At BIGA, we provide various tools for cross-trait genetic correlation analysis. You have the flexibility to upload your own genome-wide association studies (GWAS) summary statistics or query a wealth of data from third-party resources. You can upload or query one trait at a time to conduct massive analysis with curated datasets from different sources (e.g., the UK Biobank and PGC). Alternatively, you can upload or query two traits to perform a pairwise analysis.

On each page, you will find a clear and brief description for each function, as well as detailed information on the requirements for each parameter involved:

   : Click on the question mark icon to view a brief description of the function.

To ensure the security and privacy of your research, we require users to create a personal account. Start by registering with your email address. After registration, access your account using your email and password. This ensures that only you can view and manage your analysis results. With your account, you can confidently submit your genetic analysis jobs. Our platform guarantees the confidentiality of your data and results. Once logged in, you'll have access to a user-friendly dashboard, where you can manage your submissions, track the progress of ongoing analyses, and access your completed results.

To submit a massive genetic correlation analysis job, begin by selecting one of the four methods: LDSC, LAVA, Popcorn, or SumHer. Next, build your input files; you have the option to upload your GWAS data in either .txt or .gz format. BIGA accepts both tab-separated and whitespace-separated files. Alternatively, you can query data from established databases such as the GWAS Catalog, Neale Lab, or IEU OpenGWAS, or you may reuse previously input data. Note that data from the GWAS Catalog will not be harmonized as it is already pre-harmonized by GWAS Catalog. For data from other sources, BIGA will perform a harmonization step to ensure compatibility. All harmonized data within BIGA will be formatted to a common standard, meeting the input requirements for all available analytical methods. It is important to specify the human genome build version and the population of your GWAS summary statistics data. In addition, please type in trait name in input files panel. After this, choose a curated dataset from various available panels. Once everything is accurately filled, you can submit your job. Note that each analysis method—LDSC, LAVA, Popcorn, SumHer—may have minor differences in parameter settings section. Please refer to the documentation for more details.

After clicking the submit button, you will be automatically redirected to the results page. This page displays a list of all your submitted jobs. Each row corresponds to one job. The Job ID column shows the unique identifier for each job. Name is the name of your job, ideally including the input trait name and the selected curated dataset for a clearer reminder of the analysis performed. Job Type indicates the method used for the analysis. The Status column may show one of these four states: Await, Running, Failed, or Finished.
You have the option to download all the results by clicking the download link. This includes the BIGA log file, the log file of the method used, and the output of the selected method. Additionally, you can click on 'go to result' to view the main genetic correlation analysis results. If you find any result to be particularly useful, you also have the option to make it public, allowing other users to view and download it. However, you can also choose to delete a job. Please be aware that once a job is deleted, it cannot be recovered.

To submit a pairwise genetic correlation analysis job, start by preparing your input files. You are required to provide two input files. For your GWAS data, you have the flexibility to upload files in either .txt or .gz format. BIGA accepts both tab-separated and whitespace-separated files. Alternatively, you can query data from established databases such as the GWAS Catalog, Neale Lab, or IEU OpenGWAS, or you may reuse previously input data. Note that data from the GWAS Catalog will not do harmonization as it is already pre-harmonized by GWAS Catalog. For data from other sources, BIGA will perform a harmonization step to ensure compatibility. All harmonized data within BIGA is formatted to a unified standard, meeting the input requirements for all available analytical methods. It is important to specify the human genome build version and the population demographic of your GWAS summary statistics. Additionally, inputting the trait name is necessary but recommended.

Next, select the analysis method you wish to use—options include LDSC, LAVA, Popcorn, SumHer. Ensure that the information is filled in accurately before submitting your job. Each method panel includes a section for parameter settings. BIGA offers default parameter settings, making it optional for you to customize these settings. The only mandatory action is to check the run LDSC/LAVA/Popcorn/SumHer box. For more detailed information, please refer to our documentation.

After clicking the submit button, you will be automatically redirected to the results page. This is separate from massive analysis result page. This page displays a list of all your submitted jobs. Each row corresponds to one job. The Job ID column shows the unique identifier for each job. Job Name is the name of your job, ideally including the input trait name and the selected curated dataset for a clearer reminder of the analysis performed. Job Type indicates the method used for the analysis. The Status column can show one of four states: Await, Running, Failed, or Finished. You have the option to download all the results by clicking the download link. This includes the BIGA log file, the log file of the method run, and the output of the selected method. If you find a result particularly useful, you have the option to share it publicly, allowing other users to view and download it. However, you can also choose to delete a job. Please be aware that once a job is deleted, it cannot be recovered.