Genetic correlation using LDSC (linkage disequilibrium score regression).

A **genetic correlation** is the proportion of variance that two traits share due to genetic causes, the correlation between the genetic influences on a trait and the genetic influences on a different trait estimating the degree of pleiotropy or causal overlap.
**Heritability** is a measure of how well differences in people’s genes account for differences in their traits.
The tool chosen is **LDSC** (https://github.com/bulik/ldsc), a command-line tool for estimating heritability and genetic correlation from GWAS summary statistics.

Estimation of genetic correlation in European GWAS by utilizing pre-computed LD Scores.

**LDSC **performs:

LD Score regression intercept for a 1st GWAS Summary Statistics;

SNP-heritability for 1st Summary Statistics;

Genetic correlation between 1st and subsequent Summary Statistics.

A few simple steps are needed to run this pipeline:

select

*Genetic Correlation pipeline*from Pipelines section;upload your input files;

provide other parameters for the genetic correlation and the job name and finally runs the analysis;

then, you will be redirected to the results page.

The first thing to do is to load your **Summary statistics Files**, which can be:

GWAS Summary Statistics files;

European GWAS pre-computed LD scores;

SNP-list of alleles;

Sample sizes for each Summary Statistics;

Sample and Population Prevalence for stratification of summary stats along with genetic and heritability intercept for regression calculations.

Let's take a look at the **parameters**:

**Number of Samples**: Total no. of sample size (cases+control) for each summary statistics file provided;**Phenotype**: Phenotype name of the summary stats trait;**Sample Prevalence**: Value used for sample stratification respective to summary statistics;*optional, default: NULL;***Population Prevalence**: Value used for population stratification respective to summary statistics;*optional, default: NULL;***Intercept**: Value used for genetic covariance regression respective to summary statistics;*optional, default: NULL;***h2_intercept**: Value used for heritability regression respective to summary statistics;*optional, default: NULL;*

**LDScore**: Pre-calculated LDScore for 1000 European genome;*input value to be chosen from Dropdown menu with default values;***SNP**: Whole Genome reference SNPs;**Use-Intercept**: User input switch to use no-intercept parameter for calculating genetic correlation in LDSC function;*optional, default: switch on.*

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 the *Dashboard*, where you can keep an eye on which works are *In Progress*, which are *Completed*, and choose to carry out a new analysis.

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

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

**Correlation plot **is generated indicating the correlation between 1st summary stats and latter other summaries.

This example examines the evidence for the genetic correlation between psychological diseases Neuroticism, Depressive symptom and Subjective Well-Being for 10K and fills samples.

**Input files: **summary statistics folder consisting of the following files:

Neuroticism_Full.txt

DS_Full.txt

SWB_Full.txt

SWB_10K.txt

Whole-genome reference SNP list for European ancestral and Pre-computed LD scores.

**Genetic correlation results**

The *Interactive Graph *option also provides an alternative visualization method:

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