This is information of inputs or results.

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This is for optional inputs/parameters.

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1. Registration/Login

If you haven't registered yet, please do so from Register.
Before you submit your GWAS summary statistics, please log in to your account. You can login from either login page or SNP2GENE page directly.

2. Submit new job at SNP2GENE

A new job stats with a GWAS summary statistics file. A variety of file formats are supported. Please refer the section of Input files for details. If your input file is an output from PLINK, SNPTEST or METAL, you can directly submit the file without specifying column names.
The input GWAS summary statistics file could be a subset of SNPs (e.g. only SNPs which are interesting in your study), but in this case, MAGMA results are not relevant anymore.
Optionally, if you would like to pre-specify lead SNPs, you can upload a file with 3 columns; rsID, chromosome and position. FUMA will then use these SNPs to select LD-related SNPs for annotation and mapping, instead of using lead SNPs identified by FUMA (it requires to disable an option for "identify additional lead SNPs").
In addition, if you are interested in specific genomic regions, you can also provide them by uploading a file with 3 columns; chromosome, start and end position. FUMA will then use these genomic regions to select LD-related SNPs for annotation and mapping, instead of determining the regions itself.

3. Set parameters

On the same page as where you specify the input files, there are a variety of optional parameters that control the prioritization of genes. Please check your parameters carefully. The default settings are to perform identification of independent genome-wide significant SNPs at r² 0.6 and lead SNPs at r² 0.1, to maps SNPs to genes up to 10kb apart.
To filter SNPs by specific functional annotations and to use eQTL mapping, please change parameters (please refer the parameter section of this tutorial from here).
If all inputs are valid, 'Submit Job' button will be activated. Once you submit a job, this will be listed in My Jobs.
Please do not navigate away from the page while your file is uploading (this may take up to couple of minutes depending on the file size and your internet speed).

4. Check your results

After you submit files and parameter settings, a JOB has the status NEW which will be updated to QUEUES to RUNNING. Depending on the number of significant genomic regions, this may take between a couple of minutes and an hour. Once a JOB has finished running, you will receive an email. Unless an error occurred during the process, the email includes the link to the result page (this again requires login). You can also access to the results page from My Jobs page.
The result page displays 4 additional side bars.
Genome-wide plots: Manhattan plots and Q-Q plots for GWAS summary statistics and gene-based test by MAGMA, results of MAGMA gene-set analysis and tissue expression analysis.
Summary of results: Summary of results such as the number of lead and LD-related SNPs, and mapped genes for overall and per identified genomic risk locus.
Results: Tables of lead SNPs, genomic risk loci, candidate SNPs with annotations, eQTLs (only when eQTL mapping is performed), mapped genes and GWAS-catalog reported SNPs matched with candidate SNPs. You can also create interactive regional plots with functional annotations from this tab.
Downloads: Download all results as text files.
Details of all FUMA outputs are provided in the SNP2GENE Outputs section of this tutorial.

1. Submit a list of genes

Both a list of genes of interest and background genes (for hypergeometric test) are mandatory input.
You can use mapped genes from SNP2GENE by clicking the "Submit" button in the result page (Results tab).

2. Results

Once genes are submitted, four extra side bars are shown.
Gene Expression: An interactive heatmap of gene expression of user selected data sets.
Tissue Specificity: Bar plots for enrichment test of differentially expressed genes in a certain label compared to all other samples for a use selected data sets. See GENE2FUNC Outputs section for details.
Gene Sets: Plots and tables of enriched gene sets.
Gene Table: Table of input genes with links to external databases; OMIM, Drugbank and GeneCards.
Further details are provided in the GENE2FUNC Outputs section of this tutorial.

1. GWAS summary statistics

GWAS summary statistics is a mandatory input of SNP2GENE process. FUMA accept various types of format. For example, PLINK, SNPTEST and METAL output formats can be used as it is. For other formats, column names can be provided. Input files should be prepared in ascii txt or (preferably) gzipped or zipped. Every row should contain information on one SNP. An input GWAS summary statistics file could contain only subset of SNPs (e.g. SNPs of interest for your study to annotate them), but in this case, results of MAGMA will not be relevant anymore. Please note that variants which do not exists in the selected reference panel will not be included in any analyses.
For indels, both alleles need to be matched exactly with reference panel to be included in the analysis. For example, an indel rs144029872 needs to be encoded with AG/A (the order of alleles does not matter), anything else such as G/- or I2/D will not match with the selected reference panel.

Input Build
The reference data included in FUMA SNP2GENE is on build GRCh37 (hg19).
If your data is build GRCh37, you can upload your file.
If your data is build GRCh38, you can upload your file if it includes rsIDs and does not include chromosome and position columns.
As of FUMA v1.6.0, there is now the option to upload data with build GRCh38 if it includes columns for chromosome, position, effect allele, and noneffect allele. If your input data includes all of these columns and you select "Input is build GRCh38" in the submission page, then your data will be annotated with rsIDs based on dbSNP v150 data. Only variants included in the user selected reference panel ("Reference panel population") will be given an rsID. The variants that could not be given rsIDs can be downloaded after your job is completed (GRCh38_droppedvariants.txt.gz).

Mandatory columns
The input file must include a P-value and either an rsID or chromosome index + genetic position on hg19 reference genome. When either chromosome or position is missing, they are extracted from dbSNP build 146 based on rsID. In this case, input rsID is updated to dbSNP build 146. When rsID is missing, it is extracted from dbSNP build 146 based on chromosome and position. The column of chromosome can be a string such as "chr1" or just an integer such as 1. When "chr" is attached, this will be removed in output files. When the input file contains chromosome X, this will be encoded as chromosome 23, however, the input file can contain "X".

Allele columns
Alleles are not mandatory but if only one allele is provided, that is considered to be the effect allele. When two alleles are provided, the effect allele will be defined depending on column name. If alleles are not provided, they will be extracted from the dbSNP build 146 and minor alleles will be assumed to be the effect alleles. Effect and non-effect alleles are not distinguished during annotations, but used for alignment with eQTLs. Whenever alleles are provided, they are matched with dbSNP build 146 if extraction of rsID, chromosome or position is necessary.
Alleles are case insensitive.

Headers
Column names are automatically detected based on the following headers (case insensitive).

• SNP | snpid | markername | rsID: rsID
• CHR | chromosome | chrom: chromosome
• BP | pos | position: genomic position (hg19)
• A1 | effect_allele | allele1 | alleleB: affected allele
• A2 | non_effect_allele | allele2 | alleleA: another allele
• P | pvalue | p-value | p_value | pval: P-value (Mandatory)
• OR: Odds Ratio
• Beta | be: Beta
• SE: Standard error

If your input file has alternative names, these can be entered in the respective input boxes when specifying the input file. Note that any columns with the name listed above but with different element need to be avoided. For example, when the column name is "SNP" but the actual element is an id such as "chr:position" rather than rsID will cause an error.
Extra columns will be ignored.
Rows that start with "#" will be ignored.
Column "N" is described in the Parameters section.
Be careful with the alleles header in which A1 is defined as effect allele by default. Please specify both effect and non-effect allele column to avoid mislabeling.
If wrong labels are provided for alleles, it does not affect any annotation and prioritization results. It does however affect eQTLs results (alignment of risk increasing allele of GWAS and tested allele of eQTLs). Be aware of that when you interpret results.

Delimiter
Delimiter can be any of white space including single space, multiple space and tab. Because of this, each element including column names must not include any space.

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Note and Tips

When the input file has all of the following columns; rsID, chromosome, position, allele1 and allele2, the process will be much quicker than extracting information.

The pipeline currently supports human genome hg19. If your input file is not based on hg19, please update the genomic position using liftOver from UCSC. However, there is an option for you!! When you provide only rsID without chromosome and genomic position, FUMA will extract them from dbSNP build 146 based on hg19. To do this, remove columns of chromosome and genomic position or rename headers to ignore those columns. Note that extracting chromosome and genomic position will take extra time.

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2. Pre-defined lead SNPs

This is an optional input file.
This option would be useful when
1. You have lead SNPs of interest but they do not reach significant P-value threshold.
2. You are only interested in specific lead SNPs and do not want to identify additional lead SNPs which are independent. In this case, you also have to UNCHECK option of Identify additional independent lead SNPs.
If you want to specify lead SNPs, input file should have the following 3 columns:

• rsID : rsID of the lead SNPs
• chr : chromosome
• pos : genomic position (hg19)

The order of columns has to be exactly the same as shown above but header could be anything (the first row is ignored). Extra columns will be ignored.

3. Pre-defined genomic region

This is an optional input file. This option would be useful when you have already done some follow-up analyses of your GWAS and are interested in specific genomic regions. When pre-defined genomic region is provided, regardless of parameters, only lead SNPs and SNPs in LD with them within provided regions will be reported in outputs.
If you want to analyze only specific genomic regions, the input file should have the following 3 columns:

• chr : chromosome
• start : start position of the genomic region of interest (hg19)
• end : end position of the genomic region of interest (hg19)

The order of columns has to be exactly the same as shown above but header could be anything (the first row is ignored). Extra columns will be ignored.

1. Input files

Parameter	Mandatory	Description	Type	Default
GWAS summary statistics	Mandatory	Input file of GWAS summary statistics. Plain text file or zipped or gzipped files are acceptable. The maximum file size which can be uploaded is 600Mb. As well as full results of GWAS summary statistics, subset of results can also be used. e.g. If you would like to look up specific SNPs, you can filter out other SNPs. Please refer to the Input files section for specific file format.	File upload	none
Pre-defined lead SNPs	Optional	Optional pre-defined lead SNPs. The file should have 3 columns, rsID, chromosome and position.	File upload	none
Identify additional lead SNPs	Optional only when predefined lead SNPs are provided	If this option is CHECKED, FUMA will identify additional independent lead SNPs after defining the LD block for pre-defined lead SNPs. Otherwise, only given lead SNPs and SNPs in LD of them will be used for further annotations.	Check	Checked
Pre-defined genetic region	Optional	Optional pre-defined genomic regions. FUMA only looks at provided regions to identify lead SNPs and SNPs in LD of them. If you are only interested in specific regions, this option will increase the speed of process.	File upload	none

2. Parameters for lead SNPs and candidate SNPs identification

Parameter	Mandatory	Description	Type	Default	Direction
Sample size (N)	Mandatory	The total number of individuals in the GWAS or the number of individuals per SNP. This is only used for MAGMA to compute the gene-based P-values. For total sample size, input should be an integer. When the input file of GWAS summary statistics contains a column of sample size per SNP, the column name can be provided in the second text box. When column name is provided, please make sure that the column only contains integers (no float or scientific notation). If there are any float values, they will be rounded up by FUMA.	Integer or text	none	Does not affect any candidates
Maximum lead SNP P-value (<)	Mandatory	FUMA identifies lead SNPs with P-value less than or equal to this threshold and independent from each other.	numeric	5e-8	lower: decrease #lead SNPs. higher: increase #lead SNPs.
Maximum GWAS P-value (<)	Mandatory	This is the P-value threshold for candidate SNPs in LD of independent significant SNPs. This will be applied only for GWAS-tagged SNPs as SNPs which do not exist in the GWAS input but are extracted from 1000 genomes reference do not have P-value.	numeric	0.05	higher: decrease #candidate SNPs. lower: increase #candidate SNPs.
r2 threshold for independent significant SNPs (≥)	Mandatory	The minimum r2 for defining independent significant SNPs, which is used to determine the borders of the genomic risk loci. SNPs with r2 ≥ user defined threshold with any of the detected independent significant SNPs will be included for further annotations and are used fro gene prioritisation.	numeric	0.6	higher: decrease #candidate SNPs and increase #independent significant SNPs. lower: increase #candidate SNPs and decrease #independent significant SNPs.
2nd r2 threshold for lead SNPs (≥)	Mandatory	The minimum r2 for defining lead SNPs, which is used for the second clumping (clumping of the independent significant SNPs). Note that when this threshold is same as the first r2 threshold, lead SNPs are identical to independent significant SNPs.	numeric	0.1	higher: increase #lead SNPs. lower: decrease #lead SNPs.
Reference panel	Mandatory	The reference panel to compute r2 and MAF. Five populations from 1000 genomes Phase 3 and 3 versions of UK Biobank are available. See here for details.	Select	1000G Phase EUR	-
Include variants from reference panel	Mandatory	If Yes, all SNPs in strong LD with any of independent significant SNPs including non-GWAS-tagged SNPs will be included and used for gene mapping.	Yes/No	Yes	-
Minimum MAF (≥)	Mandatory	The minimum Minor Allele Frequency to be included in annotation and prioritisation. MAF is based the user selected reference panel. This filter also applies to lead SNPs. If there is any pre-defined lead SNPs with MAF less than this threshold, those SNPs will be skipped. When this value is 0 (by default), SNPs with MAF>0 are considered.	numeric	0	higher: decrease #candidate SNPs. lower: increase #candidate SNPs.
Maximum distance of LD blocks to merge (≤)	Mandatory	This is the maximum distance between LD blocks of independent significant SNPs to merge into a single genomic locus. When this is set at 0, only physically overlapping LD blocks are merged. Defining genomic loci does not affect identifying which SNPs fulfil selection criteria to be used for annotation and prioritization. It will only result in a different number of reported risk loci, which can be desired when certain loci are partly overlapping or physically very close.	numeric	250kb	higher: decrease #genomic loci. lower: increase #genomic loci.

3. Parameters for gene mapping

There are two options for gene mapping; positional and eQTL mappings. By default, positional mapping with maximum distance 10kb is performed. Since parameters in this section largely affect the result of mapped genes, please set carefully.

3.1 Positional mapping

Parameter	Mandatory	Description	Type	Default	Direction
Positional mapping	Optional	Check this option to perform positional mapping. Positional mapping is based on ANNOVAR annotations by specifying the maximum distance between SNPs and genes or based on functional consequences of SNPs on genes. These parameters can be specified in the option below.	Check	Checked	-
Distance to genes or functional consequences of SNPs on genes to map	Mandatory if positional mapping is activated.	Positional mapping criterion either map SNPs to genes based on physical distances or functional consequences of SNPs on genes. When maximum distance is provided SNPs are mapped to genes based on the distance given the user defined maximum distance. Alternatively, specific functional consequences of SNPs on genes can be selected which filtered SNPs to map to genes. Note that when functional consequences are selected, all SNPs are locating on the gene body (distance 0) except upstream and downstream SNPs which are up to 1kb apart from TSS or TSE. When the maximum distance is set at > 0kb and < 1kb all upstream and downstream SNPs are included since the actual distance is not provided by ANNOVAR. Therefore, the maximum distance > 0kb and < 1kb is same as the maximum distance 1 kb. For SNPs which are locating on a genomic region where multiple genes are overlapped, ANNOVAR has its own prioritization criteria to report the most deleterious function. For those SNPs, only prioritized annotations are used.	Integer / Multiple selection	Maximum distance 10 kb	-

3.2 eQTL mapping

Parameter	Mandatory	Description	Type	Default	Direction
eQTL mapping	Optional	Check this option to perform eQTL mapping. eQTL mapping will map SNPs to genes which likely affect expression of those genes up to 1 Mb (cis-eQTL). eQTLs are highly tissue specific and tissue types can be selected in the following option. eQTL mapping can be used together with positional mapping.	Check	Unchecked	-
Tissue types	Mandatory if eQTL mapping is CHECKED	All available tissue types with data sources are shown in the select boxes. From FUMA v1.3.0, GTEx v7 became available but GTEx v6 are kept available. Therefore, when "all" is selected, both GTEx v6 and v7 are used for mapping. For detail of eQTL data resources, please refer to the eQTL section in this tutorial.	Multiple selection	none	-
eQTL maximum P-value (≤)	Optional	The P-value threshold of eQTLs. Two options are available, Use only significant snp-gene pairs or nominal P-value threshold. When Use only significant snp-gene pairs is checked, only eQTLs with FDR ≤ 0.05 will be used. Otherwise, defined nominal P-value is used to filter eQTLs. Some of eQTL data source only contained eQTLs with a certain FDR threshold. Please refer to the eQTLs section for details of each data sources.	Check / Numeric	Checked / 1e-3	lower: increase #eQTLs and #mapped genes. higher: decrease #eQTLs and #mapped genes.

3.3 Chromatin interaction mapping

Parameter	Mandatory	Description	Type	Default	Direction
chromatin interaction mapping	Optional	Check this option to perform chromatin interaction mapping.	Check	Unchecked	-
Builtin chromatin interaction data	Optional	Build in chromatin interaction data can be selected in this option. Details of available build in data are available in the Chromatin interactions section in this tutorial.	Multiple selection	none	-
Custom chromatin interaction matrices	Optional	In addition to build in chromatin interaction data, user can upload custom data. The data should be pre-computed chromatin loops with significance (ideally FDR but another score can be used, see the Chromatin interactions section for details). The file should be gzipped and named as "(name-of-data).txt.gz". Multiple files can be uploaded. For each data, user can also provide data type, such as Hi-C, ChIA-PET or C5 which is not mandatory but will be used in the result table and regional plot. The file format is described in the Chromatin interactions section in this tutorial. Please avoid uploading more than one file with identical file names. In that case, the files are over-written by the last uploaded one.	File upload (multiple)	none	-
FDR threshold (≤)	Mandatory if chromatin interaction mapping is CHECKED	FDR threshold for significant loops. The default value is set at 1e-6 which is suggested by Schmitt et al. (2016) This threshold will be applied both build in and user uploaded chromatin loops.	Numeric	1e-6	lower: increase #chromatin interactions and #mapped genes. higher: decrease #chromatin interactions and #mapped genes.
Promoter region window	Mandatory if chromatin interaction mapping is CHECKED	Promoter regions of genes to map in significantly interacting regions. The input format should be "(upstream bp)-(donwstream bp)" from transcription start site (TSS). For example, the default "250-500" means that promoter regions are defined as 250bp upstream and 500bp downstream of the TSS. By the chromatin interaction mapping, genes whose user defined promoter regions are overlapped with the significantly interacting regions will be mapped. Please refer the Chromatin interactions section in this tutorial for details.	text	250-500	lower: increase #mapped genes. smaller: decrease #mapped genes.
Annotate enhancer/promoter regions (Roadmap 111 epigenomes)	Optional	Predicted enhancer and promoter regions from Roadmap epigenomics project for 111 epigenomes can be annotated to significantly interaction regions. If any epigenome is not selected, enhancer and promoter regions are not annotated. Annotated enhancer/promoter regions can be used to filter SNPs and mapped genes in the next two options.	Multiple selection	none	-
Filter SNPs by enhancers	Optional	This option is only available when at least one epigenome is selected in the previous option to annotate enhancer/promoter regions. When this option is checked, SNPs are filtered on such that overlap with one of the annotated enhancer regions for chromatin interaction mapping. Please refer the Chromatin interactions section in this tutorial for details.	Check	Unchecked	-
Filter genes by promoters	Optional	This option is only available when at least one epigenome is selected in the previous option to annotate enhancer/promoter regions. When this option is checked, chromatin interaction mapping is only performed for genes whose promoter regions are overlap with one of the annotated promoter regions. Please refer the Chromatin interactions section in this tutorial for details.	Check	Unchecked	-

3.4 Functional annotation filtering

Positional, eQTL and chromatin interaction mappings have the following options separately, for the filtering of SNPs based on functional annotation. All filters below apply to selected SNPs in LD with independent significant SNPs that are used to prioritize genes and influence the number of SNPs that are mapped to genes, and consequently influence the number of prioritized genes.

Parameter	Mandatory	Description	Type	Default	Direction
CADD score	Optional	Check this if you want to perform filtering of SNPs by CADD score. This applies to selected SNPs in LD with independent significant SNPs that are used to prioritize genes. CADD score is the score of deleteriousness of SNPs predicted by 63 functional annotations. 12.37 is the threshold to be deleterious suggested by Kicher et al (2014). Please refer to the original publication for details from links.	Check	Unchecked	-
Minimum CADD score (≥)	Mandatory if CADD score is checked	The higher the CADD score, the more deleterious.	numeric	12.37	higher: less SNPs will be mapped to genes. lower: more SNPs will be mapped to genes.
RegulomeDB score	Optional	Check if you want to perform filtering of SNPs by RegulomeDB score. This applies to selected SNPs in LD with independent significant SNPs that are used to prioritize genes. RegulomeDB score is a categorical score representing regulatory functionality of SNPs based on eQTLs and chromatin marks. Please refer to the original publication for details from links.	Check	Unchecked	-
Minimum RegulomeDB score (≥)	Mandatory if RegulomeDB score is checked	RegulomeDB score is a categorical score from 1a to 7) Score 1a means that those SNPs are most likely affecting regulatory elements and 7 means that those SNPs do not have any annotations. SNPs are recorded as NA if they are not present in the database. SNPs with NA will not be included for filtering on RegulomeDB score.	string	7	higher: more SNPs will be mapped to genes. lower: less SNPs will be mapped to genes.
15-core chromatin state	Optional	Check if you want to perform filtering of SNPs by chromatin state. This applies to selected SNPs in LD with independent significant SNPs that are used to prioritize genes. The chromatin state represents accessibility of genomic regions (every 200bp) with 15 categorical states predicted by ChromHMM based on 5 chromatin marks for 127 epigenomes.	Check	Unchecked	-
15-core chromatin state tissue/cell types	Mandatory if 15-core chromatin state is checked	Multiple tissue/cell types can be selected from the list.	Multiple selection	none	-
Maximum state of chromatin(≤)	Mandatory if 15-core chromatin state is checked	The maximum state to filter SNPs. Between 1 and 15. Generally, between 1 and 7 is open state.	numeric	7	higher: more SNPs will be mapped to genes. lower: less SNPs will be mapped to genes.
Method for 15-core chromatin state filtering	Mandatory if 15-core chromatin state is checked	When multiple tissue/cell types are selected, either any (filtered on SNPs which have state above than threshold in any of selected tissue/cell types), majority (filtered on SNPs which have state above than threshold in majority (≥50%) of selected tissue/cell type), or all (filtered on SNPs which have state above than threshold in all of selected tissue/cell type).	Selection	any	-
Annotation datasets	Optional	Additional functional annotations can be annotated to candidate SNPs. All available data are regional based annotation (bed file format).	Multiple selection	none	-
Annotation filtering method	Mandatory if any of Annotation datasets is selected.	By default, SNPs are not filtered by the annotations selected in Annotation datasets. To filter SNPs based on the selected annotation, select this options from any (filtered on SNPs which are overlapping with any selected annotations), majority (filtered on SNPs which are overlapping with majority (≥50%) of selected annotations), or all (filtered on SNPs which are overlapping with all of selected annotations).	Selection	No filtering	-

4. Gene types

Biotype of genes to map can be selected. Please refer to Ensembl for details of biotypes.

Parameter	Mandatory	Description	Type	Default
Gene type	Mandatory	Gene type to map. This is based on gene_biotype obtained from BioMart of Ensembl. Please see here for details	Multiple selection.	Protein coding genes.

5. MHC region

The MHC region is often excluded due to its complicated LD structure. Therefore, this option is checked by default. Please uncheck to include MHC region. Note that it doesn't change any results if there is no significant hit in the MHC region.

Parameter	Mandatory	Description	Type	Default
Exclude MHC region	Optional	Check if you want to exclude the MHC region. The default region is defined as between "MOG" and "COL11A2" genes.	Check	Checked
Options for excluding MHC region	Optional	MHC region can be excluded only from either annotations or MAGMA gene analysis, or from both by selecting this option.	Select	Only from annotations
Extended MHC region	Optional	User specified MHC region to exclude (for extended or shorter region). The input format should be like "25000000-34000000" on hg19.	Text	Null

6. MAGMA analysis

Starting from FUMA version 1.5.1, user needs to check the magma checkbox to perform MAGMA. MAGMA gene and gene-set analyses are performed for the input summary statistics. Gene expression data sets for MAGMA gene expression analysis can be also selected from here.

Parameter	Mandatory	Description	Type	Default
Perform MAGMA	Optional	UNCHECK to SKIP MAGMA analyses.	Check	Checked
MAGMA gene annotation window	Mandatory when MAGMA is active.	The window of the genes to assign SNPs (symmetric). e.g. when 5kb is selected, SNPs within 5kb window of a gene (both side) will be assigned to that gene. The option is available from 0, 5, 10, 15, 20kb window.	Select	0kb from both side of the genes
MAGMA gene expression analysis	Mandatory when MAGMA is active.	Gene expression data sets used for MAGMA gene-property analysis to test positive association between genetic associations and gene expression in a given label.	Select	GTEx v6

7. Title of job submission

Title of job submission can be provided at above the "Submit Job" button. This is not mandatory but this would be useful to keep track your jobs.

1. Select a jobID to duplicate

At the top of the page, users can select a jobID of existing job on the account. Note that only jobs which are succeeded are selectable. This is only available for users who already have SNP2GENE jobs.

2. Modify parameters

Once a jobID is selected, the previous parameters are automatically loaded. Modify parameters before submitting, otherwise the results will be same as the selected job. For chromatin interaction mapping, user custom files need to be re-uploaded.
Users are allowed to provide new title and suffix "_copied_(jobID)" will be automatically added to the title.
Users are only allowed to modify gene mapping parameters. Other parameters such as P-value or r2 threshold for defining independent significant SNPs cannot be changed.

3. Submit

User can submit the job by clicking the button at the bottom of the page. After submission, the process is same as submitting a new SNP2GENE job, you will get an email once the process is done and results are accessible from your job list table.

1. 1000 Genome Phase3

Genotype data for chromosome 1-22 and X was downloaded from ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/.
Multi allelic SNPs were first split into separate columns using vcfmulti2oneallele.jar from JVARKIT (http://lindenb.github.io/jvarkit/). VCF files were then converted to PLINK bfile (PLINK v1.9). Any CNVs were removed, while any indels were kept. Unique ID (consists of chr:position:allele1:allele2 where alleles were alphabetically ordered) was assigned to each SNP and duplicated SNPs (with identical unique ID) were excluded. Genotype data were split into 5 (super) populations based on panel file (ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/release/20130502/integrated_call_samples_v3.20130502.ALL.panel) using PLINK.
MAF and pairwise LD were computed by PLINK (--r2 --ld-window 99999 --ld-window-r2 0.05) for each population and all samples together (ALL), and SNPs with MAF=0 were excluded for each population.
Reference panel ALL covers most number of SNPs. To avoid missing SNPs from FUMA annotations, reference panel ALL might be preferred. However, the LD is not population specific and need caution for the definition of independent significant SNPs and lead SNPs.

Number of samples and SNPs in the reference panels (click on a row to download the corresponding variant file):

Population	Sample size	Number of SNPs	Download size
ALL	2,504	84,853,668	870M
AFR	661	43,676,209	461M
AMR	347	29,501,504	305M
EAS	504	24,507,348	254M
EUR	503	25,063,419	260M
SAS	489	27,691,316	287M

2. UK Biobank release 2b

Genotype data was obtained under application ID 16406. The reference panel is based on genotype data released in May 2018 (including SNPs imputed UK10K/1000G). Two reference panels were created; white British and European subjects. For white British, 10,000 unrelated individuals were randomly selected. For European, each individuals were first assigned to one of the 5 1000G populations based on the minimum Mahalanobis distance. Then randomly selected 10,000 unrelated EUR individuals were used.
SNPs were filtered on INFO score > 0.9. MAF and pairwise LD were computed by PLINK (--r2 --ld-window 99999 --ld-window-r2 0.05) and SNPs with MAF=0 were excluded.
In both reference panels, 16,972,700 SNPs are available.

To avoid mismatch of rsID, unique ID (chr:pos:allele1:allele2) is used for MAGMA.

1. 1000 Genome Phase3

Same as described in Reference panel section.

2. UK Biobank release2

Same as described in Reference panel section, except further 1,000 individuals were randomly selected to reduce runtime of MAGMA (it takes >3 hours with 10,000 individuals).

Gene analysis is performed with default parameters (SNP-wide mean model) with the user selected reference panel.
The command FUMA uses is the following.
magma --bfile [path to the selected reference panel] \
--pval [magma input file] ncol=3 (or N=[total sample size]) \
--gene-annot [path to the annotation file with use selected window size] \
--out [output file]

In FUMA, curated gene sets (c2.all) and go terms (c5.bp, c5.cc and c5.mf) from MsigDB are tested. For FUMA ≤ v1.3.0, 10894 gene sets (curated gene sets: 4728, GO terms: 6166) from MsigdB v5.2 are used. For FUMA ≥ v1.3.1, 10655 gene sets (curated gene sets: 4738, GO terms: 5917) from MsigDB v6.1 are used. For FUMA ≥ v1.3.4, 10678 gene sets (curated gene sets: 4761, GO terms: 5917) from MsigDB v6.2 are used. Bonferroni correction was performed for the all tested gene sets. To customise, you can download the output file and select a specific gene sets.

The MSigDB v7.0 gene-set file used in FUMA from version 1.3.5d to 1.5.5 can be downloaded here: The MSigDB v2023.1Hs gene-set file used in FUMA from version 1.5.6+ can be downloaded here:

The gene-sets were downloaded from MSigDB with Entrez gene IDs and then converted to Ensembl gene IDs using biomaRt. When one Entrez gene ID matched multiple Ensembl gene IDs, all of the matching Ensembl gene IDs were included.

Gene set analysis is performed by the following command.
magma --gene-results [path to]/magma.genes.raw \
--set-annot [path to gene set file] \
--out [output file]

To identify tissue specificity of the phenotype, FUMA performs MAGMA gene-property analyses to test relationships between tissue specific gene expression profiles and disease-gene associations. The gene-property analysis is based on the regression model, $$Z \sim \beta_0 + E_t\beta_E + A\beta_A + B\beta_B + \epsilon$$ where \(Z\) is a gene-based Z-score converted from the gene-based P-value, \(B\) is a matrix of several technical confounders included by default. \(E_t\) is the gene expression value of a testing tissue type c and \(A\) is the average expression across tissue types in a data set, defined as follows: $$E_t = \sum_{i}^{n} log_2(e_i + 1)/n$$ $$A = \sum_{j \in T}^{N} E_j/N$$ where \(n\) is the number of samples in tissue type t, \(e_i\) is the expression value of a sample in the tissue type t (e.g. RPKM count or TPM), \(N\) is the number of tissue types in a data set and \(T = \{tissue\ type\ 1, tissue\ type\ 2, ..., tissue\ type\ N\}\). We performed a one-sided test (\(\beta_E>0\)) which is essentially testing the positive relationship between tissue specificity and genetic association of genes.

MAGMA gene-property analysis is run with the following command,
magma --gene-results [input file name].genes.raw \
--gene-covar [file name of selected RNA-seq data set] \
--model direction-covar=greater condition-hide=Average \
--out [output file name]

1. GTEx v6

Data source
RNAseq data set was downloaded from http://www.gtexportal.org/home/datasets. Gene level RPKM was used (GTEx_Analysis_v6_RNA-seq_RNA-SeQCv1.1.8_gene_rpkm.gct.gz).

Pre-process
Primary gene ID was Ensemble ID. In total, 8,555 samples were available. From 56,318 annotated genes, genes were filtered on such that average RPKM per tissue is >1 in at least on of the 53 tissues. This resulted in 28,577 genes. RPKM was winsorized at 50 (replaced RPKM>50 with 50). Then average of log transformed RPKM with pseudocount 1 (log2(RPKM+1)) per tissue (for either 53 detail or 30 general tissues) was used as the covariates conditioning on the average across all the tissues.

2. GTEx v7

Data source
RNAseq data set was downloaded from http://www.gtexportal.org/home/datasets. Gene level TPM was used (GTEx_Analysis_2016-01-15_v7_RNASeQCv1.1.8_gene_rpm.gct.gz).

Pre-process
Primary gene ID was Ensemble ID. In total, 11,688 samples were available. From 56,203 annotated genes, genes were filtered on such that average TPM per tissue is >1 in at least on of the 53 tissues. This resulted in 32,335 genes. TPM was winsorized at 50 (replaced TPM>50 with 50). Then average of log transformed TPM with pseudocount 1 (log2(TPM+1)) per tissue (for either 53 detail or 30 general tissues) was used as the covariates conditioning on the average across all the tissues.

3. BrainSpan

Data source
RNAseq data set was downloaded from http://www.brainspan.org/static/download. Gene level RPKM was used (genes_matrix_csv.zip).

Pre-process
Primary gene ID was Ensemble ID. In total, 524 samples were available. General developmental stages were annotated for each sample based on the age. We used 11 developmental stages and 29 ages as the label. For the label of age, we excluded age groups with <3 samples (25 pcw and 35 pcw). From 52,376 annotated genes, genes were filtered on such that average RPKM per label is >1 in at least one of the either developmental stage or age. This resulted in 19,601 and 21,001 genes for developmental stages and age groups, respectively. RPKM was winsorized at 50 (replaced RPKM>50 with 50). Then average of log transformed RPKM with pseudocount 1 (log2(RPKM+1)) per label (for either 11 developmental stages or 29 age groups) was used as the covariates conditioning on the average across all the labels.

1. Independent significant SNPs (Ind. sig. SNPs)

Ind. sig. SNPs are defined as SNPs that have a P-value ≤ the user define threshold for genome-wide significance (5e-8 by default) and are independent from each other at the user defined r² (0.6 by default). Therefore, ind. sig. SNPs are essentially the same as SNPs that are contained after clumping GWAS tagged SNPs at the same P-value and r². Ind. sig. SNPs are used to select candidate SNPs that are in LD with the ind. sig. SNPs.
The candidate SNPs (and ind. sig. SNPs) are used for gene prioritization.
Relaxing the threshold for the genome-wide significant P-value results in an increased number of ind. sig. SNPs. When you would like to identify ind. sig. SNPs in genomic loci which do not reach the commonly adopted genome-wide significance level of 5e-8, less significant P-value can be used. Alternatively, by providing pre-defined lead SNPs in a separate file, these provided SNPs will be defined as ind. sig. SNPs regardless of their P-value.
The higher the threshold for r², the more SNPs are defined as ind. sig. SNPs. At the same time, the number of SNPs in the LD with the ind. sig. SNPs (the candidate SNPs; which are the SNPs annotated in FUMA and used for gene prioritization) decreases.

2. Lead SNPs

Lead SNPs are defined as SNPs which are ind. sig. SNPs and are independent from each other at r² < 0.1 (from v1.3.5, this value can be specified by users). Therefore, lead SNPs are same as the SNPs clumped ind. sig. SNPs at the user defined P-value and r² = 0.1 by plink.
When r² is set at 0.1, lead SNPs are exactly the same as ind. sig. SNPs. However, this will also result in selecting candidate SNPs that have r² above 0.1 with any of ind. sig. SNPs. We thus advise to set r² at 0.6 or higher.

3. Genomic risk loci

On top of lead SNPs, FUMA defines genomic risk loci, including all independent signals that are physically close or overlapping in a single locus. First, ind. sig. SNPs which are dependent each other at r2 ≥ 0.1 are assigned to the same genomic risk locus. Then, ind. sig. SNPs which are closer than the user defined distance (250 kb by default) are merged into one genomic risk locus. The distance between two LD blocks of two ind. sig. SNPs is the distance between the closest SNPs (which are in LD of the ind. sig. SNPs at user defined r²) from each LD block.
Each locus is represented by the top lead SNP which has the minimum P-value in the locus.

4. Candidate SNPs (SNPs in LD of ind. sig. SNPs)

Candidate SNPs are SNPs that are in LD with any of the ind. sig. SNPs at the user defined r². Candidate SNPs, together with the ind. sig. SNPs, are the SNPs that are used to prioritize genes. The most left and most right SNPs which are in LD of a ind. sig. SNP define a LD block in which those SNPs are used to compute distance between LD blocks.
Note that not all SNPs are necessary in LD with lead SNPs, although they must be in LD with ind. sig. SNPs at the user defined r².
All candidate SNPs are annotated and their functions and listed in the "SNPs" table.
The higher the threshold r², the less candidate SNPs are identified. The number of candidate SNPs can also be controlled by the parameter of the maximum P-value for gwas-tagged SNPs (0.05 by default). For example, when r² is set at less than 0.6, a parameter of P-value threshold for GWAS tagged SNPs might need to be set at more significant since SNPs with r² often have very high P-value.

Effect of r² parameter

1. GTEx v6

Data source
eQTL data was downloaded from http://www.gtexportal.org/home/datasets. Under the section of GTEx V6, from single tissue eQTL data both GTEx_analysis_V6_eQTLs.tar.gz for significant SNP-gene association based on permutation, and GTEx_Analysis_V6_all-snp-gene-associations.tar for every SNP-gene association test (including non-significant paris) were downloaded.
GTEx eQTL v6 contains 44 different tissue types across 30 general tissue types.

Description
FUMA contains all SNP-gene pairs of cis-eQTL with nominal P-value < 0.05 (including non-significant associations). Significant eQTLs are defined as FDR (gene q-value) ≤ 0.05. The gene FDR is pre-calculated by GTEx and every gene-tissue pair has a defined P-value threshold for eQTLs based on permutation.
Signed statistics are t-statistics.

Samples

2. Blood eQTL browser (Westra et al. 2013)

Data source
eQTL data was downloaded from http://genenetwork.nl/bloodeqtlbrowser/.

Description
The data only include eQTLs with FDR ≤ 0.5. Genes in the original files were mapped to Ensembl ID in which genes are removed if they are not mapped to Ensembl ID.
Signed statistics are Z-scores.

Samples
5,311 peripheral blood samples from 7 studies (Westra et al. 2013).

3. BIOS QTL browser (Zhernakova et al. 2017)

Data source
eQTL data was downloaded from http://genenetwork.nl/biosqtlbrowser/. Cis-eQTLs Gene-level all primary effects was downloaded which includes all SNP-gene pairs with FDR ≤ 0.05.

Description
The data only include eQTLs with FDR ≤ 0.05.
Signed statistics are betas.

Samples
2,116 whole peripheral blood samples of healthy adults from 4 Dutch cohorts (Zhernakova et al. 2017).

4. BRAINEAC

Data source
eQTL was obtained from http://www.braineac.org/.

Description
The data include all eQTLs with nominal P-value < 0.05. Since tested allele was not provided in the original data source, minor alleles in 1000 genome phase 3 are assigned as tested alleles.
Signed statistics are t-statistics.
eQTLs were identified for each of the following 10 brain regions and based on averaged expression across all of them.
Alignment of risk increasing allele and eQTL tested allele was not performed for this data source, since tested allele is not available in the original data source (replaced with "NA" in the result table).

• Cerebellar cortex
• Frontal cortex
• Hippocampus
• Inferior olivary nucleus (sub-dissected from the medulla)
• Occipital cortex
• Putamen (at the level of the anterior commissure)
• Substantia nigra
• Temporal cortex
• Thalamus (at the level of the lateral geniculate nucleus)
• Intralobular white matter

Samples
134 neuropathologically confirmed control individuals of European descent from UK Brain Expression Consortium (Ramasamy et al. 2014).

5. GTEx v7

Data source
eQTL data was downloaded from http://www.gtexportal.org/home/datasets. Under the section of GTEx V7, from single tissue eQTL data both GTEx_analysis_v7_eQTLs.tar.gz for significant SNP-gene association based on permutation, and GTEx_Analysis_v7_all_associations.tar.gz for every SNP-gene association test (including non-significant pairs) were downloaded.
GTEx eQTL v7 contains 53 different tissue types across 30 general tissue types.

Description
FUMA contains all SNP-gene pairs of cis-eQTL with nominal P-value < 0.05 (including non-significant associations). Significant eQTLs are defined as FDR (gene q-value) ≤ 0.05. The gene FDR is pre-calculated by GTEx and every gene-tissue pair has a defined P-value threshold for eQTLs based on permutation.
Signed statistics are betas.

Samples

6. MuTHER (Grundberg et al. 2012)

Data source
eQTL data was downloaded from http://www.muther.ac.uk/.

Description
Chromosome coordinate was lifted over to hg19 from hg18 using liftOver software. Gene names are mapped to Ensembl ID (excluded genes which are not mapped to ENSG ID). Since only tested allele was provided, other allele was extracted from 1000G EUR population. FDR (or any corrected P-value) was not available in the original data (in the FUMA, FDR column was replaced with NA).
Signed statistics are betas.
Since FDR is not available, MuTHER eQTLs can be only used when P-value threshold provided by user, not "only significant snp-gene pairs" option.

Samples
856 female individuals of European descent recruited from the TwinsUK Adult twin registry (Grundberg et al. 2012).

• Adipose (N=855)
• Skin (N=847)
• LCL (N=837)

7. xQTLServer (Ng et al. 2017)

Data source
eQTL data was downloaded from http://mostafavilab.stat.ubc.ca/xqtl/.

Description
Gene names are mapped to Ensembl ID (excluded genes which are not mapped to ENSG ID). Since alleles were not available in the original data, extracted from 1000G EUR population based on chromosome coordinate. FDR was not provided in the original data source, but the FDR column was replaced with Bonferroni corrected p-value, as it was used in the original study (corrected for all tested SNP-gene pairs 60,456,556).
Signed statistics are not available.
Alignment of risk increasing allele and eQTL tested allele was not performed for this data source, since tested allele and signed statistics are not available in the original data source (replaced with "NA" in the result table).

Samples
494 dorsolateral prefrontal cortex samples (Ng et al. 2017).

8. CommonMind Consortium (Fromer et al. 2016)

Data source
eQTL data was downloaded from https://www.synapse.org//#!Synapse:syn5585484. Both eQTLs with and without SVA are included.

Description
Publicly available eQTLs from CMC (without application) is binned by FDR. Therefore, nominal P-value is not available (replaced with NA). FDR was binned into the following four groups, <0.2, <0.1, <0.05 and <0.01. As numeric value is required for filtering during SNP2GENE process, those categorical values are replaced with 0.199, 0.099, 0.049 and 0.009 respectively.
Signed statistics are not available but since expressed increasing allele was provided, signed_stats column is replaced with 1.
Trans eQTLs are also available for CMC data set (as a separated option from cis-eQTLs).

Samples
Post-mortem brain samples from 467 Caucasian individuals (209 with SCZ, 206 controls and 52 AFF cases; Fromer et al. 2016).

9. eQTLGen (Vosa et al. 2018)

Data source
eQTL data was downloaded from http://www.eqtlgen.org/index.html. For cis-eQTLs, cis-eQTLs_full_20180905.txt.gz, for trans-eQTLs, trans-eQTL_significant_20181017.txt.gz was used.

Description
Full summary statistics were downloaded. For cis-eQTLs, full summary statistics was downloaded. In the dataset, every SNP-gene pair with a distance <1Mb from the center of the gene and tested in at least 2 cohorts was included. For trans-eQTLs, only significant eQTLs were included in FUMA since the cross-mapping effects were not filtered in the downloadable full summary statistics. In the original study, every SNP-gene pair with a distance >5Mb and tested in at least 2 cohorts was included. FDR was estimated based on permutations. Please refer the original study for more details (Vosa et al. 2018). Ensembl gene ID is used as provided in the original file.
Signed statistics are z-scores.

Samples
Meta-analysis of cis-/trans-eQTLs from 37 datasets with a total of 31,684 individuals.

10. PsychENCODE (Wang et al. 2018)

Data source
eQTL data was downloaded from http://resource.psychencode.org. We used significant (DER-08a_hg19_eQTL.significant).

Description
The available eQTLs were filtered based on an FDR <0.05 and an expression >0.1 FPKM in at least 10 samples. Please refer the original study for more details (Wang et al. 2018). Ensembl gene ID is used as provided in the original file.
The signed statistics are betas.

Samples
The eQTLs were identified from 1387 individuals.

11. DICE (Schmiedel et al. 2018)

Data source
eQTL data was downloaded from https://dice-database.org/downloads#eqtl_download. The cis-eQTLs were obtained from the DICE eQTL section of the website.

Description
Only significant eQTLs are present in the dataset. The available eQTLs were filtered based on FDR<0.05, nominal P-value<0.0001, and TPM>0.1. FDR was estimated using permutation. Please refer the original study for more details (Schmiedel et al. 2018). Ensembl gene ID is used as provided in the original file. FDR was not provided in the original source, but since the eQTLs were already filtered on FDR<0.05 all eQTLs were assigned to FDR 0.049 to be able to pass the filtering of the "only significant snp-gene pairs" option.
Signed statistics are betas. The cell types were:

• Naive B cells
• Activated CD4 T cells
• Naive CD4 T cells
• Activated CD8 T cells
• Naive CD8 T cells
• Classical Monocytes
• Non-classical Monocytes
• NK cell, CD56dim CD16+
• TFH CD4 T cells
• TH117 CD4 T cells
• TH1 CD4 T cells
• TH2 CD4 T cells
• Memory TREG CD4 T cells
• Naive TREG CD4 T cells

Samples
The eQTLs were identified in 13 immune cell types isolated from 106 leukapheresis samples provided by 91 healthy subjects.

12. van der Wijst et al. scRNA eQTLs (van der Wijst et al. 2018)

Data source
eQTL data was downloaded from https://molgenis26.target.rug.nl/downloads/scrna-seq/.

Description
The tested allele was specified in the data, but the other allele was not. FDR was estimated using permutation. Please refer the original study for more details (van der Wijst et al. 2018). Ensembl gene ID is used as provided in the original file. The summary statistics are Z scores.
The cell types were:

• B cells
• CD4 T cells
• CD8 T cells
• Peripheral blood mononuclear cells (PBMC)
• Monocytes
• Classical monocytes
• Non-classical monocytes
• Natural killer (NK) cells
• Dendritic cells (DC)

Samples
The eQTLs were identified from 25,000 peripheral blood mononuclear cells (PBMCs) from 45 donors.

13. GTEx v8

Data source
eQTL data was downloaded from http://www.gtexportal.org/home/datasets. Under the section of GTEx V8, from single tissue eQTL data both GTEx_Analysis_v8_eQTL.tar for significant SNP-gene associations, and all tested pairs of SNP-gene were obtained from GCP (including non-significant pairs).
GTEx eQTL v8 contains 54 different tissue types across 30 general tissue types.

Description
FUMA contains all SNP-gene pairs of cis-eQTL with nominal P-value < 0.05 (including non-significant associations). Significant eQTLs are defined as FDR (gene q-value) ≤ 0.05. The gene FDR is pre-calculated by GTEx and every gene-tissue pair has a defined P-value threshold for eQTLs based on permutation.
Signed statistics are betas.

Samples

14. eQTL Catalogue

Data source
eQTL data was downloaded from the eQTLcatalogue (not from the original data source). The paths to individual datasets can be found at https://github.com/eQTL-Catalogue/eQTL-Catalogue-resources/blob/master/tabix/tabix_ftp_paths.tsv. Only the gene level (ge) files were included. Details of each dataset are described below. Datasets which were already present on FUMA have not been included (DICE & xQTLServer). As of FUMA v1.6.1, only nominally significant (P<0.05) eQTLs identified in the data were included.

Description
The eQTLs were mapped to hg19 from hg38 using liftOver software. Significant eQTLs are defined using a nominal p-value (0.00001). More information on the methods used to generate the eQTL data can be found at https://www.ebi.ac.uk/eqtl/Methods/.

Datasets

15. EyeGEx

Data source
eQTL data was downloaded from the GTEx website https://gtexportal.org/home/datasets. The file containing the cis-eQTLs can be downloaded from https://storage.googleapis.com/gtex_external_datasets/eyegex_data/single_tissue_eqtl_data/Retina.nominal.eQTLs.with_thresholds.tar. All eQTLs identified in the data were included.

Description
Please refer to the original study for more details (Ratnapriya et al. 2019). Ensembl gene ID is used as provided in the original file. FDR adjusted P-values were calculated based on gene-level FDR threshold.
The signed statistics are betas.

Samples
The eQTLs were identified from 406 individuals.

16. InsPIRE

Data source
eQTL data was downloaded from zenodo https://zenodo.org/record/3408356. The file containing the cis-eQTLs can be downloaded from https://zenodo.org/record/3408356/files/InsPIRE_Islets_Gene_eQTLs_Nominal_Pvalues.txt.gz?download=1. All nominally significant (P<0.05) eQTLs identified in the data were included except two variant-gene connections which were reported twice (5:150176501:C:A-ENSG00000197083.7 and 5:150176501:C:A-ENSG00000211445.7).

Description
Human pancreatic islets were the tested tissue. Please refer to the original study for more details (Viñuela et al. 2020). Ensembl gene ID is used as provided in the original file after the version number was removed (e.g. ENSG00000211445.7 became ENSG00000211445). The FDR reported in the paper could not be calculated using the downloaded data. Instead the FDR value was set to 1 for all variants except those found to be independently significant (included in PacreaticIslets_independent_gene_eQTLs.txt). The FDR value of the independently significant variants was set to 1e-5.
The signed statistics are betas.

Samples
The eQTLs were identified from 420 individuals.

17. TIGER

Data source
eQTL data was downloaded from the TIGER website http://tiger.bsc.es/downloads. The file containing the cis-eQTLs can be downloaded from http://tiger.bsc.es/assets/tiger_eqtl_stats.tar.gz. All nominally significant (P<0.05) eQTLs identified in the data were included. The eQTLs on the X chromosome were not included because the X chromosome was analysed separately for males and females, which would have led to duplicate measurements for the same variant-gene connection.

Description
Human pancreatic islets were the tested tissue. Please refer to the original study for more details (Alonso et al. 2021). Ensembl gene ID is used as provided in the original file. FDR values were calculated by adjusting the P-values so that an FDR of 0.05 was equal to a P-value of 6.2e-4. This was the significance threshold described in the original paper.
The signed statistics are Z-scores.

Samples
The eQTLs were identified from 404 individuals.

Risk increasing allele in GWAS

When "beta" or "OR" column is provided in the input GWAS file, risk increasing alleles are defined as follows: if beta > 0 or OR > 1, effect/risk allele is defined as the risk increasing allele, if beta < 0 or OR < 1, non-effect/non-risk allele is defined as the risk increasing allele.
If signed effect is not provided in the input GWAS file, risk increasing allele is not defined ("NA"). SNPs which are not in the input GWAS file but obtained from reference panel due to high LD are also encoded as "NA". When both effect and non-effect alleles are not provided in the input GWAS file, this alignment is not relevant. Please be careful to interpret the results.

Aligned direction of eQTLs

The sign of the t-statistics or z-score of the original eQTL data sources represents the direction of effect of tested allele. To obtain the direction of effect for risk increasing allele of GWAS, risk increasing allele and tested allele of eQTLs are aligned as follows: if risk increasing allele is the same allele as tested allele of the eQTL, direction is the same as the sign of the original t-statistics/z-score, if risk increasing allele is not same allele as tested allele of the eQTL, direction of t-statistics/z-score was flipped.
Direction is either "+" (risk increasing allele increases the expression of the gene) or "-" (risk increasing allele decreases the expression of the gene).

Examples

Here are some examples how the alleles are aligned.

uniqID	effect allale	non-effect allele	beta	risk increasing allele	tested allele of eQTL	t-statistics of eQTL	aligned direction
1:201885026:C:T	T	C	0.22	T	T	-7.98	-
11:43843579:C:G	C	G	0.004	C	G	17.23	-
16:28537971:C:T	T	C	-0.028	C	C	5.04	+

Terminology

Region 1
One end of a significant interaction which overlap with one of the candidate SNPs (independent significant SNPs and SNPs which are in LD of them). This region is always overlap with one of the genomic risk loci identified by FUMA.
Region 2
Another end of the significant interaction. This region is used to map to genes. Region 2 could also be overlapped with one of the genomic risk loci.

Direction of interactions

Input files of chromatin interaction consist of 7 columns: chr1, start1, end1, chr2, start2, end2, FDR (or other score). For loops identified by HiC, there is no directionality, i.e. both directions (chr1:start1-end1 <-> chr2:start2-end2) were considered regardless of the order in the file. For enhancer-promoter (EP) links, only one way (enhancer -> promoter) is considered for the mapping. The directionality is specified for each dataset below.

Build in chromatin interaction data

1. Hi-C of 21 tissue/cell types from GSE87112.
Pre-processed significant loops computed by Fit-Hi-C were obtained from GSE87112. Loops were filtered at FDR 0.05. For mapping, loops can be further filter by the user defined FDR threshold. Both directions are considered. Available tissue/cell types are listed below.

• Adrenal
• Aorta
• Bladder
• Dorsolateral Prefrontal Cortex
• Hippocampus
• Left Ventricle
• Liver
• Lung
• Ovary
• Pancreas
• Psoas
• Right Ventricle
• Small Bowel
• Spleen
• GM12878
• IMR90
• Mesenchymal Stem Cell
• Mesendoderm
• Neural Progenitor Cell
• Trophoblast-like Cell
• hESC

2. Hi-C loops from Giusti-Rodriguez et al. 2019
Pre-processed enhancer-promoter and promoter-promoter interactions based on HiC data for adult and fetal human brain samples. The data was provided by Prof. Patric F. Sullivan. Only significant interaction with P < 2.31e-11 (after Bonferroni correction) were included. Both directions are considered.

3. Hi-C based data from PsychENCODE
3.1 Enhancer-Promoter links based on Hi-C
The data was downloaded from PsychENCODE resource (file: INT-16_HiC_EP_linkages.csv).
Promoter regions were defined as 1000 around the provided TSS site. Since there is no P-value/FDR/score, all interactions were assigned to 0. Only one way (enhancer -> promoter) is considered.
3.2 Promoter anchored Hi-C loops
The data was downloaded from PsychENCODE resource (file: Promoter-anchored_chromatin_loops.bed).
Since there is no P-value/FDR/score, all interactions were assigned to 0. Only one way (region -> promoter) is considered.

4. Enhancer-Promoter correlations from FANTOM5 The data was downloaded from FANTOM5 human Enhancer Tracks (file: hg19_enhancer_promoter_correlations_distances_cell_type.txt and hg19_enhancer_promoter_correlations_distances_organ.txt). Only one way (enhancer -> promoter) is considered.

Custom chromatin interaction matrices file format

1. Input file format
The chromatin interaction files should have the following 7 columns in the order as listed below. Header line is mandatory but the column names do not need to be the same as the below as long as the order is the same. Delimiter should be tab or white space(s). The input file should be gzipped and named as "(name_of_data).txt.gz" in which "(name_of_data)" will be used in the result table and regional plot.

Columns:

1. chromosome of region 1
2. start position of region 1
3. end position of region 1
4. chromosome of region 2
5. start position of region 2
6. end position of region 2
7. FDR

Example:
chr1 start1 end1 chr2 start2 end2 FDR
1 2920001 2960000 1 3160001 3200000 0.03186403
1 4160001 4200000 1 5880001 5920000 5.3e-8
1 4520001 4560000 3 83200001 83240000 0.03920674

Chromosome can be coded as string like "chr1" and "chrX" which will be converted into integer.
Order of regions does not matter, unless a word "oneway" is in the file name (e.g. hic_loops_oneway.txt.gz). In that case only one direction (1st region -> 2nd region) is considered.
Inter-chromosomal interactions can be encoded in the same file by specifying chromosome of region 1 and region 2.
The column of FDR will be used to filter interaction by the user defined threshold.
The maximum size of each file is 600Mb. If the file is larger than this, please filter interactions or split them into multiple files.

2. Data types
When uploading custom chromatin interaction matrices, users can specify the type of data such as Hi-C or ChIA-PET. Specifying the data type is not mandatory since it is only used to specify in the result table and regional plot for convenience.

3. Filtering of chromatin interactions
The 7th column (FDR) will be used to filter interactions. To prevent from this filtering, either set filtering threshold to 1 or assign 0 to the FDR column. Technically, the 7th column does not have to be FDR but any other scores. When one prefers to use different score or nominal P-value, that is also possible by setting proper filtering threshold. Note that, interactions will be filtered on which have score less than or equal to the threshold.

Enhancer and promoter regions

Enhancer and promoter regions were obtained from Roadmap Epigenomics Projects for 111 epigenomes. Those regions were predicted using DNase peaks and core 15-state chromatin state model. Please refer here for details.
For selected epigenomes, enhancer regions are annotated to region 1 and promoter regions are annotated to region 2. Dyadic enhancer/promoter regions are annotated for both.
Annotated enhancer and promoter regions can be used to filter SNPs or mapped genes which is described in the next section.

Chromatin interaction mapping

1. Basic mapping (without filtering)
Chromatin interaction mapping is performed with significant chromatin interactions at the user defined threshold. Regions 2 is mapped to genes whose promoter regions (250bp up- and 50bp down-stream of the TSS by default) are overlapped with the region 2. Those genes were considered as mapped by candidate SNPs which are overlapped with region 1.
In the case there is not genes in region 2, those interactions are not mapped to any genes.


2. Enhancer filtering
When enhancers are annotated to region 1, user can select the option to filter candidate SNPs on such that are overlapped with enhancer regions of selected epigenomes. Note that, in the result table, all significant interactions are included but not all are necessary used for mapping.


3. Promoter filtering
When promoters are annotated to region 2, user can select the option to limit the chromatin interaction mapping to only genes whose promoter regions are overlapped with annotated promoter regions of selected epigenomes. Note that, in the result table, all significant interactions are included but not all are necessary mapped to genes.


In very rare cares, when the promoter filtering is activated, genes whose promoter regions (250bp up- and 500bp down-stream of TSS) do not overlap with region 2 but do overlap with promoters from Roadmap that are overlapping with region 2 are mapped. In this case, these genes are not in "ci.txt" file but in "ciProm.txt" file which can be linked to "ci.txt" by region 2.

Option 1. Use mapped genes from SNP2GENE

If you want to use mapped genes from SNP2GENE, just click a button in Mapped genes panel of the result page. It will open a new tab and automatically starts analyses. This will take all mapped genes and use background genes with selected gene types for gene mapping (such as "protein-coding" or "ncRNA"). The method for multiple testing correction (FDR BH), adjusted P-value cutoff (0.05) and minimum number of overlapped genes (2) are set at default values. These options can be adjusted by resubmitting your query (click "Submit" button in New Query tab).

Option 2. Use a list of genes of interest

To analyze a custom list of genes, you have to prepare a list of genes as either ENSG ID, entrez ID or gene symbol. Genes can be provided in the text are (one gene per line) or by uploading a file in the left panel. When you upload a file, genes have to be in the first column with a header. Header can be anything (even just a new line is fine) but FUMA will start reading your genes from the second row.

To analyze your genes, you need to specify background genes, which are used in the 2x2 enrichment tests. You can choose from the provided gene types. Alternatively, you can provide a custom list of background genes. Please provide this list either in the text area or by uploading a file of the right panel. File format should be the same as described for genes of interest.

1. Summary of input genes and download files

1) Summary of input genes
The table summarised the input genes and background genes. Input genes which are not used in the GENE2FUNC analyses due to lack of matching gene ID are also listed. Since the primary gene ID of FUMA is Ensembl ID and not all Ensembl IDs are mapped to unique entrez ID (NCBI gene ID), the number of unique entrez ID can be smaller than the number of input genes with Ensembl ID. Ensembl ID is used for expression heatmap and tissue specificity analyses, and entrez ID is used for gene set enrichment analysis.
2) Download files
Results of GENE2FUNC can be downloaded as text file from here.
3) Parameters
The table contains input parameters. This can be also downloaded from the option above.

2. Gene Expression Heatmap

The heatmap displays two expression values.
1) Average expression per label
This is an averaged expression value per label (e.g. tissue types or developmental stage) per gene following to winsorization at 50 and log 2 transformation with pseudocount 1. The expression value depends on the data set, RPKM (Read Per Kilobase per Million) for GTEx v6 and BrainSapn, TPM (Transcripts Per Million) for GTEx v7. This allows for comparison across labels and genes. Hence, cells filled in red represent higher expression compared to cells filled in blue across genes and labels.
2) Average of normalized expression per label
This is the average of normalized expression (zero mean across samples) following to winsorization at 50 and log 2 transformation of the expression value with pseudocount 1. This allows comparison of gene expression across labels (horizontal comparison) within a gene. Thus expression values of different genes within a label (vertical comparison) are not comparable. Hence, cells filled in red represents higher expression of the genes in a corresponding label compared to other labels, but it DOES NOT represent higher expression compared to other genes.

Labels (columns) and genes (rows) can be ordered by alphabetically or cluster (hierarchical clustering). Hierarchical clustering is performed using python scipy package (using "average" method).
The heatmap is downloadable in several file formats. Note that the image will be downloaded as displayed.

3. Tissue specificity

Tissue specificity is tested using the differentially expressed genes defined for each label of each expression data set

Differentially Expressed Gene (DEG) Sets
DEG sets were pre-calculated by performing two-sided t-test for any one of labels against all others. For this, expression values were normalized (zero-mean) following to a log 2 transformation of expression value (EPKM or TPM). Genes which with P-value ≤ 0.05 after Bonferroni correction and absolute log fold change ≥ 0.58 were defined as differentially expressed genes in a given label compared to others. On top of DEG, up-regulated DEG and down-regulated DEG were also pre-calculated by taking sign of t-statistics into account.

Input genes were tested against each of the DEG sets using the hypergeometric test. The background genes are genes that have average expression value > 1 in at least one of the labels and exist in the user selected background genes. Significant enrichment at Bonferroni corrected P-value ≤ 0.05 are coloured in red.
Note that for DEG sets, Bonferroni correction is performed for each of up-regulated, down-regulated and both-sided DEG sets separately.

Results and images are downloadable as text files and in several image file formats.

4. Gene Sets

Hypergeometric tests are performed to test if genes of interest are overrepresented in any of the pre-defined gene sets. Multiple test correction is performed per category, (i.e. canonical pathways, GO biological processes and so on, separately). Gene sets were obtained from MSigDB, WikiPathways and reported genes from the GWAS-catalog. The MSigDB and WikiPathways data were downloaded with entrez IDs and included without modification. The GWAS catalog data was downloaded with gene symbols and then converted to entrez ID using biomaRt. If a single gene symbol matched multiple entrez IDs, then all matching entrez IDs were included in the geneset.
The following files were used to make the GENE2FUNC genesets:

GENE2FUNC name	File used
Hallmark gene sets (MsigDB h)	h.all.v2023.1.Hs.entrez.gmt
Positional gene sets (MsigDB c1)	c1.all.v2023.1.Hs.entrez.gmt
Curated_gene_sets	c2.all.v2023.1.Hs.entrez.gmt
Chemical and Genetic pertubation gene sets (MsigDB c2)	c2.cgp.v2023.1.Hs.entrez.gmt
All Canonical Pathways (MsigDB c2)	c2.cp.v2023.1.Hs.entrez.gmt
BioCarta (MsigDB c2)	c2.cp.biocarta.v2023.1.Hs.entrez.gmt
KEGG (MsigDB c2)	c2.cp.kegg.v2023.1.Hs.entrez.gmt
Reactome (MsigDB c2)	c2.cp.reactome.v2023.1.Hs.entrez.gmt
microRNA targets (MsigDB c3)	c3.mir.v2023.1.Hs.entrez.gmt
TF targets (MsigDB c3)	c3.tft.v2023.1.Hs.entrez.gmt
All computational gene sets (MsigDB c4)	c4.all.v2023.1.Hs.entrez.gmt
Cancer gene neighborhoods (MsigDB c4)	c4.cgn.v2023.1.Hs.entrez.gmt
Cancer gene modules (MsigDB c4)	c4.cm.v2023.1.Hs.entrez.gmt
GO biological processes (MsigDB c5)	c5.go.bp.v2023.1.Hs.entrez.gmt
GO cellular components (MsigDB c5)	c5.go.cc.v2023.1.Hs.entrez.gmt
GO molecular functions (MsigDB c5)	c5.go.mf.v2023.1.Hs.entrez.gmt
Oncogenic signatures (MsigDB c6)	c6.all.v2023.1.Hs.entrez.gmt
Immunologic signatures (MsigDB c7)	c7.all.v2023.1.Hs.entrez.gmt
WikiPathways	c2.cp.wikipathways.v2023.1.Hs.entrez.gmt
Cell_type_signature (MSigDB c8)	c8.all.v2023.1.Hs.entrez.gmt

The genesets used in the GENE2FUNC module can be downloaded here:

The full results are downloadable as a text file at the top of the page.
In each category, plot view and table view are selectable. In the plot view, images are downloadable in several file formats.

5. Gene Table

Input genes are mapped to OMIM ID, UniProt ID, Drug ID of DrugBank and links to GeneCards. Drug IDs are assigned if the UniProt ID of the gene is one of the targets of the drug.
Each link to OMIM, Drugbank and GeneCards will open in a new tab.

Forward selection criteria when cell type \(a\) showed lower marginal P-value than cell type \(b\)
(scenarios are ordered by the priority)

Scenario	Cell type a	Cell type b	Cell type a state	Cell type b state	Description
1	\(PS_{a,b}≥0.8\)	\(PS_{b,a}≥0.8\)	indep	indep	The association of cell type \(a\) and \(b\) are independent.
2	\(p_{a,b}≥0.05\)	\(p_{b,a}≥0.05\)	join	joint-drop	The association of cell type \(a\) and \(b\) are depending each other, and the model cannot distinguish association of two cell types. In this case, cell type \(a\) is retained and \(b\) is dropped as cell type \(a\) has more significant marginal P-value, but it does not mean association of cell type \(a\) is true and \(b\) is not.
3	\(PS_{a,b}<0.2\)	\(PS_{b,a}<0.2\)	joint	joint-drop	Similar to the scenario 2, but the association of cell type \(a\) and \(b\) are not completely explained by each other. In this case, only cell type \(a\) is retained as the significance of cell type \(b\) drop to less than 20% of the marginal association. The output (state of cell types) is exactly the same as scenario 2, however there might be still some signals specific to each cell type \(a\) and \(b\).
4	\(PS_{a,b}≥0.5\)	\(p_{b,a}≥0.05\)	main	drop	The association of cell type \(b\) is completely depending on the association of cell type \(a\). Only cell type \(a\) is retained.
5	\(PS_{a,b}≥0.8\)	\(PS_{b,a}<0.2\)	main	partial-drop	The association of cell type \(b\) is mostly depending on the association of cell type \(a\) but cell type \(a\) cannot completely explain the association of cell type \(b\). In this case, only cell type \(a\) is retained as the significance of cell type \(b\) drop to less than 20% of the marginal association, however there are some amount of signals remained (since P-value is still less than 0.05).
6	\(PS_{a,b}≥0.5\)	\(PS_{b,a}≥0.5\)	partial-joint	partial-joint	The association of cell type \(a\) and \(b\) are only partially explained by each other but majority of signals are coming from the independent associations. Both cell type \(a\) and \(b\) are retained.
7	\(PS_{a,b}≥0.2\)	\(PS_{b,a}≥0.2\)	partial-joint	partial-joint-drop	Similar to scenario 6 but larger proportion of signals are explained by each other. In this case, only cell type \(a\) is retained as cell type \(b\) remain less than 20% of marginal significance, however there might still be specific underlying signal for cell type \(b\).
8	\(PS_{a,b}≥0.2\)	\(p_{b,a}≥0.05\)	partial-joint	joint-drop	The association of cell type \(b\) is completely explained by cell type \(a\) but there are part of association of cell type \(a\) dependent on cell type \(b\). In this case, only cell type \(a\) is retained.
9	\(PS_{a,b}≥0.2\)	\(PS_{b,a}<0.2\)	partial-joint	partial-joint-drop	The association of cell type \(b\) is mostly explained by cell type \(a\) but there are part of associations dependent on each other. In this case, only cell type \(a\) is retained.

Note that when associations of two cell types are jointly explained, only one cell type with the lowest marginal P-value is retained for the third step. However, this does not mean the discarded cell type is less important than the retained cell type, but the result suggests that the associations of these two cell types cannot be distinguished.
Although conditional P-values are often proportional to marginal P-values, it is possible that cell type with higher marginal P-value results in less conditional P-value for a pair of cell types (i.e. \(p_{b,a}\)<\(p_{a,b}\)). Therefore, when \(PS_{a,b}\)<0.2 and \(PS_{b,a}\)≥0.2, the order of cell types was flipped for forward selection.
Although only retained cell types were used for the third step, the results of within dataset conditional analyses for any pair of cell types were further breakdown into 8 categories as described in the table. This is to provide better understanding of the relationship of two significantly associated cell types. For example, in both scenario 4 and 5, cell type B is dropped and cell type A is considered as the main driver of the association. However, in scenario 4, association of cell type B cannot be completely explained by cell type A as conditional P-value of cell type B is still <0.05. Therefore, there might still be a unique signal to cell type B, however, as large amount of significance is dropped, the cell type B is not retained for the further step.
Note that step 2 is only performed for dataset where more than one cell types reached significance after multiple testing correction across datasets.

Step 3. cross datasets conditional analysis

The last step is to unravel relationships between significantly associated cell types across datasets. Although the absolute gene expression values in different datasets are not directly comparable, cross-datasets conditional analysis allows us to test the extent to which the significant gene expression profiles found in different data sets reflect the same or similar association signals. The analysis is performed for all possible cross-dataset pairs of significant cell types retained from the second step. Then the \(PS\) of the cross-datasets (CD) conditional P-value of a cell type relative to the CD marginal P-value is computed for each cell type of all possible pairs.
For each pair of cell types from different datasets, the following three regression models were tested to incorporate the effect of the average expression from the other dataset:
$$Z=\beta_0 + E_c1\beta_{E_{c1}} + A_1\beta_{A_1} + A_2\beta_{A_2} + B\beta_B + \epsilon$$ $$Z=\beta_0 + E_c2\beta_{E_{c2}} + A_1\beta_{A_1} + A_2\beta_{A_2} + B\beta_B + \epsilon$$ $$Z=\beta_0 + E_c1\beta_{E_{c1}} + E_c2\beta_{E_{c2}} + A_1\beta_{A_1} + A_2\beta_{A_2} + B\beta_B + \epsilon$$ where \(E_{cx}\) is an average log transformed expression of cell type \(c\) from dataset \(x\), and \(A_x\) is an average expression across cell types in dataset \(x\). In this step, we define P-value of testing alternative hypothesis \(\beta_{E_{cx}}>\)0 from 1st and 2nd models as CD marginal P-value, and \(\beta_{E_{cx}}\)>0 from 3rd model as CD conditional P-value for a cell type \(c\) from a dataset \(x\).
Note that, when associations of two cell types from different datasets with a trait are largely disappeared by conditioning on each other, it suggests that associations of those cell types were driven by similar genetic signals but this does not measure the similarity of two cell types (i.e. it cannot be concluded that the cell types from the different datasets are the same).
Note that step 3 is only performed where there are significant cell types from more than one datasets.
Please be aware that, some of scRNA-seq, there are multiple datasets available from a single scRNA-seq data resource. For example, Tabula Muris FACS data, one dataset contains all cell types from all 20 tissues, and there are 20 datasets for each tissue separately. When both TabulaMuris_FACS_all and TabulaMuris_FACS_Aorta datasets are selected, for instance, exact same cell types in the Aorta dataset exist in the dataset with all tissues. Testing both datasets are still relevant as average expression across cell types is different for each dataset, however, step 3 is not relevant in this case as they are exactly the same cell type. When step 3 is activated, FUMA will still perform all possible pair of significant cell types across datasets. However the pair of exact same cell type will be collinear (MAGMA outputs NA for such pairs).

1. Prepare jobs in SNP2GENE and GENE2FUNC

You can publish any of existing SNP2GENE job in your account but only the ones without any error. MAGMA results are optional but it's highly recommended to include them too. You can also publish GENE2FUNC results together with SNP2GENE results if the GENE2FUNC job is performed for mapped genes from the corresponding SNP2GENE job.

2. Publish results

You can publish your results from your job list on SNP2GENE page. There is a "publish" button for each SNP2GENE job.



When you click the "publish" button, a popup will open where you can specify some features of the job. Please fill the features in the table below as much as possible before submit your job.

Features	Description
Selected SNP2GENE jobID	Auto filled when you click the "publish" button. This value is not changeable.
Corresponding GENE2FUNC jobID	Auto filled when there is a recognized GENE2FUNC job. FUMA recognizes a matched GENE2FUNC job only when the GENE2FUNC job has been performed by using "GENE2FUNC" button (internal submission). If you manually submit GENE2FUNC for the corresponding SNP2GENE job, you can manually specify here.
Title	Title of the published job should be self-descriptive, although it is auto filled by the title of the selected SNP2GENE job. If the title is not clear enough, the developer might contact you to provide a sufficient information.
Author	Auto filled with the user name but please provide your full name.
Email	Auto filled with your registered email address. Please provide an email address that is reachable to you. Any future modification/deletion of the published job will be only processed when it is requested by the matched email.
Phenotype	Please provide phenotype of the GWAS if applicable.
Publication	This is the publication where the selected SNP2GENE job is described (not the reference to the summary statistics). This can be any format as long as users are able to find the publication. Please provide PubMed ID if possible (e.g. PMID: 29184056). If you don't have publication yet, please let the developer know once the publication becomes available. You can also provide preprint DOI.
Link to summary statistics	If the summary statistics used in this job is publicly available, please provide the original link.
Reference of summary statistics	This should be the original publication of the summary statistics. This can be same as the publication above when a new GWAS result is presented in the publication.
Notes	You can provide any additional information here (max 300 characters). For example, when there are multiple summary statistics available from the same study, you should specify which result this is referring to.

3. Check your published results

Published job will be listed in the "Browse Public Results" page. Please have a look at your published job to check if there is any problem.

Modification and deletion of published jobs can be done by similar way as publishing the job. From the SNP2GENE job list, the published jobs now have "edit" button instead of "publish" button. By clicking this, you can update the features listed above or delete the published job. Note that deleting the published job does not delete the job from your account. At the same time, when you delete the original job from your account, it does not delete the corresponding published job. If you want to modify/delete published job whose original job is deleted from your account, please contact to the FUMA developer, (Kyoko Watanabe: k.watanabe@vu.nl). Please also provide id of the published job together.
Modification/deletion is only possible when the user is logged in with the same email address as the entry.

We do not take any responsibility for your published results. Any question specific to a published result from other users is required to answer by the user who published the result not the FUMA developer.

Any FUMA users are able to browse your published results and download any text files and images. They are also able to create regional plot with annotations.
The "Browse Public Result" page does not require users to login.