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Command-line arguments | Input | Output | Fine-mapping example | Correlation example

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FINEMAP is a program for identifying causal SNPs in genomic regions associated with complex traits and disease. FINEMAP is computationally efficient by using summary statistics from genome-wide association studies and robust by using a shotgun stochastic search algorithm (Hans et al., 2007). It produces accurate results in a fraction of processing time of existing approaches and is therefore the ideal tool for analyzing growing amounts of data produced in genome-wide association studies and emerging sequencing or biobank projects.

Download

(license)

• Be aware that FINEMAP v1.1 cannot handle large effect size regions!
• finemap_v1.1_MacOSX.tgz (Mac OS X)
• finemap_v1.1_x86_64.tgz (Unix)
• (Updated 20-July-2016)
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• To receive email reminders about updates of FINEMAP, send an email to finemap@christianbenner.com.

Command-line arguments

--corr		Determine highly correlated SNPs		Subprogram
--corr-config		The posterior probability of a causal configuration is set to zero if it includes a pair of SNPs with absolute correlation above this threshold		Default is 0.95 (with --sss)
--corr-filter		SNPs are discarded such that no pair of SNPs remains with absolute correlation greater above this threshold		Default is 0.95 (with --corr)
--help		Command-line help
--in-files		Master file (see below)		With --sss/--corr
--log		Option to write output to log files specified in column 'log' in the master file		No log files are written by default
--n-causal-max		Maximum number of allowed causal SNPs		Default is 5
--n-configs-top		Number of top causal configurations to be saved		Default is 50000
--n-convergence		Number of iterations that the added probability mass is required to be below the specified threshold (--prob-tol) before the shotgun stochastic search is terminated		Default is 1000
--n-iterations		Maximum number of iterations before the shotgun stochastic search is terminated		Default is 100000
--prior-k		Option to use prior probabilities for the number of causal SNPs as specified in K files (see below) in the master file		SNPs are by default assumed to be causal with probability 1 / (# of SNPs in the genomic region)
--prior-k0		Prior probability that there is no causal SNP in the genomic region. Only used when computing posterior probabilities for the number of causal SNPs but not during fine-mapping itself		Default is 0.0
--prior-std		Prior standard deviation of effect sizes		Default is 0.05
--prob-tol		Tolerance at which the added probability mass (over --n-convergence iterations) is considered small enough to terminate the shotgun stochastic search		Default is 0.001
--regions		Option to specify delimiter-separated list of datasets for fine-mapping as given in the master file (e.g. 1,2 or 1|2)		All regions are processed by default
--sss		Fine-mapping with shotgun stochastic search		Subprogram

Input

(1) Master file

The master file is a semicolon-separated text file and contains no space. It contains the following column names and one dataset per line.

• z column contains the names of Z files (input).

• ld column contains the names of LD files (input).

• snp column contains the names of SNP files (output).

• config column contains the names of CONFIG files (output).

• n-ind column contains the GWAS sample sizes.

• k column contains the optional K files (input).

• log column contains the optional LOG files (output).

• File extensions must correspond with the column names in the header line!

A master file with two datasets could look as follows.

z;ld;snp;config;log;n-ind

dataset1.z;dataset1.ld;dataset1.snp;dataset1.config;dataset1.log;5363

dataset2.z;dataset2.ld;dataset2.snp;dataset2.config;dataset2.log;5363

(2) Z file

The dataset.z file is a space-delimited text file. It contains the following two columns and one SNP per line.

• 1st column contains the SNP identifiers. The identifier can be a rsID number or combination of chromosome name and genomic position (e.g. XXX:yyy).

• 2nd column contains the z-scores.

A dataset.z file with three SNPs could look as follows.

rs1 0.240

rs2 0.483

rs3 1.145

• The order of SNPs in dataset.z must correspond to the order of SNPs in dataset.ld!

(3) LD file

The dataset.ld file is a space-delimited text file and contains the SNP correlation matrix (Pearson's correlation). The order of the SNPs in the dataset.ld must correspond to the order of variants in dataset.z.

A dataset.ld file with three SNPs could look as follows.

1.00 0.95 0.98

0.95 1.00 0.96

0.97 0.96 1.00

• Ideally, the SNP correlation matrix is computed from the genotype data on the same samples from which the GWAS summary statistics orginate. Read here what could happen if SNP correlations from reference genotypes (e.g. 1000 Genomes Project) do not match well with the GWAS summary statistics.

(4) Optional K file

By default, FINEMAP assumes that SNPs are causal with prior probability 1 / (# of SNPs in the genomic region). As an alternative, it is possible to specify prior probabilities for the number of causal SNPs in the genomic region by using a dataset.k file. This is a space-delimited text file and contains the prior probabilities p_k = Pr(# of causal SNPs is k) for k = 1,...,K, where K is the number of entries in the dataset.k file. The prior probabilities must be non-negative and will be normalized to sum to one.

A dataset.k file allowing for three causal SNPs with p₁ = 0.6, p₂ = 0.3 and p₃ = 0.1 would look as follows.

0.6 0.3 0.1

• We assume that the genomic region includes at least one causal SNP and thus p₀ = 0. A non-zero prior probability p₀ that there is no causal SNP in the genomic region can be specified with the command-line argument --prior-k0. This value is only used when computing posterior probabilities p_k|data = Pr(# of causal SNPs is k | data) but not during fine-mapping itself. We further assumen that p_k = 0 for k = K +1,...,m, where m is the number of SNPs in the dataset.z file.

Output

(1) SNP file

The dataset.snp file is a space-delimited text file. It contains the model-averaged posterior summaries for each SNP one per line.

• index column contains the line numbers in which SNPs appear in the dataset.z file.

• snp column contains the SNP identifiers.

• snp_prob column contains the marginal Posterior Inclusion Probabilities (PIP). The PIP for the l th SNP is the posterior probability that this SNP is causal. It is computed by summing up the posterior probabilities of all causal configurations in the dataset.config file in which l th SNP is included.

• snp_log10bf column contains the log₁₀ Bayes factors. The Bayes factor quantifies the evidence that the l th SNP is causal with log₁₀ Bayes factors greater than 2 reporting decisive evidence.

(2) CONFIG file

The dataset.config file is a space-delimited text file. It contains the posterior summaries for each causal configuration one per line.

• rank column contains the ranking.

• config column contains the SNP identifiers.

• config_prob column contains the posterior probability. For a causal configuration γ, an entry in this column is the posterior probability that SNPs in this configuration are causal.

• config_log10bf column contains the log₁₀ Bayes factors. The Bayes factor quantifies the evidence for a causal configuration γ over the null configuration γ₀.

(3) LOG file

The dataset.log file outputs additional information. It contains the following output.

• Posterior probabilities Pr(# of causal SNPs is k | data) for k = 1,...,K, where K is the maximum number of allowed causal SNPs.

• A log₁₀ Bayes factor to quantify the evidence of at least one causal SNP in the genomic region.

Fine-mapping example

Using genotype data with 50 SNPs and 5363 individuals, a quantitative phenotype was simulated using a linear model with 2 causal SNPs. Single-SNP testing was performed to obtain z-scores. SNP correlations were computed from individual-level genotype data.

Fine-mapping the SNPs in genomic region 1 in the example folder is done follows:

./finemap --sss --in-files example/data --regions 1

Correlation example

The same data as in the fine-mapping example above are used. Repairing non-positive definiteness of a SNP correlation matrix (with Pearson's correlation coefficients) can sometimes be done by discarding SNPs such that no pair of SNPs remains with absolute correlation greater than some specified threshold (--corr-filter, default is 0.95). A search through the correlation matrix is performed to determine SNPs that need to be removed. The absolute values of pair-wise correlations are considered. If two SNPs have a high correlation, the mean absolute correlation of each SNP is considered and the SNP with the largest mean absolute correlation is removed.

Pair-wise SNP correlations can be reduced, for instance according to threshold |r_ij |<0.98, as follows:

./finemap --corr --in-files example/data --corr-filter 0.98

This removed the SNPs 1, 24, 27, 35, 37.

References

Benner, C. et al. FINEMAP: Efficient variable selection using summary data from genome-wide association studies. Bioinformatics 32, 1493-1501 (2016).

Hans, D. et al. Shotgun stochastic search for "large p" regression. J Am Stat Assoc 102, 507-516 (2007).

Acknowledgements

Matti Pirinen contributed to the design and implementation of FINEMAP.