Related Experiment Video
Updated: Jul 12, 2026
08:27
Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
Published on: July 27, 2021
Genome-wide association studies of missing metabolite measures from two population-based studies.
Tariq O Faquih1,2,3, Mohammed Aslam Imtiaz4, Valentina Talevi4
1Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.
Genome Biology
|July 10, 2026
Summary
Missing values in metabolomics may not be random. Genetic variations influence metabolite detection, suggesting a biological basis for missing data in metabolic research.
Area of Science:
- Genetics
- Metabolomics
- Epidemiology
Background:
- Metabolomic studies often encounter missing values, typically handled by imputation or removal.
- These missing values are rarely investigated as indicators of altered metabolism.
- Interindividual genetic variation is hypothesized to contribute to missingness in metabolomic data.
Purpose of the Study:
- To investigate the hypothesis that genetic variation influences missingness in metabolomic data.
- To identify genetic loci associated with the probability of metabolite missingness.
Main Methods:
- Logistic Genome-Wide Association Study (GWAS) of metabolite missingness.
- Analysis of untargeted mass spectrometry data from two large cohorts: Netherlands Epidemiology of Obesity Study and the Rhineland Study.
- Meta-analysis of GWAS results for metabolites missing in 10%-90% of individuals.
Main Results:
- Identified 55 metabolome-wide significant associations between single nucleotide polymorphisms (SNPs) and metabolite missingness.
- Discovered 42 novel associations, involving 28 metabolites and 41 lead SNPs.
- Significant associations were found for metabolites related to beta-oxidation, bile acids, steroids, and xenobiotics metabolism.
Conclusions:
- Missing values in metabolomics are partially non-random and influenced by genetic factors.
- Genetic variation plays a role in the detectability of metabolites.
- These findings have implications for the interpretation of missing data in metabolomic research.
Related Concept Videos
01:11Genome-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...
GWAS does not require the identification of the target gene involved in...
01:12Analysis of Population Pharmacokinetic Data
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
01:27Pharmacogenetics of Drug Metabolism: Overview
Genetic polymorphism in drug metabolism is crucial to the inter-individual variability observed in drug responses. Drug metabolism primarily involves the chemical modification of drugs and other xenobiotics to enhance their elimination by increasing their polarity. Two main classes of enzymes mediate this biotransformation process: Phase I enzymes, primarily cytochrome P450s, catalyze oxidation and reduction reactions, while other enzymes, such as esterases, mediate hydrolysis, and Phase II...