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Abstract BackgroundEstimating 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. ResultsHere 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. ConclusionsTo help researchers use PCA in their QTL analysis, we provide an R package along with a detailed guide, both of which are freely available athttps://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.
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This content will become publicly available on September 4, 2026
Evaluating the Effectiveness of Data Reduction Techniques in QTL Mapping
Abstract Data reduction methods are frequently employed in large genomics and phenomics studies to extract core patterns, reduce dimensionality, and alleviate multiple testing effects. Principal component analysis (PCA), in particular, identifies the components that capture the most variance within omics datasets. While data reduction can simplify complex datasets, it remains unclear how the use of PCA impacts downstream analyses such as quantitative trait loci (QTL) or genome-wide association (GWA) approaches and their biological interpretation. In QTL studies, an alternative to data reduction is the use of post-hoc data summarization approaches, such as hotspot analysis, which involves mapping individual traits and consolidating results based on shared genomic locations. To evaluate how different analytical approaches may alter the biological insights derived from multi-dimensional QTL datasets, we compared individual trait hotspots with PCA-based QTL mapping using transcriptomic and metabolomic data from a structured recombinant inbred line population. Interestingly, these two approaches identified different genomic regions and genetic architectures. These findings suggest that mapping PCA-reduced data does not merely streamline analyses but may generate a fundamentally different view of the underlying genetic architecture compared to individual trait mapping and hotspot analysis. Thus, the use of PCA and other data reduction techniques prior to QTL or GWAS mapping should be carefully considered to ensure alignment with the specific biological question being addressed.
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- PAR ID:
- 10651090
- Publisher / Repository:
- bioRxiv
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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