Functional group-based linkage analysis of gene expression trait loci

Introduction: The majority of information on the genetic background of atrial fibrillation (AF) results from genomic DNA variant analysis without consideration of tissue expression. Hypothesis: Analysis of tissue-specific gene expression in left atrium (LA) can further understanding of the molecular mechanism of identified AF risk variants, and identify novel genes and gene variants associated with AF. Methods: We isolated mRNA from samples of the LA free wall taken during mitral valve surgery in 62 Caucasian individuals. Gene expression in the LA was compared between patients who did and did not have post-operative AF (poAF) using high-throughput RNA expression. Using genotypes of 1.4 million single nucleotide polymorphisms (SNP) we performed cis expression quantifying trait loci (eQTL) analysis, correlating gene expression of each gene with the genotypes of adjacent (<1Mbp) SNPs. Results: We identified 23 differentially expressed genes in the LA of patients with poAF, including three potassium channel genes (KCNA7, KCNH8 and KCNK17). The largest expression difference was in LOC645323, a long non-coding RNA. The expression of PITX2, ZFHX3 and KCNN3, previously shown to be associated with AF, did not differ between patients with and without poAF. We identified 12,476 cis eQTL relationships in the LA, several of those included genetic regions and genes previously associated with AF. We confirmed an eQTL relationship between rs3744029 genotype and the expression of MYOZ1. Furthermore we describe a novel eQTL relationship between rs6795970 genotype and the expression of the SCN10A gene. Conclusions: We have analysed the human LA expression via high-throughput RNA sequencing, and identified novel genes and gene variants likely involved in the molecular pathophysiology of AF.

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Leveraging DNA-Methylation Quantitative-Trait Loci to Characterize the Relationship between Methylomic Variation, Gene Expression, and Complex Traits

The American Journal of Human Genetics ◽

10.1016/j.ajhg.2018.09.007 ◽

2018 ◽

Vol 103 (5) ◽

pp. 654-665 ◽

Cited By ~ 29

Author(s):

Eilis Hannon ◽

Tyler J. Gorrie-Stone ◽

Melissa C. Smart ◽

Joe Burrage ◽

Amanda Hughes ◽

...

Keyword(s):

Gene Expression ◽

Dna Methylation ◽

Quantitative Trait Loci ◽

Quantitative Trait ◽

Complex Traits ◽

Trait Loci ◽

The Relationship ◽

Methylation Quantitative Trait Loci

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The Functional False Discovery Rate with Applications to Genomics

10.1101/241133 ◽

2017 ◽

Cited By ~ 2

Author(s):

Xiongzhi Chen ◽

David G. Robinson ◽

John D. Storey

Keyword(s):

Gene Expression ◽

False Discovery Rate ◽

Genetic Marker ◽

Read Depth ◽

False Discovery Rates ◽

Additional Information ◽

False Discovery ◽

Gene Expression Trait ◽

Genetics Of Gene Expression ◽

False Discoveries

AbstractThe false discovery rate measures the proportion of false discoveries among a set of hypothesis tests called significant. This quantity is typically estimated based on p-values or test statistics. In some scenarios, there is additional information available that may be used to more accurately estimate the false discovery rate. We develop a new framework for formulating and estimating false discovery rates and q-values when an additional piece of information, which we call an “informative variable”, is available. For a given test, the informative variable provides information about the prior probability a null hypothesis is true or the power of that particular test. The false discovery rate is then treated as a function of this informative variable. We consider two applications in genomics. Our first is a genetics of gene expression (eQTL) experiment in yeast where every genetic marker and gene expression trait pair are tested for associations. The informative variable in this case is the distance between each genetic marker and gene. Our second application is to detect differentially expressed genes in an RNA-seq study carried out in mice. The informative variable in this study is the per-gene read depth. The framework we develop is quite general, and it should be useful in a broad range of scientific applications.

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