Skip to content

FAQ

General

Why do I need KGGSum?

KGGSum is fast, convenient, and integrates multiple analysis functions for large-scale GWAS summary data mining.

How do I cite KGGSum?

The publication of the specific method(s) you use should be cited to allow readers to understand the principles of your analyses.

Feedback and Support

Where do I report bugs or feedback?

You can send an email to limx54@163.com for this.

Using AI Assistance

How can I use generative AI to assist with KGGSum?

You can print the full documents of the online user manual as a PDF file and upload the PDF file to the AI and then ask questions like:

“Please generate commands for me to perform gene-based analysis using KGGSum with my GWAS summary file (abc.tsv.gz) and reference genome = hg19”.

Troubleshooting

Java Version Issue (KGGSum)

Symptoms

  • When running Association → Region/Gene-based association analysis, you may see:

  • java.lang.UnsupportedClassVersionError: org/apache/arrow/memory/BufferAllocator ... class file version 55.0 ... up to 52.0

  • Followed by: java.lang.ArrayIndexOutOfBoundsException: -1

Root Cause

  • Apache Arrow integrated in KGGSum v1.0 is compiled with Java 11 (class version 55).
  • Your runtime environment only provides Java 8 (class version 52), causing class loading to fail.
  • Thread-pool tasks exit unexpectedly, triggering a secondary exception (array out-of-bounds).

Solution

  1. Install OpenJDK 17 on Ubuntu: sudo apt install openjdk-17-jre
  2. Run KGGSum with the newer Java runtime: /usr/lib/jvm/java-17-openjdk-amd64/bin/java -jar kggsum.jar
  3. Result: the issue disappears; the Association example command completes normally with full output.

Conclusion

  • In practice, Java Runtime ≥ 21 is more reliable (even though the manual states ≥ 17).

gsMap Installation Environment Issue

Symptoms

  • In Conda (Python 3.13), running pip install gsmap may fail because numpy<2.0.0 needs to be built from source, with errors such as:

  • ERROR: Unknown compiler(s)... [Errno 2] No such file or directory: 'gcc'

Root Cause

  • Python 3.13 may not have an available prebuilt NumPy wheel, so pip falls back to building from source.
  • Your system does not have a C/C++ compiler installed (gcc/clang), so the build fails.
  • Ecosystem support is still catching up: gsmap is not yet well-supported on Python 3.13.

Solution

  1. Create a new Python 3.10 environment: conda create -n gsMap python=3.10
  2. Install dependencies in the new environment: conda install numpy, then pip install gsmap
  3. Result: gsmap installs and works normally.

Note

  • TC-04 depends on gsmap. Due to local Python 3.13 and a missing compiler, this testing round does not cover sDESE.