PCA outperforms popular hidden variable inference methods for molecular QTL mapping

Heather J Zhou; Lei Li; Yumei Li; Wei Li; Jingyi Jessica Li

doi:10.1186/s13059-022-02761-4

PCA outperforms popular hidden variable inference methods for molecular QTL mapping

Genome Biol. 2022 Oct 11;23(1):210. doi: 10.1186/s13059-022-02761-4.

Authors

Heather J Zhou¹, Lei Li², Yumei Li³, Wei Li³, Jingyi Jessica Li^{4

5

6

7}

Affiliations

¹ Department of Statistics, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
² Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518055, China.
³ Division of Computational Biomedicine, Department of Biological Chemistry, School of Medicine, University of California, Irvine, Irvine, CA, 92697, USA.
⁴ Department of Statistics, University of California, Los Angeles, Los Angeles, CA, 90095, USA. lijy03@g.ucla.edu.
⁵ Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, 90095, USA. lijy03@g.ucla.edu.
⁶ Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA. lijy03@g.ucla.edu.
⁷ Department of Biostatistics, University of California, Los Angeles, Los Angeles, CA, 90095, USA. lijy03@g.ucla.edu.

Abstract

Background: Estimating and accounting for hidden variables is widely practiced as an important step in molecular quantitative trait locus (molecular QTL, henceforth "QTL") analysis for improving the power of QTL identification. However, few benchmark studies have been performed to evaluate the efficacy of the various methods developed for this purpose.

Results: Here we benchmark popular hidden variable inference methods including surrogate variable analysis (SVA), probabilistic estimation of expression residuals (PEER), and hidden covariates with prior (HCP) against principal component analysis (PCA)-a well-established dimension reduction and factor discovery method-via 362 synthetic and 110 real data sets. We show that PCA not only underlies the statistical methodology behind the popular methods but is also orders of magnitude faster, better-performing, and much easier to interpret and use.

Conclusions: To help researchers use PCA in their QTL analysis, we provide an R package PCAForQTL along with a detailed guide, both of which are freely available at https://github.com/heatherjzhou/PCAForQTL . We believe that using PCA rather than SVA, PEER, or HCP will substantially improve and simplify hidden variable inference in QTL mapping as well as increase the transparency and reproducibility of QTL research.

Publication types

Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.
Research Support, N.I.H., Extramural

MeSH terms

Chromosome Mapping / methods
Models, Genetic*
Principal Component Analysis
Quantitative Trait Loci*
Reproducibility of Results

Abstract

Publication types

MeSH terms

Grants and funding