Fast eQTL Analysis for Twin Studies

TwinEQTL: Ultra Fast and Powerful Association Analysis for eQTL and GWAS in Twin Studies

We develop a computationally efficient alternative, TwinEQTL, to a linear mixed-effects model (LMM) for twin genome-wide association study (GWAS) data. Instead of analyzing all twin samples together with LMM, TwinEQTL first splits twin samples into two independent groups on which multiple linear regression analysis can be validly performed separately, followed by an appropriate meta-analysis-like approach to combine the two non-independent test results. Through mathematical derivations, we prove the validity of TwinEQTL algorithm and show that the correlation between two dependent test statistics at each single-nucleotide polymorphism (SNP) are independent of its minor allele frequency (MAF). Thus the correlation is constant across all SNPs. Through simulations, we show empirically that TwinEQTL has well controlled type I error with negligible power loss compared to the gold-standard linear mixed effects models. To accommodate eQTL analysis with twin subjects, we further implement TwinEQTL into a R package with much improved computational efficiency. Our approaches provide a significant leap in terms of computing speed for GWAS and eQTL analysis with twin samples.

A Genome-Wide Integrative Association Study of DNA Methylation and Gene Expression Data and Later Life Cognitive Functioning in Monozygotic Twins

Monozygotic twins are genetically identical but rarely phenotypically identical. Epigenetic and transcriptional variation could influence this phenotypic discordance. Investigation of intra-pair differences in molecular markers and a given phenotype in monozygotic twins controls most of the genetic contribution, enabling studies of the molecular features of the phenotype. This study aimed to identify genes associated with cognition in later life using integrated enrichment analyses of the results of blood-derived intra-pair epigenome-wide and transcriptome-wide association analyses of cognition in 452 middle-aged and old-aged monozygotic twins (56–80 years). Integrated analyses were performed with an unsupervised approach using KeyPathwayMiner, and a supervised approach using the KEGG and Reactome databases. The supervised approach identified several enriched gene sets, including “neuroactive ligand receptor interaction” (p-value = 1.62^∗10-2), “Neurotrophin signaling” (p-value = 2.52^∗10-3), “Alzheimer’s disease” (p-value = 1.20^∗10-2), and “long-term depression” (p-value = 1.62^∗10-2). The unsupervised approach resulted in a 238 gene network, including the Alzheimer’s disease gene APP (Amyloid Beta Precursor Protein) as an exception node, and several novel candidate genes. The strength of the unsupervised method is that it can reveal previously uncharacterized sub-pathways and detect interplay between biological processes, which remain undetected by the current supervised methods. In conclusion, this study identified several previously reported cognition genes and pathways and, additionally, puts forward novel candidates for further verification and validation.

Identification of expression quantitative trait loci (eQTLs) in human peripheral blood mononuclear cells (PBMCs) and shared with liver and brain

PBMCs are essential for immunity and involved in various diseases. To identify genetic variations contributing to PBMCs transcriptome-wide gene expression, we performed a genome-wide eQTL analysis by using genome-wide SNPs data and transcriptome-wide mRNA expression data. To assess whether there are common regulation patterns shared among different tissues/organs, public datasets were utilized to identify common eQTLs shared with PBMCs in lymphoblastoid, monocytes, liver, and brain. Allelic expression imbalance (AEI) assay was employed to validate representative eQTLs identified. We identified 443 cis- and 2386 trans-eSNPs (FDR <0.05), which regulated 128 and 635 target genes, respectively. A transcriptome-wide expression regulation network was constructed, highlighting the importance of 28 pleiotropic eSNPs and 18 dually (cis- and trans-) regulated genes. Three genes, that is, TIPRL, HSPB8, and EGLN3, were commonly regulated by hundreds of eSNPs and constituted a very complex interaction network. Strikingly, the missense SNP rs371513 trans- regulated 25 target genes, which were functionally related to poly(A) RNA binding. Among 8904 eQTLs (P < 0.001) identified herein in PBMCs, a minority (163) was overlapped with lymphoblastoid, monocytes, liver, and/or brain. Besides, two cis-eSNPs in PBMC were confirmed by AEI. The present results demonstrated a comprehensive expression regulation network for human PBMCs and may provide novel insights into the pathogenesis of immunological diseases related to PBMCs.

Functional Architectures of Local and Distal Regulation of Gene Expression in Multiple Human Tissues

Genetic variants that modulate gene expression levels play an important role in the etiology of human diseases and complex traits. Although large-scale eQTL mapping studies routinely identify many local eQTLs, the molecular mechanisms by which genetic variants regulate expression remain unclear, particularly for distal eQTLs, which these studies are not well powered to detect. Here, we leveraged all variants (not just those that pass stringent significance thresholds) to analyze the functional architecture of local and distal regulation of gene expression in 15 human tissues by employing an extension of stratified LD-score regression that produces robust results in simulations. The top enriched functional categories in local regulation of peripheral-blood gene expression included coding regions (11.41×), conserved regions (4.67×), and four histone marks (p < 5 × 10^-5 for all enrichments); local enrichments were similar across the 15 tissues. We also observed substantial enrichments for distal regulation of peripheral-blood gene expression: coding regions (4.47×), conserved regions (4.51×), and two histone marks (p < 3 × 10^-7 for all enrichments). Analyses of the genetic correlation of gene expression across tissues confirmed that local regulation of gene expression is largely shared across tissues but that distal regulation is highly tissue specific. Our results elucidate the functional components of the genetic architecture of local and distal regulation of gene expression.