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Molecular quantitative trait loci

Nature Reviews Methods Primers volume 3, Article number: 4 (2023) Cite this article

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Abstract

Understanding functional effects of genetic variants is one of the key challenges in human genetics, as much of disease-associated variation is located in non-coding regions with typically unknown putative gene regulatory effects. One of the most important approaches in this field has been molecular quantitative trait locus (molQTL) mapping, where genetic variation is associated with molecular traits that can be measured at scale, such as gene expression, splicing and chromatin accessibility. The maturity of the field and large-scale studies have produced a rich set of established methods for molQTL analysis, with novel technologies opening up new areas of discovery. In this Primer, we discuss the study design, input data and statistical methods for molQTL mapping and outline the properties of the resulting data as well as popular downstream applications. We review both the limitations and caveats of molQTL mapping as well as future potential approaches to tackle them. With technological development now providing many complementary methods for functional characterization of genetic variants, we anticipate that molQTLs will remain an important part of this toolkit as the only existing approach that can measure human variation in its native genomic, cellular and tissue context.

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Fig. 1: Illustration of molQTLs.
Fig. 2: Overview of input data and processing steps for molQTL mapping and quality control.
Fig. 3: Transcriptome phenotypes that can be quantified from short-read RNA-seq data for molQTL mapping.
Fig. 4: QTL discovery power as a function of sample size.
Fig. 5: Visualization of molQTLs.
Fig. 6: Illustration of LD contamination in analysis of molQTL sharing.
Fig. 7: Questions in functional characterization of genetic variants, with examples of upcoming approaches for addressing these challenges.

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Acknowledgements

T.L. is supported by the National Institutes of Health (NIH) (grants R01GM122924, R01AG057422, R01MH106842 and U24HG012090) and by the European Research Council (grant 101043238). S.B.M. is supported by the National Institutes of Health (NIH) (grants R01AG066490, R01MH125244, U01HG012069 and U24HG010090). K.A. is supported by funding from the European Union’s Horizon 2020 research and innovation programme (grant no. 825775), Estonian Research Council (grant no. PSG415), Open Targets (grant nos. OTAR2067, OTAR2069 and OTAR2077) and Estonian Centre of Excellence in ICT Research (EXCITE), funded by the European Regional Development Fund.

Author information

Authors and Affiliations

  1. Illumina Artificial Intelligence Laboratory, Illumina, Inc., San Diego, CA, USA

    François Aguet

  2. Institute of Computer Science, University of Tartu, Tartu, Estonia

    Kaur Alasoo

  3. Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA

    Yang I. Li & Hae Kyung Im

  4. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA

    Alexis Battle

  5. Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA

    Alexis Battle

  6. Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA

    Alexis Battle

  7. Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA

    Alexis Battle

  8. Department of Pathology, Stanford University, Stanford, CA, USA

    Stephen B. Montgomery

  9. Department of Genetics, Stanford University, Stanford, CA, USA

    Stephen B. Montgomery

  10. Science for Life Laboratory, Department of Gene Technology, KTH Royal Institute of Technology, Stockholm, Sweden

    Tuuli Lappalainen

  11. New York Genome Center, New York, NY, USA

    Tuuli Lappalainen

  12. Department of Systems Biology, Columbia University, New York, NY, USA

    Tuuli Lappalainen

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  1. François Aguet
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  3. Yang I. Li
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  4. Alexis Battle
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Contributions

Introduction (T.L., F.A., K.A., Y.I.L., A.B., H.K.I. and S.B.M.); Experimentation (T.L., F.A., K.A., Y.I.L., A.B., H.K.I. and S.B.M.); Results (T.L., F.A., K.A., Y.I.L., A.B., H.K.I. and S.B.M.); Applications (T.L., F.A., K.A., Y.I.L., A.B., H.K.I. and S.B.M.); Reproducibility and data deposition (T.L., F.A., K.A., Y.I.L., A.B., H.K.I. and S.B.M.); Limitations and optimizations (T.L., F.A., K.A., Y.I.L., A.B., H.K.I. and S.B.M.); Outlook (T.L., F.A., K.A., Y.I.L., A.B., H.K.I. and S.B.M.); Overview of the Primer (T.L. and F.A.).

Corresponding authors

Correspondence to François Aguet or Tuuli Lappalainen.

Ethics declarations

Competing interests

F.A. is an employee of Illumina, Inc. and an inventor on a patent application related to TensorQTL. A.B. consults for Third Rock Ventures, Inc. and is a shareholder in Alphabet, Inc. T.L. advises GSK, Variant Bio and Goldfinch Bio, and has equity in Variant Bio. S.B.M. advises BioMarin, MyOme and Tenaya Therapeutics. The other authors declare no competing interests.

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Nature Reviews Methods Primers thanks Jan Korbel and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Glossary

Collider bias

A statistical distortion when an exposure and an outcome each influence a common third variable and that variable (collider) is controlled for in the analysis.

Co-localization

Sharing of causal variants between two association signals in the same locus.

eGenes

Genes whose expression is affected by at least one significant expression quantitative trait locus (eQTL).

Federated analysis

Analysis of different data sets separately in a coordinated and uniform manner with subsequent meta-analysis to integrate the results.

Gaussian residuals

An assumption that the linear regression residuals are normally distributed.

Homoscedasticity

An assumption of equal variances.

LD contamination

A given variant showing an association signal for a trait not because the variant itself is causally affecting this trait but because it is in linkage disequilibrium (LD) with a true causal variant.

Minor allele frequency

(MAF). The population frequency of the less common allele of a genetic variant.

Molecular traits

Phenotypes that are defined and measured at the molecular level.

Quantile normalization

A procedure applied to a data set such that the distribution of the values of each sample is the same.

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Aguet, F., Alasoo, K., Li, Y.I. et al. Molecular quantitative trait loci. Nat Rev Methods Primers 3, 4 (2023). https://doi.org/10.1038/s43586-022-00188-6

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