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Related Concept Videos

Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
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Updated: Jun 14, 2026

RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level
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FlexLMM: a Nextflow linear mixed model framework for GWAS.

Saul Pierotti1, Tomas Fitzgerald1, Ewan Birney1

  • 1European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Cambridge CB10 1SD, United Kingdom.

Bioinformatics (Oxford, England)
|January 15, 2025
PubMed
Summary
This summary is machine-generated.

FlexLMM is a new Nextflow pipeline for accurate statistical analysis in genome-wide association studies. It correctly handles population structure and covariates using a novel two-step permutation method for reliable significance testing.

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Area of Science:

  • Genetics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Linear mixed models (LMMs) are standard for genome-wide association studies (GWAS) with population structure.
  • Standard permutation tests are invalid with population structure or covariates, as samples lack exchangeability and covariate relationships are disrupted.

Purpose of the Study:

  • To develop a flexible Nextflow pipeline, FlexLMM, for performing appropriate permutations in LMMs.
  • To enable accurate significance threshold determination in GWAS by addressing limitations of naive permutation methods.

Main Methods:

  • FlexLMM implements a two-step permutation process.
  • Population structure is regressed out first, followed by permutations on uncorrelated residuals.

Main Results:

  • FlexLMM provides a robust method for empirical null distribution estimation in LMMs.
  • The pipeline ensures valid statistical inference in GWAS with complex sample structures.

Conclusions:

  • FlexLMM offers a flexible and accurate solution for permutation testing in LMMs.
  • This pipeline is valuable for genetic studies involving multi-parental crosses in model organisms and agricultural species.