Longitudinal trends in the association of metabolic syndrome with 550 k single-nucleotide polymorphisms in the Framingham Heart Study

Longitudinal genome-wide DNA methylation changes in response to kidney failure replacement therapy

Chronic kidney disease (CKD) is an emerging public health priority associated with high mortality rates and demanding treatment regimens, including life-style changes, medications or even dialysis or renal transplantation. Unavoidably, the uremic milieu disturbs homeostatic processes such as DNA methylation and other vital gene regulatory mechanisms. Here, we aimed to investigate how dialysis or kidney transplantation modifies the epigenome-wide methylation signature over 12 months of treatment. We used the Infinium HumanMethylation450 BeadChip on whole blood samples from CKD-patients undergoing either dialysis (n = 11) or kidney transplantation (n = 12) and 24 age- and sex-matched population-based controls. At baseline, comparison between patients and controls identified several significant (P_FDR < 0.01) CpG methylation differences in genes with functions relevant to inflammation, cellular ageing and vascular calcification. Following 12 months, the global DNA methylation pattern of patients approached that seen in the control group. Notably, 413 CpG sites remained differentially methylated at follow-up in both treatment groups compared to controls. Together, these data indicate that the uremic milieu drives genome-wide methylation changes that are partially reversed with kidney failure replacement therapy. Differentially methylated CpG sites unaffected by treatment may be of particular interest as they could highlight candidate genes for kidney disease per se.

RNA sequencing of blood in coronary artery disease: involvement of regulatory T cell imbalance

BACKGROUND

Cardiovascular disease had a global prevalence of 523 million cases and 18.6 million deaths in 2019. The current standard for diagnosing coronary artery disease (CAD) is coronary angiography. Surprisingly, despite well-established clinical indications, up to 40% of the one million invasive cardiac catheterizations return a result of 'no blockage'. The present studies employed RNA sequencing of whole blood to identify an RNA signature in patients with angiographically confirmed CAD.

METHODS

Whole blood RNA was depleted of ribosomal RNA (rRNA) and analyzed by single-molecule sequencing of RNA (RNAseq) to identify transcripts associated with CAD (TRACs) in a discovery group of 96 patients presenting for elective coronary catheterization. The resulting transcript counts were compared between groups to identify differentially expressed genes (DEGs).

RESULTS

Surprisingly, 98% of DEGs/TRACs were down-regulated ~ 1.7-fold in patients with mild to severe CAD (> 20% stenosis). The TRACs were independent of comorbid risk factors for CAD, such as sex, hypertension, and smoking. Bioinformatic analysis identified an enrichment in transcripts such as FoxP1, ICOSLG, IKZF4/Eos, SMYD3, TRIM28, and TCF3/E2A that are likely markers of regulatory T cells (Treg), consistent with known reductions in Tregs in CAD. A validation cohort of 80 patients confirmed the overall pattern (92% down-regulation) and supported many of the Treg-related changes. TRACs were enriched for transcripts associated with stress granules, which sequester RNAs, and ciliary and synaptic transcripts, possibly consistent with changes in the immune synapse of developing T cells.

CONCLUSIONS

These studies identify a novel mRNA signature of a Treg-like defect in CAD patients and provides a blueprint for a diagnostic test for CAD. The pattern of changes is consistent with stress-related changes in the maturation of T and Treg cells, possibly due to changes in the immune synapse.

Integration of Transformative Platforms for the Discovery of Causative Genes in Cardiovascular Diseases

Cardiovascular diseases are the leading cause of morbidity and mortality worldwide. Genome-wide association studies (GWAS) are powerful epidemiological tools to find genes and variants associated with cardiovascular diseases while follow-up biological studies allow to better understand the etiology and mechanisms of disease and assign causality. Improved methodologies and reduced costs have allowed wider use of bulk and single-cell RNA sequencing, human-induced pluripotent stem cells, organoids, metabolomics, epigenomics, and novel animal models in conjunction with GWAS. In this review, we feature recent advancements relevant to cardiovascular diseases arising from the integration of genetic findings with multiple enabling technologies within multidisciplinary teams to highlight the solidifying transformative potential of this approach. Well-designed workflows integrating different platforms are greatly improving and accelerating the unraveling and understanding of complex disease processes while promoting an effective way to find better drug targets, improve drug design and repurposing, and provide insight towards a more personalized clinical practice.

Comparing Analytic Methods for Longitudinal GWAS and a Case-Study Evaluating Chemotherapy Course Length in Pediatric AML. A Report from the Children's Oncology Group

Regression analysis is commonly used in genome-wide association studies (GWAS) to test genotype-phenotype associations but restricts the phenotype to a single observation for each individual. There is an increasing need for analytic methods for longitudinally collected phenotype data. Several methods have been proposed to perform longitudinal GWAS for family-based studies but few methods are described for unrelated populations. We compared the performance of three statistical approaches for longitudinal GWAS in unrelated subjectes: (1) principal component-based generalized estimating equations (PC-GEE); (2) principal component-based linear mixed effects model (PC-LMEM); (3) kinship coefficient matrix-based linear mixed effects model (KIN-LMEM), in a study of single-nucleotide polymorphisms (SNPs) on the duration of 4 courses of chemotherapy in 624 unrelated children with de novo acute myeloid leukemia (AML) genotyped on the Illumina 2.5 M OmniQuad from the COG studies AAML0531 and AAML1031. In this study we observed an exaggerated type I error with PC-GEE in SNPs with minor allele frequencies < 0.05, wheras KIN-LMEM produces more than expected type II errors. PC-MEM showed balanced type I and type II errors for the observed vs. expected P-values in comparison to competing approaches. In general, a strong concordance was observed between the P-values with the different approaches, in particular among P < 0.01 where the between-method AUCs exceed 99%. PC-LMEM accounts for genetic relatedness and correlations among repeated phenotype measures, shows minimal genome-wide inflation of type I errors, and yields high power. We therefore recommend PC-LMEM as a robust analytic approach for GWAS of longitudinal data in unrelated populations.

Association of FURIN and ZPR1 polymorphisms with metabolic syndrome

Although genome-wide association studies (GWASs) have identified various genes and loci in predisposition to metabolic syndrome (MetS) or each component of this condition, the genetic basis of MetS in individuals remains to be identified definitively. The aim of the present study was to examine the possible association of MetS in individuals with 29 polymorphisms that were previously identified as susceptibility loci for coronary artery disease or myocardial infarction by meta-analyses of GWASs. The study population comprised 1,822 subjects with MetS and 1,096 controls. Subjects with MetS had ≥3 of the 5 components of the diagnostic criteria for MetS, whereas control individuals had 0-1 of the 5 components. The genotypes for the 29 polymorphisms were determined by the multiplex bead-based Luminex assay. Comparisons of allele frequencies by the χ2 test revealed that rs17514846 (A→C) of the furin (paired basic amino acid-cleaving enzyme) gene (FURIN; P=0.0006), rs964184 (C→G) of the ZPR1 zinc finger gene (ZPR1; P=0.0078) and rs599839 (G→A) of the proline/serine-rich coiled-coil 1 gene (P=0.0486) were significantly (P<0.05) associated with the prevalence of MetS. Multivariable logistic regression analysis with adjustment for age, gender and smoking status revealed that rs17514846 of FURIN (P=0.0016; odds ratio, 0.76; dominant model) and rs964184 of ZPR1 (P=0.0164; odds ratio, 1.21; dominant model) were significantly associated with MetS. The minor A allele of rs17514846 of FURIN was significantly associated with a decrease in the serum concentration of triglycerides (P=0.0293) and to an increase in the serum concentration of high-density lipoprotein (HDL) cholesterol (P=0.0460). The minor G allele of rs964184 of ZPR1 was significantly associated with increases in the serum concentration of triglycerides (P=6.2×10-9) and fasting plasma glucose level (P=0.0028) and to a decrease in the serum concentration of HDL cholesterol (P=0.0105). FURIN and ZPR1 may thus be susceptibility loci for MetS.

Large-scale analysis reveals a functional single-nucleotide polymorphism in the 5'-flanking region of PRDM16 gene associated with lean body mass

Genetic factors are important for the development of sarcopenia, a geriatric disorder characterized by low lean body mass. The aim of this study was to search for novel genes that regulate lean body mass in humans. We performed a large-scale search for 250K single-nucleotide polymorphisms (SNPs) associated with bone mineral density (BMD) using SNP arrays in 1081 Japanese postmenopausal women. We focused on an SNP (rs12409277) located in the 5′-flanking region of the PRDM16 (PRD1-BF-1-RIZ1 homologous domain containing protein 16) gene that showed a significant P value in our screening. We demonstrated that PRDM16 gene polymorphisms were significantly associated with total body BMD in 1081 postmenopausal Japanese women. The rs12409277 SNP affected the transcriptional activity of PRDM16. The subjects with one or two minor allele(s) had a higher lean body mass than the subjects with two major alleles. Genetic analyses uncovered the importance of the PRDM16 gene in the regulation of lean body mass.

Analysis of metabolic syndrome components in >15 000 african americans identifies pleiotropic variants: results from the population architecture using genomics and epidemiology study

BACKGROUND

Metabolic syndrome (MetS) refers to the clustering of cardiometabolic risk factors, including dyslipidemia, central adiposity, hypertension, and hyperglycemia, in individuals. Identification of pleiotropic genetic factors associated with MetS traits may shed light on key pathways or mediators underlying MetS.

METHODS AND RESULTS

Using the Metabochip array in 15 148 African Americans from the Population Architecture using Genomics and Epidemiology (PAGE) study, we identify susceptibility loci and investigate pleiotropy among genetic variants using a subset-based meta-analysis method, ASsociation-analysis-based-on-subSETs (ASSET). Unlike conventional models that lack power when associations for MetS components are null or have opposite effects, Association-analysis-based-on-subsets uses 1-sided tests to detect positive and negative associations for components separately and combines tests accounting for correlations among components. With Association-analysis-based-on-subsets, we identify 27 single nucleotide polymorphisms in 1 glucose and 4 lipids loci (TCF7L2, LPL, APOA5, CETP, and APOC1/APOE/TOMM40) significantly associated with MetS components overall, all P<2.5e-7, the Bonferroni adjusted P value. Three loci replicate in a Hispanic population, n=5172. A novel African American-specific variant, rs12721054/APOC1, and rs10096633/LPL are associated with ≥3 MetS components. We find additional evidence of pleiotropy for APOE, TOMM40, TCF7L2, and CETP variants, many with opposing effects (eg, the same rs7901695/TCF7L2 allele is associated with increased odds of high glucose and decreased odds of central adiposity).

CONCLUSIONS

We highlight a method to increase power in large-scale genomic association analyses and report a novel variant associated with all MetS components in African Americans. We also identify pleiotropic associations that may be clinically useful in patient risk profiling and for informing translational research of potential gene targets and medications.

Obesity-insulin targeted genes in the 3p26-25 region in human studies and LG/J and SM/J mice

Identifying metabolic syndrome (MetS) genes is important for novel drug development and health care. This study extends the findings on human chromosome 3p26-25 for an identified obesity-insulin factor QTL, with an LOD score above 3. A focused association analysis comprising up to 9578 African American and Caucasian subjects from the HyperGEN Network (908 African Americans and 1025 whites), the Family Heart Study (3035 whites in time 1 and 1943 in time 2), and the Framingham Heart Study (1317 in Offspring and 1320 in Generation 3) was performed. The homologous mouse region was explored in an F(16) generation of an advanced intercross between the LG/J and SM/J inbred strains, in an experiment where 1002 animals were fed low-fat (247 males; 254 females) or high-fat (253 males; 248 females) diets. Association results in humans indicate pleiotropic effects for SNPs within or surrounding CNTN4 on obesity, lipids and blood pressure traits and for SNPs near IL5RA, TRNT1, CRBN, and LRRN1 on central obesity and blood pressure. Linkage analyses of this region in LG/J×SM/J mice identify a highly significant pleiotropic QTL peak for insulin and glucose levels, as well as response to glucose challenge. The mouse results show that insulin and glucose levels interact with high and low fat diets and differential gene expression was identified for Crbn and Arl8b. In humans, ARL8B resides ~137kbps away from BHLHE40, expression of which shows up-regulation in response to insulin treatment. This focused human genetic analysis, incorporating mouse research evidenced that 3p26-25 has important genetic contributions to MetS components. Several of the candidate genes have functions in the brain. Their interaction with MetS and the brain warrants further investigation.

Genome-wide screen for metabolic syndrome susceptibility Loci reveals strong lipid gene contribution but no evidence for common genetic basis for clustering of metabolic syndrome traits

BACKGROUND

Genome-wide association (GWA) studies have identified several susceptibility loci for metabolic syndrome (MetS) component traits, but have had variable success in identifying susceptibility loci to the syndrome as an entity. We conducted a GWA study on MetS and its component traits in 4 Finnish cohorts consisting of 2637 MetS cases and 7927 controls, both free of diabetes, and followed the top loci in an independent sample with transcriptome and nuclear magnetic resonance-based metabonomics data. Furthermore, we tested for loci associated with multiple MetS component traits using factor analysis, and built a genetic risk score for MetS.

METHODS AND RESULTS

A previously known lipid locus, APOA1/C3/A4/A5 gene cluster region (SNP rs964184), was associated with MetS in all 4 study samples (P=7.23×10(-9) in meta-analysis). The association was further supported by serum metabolite analysis, where rs964184 was associated with various very low density lipoprotein, triglyceride, and high-density lipoprotein metabolites (P=0.024-1.88×10(-5)). Twenty-two previously identified susceptibility loci for individual MetS component traits were replicated in our GWA and factor analysis. Most of these were associated with lipid phenotypes, and none with 2 or more uncorrelated MetS components. A genetic risk score, calculated as the number of risk alleles in loci associated with individual MetS traits, was strongly associated with MetS status.

CONCLUSIONS

Our findings suggest that genes from lipid metabolism pathways have the key role in the genetic background of MetS. We found little evidence for pleiotropy linking dyslipidemia and obesity to the other MetS component traits, such as hypertension and glucose intolerance.

Association between ADRA1A gene and the metabolic syndrome: candidate genes and functional counterpart in the PAMELA population

OBJECTIVES

There is currently uncertainty about whether metabolic syndrome has a common underlying process. We performed a gene-centric association study of metabolic syndrome in 98 major cardiometabolic genes in the large, well phenotyped Pressioni Arteriose Monitorate e Loro Associazioni (PAMELA) study. We followed this with functional studies to elucidate a possible mechanism for the top association signal.

METHODS

From the PAMELA cohort, we sampled 1407 individuals with information on the metabolic syndrome (ATPIII criteria). We analyzed 1324 tagging single-nucleotide polymorphisms (SNPs) in 98 candidate genes selected, based on known pathways involved in sympathetic nervous system, oxidative stress, renin-angiotensin system and sodium balance.

RESULTS

The SNP rs17055869 near the alpha-1A-adrenoreceptor gene (ADRA1A) showed the strongest association with metabolic syndrome (odds ratio 1.7, CI 1.3-2.2; P = 0.00007, P = 0.000098 after permutation). In order to determine a functional basis for this association, we examined in a subgroup of metabolic syndrome patients whether the allelic distribution of the above-mentioned gene is different according to the different degree of the metabolic syndrome-related sympathetic activation, directly assessed by the gold standard method to assess neuroadrenergic drive, that is microneurographic recording of efferent postganglionic muscle sympathetic nerve traffic. All metabolic syndrome patients with a lesser degree of sympathetic activation were homozygous for the major allele (C), whereas those with a very pronounced sympathetic overdrive had an over-representation of the minor T allele (P < 0.0001).

CONCLUSION

Thus, the rs17055869 SNP near the 3' end of ADRA1A is significantly associated with metabolic syndrome and it may be involved in determining a greater level of sympathetic activation in metabolic syndrome patients.

The genetics of obesity and the metabolic syndrome

In this review, we discuss the genetic architecture of obesity and the metabolic syndrome, highlighting recent advances in identifying genetic variants and loci responsible for a portion of the variation in components of the metabolic syndrome, namely, adiposity traits, serum HDL and triglycerides, blood pressure, and glycemic traits. We focus particularly on recent progress from large-scale genome-wide association studies (GWAS), by detailing their successes and how lessons learned can pave the way for future discovery. Results from recent GWAS coalesce with earlier work suggesting numerous interconnections between obesity and the metabolic syndrome, developed through several potentially pleiotropic effects. We detail recent work by way of a case study on the cadherin 13 gene and its relation with adiponectin in the HyperGEN and the Framingham Heart Studies, and its association with obesity and the metabolic syndrome. We provide also a gene network analysis of recent variants related to obesity and metabolic syndrome discovered through genome-wide association studies, and 4 gene networks based on searching the NCBI database.

Use of longitudinal data in genetic studies in the genome-wide association studies era: summary of Group 14

Participants analyzed actual and simulated longitudinal data from the Framingham Heart Study for various metabolic and cardiovascular traits. The genetic information incorporated into these investigations ranged from selected single-nucleotide polymorphisms to genome-wide association arrays. Genotypes were incorporated using a broad range of methodological approaches including conditional logistic regression, linear mixed models, generalized estimating equations, linear growth curve estimation, growth modeling, growth mixture modeling, population attributable risk fraction based on survival functions under the proportional hazards models, and multivariate adaptive splines for the analysis of longitudinal data. The specific scientific questions addressed by these different approaches also varied, ranging from a more precise definition of the phenotype, bias reduction in control selection, estimation of effect sizes and genotype associated risk, to direct incorporation of genetic data into longitudinal modeling approaches and the exploration of population heterogeneity with regard to longitudinal trajectories. The group reached several overall conclusions: (1) The additional information provided by longitudinal data may be useful in genetic analyses. (2) The precision of the phenotype definition as well as control selection in nested designs may be improved, especially if traits demonstrate a trend over time or have strong age-of-onset effects. (3) Analyzing genetic data stratified for high-risk subgroups defined by a unique development over time could be useful for the detection of rare mutations in common multifactorial diseases. (4) Estimation of the population impact of genomic risk variants could be more precise. The challenges and computational complexity demanded by genome-wide single-nucleotide polymorphism data were also discussed.