Allele-specific enhancers mediate associations between LCAT and ABCA1 polymorphisms and HDL metabolism

The importance of functional genomics studies in precision rheumatology

Rheumatic diseases, those that affect the musculoskeletal system, cause significant morbidity. Among risk factors of these diseases is a significant genetic component. Recent advances in high-throughput omics techniques now allow a comprehensive profiling of patients at a genetic level through genome-wide association studies. Without functional interpretation of variants identified through these studies, clinical insight remains limited. Strategies include statistical fine-mapping that refine the list of variants in loci associated with disease, whilst colocalization techniques attempt to attribute function to variants that overlap a genetically active chromatin annotation. Functional validation using genome editing techniques can be used to further refine genetic signals and identify key pathways in cell types relevant to rheumatic disease biology. Insight gained from the combination of genetic studies and functional validation can be used to improve precision medicine in rheumatic diseases by allowing risk prediction and drug repositioning.

Antithrombin, Protein C, and Protein S: Genome and Transcriptome-Wide Association Studies Identify 7 Novel Loci Regulating Plasma Levels

BACKGROUND

Antithrombin, PC (protein C), and PS (protein S) are circulating natural anticoagulant proteins that regulate hemostasis and of which partial deficiencies are causes of venous thromboembolism. Previous genetic association studies involving antithrombin, PC, and PS were limited by modest sample sizes or by being restricted to candidate genes. In the setting of the Cohorts for Heart and Aging Research in Genomic Epidemiology consortium, we meta-analyzed across ancestries the results from 10 genome-wide association studies of plasma levels of antithrombin, PC, PS free, and PS total.

METHODS

Study participants were of European and African ancestries, and genotype data were imputed to TOPMed, a dense multiancestry reference panel. Each of the 10 studies conducted a genome-wide association studies for each phenotype and summary results were meta-analyzed, stratified by ancestry. Analysis of antithrombin included 25 243 European ancestry and 2688 African ancestry participants, PC analysis included 16 597 European ancestry and 2688 African ancestry participants, PSF and PST analysis included 4113 and 6409 European ancestry participants. We also conducted transcriptome-wide association analyses and multiphenotype analysis to discover additional associations. Novel genome-wide association studies and transcriptome-wide association analyses findings were validated by in vitro functional experiments. Mendelian randomization was performed to assess the causal relationship between these proteins and cardiovascular outcomes.

RESULTS

Genome-wide association studies meta-analyses identified 4 newly associated loci: 3 with antithrombin levels (GCKR, BAZ1B, and HP-TXNL4B) and 1 with PS levels (ORM1-ORM2). transcriptome-wide association analyses identified 3 newly associated genes: 1 with antithrombin level (FCGRT), 1 with PC (GOLM2), and 1 with PS (MYL7). In addition, we replicated 7 independent loci reported in previous studies. Functional experiments provided evidence for the involvement of GCKR, SNX17, and HP genes in antithrombin regulation.

CONCLUSIONS

The use of larger sample sizes, diverse populations, and a denser imputation reference panel allowed the detection of 7 novel genomic loci associated with plasma antithrombin, PC, and PS levels.

Effects of SNVs in ABCA1, ABCG1, ABCG5, ABCG8, and SCARB1 Genes on Plasma Lipids, Lipoproteins, and Adiposity Markers in a Brazilian Population

Several proteins are involved in cholesterol homeostasis, as scavenger receptor class B type I and ATP-binding cassette (ABC) transporters including ABCA1, ABCG1, ABCG5, and ABCG8. This study aimed to determine the effects of single nucleotide variants (SNVs) rs2275543 (ABCA1), rs1893590 (ABCG1), rs6720173 (ABCG5), rs6544718 (ABCG8), and rs5888 (SCARB1) on plasma lipids, lipoproteins, and adiposity markers in an asymptomatic population and its sex-specific effects. Volunteers (n = 590) were selected and plasma lipids, lipoproteins, and adiposity markers (waist-to-hip and waist-to-height ratios, lipid accumulation product and body adiposity index) were measured. Genomic DNA was isolated from peripheral blood cells according to the method adapted from Gross-Bellard. SNVs were detected in the TaqMan® OpenArray® Real-Time polymerase chain reaction platform and data analyses were performed using the TaqMan® Genotyper Software. The rs2275543*C point to an increase of high-density lipoprotein size in females while in males very-low-density lipoprotein, cholesterol, and triglycerides were statistically lower (P value < 0.05). The rs1893590*C was statistically associated with lower apolipoprotein A-I levels and higher activities of paraoxonase-1 and cholesteryl ester transfer protein (P value < 0.05). The rs6720173 was statistically associated with an increase in cholesterol and low-density lipoprotein cholesterol in males; moreover, rs6544718*T reduced adiposity markers in females (P value < 0.05). Regarding the rs5888, a decreased adiposity marker in the total population and in females occurred (P value < 0.05). Multivariate analysis of variance showed that SNVs could influence components of high-density lipoprotein metabolism, mainly through ABCG1 (P value < 0.05). The ABCA1 and ABCG5 variants showed sex-specific effects on lipids and lipoproteins, while SCARB1 and ABCG8 variants might influence adiposity markers in females. Our data indicate a possible role of ABCG1 on HDL metabolism.

A transcription-centric model of SNP-age interaction

Complex age-associated phenotypes are caused, in part, by an interaction between an individual's genotype and age. The mechanisms governing such interactions are however not entirely understood. Here, we provide a novel transcriptional mechanism-based framework-SNiPage, to investigate such interactions, whereby a transcription factor (TF) whose expression changes with age (age-associated TF), binds to a polymorphic regulatory element in an allele-dependent fashion, rendering the target gene's expression dependent on both, the age and the genotype. Applying SNiPage to GTEx, we detected ~637 significant TF-SNP-Gene triplets on average across 25 tissues, where the TF binds to a regulatory SNP in the gene's promoter or putative enhancer and potentially regulates its expression in an age- and allele-dependent fashion. The detected SNPs are enriched for epigenomic marks indicative of regulatory activity, exhibit allele-specific chromatin accessibility, and spatial proximity to their putative gene targets. Furthermore, the TF-SNP interaction-dependent target genes have established links to aging and to age-associated diseases. In six hypertension-implicated tissues, detected interactions significantly inform hypertension state of an individual. Lastly, the age-interacting SNPs exhibit a greater proximity to the reported phenotype/diseases-associated SNPs than eSNPs identified in an interaction-independent fashion. Overall, we present a novel mechanism-based model, and a novel framework SNiPage, to identify functionally relevant SNP-age interactions in transcriptional control and illustrate their potential utility in understanding complex age-associated phenotypes.