Efficient and effective control of confounding in eQTL mapping studies through joint differential expression and Mendelian randomization analyses

reg-eQTL: Integrating transcription factor effects to unveil regulatory variants

Regulatory single-nucleotide variants (rSNVs) in noncoding regions of the genome play a crucial role in gene transcription by altering transcription factor (TF) binding, chromatin states, and other epigenetic modifications. Existing expression quantitative trait locus (eQTL) methods identify genomic loci associated with gene-expression changes, but they often fall short in pinpointing causal variants. We introduce reg-eQTL, a computational method that incorporates TF effects and interactions with genetic variants into eQTL analysis. This approach provides deeper insights into the regulatory mechanisms, bringing us one step closer to identifying potential causal variants by uncovering how TFs interact with SNVs to influence gene expression. This method defines a trio consisting of a genetic variant, a target gene, and a TF and tests its impact on gene transcription. In comprehensive simulations, reg-eQTL shows improved power of detecting rSNVs with low population frequency, weak effects, and synergetic interaction with TF as compared to traditional eQTL methods. Application of reg-eQTL to GTEx data from lung, brain, and whole-blood tissues uncovered regulatory trios that include eQTLs and increased the number of eQTLs shared across tissue types. Regulatory networks constructed on the basis of these trios reveal intricate gene regulation across tissue types.

AC-PCoA: Adjustment for confounding factors using principal coordinate analysis

Confounding factors exist widely in various biological data owing to technical variations, population structures and experimental conditions. Such factors may mask the true signals and lead to spurious associations in the respective biological data, making it necessary to adjust confounding factors accordingly. However, existing confounder correction methods were mainly developed based on the original data or the pairwise Euclidean distance, either one of which is inadequate for analyzing different types of data, such as sequencing data.

In this work, we proposed a method called Adjustment for Confounding factors using Principal Coordinate Analysis, or AC-PCoA, which reduces data dimension and extracts the information from different distance measures using principal coordinate analysis, and adjusts confounding factors across multiple datasets by minimizing the associations between lower-dimensional representations and confounding variables. Application of the proposed method was further extended to classification and prediction. We demonstrated the efficacy of AC-PCoA on three simulated datasets and five real datasets. Compared to the existing methods, AC-PCoA shows better results in visualization, statistical testing, clustering, and classification.

With today’s unprecedented amount of data, researchers are challenged by the need to enhance meaningful signals without the interference of unwanted confounders hidden inside the data. Data visualization is an important step toward exploring and explaining data in order to intuitively identify the dominant patterns. Principal coordinate analysis (PCoA), as a visualization tool, allows flexible ways to define pairwise distances and project the samples into lower dimensions without changing the distances. However, when visualizing large-scale biological datasets, the true patterns are often hindered by unwanted confounding variations, either biologically or technically in origin. To eliminate these confounding factors and recover underlying signals, we proposed a method called Adjustment for Confounding factors using Principal Coordinate Analysis, or AC-PCoA, and showed that it significantly outperforms existing methods in visualization through three simulation studies and five real datasets. We further showed that the low-dimensional representations given by AC-PCoA provide promising results in statistical testing, clustering, and classification as well.

LORSEN: Fast and Efficient eQTL Mapping With Low Rank Penalized Regression

Characterization of genetic variations that are associated with gene expression levels is essential to understand cellular mechanisms that underline human complex traits. Expression quantitative trait loci (eQTL) mapping attempts to identify genetic variants, such as single nucleotide polymorphisms (SNPs), that affect the expression of one or more genes. With the availability of a large volume of gene expression data, it is necessary and important to develop fast and efficient statistical and computational methods to perform eQTL mapping for such large scale data. In this paper, we proposed a new method, the low rank penalized regression method (LORSEN), for eQTL mapping. We evaluated and compared the performance of LORSEN with two existing methods for eQTL mapping using extensive simulations as well as real data from the HapMap3 project. Simulation studies showed that our method outperformed two commonly used methods for eQTL mapping, LORS and FastLORS, in many scenarios in terms of area under the curve (AUC). We illustrated the usefulness of our method by applying it to SNP variants data and gene expression levels on four chromosomes from the HapMap3 Project.