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Related Experiment Video

Updated: May 19, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Fast linear mixed model computations for genome-wide association studies with longitudinal data.

Karolina Sikorska¹, Fernando Rivadeneira, Patrick J F Groenen

¹Department of Biostatistics, Erasmus Medical Center, Rotterdam, Netherlands.

Statistics in Medicine

|August 23, 2012

Summary

This study introduces a faster, approximate method for analyzing how genetic variants, specifically single-nucleotide polymorphisms (SNPs), influence traits over time. The new approach efficiently explores longitudinal relationships, aiding genetic research on trait evolution.

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Infinium Assay for Large-scale SNP Genotyping Applications

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

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Last Updated: May 19, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization

Published on: July 27, 2021

Infinium Assay for Large-scale SNP Genotyping Applications

Infinium Assay for Large-scale SNP Genotyping Applications

Published on: November 19, 2013

Area of Science:

Genetics
Statistical Genetics
Bioinformatics

Background:

Genome-wide association studies (GWAS) identify genetic variants (SNPs) associated with diseases using many statistical tests.
There's increasing interest in understanding the genetic basis of trait changes over time (longitudinal data).
Analyzing longitudinal data requires advanced statistical methods that can handle correlated observations within individuals.

Purpose of the Study:

To propose and evaluate a computationally efficient approximate method for exploring the longitudinal relationship between SNPs and traits.
To compare the accuracy and speed of this new method against existing approaches.

Main Methods:

A conditional two-step approach is proposed as an approximation to linear mixed models for longitudinal genetic analysis.
A simulation study was conducted to assess the performance (accuracy and speed) of various fast methods.
The proposed method was applied to real-world data from the Rotterdam Study, examining SNPs and longitudinal bone mineral density.

Main Results:

The conditional two-step approach demonstrated efficiency in exploring longitudinal SNP-trait associations.
Simulation results indicated the method's viability as a fast alternative for analyzing genetic influences on trait evolution.
The application to bone mineral density data illustrated its practical utility in large cohort studies.

Conclusions:

The conditional two-step approach offers a computationally feasible alternative for genetic studies of longitudinal phenotypes.
This method facilitates the investigation of genetic determinants underlying trait changes over time.
Efficient analysis of longitudinal genetic data is crucial for advancing our understanding of trait evolution and disease risk.