Jiajin Li, Nahyun Kong, Buhm Han, Jae Hoon Sul
The rapid decrease in sequencing cost has enabled genetic studies to discover rare variants associated with complex diseases and traits. Once this association is identified, the next step is to understand the genetic mechanism of rare variants on how the variants influence diseases. Similar to the hypothesis of common variants, rare variants may affect diseases by regulating gene expression, and recently, several studies have identified the effects of rare variants on gene expression using heritability and expression outlier analyses.
Over the past decade, genome-wide association studies (GWAS) have successfully discovered numerous associations between common genetic variants and human complex diseases and traits[1,2]. These studies also found that those common variants typically have small effects and explain a small fraction of heritability[3,4]. Motivated by this finding, many sequencing studies have attempted to identify rare variants associated with complex traits[5,6]. It is hypothesized that rare variants may have larger effect sizes than common variants due to purifying selection and may explain some of the missing heritability[7,8]. Candidate-gene and large-scale sequencing studies have indeed found associations of rare variants with complex diseases and traits[9–11].
Materials and methods
Suppose that we have genotype and gene expression data of a population with size N, and perform an association test for a gene with k rare variants.
To compare the performance of LRT-q with the widely used existing rare variant association tests, we measure their type I error rates and statistical power in simulation studies. In this study, data are simulated with a similar framework described in Wu et al.’s work.
We have proposed LRT-q as a powerful rare variant association test for identifying the effects of rare variants on gene expression. Our simulation studies showed that the proposed method had a well-controlled false positive rate and higher statistical power compared with other methods. Through the analysis of gene expression data of 49 tissues from the GTEx dataset, we demonstrated that LRT-q detected more genes whose expression was regulated by nearby rare variants, which we call RV eGenes, compared to other approaches including SKAT-O.
We acknowledge the GTEx Consortium for providing the GTEx v8 dataset.
Citation: Li J, Kong N, Han B, Sul JH (2021) Rare variants regulate expression of nearby individual genes in multiple tissues. PLoS Genet 17(6): e1009596. https://doi.org/10.1371/journal.pgen.1009596
Editor: Chris Cotsapas, Yale School of Medicine, UNITED STATES
Received: January 31, 2021; Accepted: May 11, 2021; Published: June 1, 2021.
Copyright: © 2021 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The GTEx v8 genotype data were obtained from dbGaP accession number phs000424.v8.p2. The GTEx v8 expression data and covariates data used in this study were downloaded from the GTEx Portal (https://gtexportal.org/home/datasets). Other relevant data generated or analyzed during the current study are within the manuscript and its Supporting Information files. LRT-q, analysis scripts, and results are publicly available at https://github.com/avallonking/LRTq.
Funding: JHS is supported by the National Institute of Environmental Health Sciences (NIEHS https://www.niehs.nih.gov/) grant K01 ES028064, the National Science Foundation (https://www.nsf.gov/) grant #1705197, National Institute of Neurological Disorders and Stroke (https://www.ninds.nih.gov/) R01NS102371, and National Heart, Lung, and Blood Institute (https://www.nhlbi.nih.gov/) R03HL150604. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.