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Genome-wide Association Studies-GWAS

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.
GWAS does not require the identification of the target gene involved in...

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Updated: Jul 6, 2025

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

BulkLMM: Real-time genome scans for multiple quantitative traits using linear mixed models.

Zifan Yu¹, Gregory Farage¹, Robert W Williams²

¹Department of Preventive Medicine, University of Tennessee Health Science Center, Memphis, TN 38163, USA.

Biorxiv : the Preprint Server for Biology

|January 8, 2024

Summary

This summary is machine-generated.

Researchers developed BulkLMM, a new algorithm for fast genome scans. This method significantly speeds up the analysis of numerous quantitative traits using linear mixed models (LMMs), making genetic studies more efficient.

Keywords:

Computing Genome Scan Julia Linear Mixed Models Parallel

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Area of Science:

Genetics
Bioinformatics
Computational Biology

Background:

High-throughput phenotyping in genetic studies generates vast datasets.
Analyzing numerous quantitative traits using linear mixed models (LMMs) is computationally intensive.
Current methods of performing genome scans trait-by-trait are time-consuming.

Approach:

Developed BulkLMM, a novel algorithm for efficient genome-wide scans across multiple quantitative traits.
Utilized vectorized, multi-threaded operations, regularization, and numerical approximations to accelerate computations.
Implemented BulkLMM in the Julia programming language, offering permutation testing for LMMs.

Key Points:

BulkLMM achieves orders-of-magnitude speedup compared to single-trait analysis.
Successfully processed over 35,000 traits and 7,000 markers on mouse BXD Liver Proteome data in seconds.
Optimized computations through advanced algorithmic techniques and parallel processing.

Conclusions:

BulkLMM offers a highly efficient solution for large-scale genetic analyses involving numerous traits.
The developed software accelerates genomic discovery by drastically reducing analysis time.
Available open-source software facilitates broader adoption in genetic research.