Estimation of Interpretable eQTL Effect Sizes Using a Log of Linear Model

TL;DR

This paper introduces ACME, a new log-of-linear model for estimating eQTL effect sizes that aligns with biological expectations, improves accuracy, and controls false positives without needing rank-based normalization.

Contribution

The paper proposes the ACME model, a biologically interpretable log-of-linear approach for eQTL effect size estimation, with a non-linear least-squares fitting algorithm and validation on GTEx data.

Findings

ACME effectively captures additive allelic effects in eQTLs.

Type-I error is well-controlled under the ACME model.

Rank-based normalization can reduce power and estimation accuracy.

Abstract

The study of expression Quantitative Trait Loci (eQTL) is an important problem in genomics and biomedicine. While detection (testing) of eQTL associations has been widely studied, less work has been devoted to the estimation of eQTL effect size. To reduce false positives, detection methods frequently rely on linear modeling of rank-based normalized or log-transformed gene expression data. Unfortunately, these approaches do not correspond to the simplest model of eQTL action, and thus yield estimates of eQTL association that can be uninterpretable and inaccurate. In this paper we propose a new, log-of-linear model for eQTL action, termed ACME, that captures allelic contributions to cis-acting eQTLs in an additive fashion, yielding effect size estimates that correspond to a biologically coherent model of cis-eQTLs. We describe a non-linear least-squares algorithm to fit the model by maximum likelihood, and obtain corresponding $p$-values. We perform careful investigation of the model using a combination of simulated data and data from the Genotype Tissue Expression (GTEx) project. Our results reveal little evidence for dominance effects, a parsimonious result that accords with a simple biological model for allele-specific expression and supports use of the ACME model. We show that Type-I error is well-controlled under our approach in a realistic setting, so that rank-based normalizations are unnecessary. Furthermore, we show that such normalizations can be detrimental to power and estimation accuracy under the proposed model. We then provide summaries of ACME effect sizes for whole-genome cis-eQTLs in the GTEx data.