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Flashfm-ivis: interactive visualization for fine-mapping of multiple quantitative traits.

Feng Zhou1, Adam S Butterworth2,3,4,5, Jennifer L Asimit1

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This summary is machine-generated.

flashfm-ivis is an open-source software tool that visualizes genetic variants influencing multiple traits. It aids in comparing single- and multi-trait fine-mapping for identifying causal variants.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Identifying causal genetic variants underlying complex traits is crucial for understanding disease mechanisms.
  • Integrating information across multiple quantitative traits can improve the power and precision of genetic fine-mapping.
  • Existing fine-mapping tools often focus on single traits, limiting comprehensive analysis of shared genetic architecture.

Purpose of the Study:

  • To introduce flashfm-ivis, an interactive visualization tool for exploring genetic associations across multiple quantitative traits.
  • To facilitate the comparison of single-trait versus multi-trait fine-mapping results.
  • To enable interactive exploration and identification of potential causal genetic variants.

Main Methods:

  • Development of an interactive web-based tool and an R package for flashfm-ivis.
  • Implementation of network diagrams to visualize joint variant effects across traits.
  • Integration of regional association plots with fine-mapping results and user-controlled zoom functionality.

Main Results:

  • flashfm-ivis provides interactive visualization plots for genetic fine-mapping.
  • The tool allows for the comparison of single- and multi-trait fine-mapping results.
  • Network diagrams and integrated regional plots enhance the exploration of potential causal variants.

Conclusions:

  • flashfm-ivis offers a unique approach to visualizing and comparing genetic fine-mapping results across multiple traits.
  • The interactive features facilitate the identification of shared and distinct genetic influences on quantitative traits.
  • The open-source nature and availability as a web tool and R package promote accessibility and reuse in genetic research.