string",
help="reset the maximum upper bound of quality scores for "
"a VCCF file. Default is Inf")
parser.add_option("--allow-no-gender",
dest="filt_allow_no_sex",
type="string",
help="allow individuals with gender missing")
parser.add_option("--enforce-gender",
dest="filt_enforce_sex",
type="string",
help="only include individuals with non-missing gender "
"information")
parser.add_option("--subset-filter",
dest="filt_subset_filter",
type="choice",
choices=[
"cases", "controls", "males", "females", "founders",
"nonfounders"
],
help="only apply filters to the specific subset of "
"individuals supplied")
parser.add_option(
"--extract-snps",
dest="filt_extract",
type="string",
help="text file of variant IDs to include in the analysis, "
"ignoring all others")
parser.add_option("--exclude-snps",
dest="filt_exclude",
type="string",
help="a file of variant IDs to exclude from analysis")
parser.add_option("--restrict-chromosome",
dest="filt_chromosome",
type="string",
help="restict analysis to either a single chromosome, "
"or a comma-separated list of chromosomes")
parser.add_option("--exclude-chromosomes",
dest="filt_exclude_chromosome",
type="string",
help="exclude all variants on these "
"chromosome(s)")
parser.add_option(
"--autosome-only",
dest="filt_autosome",
action="store_true",
help="if present only autosomal variants will be analysed")
parser.add_option(
"--pseudo-autosome",
dest="filt_pseudo_autosome",
action="store_true",
help="include on the pseudo-autosomal region of chromosome X")
parser.add_option("--ignore-indels",
dest="filt_ignore_indels",
action="store_true",
help="only include bi-allelic single nucleotide "
"variants in analysis")
parser.add_option(
"--snp-range",
dest="filt_snp_bp_range",
type="string",
help="comma separated list of from, to genome co-ordinates "
"within which to include variants for analysis")
parser.add_option(
"--conditional-snp",
dest="filt_conditional_snp",
type="string",
help="condition the analysis on this SNP ID. Can only be "
"used in the linear and logistic regression models.")
parser.add_option("--snp-id-range",
dest="filt_snp_id_range",
type="string",
help="comma separate list of IDs from, to within which "
"to include variants for analysis.")
parser.add_option("--snp-id",
dest="filt_specific_snp",
type="string",
help="include a single snp in the analysis given by "
"it's variant ID.")
parser.add_option("--exclude-variant",
dest="filt_exclude_snp",
type="string",
help="exclude a single variant from the analysis, "
"given by it's variant ID")
parser.add_option(
"--covariate-filter",
dest="filt_covariate_filter",
type="string",
help="covariate column headers or column numbers on which "
"to filter on. Requries --covariate-file")
parser.add_option(
"--filter-parameter",
dest="param",
type="string",
help="parameter values to be passed to filtering function")
parser.add_option("--window-size",
dest="window_size",
type="string",
help="alters the behaviour of the --snp-range and "
"--include/exclude snp options. variants within +/- "
"half * window_size (kb) are included")
parser.add_option(
"--range-resolution",
dest="filt_range_resolution",
type="choice",
choices=["bp", "kb", "mb"],
help="alters the (from, to) range resolution to either bp, "
"kb or mb")
parser.add_option(
"--output-file-pattern",
dest="out_pattern",
type="string",
help="output file pattern prefix. file suffixes are dependent "
"on the task executed")
parser.add_option("--threads",
dest="threads",
type="int",
help="the number of threads to use for multi-threaded "
"processes")
parser.add_option("--memory",
dest="memory",
type="string",
help="amount of memory to reserve for the task")
parser.add_option("--parallel",
dest="parallel",
type="int",
help="number of jobs to split task into")
# add common options (-h/--help, ...) and parse command line
(options, args) = E.Start(parser, argv=argv)
parser.set_defaults(sum_param=None,
dup_method="same_ref",
n_perms=None,
permutation=False,
matrix_shape="triangle",
matrix_options=None,
matrix_compress="gz",
random_seed=random.randint(0, 19999),
sample_update=None,
memory="60G",
parallel=None,
covariate_file=None,
covar_col=None,
epi_report=0.001,
epi_sig=0.001)
if not options.infile_pattern:
infiles = (argv[-1]).split(",")
else:
infiles = options.infile_pattern
# create a new filegroup object
geno_files = gwas.FileGroup(files=infiles,
file_format=options.file_format,
genotype_format="imputed")
if options.pheno_file:
geno_files.set_phenotype(pheno_file=options.pheno_file,
pheno=options.pheno)
else:
pass
# add FileGroup object to the gwas program object
if options.program == "plink2":
gwas_object = gwas.Plink2(files=geno_files)
gwas_object.program_call(infiles=geno_files,
outfile=options.out_pattern)
elif options.program == "plinkdev":
gwas_object = gwas.PlinkDev(files=geno_files)
gwas_object.program_call(infiles=geno_files,
outfile=options.out_pattern)
elif options.program == "gcta":
gwas_object = gwas.GCTA(files=geno_files)
gwas_object.program_call(infiles=geno_files,
outfile=options.out_pattern)
else:
pass
# collect filtering options from options
opt_dict = options.__dict__
filter_keys = [fx for fx in opt_dict.keys() if re.search("filt", fx)]
filter_dict = {k: options.__dict__[k] for k in filter_keys if opt_dict[k]}
# iteratively add genotype filters to GWASProgram object
for fkey in filter_dict:
filt_key = fkey.lstrip("filt_")
filter_value = filter_dict[fkey]
gwas_object.apply_filters(filter_type=filt_key,
filter_value=filter_value)
# handle summary statistics
if options.method == "summary":
if options.summary_method == "allele_frequency":
gwas_object._output_statistics(allele_frequency=options.sum_param)
elif options.summary_method == "hardy_weinberg":
gwas_object._output_statistics(hardy_weinberg=options.sum_param)
elif options.summary_method == "missing_data":
gwas_object._output_statistics(missing_data=options.sum_param)
elif options.summary_method == "mendel_errors":
gwas_object._output_statistics(mendel_errors=options.sum_param)
elif options.summary_method == "inbreeding":
gwas_object._output_statistics(inbreeding=options.sum_param)
elif options.summary_method == "gender_checker":
gwas_object._output_statistics(gender_checker=options.sum_param)
elif options.summary_method == "wrights_fst":
gwas_object._output_statistics(wrights_fst=options.sum_param)
elif options.summary_method == "case_control_fst":
gwas_object._output_statistics(case_control_fst=options.sum_param)
else:
pass
elif options.method == "pca":
gwas_object.PCA(n_pcs=options.num_pcs)
elif options.method == "ld":
gwas_object.calc_ld(ld_statistic=options.ld_stat,
ld_threshold=float(options.ld_min),
ld_shape=options.ld_shape)
elif options.method == "association":
gwas_object.run_association(association=options.assoc_method,
permutation=options.permutation,
n_perms=options.n_perms,
random_seed=options.random_seed,
covariates_file=options.covariate_file,
covariates=options.covar_col)
elif options.method == "estimate_haplotypes":
gwas_object._run_tasks(estimate_haplotypes="haplotype")
elif options.method == "lmm":
print options.lmm_method
gwas_object.mixed_model(lmm_method=options.lmm_method,
grm=options.grm_prefix,
qcovar=options.covariate_file,
dcovar=options.covariate_discrete)
elif options.method == "epistasis":
gwas_object._detect_interactions(
method=options.epi_method,
modifier=options.epi_param,
set_file=options.set_file,
set_mode=options.set_method,
report_threshold=options.epi_report,
sig_threshold=options.epi_sig,
covariates_file=options.covariate_file,
covariates=options.covar_col)
elif options.method == "reml":
gwas_object.reml_analysis(method=options.reml_method,
parameters=options.reml_param,
prevalence=options.prevalence,
qcovariates=options.covariate_file,
discrete_covar=options.covariate_discrete)
elif options.method == "format":
if options.format_method == "change_format":
# adding filtering options to plink requires the --make-bed flag
try:
update_samples = opt_dict["sample_update"]
if update_samples:
E.info("updating samples from %s" % options.format_param)
gwas_object._run_tasks(change_format=options.reformat,
parameter=options.format_param)
gwas_object._run_tasks(
update_samples=options.sample_update,
parameter=options.format_param)
else:
gwas_object._run_tasks(change_format=options.reformat,
parameter=options.format_param)
except KeyError:
gwas_object._run_tasks(change_format=options.reformat,
parameter=options.format_param)
elif options.format_method == "change_missing_values":
gwas_object._run_tasks(change_missing_values=options.apply_missing,
parameter=options.format_param)
elif options.format_method == "update_variants":
gwas_object._run_tasks(update_variants=options.variant_update,
parameter=options.format_param)
gwas_object._run_tasks(change_format=options.file_format)
elif options.format_method == "update_samples":
gwas_object._run_tasks(update_samples=options.sample_update,
parameter=options.format_param)
elif options.format_method == "flip_strands":
if options.flip_subset:
gwas_object._run_tasks(flip_strands="subset",
parameter=options.format_param)
else:
gwas_object._run_tasks(flip_strands="all_samples",
parameter=options.format_param)
elif options.format_method == "flip_scan":
gwas_object._run_tasks(flip_scan=options.scan_param,
parameter=options.format_param)
elif options.format_method == "sort":
gwas_object._run_tasks(sort=options.sort_type,
parameter=options.format_param)
elif options.format_method == "merge":
if options.merge_mode:
gwas_object._run_tasks(merge_mode=options.merge_mode,
parameter=options.format_param)
else:
gwas_object._run_tasks(merge=options.merge_format,
parameter=options.format_param)
elif options.format_method == "find_duplicates":
gwas_object._run_tasks(find_duplicates=options.dup_method,
parameter=options.format_param)
else:
pass
elif options.method == "matrix":
if options.matrix_form == "distance":
if options.matrix_metric == "hamming":
gwas_object.hamming_matrix(shape=options.matrix_shape,
compression=options.matrix_compress,
options=options.matrix_options)
elif options.matrix_metric == "ibs":
gwas_object.ibs_matrix(shape=options.matrix_shape,
compression=options.matrix_compress,
options=options.matrix_options)
elif options.matrix_metric == "genomic":
gwas_object.genome_matrix(shape=options.matrix_shape,
compression=options.matrix_compress,
options=options.matrix_options)
elif options.matrix_form == "grm":
gwas_object.genetic_relationship_matrix(
shape=options.matrix_shape,
compression=options.matrix_compress,
metric=options.matrix_metric,
options=options.matrix_options)
else:
pass
gwas_object.build_statement(infiles=geno_files,
outfile=options.out_pattern,
threads=options.threads,
memory=options.memory,
parallel=options.parallel)
# write footer and output benchmark information.
E.Stop()
def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if argv is None:
argv = sys.argv
# setup command line parser
parser = E.OptionParser(version="%prog version: $Id$",
usage=globals()["__doc__"])
parser.add_option("-t", "--test", dest="test", type="string",
help="supply help")
parser.add_option("--plot-type", dest="plot_type", type="choice",
choices=["manhattan", "qqplot", "epistasis"],
help="plot type to generate")
parser.add_option("--resolution", dest="resolution", type="choice",
choices=["genome_wide", "chromosome",
"fine_map"],
help="the resolution of plotting, wether the plot "
"depicts the whole genome, a single chromosome or "
"a specific locus")
parser.add_option("--save-path", dest="save_path", type="string",
help="path and filename to save image to")
# add common options (-h/--help, ...) and parse command line
(options, args) = E.Start(parser, argv=argv)
parser.set_defaults(resolution="genome_wide",
plot_type="manhattan")
# if the input is a list of files, split them
infile = argv[-1]
infiles = infile.split(",")
# need to parse epistasis output slightly differently
if options.plot_type == "epistasis":
epi = True
else:
epi = False
if len(infiles) > 1:
results = gwas.GWASResults(assoc_file=infiles,
epistasis=epi)
elif len(infiles) == 1:
results = gwas.GWASResults(assoc_file=infile,
epistasis=epi)
else:
raise IOError("no input files detected, please specifiy association "
"results files as the last command line argument")
if options.plot_type == "manhattan":
df = results.plotManhattan(resolution=options.resolution,
save_path=options.save_path)
elif options.plot_type == "qqplot":
results.plotQQ(save_path=options.save_path,
resolution=options.resolution)
elif options.plot_type == "epistasis":
results.plotEpistasis(save_path=options.save_path,
resolution=options.resolution)
else:
pass
# only output appended results for Manhattan plot, not qqplot
try:
df.to_csv(options.stdout, sep="\t", index_col=None)
except UnboundLocalError:
pass
# write footer and output benchmark information.
E.Stop()
def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if argv is None:
argv = sys.argv
# setup command line parser
parser = E.OptionParser(version="%prog version: $Id$",
usage=globals()["__doc__"])
parser.add_option("-t",
"--test",
dest="test",
type="string",
help="supply help")
parser.add_option("--method",
dest="method",
type="choice",
choices=["cases_explained", "probability_phenotype"],
help="Which results to report, either the proportion "
"of cases explained or the probability of the "
"phenotype given the number of alleles carried")
parser.add_option("--map-file",
dest="map_file",
type="string",
help="plink .map file with SNP positions")
parser.add_option("--ped-file",
dest="ped_file",
type="string",
help="plink ped file with phenotype and "
"genotype data - A2 major allele coded")
parser.add_option("--gwas-file",
dest="gwas",
type="string",
help="gwas results file, assumes Plink "
"output format. Must contain SNP, BP, "
"OR column headers. Assumes results relate "
"to the A1 allele")
parser.add_option("--flip-alleles",
dest="flip",
action="store_true",
help="force alleles to flip if OR < 1")
parser.add_option("--plot-statistic",
dest="plot_stat",
type="choice",
choices=["frequency", "cumulative"],
help="plot either cases frequency or cumulative "
"frequency of cases")
parser.add_option("--plot-path",
dest="plot_path",
type="string",
help="save path for plot")
parser.add_option("--flag-explained-recessive",
dest="explained",
action="store_true",
help="flag individuals explained by carriage of "
"2 risk alleles - NOT IMPLIMENTED")
# add common options (-h/--help, ...) and parse command line
(options, args) = E.Start(parser, argv=argv)
# required files are .ped file, .map file and gwas results file
E.info("reading GWAS results file: %s" % options.gwas)
snp_df = pd.read_table(options.gwas, sep="\t", header=0, index_col=None)
snp_list = snp_df["SNP"].values
# parse ped file
E.info("Reading ped file: %s" % options.ped_file)
ped_df = gwas.parsePed(options.ped_file, compound_geno=True)
# parse map file and get SNP indices that correspond to
# ped file genotypes
E.info("Fetching SNPs from map file: %s" % options.map_file)
snp_index = gwas.getSNPs(options.map_file, snp_list)
E.info("SNPs found: %i" % len(snp_index))
# extract SNPs and ORs as key, value pairs
or_dict = snp_df.loc[:, ["SNP", "OR"]].to_dict(orient='list')
snp_or = dict(zip(or_dict["SNP"], or_dict["OR"]))
if options.flip:
E.info("Flipping major alleles to risk alleles")
flipped_genos = gwas.flipRiskAlleles(snp_index=snp_index,
snp_results=snp_or,
genos=ped_df["GENOS"].tolist())
# merge flipped genotypes with pedigree frame to get phenotypes
geno_df = pd.DataFrame(flipped_genos, index=ped_df["FID"])
else:
# split genos into a dataframe
genos = np.array(ped_df["GENOS"].tolist())
geno_df = pd.DataFrame(genos, index=ped_df["FID"])
merged = pd.merge(geno_df, ped_df, left_index=True, right_on="FID")
# need to discount missing genotypes > 1%
# frequencies of number of risk alleles by trait frequency
E.info("count #risk alleles per individual")
risk_results = gwas.countRiskAlleles(ped_frame=merged,
snp_index=snp_index.values(),
report=options.method,
flag=options.explained)
risk_freqs = risk_results["freqs"]
cumulative = risk_results["cumulative"]
# select results upto and including cumulative freq = 1.0
max_indx = [fx for fx, fy in enumerate(cumulative) if fy == 1.0][0]
max_freqs = risk_freqs[:max_indx + 1]
max_cum = cumulative[:max_indx + 1]
bins = [ix for ix, iy in enumerate(cumulative)][:max_indx + 1]
# plot!
if options.plot_stat == "frequency":
E.info("Generating plot of #risk alleles vs. P(Phenotype)")
hist_df = gwas.plotRiskFrequency(bins=bins,
frequencies=max_freqs,
savepath=options.plot_path,
ytitle="P(Phenotype)")
elif options.plot_stat == "cumulative":
E.info("Generating plot of #risk alleles vs. cumulative frequency")
hist_df = gwas.plotRiskFrequency(bins=bins,
frequencies=max_cum,
savepath=options.plot_path,
ytitle="Cumulative frequency cases")
hist_df["freq"] = risk_results["freqs"][:max_indx + 1]
hist_df["cumulative"] = risk_results["cumulative"][:max_indx + 1]
hist_df["cases"] = risk_results["cases"][:max_indx + 1]
hist_df["controls"] = risk_results["controls"][:max_indx + 1]
hist_df["total"] = hist_df["cases"] + hist_df["controls"]
hist_df.to_csv(options.stdout, sep="\t", index=None)
# write footer and output benchmark information.
E.Stop()
def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if argv is None:
argv = sys.argv
# setup command line parser
parser = E.OptionParser(version="%prog version: $Id$",
usage=globals()["__doc__"])
parser.add_option("-t", "--test", dest="test", type="string",
help="supply help")
parser.add_option("--method", dest="method", type="choice",
choices=["cases_explained", "probability_phenotype"],
help="Which results to report, either the proportion "
"of cases explained or the probability of the "
"phenotype given the number of alleles carried")
parser.add_option("--map-file", dest="map_file", type="string",
help="plink .map file with SNP positions")
parser.add_option("--ped-file", dest="ped_file", type="string",
help="plink ped file with phenotype and "
"genotype data - A2 major allele coded")
parser.add_option("--gwas-file", dest="gwas", type="string",
help="gwas results file, assumes Plink "
"output format. Must contain SNP, BP, "
"OR column headers. Assumes results relate "
"to the A1 allele")
parser.add_option("--flip-alleles", dest="flip", action="store_true",
help="force alleles to flip if OR < 1")
parser.add_option("--plot-statistic", dest="plot_stat", type="choice",
choices=["frequency", "cumulative"],
help="plot either cases frequency or cumulative "
"frequency of cases")
parser.add_option("--plot-path", dest="plot_path", type="string",
help="save path for plot")
parser.add_option("--flag-explained-recessive", dest="explained",
action="store_true",
help="flag individuals explained by carriage of "
"2 risk alleles - NOT IMPLIMENTED")
# add common options (-h/--help, ...) and parse command line
(options, args) = E.Start(parser, argv=argv)
# required files are .ped file, .map file and gwas results file
E.info("reading GWAS results file: %s" % options.gwas)
snp_df = pd.read_table(options.gwas, sep="\t", header=0,
index_col=None)
snp_list = snp_df["SNP"].values
# parse ped file
E.info("Reading ped file: %s" % options.ped_file)
ped_df = gwas.parsePed(options.ped_file,
compound_geno=True)
# parse map file and get SNP indices that correspond to
# ped file genotypes
E.info("Fetching SNPs from map file: %s" % options.map_file)
snp_index = gwas.getSNPs(options.map_file,
snp_list)
E.info("SNPs found: %i" % len(snp_index))
# extract SNPs and ORs as key, value pairs
or_dict = snp_df.loc[:, ["SNP", "OR"]].to_dict(orient='list')
snp_or = dict(zip(or_dict["SNP"], or_dict["OR"]))
if options.flip:
E.info("Flipping major alleles to risk alleles")
flipped_genos = gwas.flipRiskAlleles(snp_index=snp_index,
snp_results=snp_or,
genos=ped_df["GENOS"].tolist())
# merge flipped genotypes with pedigree frame to get phenotypes
geno_df = pd.DataFrame(flipped_genos, index=ped_df["FID"])
else:
# split genos into a dataframe
genos = np.array(ped_df["GENOS"].tolist())
geno_df = pd.DataFrame(genos, index=ped_df["FID"])
merged = pd.merge(geno_df, ped_df, left_index=True, right_on="FID")
# need to discount missing genotypes > 1%
# frequencies of number of risk alleles by trait frequency
E.info("count #risk alleles per individual")
risk_results = gwas.countRiskAlleles(ped_frame=merged,
snp_index=snp_index.values(),
report=options.method,
flag=options.explained)
risk_freqs = risk_results["freqs"]
cumulative = risk_results["cumulative"]
# select results upto and including cumulative freq = 1.0
max_indx = [fx for fx, fy in enumerate(cumulative) if fy == 1.0][0]
max_freqs = risk_freqs[:max_indx + 1]
max_cum = cumulative[:max_indx + 1]
bins = [ix for ix, iy in enumerate(cumulative)][:max_indx + 1]
# plot!
if options.plot_stat == "frequency":
E.info("Generating plot of #risk alleles vs. P(Phenotype)")
hist_df = gwas.plotRiskFrequency(bins=bins,
frequencies=max_freqs,
savepath=options.plot_path,
ytitle="P(Phenotype)")
elif options.plot_stat == "cumulative":
E.info("Generating plot of #risk alleles vs. cumulative frequency")
hist_df = gwas.plotRiskFrequency(bins=bins,
frequencies=max_cum,
savepath=options.plot_path,
ytitle="Cumulative frequency cases")
hist_df["freq"] = risk_results["freqs"][:max_indx + 1]
hist_df["cumulative"] = risk_results["cumulative"][:max_indx + 1]
hist_df["cases"] = risk_results["cases"][:max_indx + 1]
hist_df["controls"] = risk_results["controls"][:max_indx + 1]
hist_df["total"] = hist_df["cases"] + hist_df["controls"]
hist_df.to_csv(options.stdout, sep="\t", index=None)
# write footer and output benchmark information.
E.Stop()
def main(argv=None):
"""script main.
parses command line options in sys.argv, unless *argv* is given.
"""
if argv is None:
argv = sys.argv
# setup command line parser
parser = E.OptionParser(version="%prog version: $Id$",
usage=globals()["__doc__"])
parser.add_option("--score-method",
dest="method",
type="choice",
choices=[
"PICS", "LDscore", "ABF", "R2_rank", "get_eigen",
"calc_prior", "credible_set", "summarise"
],
help="SNP scoring/prioritisation method to apply.")
parser.add_option("--database",
dest="database",
type="string",
help="SQL database containing LD information "
"in table format. Expects columns SNP_A, "
"SNP_B, R2, BP_A and BP_B (Plink --r2 output)")
parser.add_option("--ld-directory",
dest="ld_dir",
type="string",
help="directory containing tabix-index BGZIP "
"LD files. Assumes Plink used to calculate LD")
parser.add_option("--table-name",
dest="table",
type="string",
help="name of the SQL table containing the LD"
"values")
parser.add_option("--chromosome",
dest="chromosome",
type="string",
help="chromosome to subset the association results "
"file on")
parser.add_option("--ld-threshold",
dest="ld_threshold",
type="float",
help="the threshold of LD above which variants will "
"be taken forward.")
parser.add_option("--rank-threshold",
dest="rank_threshold",
type="float",
help="the threshold in terms of the top n% SNPs to "
"output based on the ranking metric. e.g. "
"--rank-threshold=0.01 is the top 1% SNPs")
parser.add_option("--credible-interval",
dest="interval",
type="float",
help="The credible set interval size to generate the "
"credible set of SNPs")
parser.add_option("--prior-variance",
dest="prior_var",
type="float",
help="the prior variance used to weight the SNP "
"variance")
parser.add_option("--fine-map-window",
dest="map_window",
type="int",
help="the region size to included around the index "
"SNP as the fine-mapping region.")
parser.add_option("--eigen-score-directory",
dest="eigen_dir",
type="string",
help="PATH to directory containing tabix indexed "
"eigen score files")
parser.add_option("--flat-prior",
dest="flat_prior",
action="store_true",
help="Ignore functional annotation information and "
"use an uninformative prior on each SNP")
parser.add_option("--snp-set",
dest="snp_set",
type="string",
help="Pre-defined SNP set as a list of SNP IDs."
"If used to calculate priors contains column of scores.")
parser.add_option(
"--distribution",
dest="dist",
type="choice",
choices=["normal", "t", "gamma", "lognormal", "exponential"],
help="distribution from which to draw prior "
"probabilities")
parser.add_option("--distribution-parameters",
dest="dist_params",
type="string",
help="distribution parameters as a comma-separated list")
parser.add_option("--lead-snp-id",
dest="lead_snp",
type="int",
help="0-based item number in filename")
parser.add_option("--filename-separator",
dest="separator",
type="string",
help="filename separator to extract information")
parser.add_option("--snp-column",
dest="snp_col",
type="int",
help="0-based index of SNP ID column number")
parser.add_option("--probability-column",
dest="prob_col",
type="int",
help="0-based index of posterior probabilities column"
" number")
parser.set_defaults(
ld_dir=None,
dist="normal",
dist_params=None,
snp_set=None,
prior_var=0.04,
interval=0.99,
eigen_dir=None,
map_window=100000,
ld_threshold=0.5,
database=None,
table=None,
flat_prior=False,
lead_snp=2,
separator="_",
snp_col=0,
prob_col=1,
)
# add common options (-h/--help, ...) and parse command line
(options, args) = E.Start(parser, argv=argv)
infile = argv[-1]
if len(infile.split(",")) > 1:
pass
else:
peek = pd.read_table(infile, nrows=5, sep="\s*", header=0)
try:
if len(peek["TEST"] != "ADD"):
clean = False
else:
clean = True
except KeyError:
clean = True
if options.method == "LDscore":
snpscores = gwas.snpPriorityScore(gwas_results=infile,
database=options.database,
table_name=options.table,
chromosome=options.chromosome,
ld_dir=options.ld_dir,
clean=clean)
# take top 1%, all SNPs doesn't achieve anything useful
ranks = int(len(snpscores.index) * 0.01)
snpscores = snpscores.iloc[:ranks]
elif options.method == "PICS":
snp_list = {}
if options.snp_set and not options.flat_prior:
with IOTools.openFile(options.snp_set, "r") as sfile:
for line in sfile.readlines():
snp = line.split("\t")[0]
try:
score = float(line.split("\t")[-1].rstrip("\n"))
except ValueError:
score = 0
snp_list[snp] = float(score)
# get the parameter estimates for the distribution
# if they have not been provided
if not options.dist_params:
dist_params = gwas.estimateDistributionParameters(
data=snp_list.values(), distribution=options.dist)
else:
dist_params = tuple(
[float(fx) for fx in options.dist_params.split(",")])
E.info("Calculating priors on SNPs")
priors = gwas.calcPriorsOnSnps(snp_list=snp_list,
distribution=options.dist,
params=dist_params)
elif options.snp_set and options.flat_prior:
with IOTools.openFile(options.snp_set, "r") as sfile:
for line in sfile.readlines():
snp = line.split("\t")[0]
snp_list[snp] = 1.0
priors = snp_list
else:
# allow for no priors or scores to be set,
# use of priors will be ignored,
# i.e. when prior and likelihood are not from
# conjugate distributions
priors = None
# PICS scores expects the gwas results file to
# only contain the region of interest, which
# represents an independent association signal
snpscores = gwas.PICSscore(gwas_results=infile,
database=options.database,
table_name=options.table,
chromosome=options.chromosome,
priors=priors,
clean=clean,
ld_dir=options.ld_dir,
ld_threshold=options.ld_threshold)
snpscores.columns = ["SNP", "PICS"]
posterior_sum = 0
snpscores.sort_values(ascending=False, inplace=True)
post_snps = []
for snp in snpscores.index:
if posterior_sum < 99.0:
posterior_sum += snpscores.loc[snp]
post_snps.append(snp)
else:
break
snpscores = snpscores.loc[post_snps]
snpscores.drop_duplicates(inplace=True)
elif options.method == "R2_rank":
# rank SNPs based on their LD with the lead
# SNP, take the top n% SNPs
snpscores = gwas.LdRank(gwas_results=infile,
database=options.database,
table_name=options.table,
ld_dir=options.ld_dir,
chromosome=options.chromosome,
ld_threshold=options.ld_threshold,
top_snps=options.rank_threshold,
clean=clean)
elif options.method == "ABF":
snpscores = gwas.ABFScore(gwas_results=infile,
region_size=options.map_window,
chromosome=options.chromosome,
prior_variance=options.prior_var,
clean=clean)
elif options.method == "get_eigen":
E.info("Fetching Eigen scores")
snpscores = gwas.getEigenScores(eigen_dir=options.eigen_dir,
bim_file=infile,
snp_file=options.snp_set)
snpscores = pd.DataFrame(snpscores).T
elif options.method == "credible_set":
E.info("Creating credible set")
snpscores = gwas.makeCredibleSet(probs_file=infile,
credible_set=options.interval,
lead_snp_indx=options.lead_snp,
filename_sep=options.separator,
snp_column=options.snp_col,
probs_column=options.prob_col)
elif options.method == "summarise":
E.info("Collating SNP prioritisation resuslts")
file_list = infile.split(",")
snpscores = gwas.summariseResults(file_list=file_list)
snpscores.to_csv(options.stdout, index_label="SNP", sep="\t")
# write footer and output benchmark information.
E.Stop()
