# MetaGWAS Pipeline

### MetaGWAS - Meta-analysis of Genome-Wide Association Studies

MetaGWAS is a common approach for improving the power of complex trait-gene mapping studies. The basic principle of meta-analysis is to combine the evidence for association from individual studies, using appropriate weights.

### Workflow

![](/files/-M8zbYNlO6MKHGECXiCg)

#### User Journey

A few simple steps are needed to run this pipeline:

1. reach *Pipelines* section of the platform;
2. select Meta-GWAS pipeline card;
3. upload your data file;
4. choose the parameters from the configuration page;
5. provide the job name and run the analysis;

then, you will be redirected to the results page.

![](/files/-MDyPQpU84rIb0uioRSK)

The design is always the same as the previous pipelines since they share all the same characteristics.

![](/files/-MDyPlvL1Z50h-5AaDYb)

The first thing to do is to load your **Summary statistics Files** in .txt format.

This file must contain information regarding *marker name*, *chromosome*, *position*, *allele*, *allele frequency*, *effect size*, *standard error* and *p-value*. 

Once the file containing this information has been uploaded, it will be possible to define in which columns the values of your interest are contained:

![](/files/-MDyPtTN3VuTF3EUgWUn)

#### List of parameters 

* **Pheno:** phenotype associated to input data;
* **Separator:** you must chose in the drop down menu options (Whitespace, Comma, Tab) the column separator in the input file;
* **Name of 'Allele Frequency' Column:**  allele frequency of the first of these alleles, (e.g., allele frequency of the first of these alleles);
* **Name of 'Allele' Column:** column name for **effect**/**tested** allele and other allele, (e.g., the two allele labels are stored in columns labelled **EFFECT\_ALLELE** and **NON\_EFFECT\_ALLELE**);
* **Name of ‘Marker/SNP’ Column:** column name for Marker/SNP;
* **Name of ‘Weight' Column:** the number of individuals analyzed for each row -- and which can be used to weight the contribution of each study in sample size and p-value based meta-analysis is stored in a column labeled N;
* **Name of ‘Standard Error' Column:** specify the label for the standard error column;
* **Name of ‘Effect' Column:** column name for Effect size; **Log transform button:** switch *on* if you want *Log transform the effect column values;*
* **Name of ‘P Value’ Column:** column name of the p-value column; **Log transform button:** switch *on* if you want *Log transform the p-value column values;*
* **Name of ‘Strand’ Column:** column name of the strand column, if present.

![](/files/-MDyQ0S_NqMoyLK_AFVy)

Now select the *analysis scheme*.

The tool chosen for the analysis is **METAL**, which implements two approaches:

1. **Sample Size** based method: converts the direction of effect and P-value observed in each study into a signed Z-score such that very negative Z-scores indicate a small P-value and an allele associated with lower disease risk or quantitative trait levels; 
   * **Minimum sample size for all Markers:** default input value 10000;
   * **Default weight:** default input value 1000;
2. **Inverse variance** based method: weights the effect size estimates, or β-coefficients, by their estimated standard errors;
   * if you want to **use sample size & p-value for analysis**, switch on; default: *on*;
   * if you want to **use effect size and standard error for analysis**, switch on; default: *on.*

**Genomic Control Correction**

If the **genomic control** is switched *off*, there is no adjustment to test statistics; indeed, if it is switched *on* it automatically corrects test statistics to account for small amounts of population stratification or unaccounted for relatedness. The default value for **Inflation factor** is *0.97*.

![](/files/-MDyQ9OsibdHK59pQXoc)

**Strand Overlap Correction**

If you want do perform *overlap correction*, switch *on* and insert a threshold value for Z-statistics; by default, this value is 1.

If the **Strand Column** is present in the input file, switch *on* the **Strand Details** button. 

If you want to track

* **Mean Allele Frequency**
* **Minimum and Maximum Allele Frequency**

switch *on*.

There is also the option to get **Detailed SNP Report**, switch *on*.

### Results

Once you have chosen the pipeline to be used, uploaded the data file and set all the parameters, you can start your analysis using the *Run Analysis* box; at this point you will be redirected to this page, where you can keep an eye on which works are *In Progress*, which are *Completed*, and choose to carry out a new analysis. 

![](/files/-ME3S3HQbJfzDdiKFdRV)

By clicking on your *JobName*, you will have access to this page, where you can monitor all the processes involved in your analysis:

![](/files/-ME3Rj_M3uekxa18wcRx)

Now, selecting the *Results* box on the right, let's take a look at the demo results obtained using the Default Parameters Set:

![](/files/-M8thfAIkP63QkOMESE7)

**Manhattan Plot** is a type of *scatter plot -* plot or mathematical diagram using Cartesian coordinates to display values for typically two variables for a set of data.

![](/files/-M8zdAzJCK7RCNqLpcma)

**Normal Quantile Plot**

![](/files/-M8zdZo4f8sJ80H0rHph)

By clicking on the Interactive Graphs option, you can also view your results like this:

![](/files/-M8zjEQ6aPlAZqXJCD7P)

![](/files/-M8zlGJAgKiqVAD2jUsJ)

Finally, using the *Export* box, you will be able to download the results of your analysis in a *.pdf* format file.

#### Reference

1. [**METAL: Fast and Efficient Meta-Analysis of Genomewide Association Scans**](https://pubmed.ncbi.nlm.nih.gov/20616382/), CJ Willer, Y Li, GR Abecasis, 2010


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