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When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
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Strengthening Causal Inference for Complex Disease Using Molecular Quantitative Trait Loci.

Sonja Neumeyer1, Gibran Hemani2, Eleftheria Zeggini1

  • 1Institute of Translational Genomics, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.

Trends in Molecular Medicine
|November 14, 2019
PubMed
Summary

Genome-wide association studies (GWAS) find genetic links to complex diseases. Molecular quantitative trait loci (molQTLs) help identify causal genes using Mendelian randomization (MR), though challenges remain.

Keywords:
GWASMendelian randomizationQTLcomplex traitgene expressiongenome-wide association study

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Area of Science:

  • Genetics
  • Genomics
  • Systems Biology

Background:

  • Genome-wide association studies (GWAS) identify genetic loci associated with complex traits and diseases.
  • Index variants from GWAS are often non-causal and located in non-coding genomic regions.
  • Understanding biological mechanisms requires investigating intermediate traits like gene expression and protein levels.

Purpose of the Study:

  • To explore the utility of molecular quantitative trait loci (molQTLs) in elucidating complex trait mechanisms.
  • To leverage molQTLs as instrumental variables in Mendelian randomization (MR) for causal inference.
  • To address challenges in using molQTLs for identifying causal features and mechanisms.

Main Methods:

  • Utilizing large-scale genetic association data.
  • Investigating intermediate traits such as gene expression and protein levels.
  • Applying Mendelian randomization (MR) with molecular quantitative trait loci (molQTLs) as instrumental variables.

Main Results:

  • Molecular quantitative trait loci (molQTLs) serve as mediators between genetic variants and complex traits.
  • Mendelian randomization (MR) approaches using molQTLs can help identify causal relationships.
  • Pleiotropy and non-specificity of molQTLs present ongoing challenges.

Conclusions:

  • Molecular quantitative trait loci (molQTLs) are valuable for understanding disease mechanisms.
  • Further methodological development is needed to overcome limitations in molQTL analysis.
  • Integrating GWAS with molQTLs and MR offers a powerful framework for causal inference in complex traits.