A Simple Test of Class-Level Genetic Association Can Reveal Novel Cardiometabolic Trait Loci

Association of the Lactase Persistence Haplotype Block With Disease Risk in Populations of European Descent

Among people of European descent, the ability to digest lactose into adulthood arose via strong positive selection of a highly advantageous allele encompassing the lactase gene. Lactose-tolerant and intolerant individuals may have different disease risks due to the shared genetics of their haplotype block. Therefore, the overall objective of the study was to assess the genetic association of the lactase persistence haplotype to disease risk. Using data from the 1000Genomes project, we estimated the size of the lactase persistence haplotype block to be 1.9 Mbp containing up to 9 protein-coding genes and a microRNA. Based on the function of the genes and microRNA, we studied health phenotypes likely to be impacted by the lactase persistence allele: prostate cancer status, cardiovascular disease status, and bone mineral density. We used summary statistics from large genome-wide metanalyses—32,965 bone mineral density, 140,306 prostate cancer and 184,305 coronary artery disease subjects—to evaluate whether the lactase persistence allele was associated with these disease phenotypes. Despite the fact that previous work demonstrated that the lactase persistence haplotype block harbors increased deleterious mutations, these results suggest little effect on the studied disease phenotypes.

Into the Wild: GWAS Exploration of Non-coding RNAs

Genome-wide association studies (GWAS) have proven a fundamental tool to identify common variants associated to complex traits, thus contributing to unveil the genetic components of human disease. Besides, the advent of GWAS contributed to expose unexpected findings that urged to redefine the framework of population genetics. First, loci identified by GWAS had small effect sizes and could only explain a fraction of the predicted heritability of the traits under study. Second, the majority of GWAS hits mapped within non-coding regions (such as intergenic or intronic regions) where new functional RNA species (such as lncRNAs or circRNAs) have started to emerge. Bigger cohorts, meta-analysis and technical improvements in genotyping allowed identification of an increased number of genetic variants associated to coronary artery disease (CAD) and cardiometabolic traits. The challenge remains to infer causal mechanisms by which these variants influence cardiovascular disease development. A tendency to assign potential causal variants preferentially to coding genes close to lead variants contributed to disregard the role of non-coding elements. In recent years, in parallel to an increased knowledge of the non-coding genome, new studies started to characterize disease-associated variants located within non-coding RNA regions. The upcoming of databases integrating single-nucleotide polymorphisms (SNPs) and non-coding RNAs together with novel technologies will hopefully facilitate the discovery of causal non-coding variants associated to disease. This review attempts to summarize the current knowledge of genetic variation within non-coding regions with a focus on long non-coding RNAs that have widespread impact in cardiometabolic diseases.

Deep RNA Sequencing Uncovers a Repertoire of Human Macrophage Long Intergenic Noncoding RNAs Modulated by Macrophage Activation and Associated With Cardiometabolic Diseases

Background

Sustained and dysfunctional macrophage activation promotes inflammatory cardiometabolic disorders, but the role of long intergenic noncoding RNA (lincRNA) in human macrophage activation and cardiometabolic disorders is poorly defined. Through transcriptomics, bioinformatics, and selective functional studies, we sought to elucidate the lincRNA landscape of human macrophages.

Methods and Results

We used deep RNA sequencing to assemble the lincRNA transcriptome of human monocyte‐derived macrophages at rest and following stimulation with lipopolysaccharide and IFN‐γ (interferon γ) for M1 activation and IL‐4 (interleukin 4) for M2 activation. Through de novo assembly, we identified 2766 macrophage lincRNAs, including 861 that were previously unannotated. The majority (≈85%) was nonsyntenic or was syntenic but not annotated as expressed in mouse. Many macrophage lincRNAs demonstrated tissue‐enriched transcription patterns (21.5%) and enhancer‐like chromatin signatures (60.9%). Macrophage activation, particularly to the M1 phenotype, markedly altered the lincRNA expression profiles, revealing 96 lincRNAs differentially expressed, suggesting potential roles in regulating macrophage inflammatory functions. A subset of lincRNAs overlapped genomewide association study loci for cardiometabolic disorders. MacORIS (macrophage‐enriched obesity‐associated lincRNA serving as a repressor of IFN‐γ signaling), a macrophage‐enriched lincRNA not expressed in mouse macrophages, harbors variants associated with central obesity. Knockdown of MacORIS, which is located in the cytoplasm, enhanced IFN‐γ–induced JAK2 (Janus kinase 2) and STAT1 (signal transducer and activator of transcription 1) phosphorylation in THP‐1 macrophages, suggesting a potential role as a repressor of IFN‐γ signaling. Induced pluripotent stem cell–derived macrophages recapitulated the lincRNA transcriptome of human monocyte‐derived macrophages and provided a high‐fidelity model with which to study lincRNAs in human macrophage biology, particularly those not conserved in mouse.

Conclusions

High‐resolution transcriptomics identified lincRNAs that form part of the coordinated response during macrophage activation, including specific macrophage lincRNAs associated with human cardiometabolic disorders that modulate macrophage inflammatory functions.

A score test for genetic class-level association with nonlinear biomarker trajectories

Emerging data suggest that the genetic regulation of the biological response to inflammatory stress may be fundamentally different to the genetic underpinning of the homeostatic control (resting state) of the same biological measures. In this paper, we interrogate this hypothesis using a single-SNP score test and a novel class-level testing strategy to characterize protein-coding gene and regulatory element-level associations with longitudinal biomarker trajectories in response to stimulus. Using the proposed class-level association score statistic for longitudinal data, which accounts for correlations induced by linkage disequilibrium, the genetic underpinnings of evoked dynamic changes in repeatedly measured biomarkers are investigated. The proposed method is applied to data on two biomarkers arising from the Genetics of Evoked Responses to Niacin and Endotoxemia study, a National Institutes of Health-sponsored investigation of the genomics of inflammatory and metabolic responses during low-grade endotoxemia. Our results suggest that the genetic basis of evoked inflammatory response is different than the genetic contributors to resting state, and several potentially novel loci are identified. A simulation study demonstrates appropriate control of type-1 error rates, relative computational efficiency, and power. Copyright © 2017 John Wiley & Sons, Ltd.

Bayesian variable selection for post-analytic interrogation of susceptibility loci

Understanding the complex interplay among protein coding genes and regulatory elements requires rigorous interrogation with analytic tools designed for discerning the relative contributions of overlapping genomic regions. To this aim, we offer a novel application of Bayesian variable selection (BVS) for classifying genomic class level associations using existing large meta-analysis summary level resources. This approach is applied using the expectation maximization variable selection (EMVS) algorithm to typed and imputed SNPs across 502 protein coding genes (PCGs) and 220 long intergenic non-coding RNAs (lncRNAs) that overlap 45 known loci for coronary artery disease (CAD) using publicly available Global Lipids Gentics Consortium (GLGC) (Teslovich et al., 2010; Willer et al., 2013) meta-analysis summary statistics for low-density lipoprotein cholesterol (LDL-C). The analysis reveals 33 PCGs and three lncRNAs across 11 loci with >50% posterior probabilities for inclusion in an additive model of association. The findings are consistent with previous reports, while providing some new insight into the architecture of LDL-cholesterol to be investigated further. As genomic taxonomies continue to evolve, additional classes such as enhancer elements and splicing regions, can easily be layered into the proposed analysis framework. Moreover, application of this approach to alternative publicly available meta-analysis resources, or more generally as a post-analytic strategy to further interrogate regions that are identified through single point analysis, is straightforward. All coding examples are implemented in R version 3.2.1 and provided as supplemental material.

Genome-wide interrogation reveals hundreds of long intergenic noncoding RNAs that associate with cardiometabolic traits

Long intergenic noncoding RNAs (lincRNAs) play important roles in disease, but the vast majority of these transcripts remain uncharacterized. We defined a set of 54 944 human lincRNAs by drawing on four publicly available lincRNA datasets, and annotated ∼2.5 million single nucleotide polymorphisms (SNPs) from each of 15 cardiometabolic genome-wide association study datasets into these lincRNAs. We identified hundreds of lincRNAs with at least one trait-associated SNP: 898 SNPs in 343 unique lincRNAs at 5% false discovery rate, and 469 SNPs in 146 unique lincRNAs meeting Bonferroni-corrected P < 0.05. An additional 64 trait-associated lincRNAs were identified using a class-level testing strategy at Bonferroni-corrected P < 0.05. To better understand the genomic context and prioritize trait-associated lincRNAs, we examined the pattern of linkage disequilibrium between SNPs in the lincRNAs and SNPs that met genome-wide-significance in the region (±500 kb of lincRNAs). A subset of the lincRNA-trait association findings was replicated in independent Genome-wide association studies data from the Pakistan Risk of Myocardial Infarction Study study. For trait-associated lincRNAs, we also investigated synteny and conservation relative to mouse, expression patterns in five cardiometabolic-relevant tissues, and allele-specific expression in RNA sequencing data for adipose tissue and leukocytes. Finally, we revealed a functional role in human adipocytes for linc-NFE2L3-1, which is expressed in adipose and is associated with waist-hip ratio adjusted for BMI. This comprehensive profile of trait-associated lincRNAs provides novel insights into disease mechanism and serves as a launching point for interrogation of the biology of specific lincRNAs in cardiometabolic disease.