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GraphLD

GraphLD provides LDGM-backed tools for working with GWAS summary statistics.

You can use it from the command line for graphREML, BLUP, clumping, simulation, and surrogate-marker workflows, or from Python for lower-level LDGM and likelihood operations.

Start Here

Choose the path that matches what you want to do:

  • If you have new annotations that you wish to test for enrichment against a prespecified set of traits, go to Enrichment Score Test.
  • If you have new annotations or new GWAS summary statistics and wish to estimate heritability enrichments, go to CLI graphREML.
  • If you need to install GraphLD or download reference data, start with Installation.
  • If you want a broader overview of the terminal workflows, start with Command Line Interface.
  • If you want to use GraphLD from Python, start with the Python Guide.
  • If you need exact signatures for modules, classes, or functions, use the autogenerated API Reference.
  • If you are unsure what file layout GraphLD expects, check File Formats.

Features

  • graphREML for heritability partitioning and enrichment from GWAS summary statistics.
  • Enrichment score tests for very fast heritability enrichment significance testing from precomputed derivative statistics at either a gene or variant level.
  • Simulation for generating GWAS summary statistics with realistic LD patterns.
  • BLUP weights for performing polygenic prediction.
  • LD clumping for selecting lead associations or performing polygenic prediction.
  • LDGM matrix operations for precision-matrix and Schur-complement operations.
  • Surrogate markers for accelerating graphREML when you have several sets of summary statistics with similar variant missingness.

Documentation Map