---
title: "GWAS and Gene Set Analysis in OmicsBox"
canonical: "https://help.biobam.com/space/OBD/3291152385/GWAS%20and%20Gene%20Set%20Analysis%20in%20OmicsBox"
format: markdown
---
> Macro (toc)

# Introduction

Performing GWAS using GAPIT3 in OmicsBox simplifies the identification of genetic variants associated with complex traits. With OmicsBox you will be able to integrate additional data to generate a covariate matrix, preprocess data, manage phenotypes and finally execute GWAS. Moreover, you will be able to visualize results seamlessly with this integrated solution.

**Dataset Description**

The dataset is obtained from the[ Sol Genomics Network](https://solgenomics.net/). This dataset comes from the Varitome Project and consists on a VCF file with variants from tomato and a traits file with different quantitative phenotype information. 

- Organism: *Solanum lycopersicum*
- [VCF File](https://solgenomics.net/ftp/varitome/GWAS/VCFs)
- [Traits File](https://solgenomics.net/ftp/varitome/GWAS/normalized_phen/): Quantitative phenotype information for samples present in the VCF file.

# Bioinformatic Analysis

## Gwas Analysis

### Application

[GWAS with GAPIT3](https://manual.omicsbox.biobam.com/user-manual/omicsbox-modules/module-genetic-variation/gwas-with-gapit3/)

### Input

- VCF File: [varitome_all.vcf.gz](https://drive.google.com/file/d/1gS60tCsIwUyuoVfbHmJCakJYB_vVS6V9/view?usp=sharing)
- Phenotypic Tratis File: [phenotypes.tsv](https://drive.google.com/file/d/1TXv3qQTsxCg2n6BrI0Rm6AqAR3KEPBa2/view?usp=sharing)

### Parameters

#### SNP filtering

- Hardy-Weinberg Equilibrium P-value: 0.0001
- MAF Threshold: 0.001
- Missingness Threshold: 0.5

#### Sample filtering

- Sample Missingness Threshold: 0.7

#### Linkage Disequilibrium Pruning

- Perform LD pruning: True
- Maximum Linkage Disequilibrium: 0.9
- Window Size: 1000

#### Phenotype standarization

- Normalize Phenotype Data: True
- Remove Phenotype Outliers: False

#### GWAS Options

- Attach Own Kinship Matrix: False
- Kinship Group: Mean
- Kinship Cluster: Average
- Kinship Algorithm: VanRaden
- Attach Own Covariate Matrix: False
- Number of Dimensions for PCA: 2
- GWAS Model: General Linear Model (GLM)

### Execution Time

8 minutes.

### Output

- [GWAS Table](https://drive.google.com/file/d/1ClqklKEuVH7tef8P5VwGfYbZmqCqSc25/view?usp=sharing)
- [Report](https://drive.google.com/file/d/1JrSrj9fnn7qj9MrXsdt36eD3EqsMZKl0/view?usp=sharing)
- [Corrected Phenotypes](https://drive.google.com/file/d/1NmhdUea_oXlXbFm3Bx4-TeZBdFAsUFFk/view?usp=sharing)

## Gene Set Analysis

### Application

[GWAS Gene Set Analysis with MAGMA](https://biobam.atlassian.net/wiki/spaces/~796203790/pages/3294298113)

### Input

- [GWAS Table](https://drive.google.com/file/d/1ClqklKEuVH7tef8P5VwGfYbZmqCqSc25/view?usp=sharing)
- [Annotation File](https://drive.google.com/file/d/1KNZkmcbLMP7LKZQq5yFdJ3ZSYchdGhDS/view?usp=sharing)
- [Gene Set File](https://drive.google.com/file/d/1MHUyblzPzNrsumXuqQbtoUqbhT4DBPWf/view?usp=sharing)

### Parameters

- Phenotype: SLC_SLL_SP_COLUMELLA_AREA_.MM.2..FAM
- Window To Include SNPs in Gene (kb): 1.5
- Gene Test Model: Mean SNPs Association Test
- Columns to Use in Gene Test: P-Value
- Make Self-Contained GSA: True
- Gene Sets Min Size: 15
- Gene Sets Max Size: 500

### Execution Time

4 minutes aprox.

### Output

- [Competitive MAGMA Table](https://drive.google.com/file/d/1sNFRmtPMIHVvnZlMY_V9j8VnQmySF5fn/view?usp=sharing)
- [Self-Contained MAGMA Table](https://drive.google.com/file/d/1j63FzIl68a0eeyG2wQ_YD4w2igfELpQg/view?usp=sharing)
- [SNPs per Gene Chart](https://drive.google.com/file/d/1pj3axWzmqZbfGO1H1-tOBHgC1l4vbNIN/view?usp=sharing)