Genome-wide association study of a heart failure related metabolomic profile among African Americans in the Atherosclerosis Risk in Communities (ARIC) study

Serum metabolite signatures of cardiac function and morphology in individuals from a population-based cohort

BACKGROUND

Changes in serum metabolites in individuals with altered cardiac function and morphology may exhibit information about cardiovascular disease (CVD) pathway dysregulations and potential CVD risk factors. We aimed to explore associations of cardiac function and morphology, evaluated using magnetic resonance imaging (MRI) with a large panel of serum metabolites.

METHODS

Cross-sectional data from CVD-free individuals from the population-based KORA cohort were analyzed. Associations between 3T-MRI-derived left ventricular (LV) function and morphology parameters (e.g., volumes, filling rates, wall thickness) and markers of carotid plaque with metabolite profile clusters and single metabolites as outcomes were assessed by adjusted multinomial logistic regression and linear regression models.

RESULTS

In 360 individuals (mean age 56.3 years; 41.9% female), 146 serum metabolites clustered into three distinct profiles that reflected high-, intermediate- and low-CVD risk. Higher stroke volume (relative risk ratio (RRR): 0.53, 95%-CI [0.37; 0.76], p-value < 0.001) and early diastolic filling rate (RRR: 0.51, 95%-CI [0.37; 0.71], p-value < 0.001) were most strongly protectively associated against the high-risk profile compared to the low-risk profile after adjusting for traditional CVD risk factors. Moreover, imaging markers were associated with 10 metabolites in linear regression. Notably, negative associations of stroke volume and early diastolic filling rate with acylcarnitine C5, and positive association of function parameters with lysophosphatidylcholines, diacylphosphatidylcholines, and acylalkylphosphatidylcholines were observed. Furthermore, there was a negative association of LV wall thickness with alanine, creatinine, and symmetric dimethylarginine. We found no significant associations with carotid plaque.

CONCLUSIONS

Serum metabolite signatures are associated with cardiac function and morphology even in individuals without a clinical indication of CVD.

Machine learning approaches for biomarker discovery to predict large-artery atherosclerosis

Large-artery atherosclerosis (LAA) is a leading cause of cerebrovascular disease. However, LAA diagnosis is costly and needs professional identification. Many metabolites have been identified as biomarkers of specific traits. However, there are inconsistent findings regarding suitable biomarkers for the prediction of LAA. In this study, we propose a new method integrates multiple machine learning algorithms and feature selection method to handle multidimensional data. Among the six machine learning models, logistic regression (LR) model exhibited the best prediction performance. The value of area under the receiver operating characteristic curve (AUC) was 0.92 when 62 features were incorporated in the external validation set for the LR model. In this model, LAA could be well predicted by clinical risk factors including body mass index, smoking, and medications for controlling diabetes, hypertension, and hyperlipidemia as well as metabolites involved in aminoacyl-tRNA biosynthesis and lipid metabolism. In addition, we found that 27 features were present among the five adopted models that could provide good results. If these 27 features were used in the LR model, an AUC value of 0.93 could be achieved. Our study has demonstrated the effectiveness of combining machine learning algorithms with recursive feature elimination and cross-validation methods for biomarker identification. Moreover, we have shown that using shared features can yield more reliable correlations than either model, which can be valuable for future identification of LAA.

Missense Genetic Variation of ICAM1 and Incident Heart Failure

BACKGROUND

Intercellular adhesion molecule-1 (ICAM-1) is a cell surface protein that participates in endothelial activation and is hypothesized to play a central role in heart failure (HF). We evaluated associations of ICAM1 missense genetic variants with circulating ICAM-1 levels and with incident HF.

METHODS AND RESULTS

We identified 3 missense variants within ICAM1 (rs5491, rs5498 and rs1799969) and evaluated their associations with ICAM-1 levels in the Coronary Artery Risk Development in Young Adults Study and the Multi-Ethnic Study of Atherosclerosis (MESA). We determined the association among these 3 variants and incident HF in MESA. We separately evaluated significant associations in the Atherosclerosis Risk in Communities (ARIC) study. Of the 3 missense variants, rs5491 was common in Black participants (minor allele frequency [MAF] > 20%) and rare in other race/ethnic groups (MAF < 5%). In Black participants, the presence of rs5491 was associated with higher levels of circulating ICAM-1 at 2 timepoints separated by 8 years. Among Black participants in MESA (n = 1600), the presence of rs5491 was associated with an increased risk of incident HF with preserved ejection fraction (HFpEF; HR = 2.30; [95% CI 1.25-4.21; P = 0.007]). The other ICAM1 missense variants (rs5498 and rs1799969) were associated with ICAM-1 levels, but there were no associations with HF. In ARIC, rs5491 was significantly associated with incident HF (HR = 1.24 [95% CI 1.02 - 1.51]; P = 0.03), with a similar direction of effect for HFpEF that was not statistically significant.

CONCLUSIONS

A common ICAM1 missense variant among Black individuals may be associated with increased risk of HF, which may be HFpEF-specific.

Identifying key multi-modal predictors of incipient dementia in Parkinson's disease: a machine learning analysis and Tree SHAP interpretation

Background

Persons with Parkinson's disease (PD) differentially progress to cognitive impairment and dementia. With a 3-year longitudinal sample of initially non-demented PD patients measured on multiple dementia risk factors, we demonstrate that machine learning classifier algorithms can be combined with explainable artificial intelligence methods to identify and interpret leading predictors that discriminate those who later converted to dementia from those who did not.

Method

Participants were 48 well-characterized PD patients (M_{baseline age} = 71.6; SD = 4.8; 44% female). We tested 38 multi-modal predictors from 10 domains (e.g., motor, cognitive) in a computationally competitive context to identify those that best discriminated two unobserved baseline groups, PD No Dementia (PDND), and PD Incipient Dementia (PDID). We used Random Forest (RF) classifier models for the discrimination goal and Tree SHapley Additive exPlanation (Tree SHAP) values for deep interpretation.

Results

An excellent RF model discriminated baseline PDID from PDND (AUC = 0.84; normalized Matthews Correlation Coefficient = 0.76). Tree SHAP showed that ten leading predictors of PDID accounted for 62.5% of the model, as well as their relative importance, direction, and magnitude (risk threshold). These predictors represented the motor (e.g., poorer gait), cognitive (e.g., slower Trail A), molecular (up-regulated metabolite panel), demographic (age), imaging (ventricular volume), and lifestyle (activities of daily living) domains.

Conclusion

Our data-driven protocol integrated RF classifier models and Tree SHAP applications to selectively identify and interpret early dementia risk factors in a well-characterized sample of initially non-demented persons with PD. Results indicate that leading dementia predictors derive from multiple complementary risk domains.

Gamma-glutamyl-leucine levels are causally associated with elevated cardio-metabolic risks

Objective

Gamma-glutamyl dipeptides are bioactive peptides involved in inflammation, oxidative stress, and glucose regulation. Gamma-glutamyl-leucine (Gamma-Glu-Leu) has been extensively reported to be associated with the risk of cardio-metabolic diseases, such as obesity, metabolic syndrome, and type 2 diabetes. However, the causality remains to be uncovered. The aim of this study was to explore the causal-effect relationships between Gamma-Glu-Leu and metabolic risk.

Materials and methods

In this study, 1,289 subjects were included from a cross-sectional survey on metabolic syndrome (MetS) in eastern China. Serum Gamma-Glu-Leu levels were measured by untargeted metabolomics. Using linear regressions, a two-stage genome-wide association study (GWAS) for Gamma-Glu-Leu was conducted to seek its instrumental single nucleotide polymorphisms (SNPs). One-sample Mendelian randomization (MR) analyses were performed to evaluate the causality between Gamma-Glu-Leu and the metabolic risk.

Results

Four SNPs are associated with serum Gamma-Glu-Leu levels, including rs12476238, rs56146133, rs2479714, and rs12229654. Out of them, rs12476238 exhibits the strongest association (Beta = -0.38, S.E. = 0.07 in discovery stage, Beta = -0.29, S.E. = 0.14 in validation stage, combined P-value = 1.04 × 10^-8). Each of the four SNPs has a nominal association with at least one metabolic risk factor. Both rs12229654 and rs56146133 are associated with body mass index, waist circumference (WC), the ratio of WC to hip circumference, blood pressure, and triglyceride (5 × 10^-5 < P < 0.05). rs56146133 also has nominal associations with fasting insulin, glucose, and insulin resistance index (5 × 10^-5 < P < 0.05). Using the four SNPs serving as the instrumental SNPs of Gamma-Glu-Leu, the MR analyses revealed that higher Gamma-Glu-Leu levels are causally associated with elevated risks of multiple cardio-metabolic factors except for high-density lipoprotein cholesterol and low-density lipoprotein cholesterol (P > 0.05).

Conclusion

Four SNPs (rs12476238, rs56146133, rs2479714, and rs12229654) may regulate the levels of serum Gamma-Glu-Leu. Higher Gamma-Glu-Leu levels are causally linked to cardio-metabolic risks. Future prospective studies on Gamma-Glu-Leu are required to explain its role in metabolic disorders.

Effects of autophagy modulators tamoxifen and chloroquine on the expression profiles of long non-coding RNAs in MIAMI cells exposed to IFNγ

Mesenchymal stromal cells (MSCs) can be utilized clinically for treatment of conditions that result from excessive inflammation. In a pro-inflammatory environment, MSCs adopt an anti-inflammatory phenotype resulting in immunomodulation. A sub-type of MSCs referred to as “marrow-isolated adult multilineage inducible” (MIAMI) cells, which were isolated from bone marrow, were utilized to show that the addition of autophagy modulators, tamoxifen (TX) or chloroquine (CQ), can alter how MIAMI cells respond to IFNγ exposure in vitro resulting in an increased immunoregulatory capacity of the MIAMI cells. Molecularly, it was also shown that TX and CQ each alter both the levels of immunomodulatory genes and microRNAs which target such genes. However, the role of other non-coding RNAs (ncRNAs) such as long non-coding RNAs (lncRNAs) in regulating the response of MSCs to inflammation has been poorly studied. Here, we utilized transcriptomics and data mining to analyze the putative roles of various differentially regulated lncRNAs in MIAMI cells exposed to IFNγ with (or without) TX or CQ. The aim of this study was to investigate how the addition of TX and CQ alters lncRNA levels and evaluate how such changes could alter previously observed TX- and CQ-driven changes to the immunomodulatory properties of MIAMI cells. Data analysis revealed 693 long intergenic non-coding RNAS (lincRNAs), 480 pseudogenes, and 642 antisense RNAs that were differentially regulated with IFNγ, IFNγ+TX and IFNγ+CQ treatments. Further analysis of these RNA species based on the existing literature data revealed 6 antisense RNAs, 2 pseudogenes, and 5 lincRNAs that have the potential to modulate MIAMI cell’s response to IFNγ treatment. Functional analysis of these genomic species based on current literature linking inflammatory response and ncRNAs indicated their potential for regulation of several key pro- and anti-inflammatory responses, including NFκB signaling, cytokine secretion and auto-immune responses. Overall, this work found potential involvement of multiple pro-and anti-inflammatory pathways and molecules in modulating MIAMI cells’ response to inflammation.

Plasma Metabolic Signature of Atherosclerosis Progression and Colchicine Treatment in Rabbits

Balloon catheter endothelial denudation in New Zealand white rabbits fed high cholesterol diet is a validated atherosclerosis model. Well-characterized in terms of atherosclerosis induction and progression, the metabolic changes associated with the atherosclerosis progression remain indeterminate. Non-targeted metabolomics permits to develop such elucidation and allows to evaluate the metabolic consequences of colchicine treatment, an anti-inflammatory drug that could revert these changes. 16 rabbits underwent 18 weeks of atherosclerosis induction by diet and aortic denudation. Thereafter animals were randomly assigned to colchicine treatment or placebo for 18 weeks while on diet. Plasma samples were obtained before randomization and at 36 weeks. Multiplatform (GC/MS, CE/MS, RP-HPLC/MS) metabolomics was applied. Plasma fingerprints were pre-processed, and the resulting matrixes analyzed to unveil differentially expressed features. Different chemical annotation strategies were accomplished for those significant features. We found metabolites associated with either atherosclerosis progression, or colchicine treatment, or both. Atherosclerosis was profoundly associated with an increase in circulating bile acids. Most of the changes associated with sterol metabolism could not be reverted by colchicine treatment. However, the variations in lysine, tryptophan and cysteine metabolism among others, have shown new potential mechanisms of action of the drug, also related to atherosclerosis progression, but not previously described.

Refining genome-wide associated loci for serum uric acid in individuals with African ancestry

OBJECTIVE

Serum uric acid is the end-product of purine metabolism and at high levels is a risk factor for several human diseases including gout and cardiovascular disease. Heritability estimates range from 0.32 to 0.63. Genome-wide association studies (GWAS) provide an unbiased approach to identify loci influencing serum uric acid. Here, we performed the first GWAS for serum uric acid in continental Africans, with replication in African Americans.

METHODS

Africans (n = 4126) and African Americans (n = 5007) were genotyped on high-density GWAS arrays. Efficient mixed model association, a variance component approach, was used to perform association testing for a total of ~ 18 million autosomal genotyped and imputed variants. CAVIARBF was used to fine map significant regions.

RESULTS

We identified two genome-wide significant loci: 4p16.1 (SLC2A9) and 11q13.1 (SLC22A12). At SLC2A9, the most strongly associated SNP was rs7683856 (P = 1.60 × 10-44). Conditional analysis revealed a second signal indexed by rs6838021 (P = 5.75 × 10-17). Gene expression and regulatory motif data prioritized a single-candidate causal variant for each signal. At SLC22A12, the most strongly associated SNP was rs147647315 (P = 6.65 × 10-25). Conditional analysis and functional annotation prioritized the missense variant rs147647315 (R (Arg) > H (His)) as the sole causal variant. Functional annotation of these three signals implicated processes in skeletal muscle, subcutaneous adipose tissue and the kidneys, respectively.

CONCLUSIONS

This first GWAS of serum uric acid in continental Africans identified three associations at two loci, SLC2A9 and SLC22A12. The combination of weak linkage disequilibrium in Africans and functional annotation led to the identification of candidate causal SNPs for all three signals. Each candidate causal variant implicated a different cell type. Collectively, the three associations accounted for 4.3% of the variance of serum uric acid.

Genome-wide association study identifies novel risk variants from RPS6KA1, CADPS, VARS, and DHX58 for fasting plasma glucose in Arab population

Consanguineous populations of the Arabian Peninsula, which has seen an uncontrolled rise in type 2 diabetes incidence, are underrepresented in global studies on diabetes genetics. We performed a genome-wide association study on the quantitative trait of fasting plasma glucose (FPG) in unrelated Arab individuals from Kuwait (discovery-cohort:n = 1,353; replication-cohort:n = 1,196). Genome-wide genotyping in discovery phase was performed for 632,375 markers from Illumina HumanOmniExpress Beadchip; and top-associating markers were replicated using candidate genotyping. Genetic models based on additive and recessive transmission modes were used in statistical tests for associations in discovery phase, replication phase, and meta-analysis that combines data from both the phases. A genome-wide significant association with high FPG was found at rs1002487 (RPS6KA1) (p-discovery = 1.64E-08, p-replication = 3.71E-04, p-combined = 5.72E-11; β-discovery = 8.315; β-replication = 3.442; β-combined = 6.551). Further, three suggestive associations (p-values < 8.2E-06) with high FPG were observed at rs487321 (CADPS), rs707927 (VARS and 2Kb upstream of VWA7), and rs12600570 (DHX58); the first two markers reached genome-wide significance in the combined analysis (p-combined = 1.83E-12 and 3.07E-09, respectively). Significant interactions of diabetes traits (serum triglycerides, FPG, and glycated hemoglobin) with homeostatic model assessment of insulin resistance were identified for genotypes heterozygous or homozygous for the risk allele. Literature reports support the involvement of these gene loci in type 2 diabetes etiology.

Genome-wide association study for proliferative diabetic retinopathy in Africans

Proliferative diabetic retinopathy (PDR) is a sight-threatening complication of diabetes that is associated with longer duration of diabetes and poor glycemic control under a genetic susceptibility background. Although GWAS of PDR have been conducted in Europeans and Asians, none has been done in continental Africans, a population at increased risk for PDR. Here, we report a GWAS of PDR among Africans. PDR cases (n = 64) were T2D patients with neovascularization in the retina and/or retinal detachment. Controls (n = 227) were T2D patients without listed eye complications despite high risk (T2D duration ≥10 years and fasting blood glucose >169 mg/dl). Replication was assessed in African Americans enrolled in the ARIC study. We identified 4 significant loci: WDR72, HLA-B, GAP43/RP11-326J18.1, and AL713866.1. At WDR72 the most strongly associated SNPs were rs12906891 (MAF = 0.071; p = 9.68 × 10-10; OR = 1.46, 95% CI [1.30,1.64]) and rs11070992 (MAF = 0.14; p = 4.23 × 10-8; OR = 1.28, 95%CI [1.17-1.40]). rs11070992 replicated in African Americans (p = 0.04). Variants in this gene have been associated with diabetic retinopathy, glycemic control, revascularization, and kidney disease.

Circular PVT1: an oncogenic non-coding RNA with emerging clinical importance

The importance of circular RNAs (circRNAs) in pathological processes like cancer is evident. Among the circRNAs, recent studies have brought circPVT1 under focus as the most potent oncogenic non-coding RNA. Recent studies on various aspects of circPVT1, including its biogenesis, molecular alteration and its probable role in oncogenesis, have been conducted for research and clinical interest. In this review, a first attempt has been made to summarise the available data on circPVT1 from PubMed and other relevant databases with special emphasis on its role in development, progression and prognosis of various malignant conditions. CircPVT1 is derived from the same genetic locus encoding for long non-coding RNA lncPVT1; however, existing literature suggested circPVT1 and lncPVT1 are transcripted independently by different promoters. The interaction between circRNA and microRNA has been highlighted in majority of the few malignancies in which circPVT1 was studied. Besides its importance in diagnostic and prognostic procedures, circPVT1 seemed to have huge therapeutic potential as evident from differential drug response of cancer cell line as well as primary tumors depending on expression level of the candidate. circPVT1 in cancer therapeutics might be promising as a biomarker to make the existing treatment protocol more effective and also as potential target for designing novel therapeutic intervention.

Epigenome wide association study of SNP-CpG interactions on changes in triglyceride levels after pharmaceutical intervention: a GAW20 analysis

In the search for an understanding of how genetic variation contributes to the heritability of common human disease, the potential role of epigenetic factors, such as methylation, is being explored with increasing frequency. Although standard analyses test for associations between methylation levels at individual cytosine-phosphate-guanine (CpG) sites and phenotypes of interest, some investigators have begun testing for methylation and how methylation may modulate the effects of genetic polymorphisms on phenotypes. In our analysis, we used both a genome-wide and candidate gene approach to investigate potential single-nucleotide polymorphism (SNP)–CpG interactions on changes in triglyceride levels. Although we were able to identify numerous loci of interest when using an exploratory significance threshold, we did not identify any significant interactions using a strict genome-wide significance threshold. We were also able to identify numerous loci using the candidate gene approach, in which we focused on 18 genes with prior evidence of association of triglyceride levels. In particular, we identified GALNT2 loci as containing potential CpG sites that moderate the impact of genetic polymorphisms on triglyceride levels. Further work is needed to provide clear guidance on analytic strategies for testing SNP–CpG interactions, although leveraging prior biological understanding may be needed to improve statistical power in data sets with smaller sample sizes.

The role of genetic variation of human metabolism for BMI, mental traits and mental disorders

OBJECTIVE

The aim was to assess whether loci associated with metabolic traits also have a significant role in BMI and mental traits/disorders METHODS: We first assessed the number of single nucleotide polymorphisms (SNPs) with genome-wide significance for human metabolism (NHGRI-EBI Catalog). These 516 SNPs (216 independent loci) were looked-up in genome-wide association studies for association with body mass index (BMI) and the mental traits/disorders educational attainment, neuroticism, schizophrenia, well-being, anxiety, depressive symptoms, major depressive disorder, autism-spectrum disorder, attention-deficit/hyperactivity disorder, Alzheimer's disease, bipolar disorder, aggressive behavior, and internalizing problems. A strict significance threshold of p < 6.92 × 10-6 was based on the correction for 516 SNPs and all 14 phenotypes, a second less conservative threshold (p < 9.69 × 10-5) on the correction for the 516 SNPs only.

RESULTS

19 SNPs located in nine independent loci revealed p-values < 6.92 × 10-6; the less strict criterion was met by 41 SNPs in 24 independent loci. BMI and schizophrenia showed the most pronounced genetic overlap with human metabolism with three loci each meeting the strict significance threshold. Overall, genetic variation associated with estimated glomerular filtration rate showed up frequently; single metabolite SNPs were associated with more than one phenotype. Replications in independent samples were obtained for BMI and educational attainment.

CONCLUSIONS

Approximately 5-10% of the regions involved in the regulation of blood/urine metabolite levels seem to also play a role in BMI and mental traits/disorders and related phenotypes. If validated in metabolomic studies of the respective phenotypes, the associated blood/urine metabolites may enable novel preventive and therapeutic strategies.

Circulating Biomarkers in Heart Failure

Biological markers have served for diagnosis, risk stratification and guided therapy of heart failure (HF). Our knowledge regarding abilities of biomarkers to relate to several pathways of HF pathogenesis and reflect clinical worsening or improvement in the disease is steadily expanding. Although there are numerous clinical guidelines, which clearly diagnosis, prevention and evidence-based treatment of HF, a strategy regarding exclusion of HF, as well as risk stratification of HF, nature evolution of disease is not well established and requires more development. The aim of the chapter is to discuss a role of biomarker-based approaches for more accurate diagnosis, in-depth risk stratification and individual targeting in treatment of patients with HF.

Whole exome sequencing in thrombophilic pedigrees to identify genetic risk factors for venous thromboembolism

BACKGROUND

Family studies have shown a strong heritability component for venous thromboembolism (VTE), but established genetic risk factors are present in only half of VTE patients.

AIM

To identify genetic risk factors in two large families with unexplained hereditary VTE.

METHODS

We performed whole exome sequencing in 10 affected relatives of two unrelated families with an unexplained tendency for VTE. We prioritized variants shared by all affected relatives from both families, and evaluated these in the remaining affected and unaffected individuals. We prioritized variants based on 3 different filter strategies: variants within candidate genes, rare variants across the exome, and SNPs present in patients with familial VTE and with low frequency in the general population. We used whole exome sequencing data available from 96 unrelated VTE cases with a positive family history of VTE from an affected sib study (the GIFT study) to identify additional carriers and compared the risk-allele frequencies with the general population. Variants found in only one individual were also retained for further analysis. Finally, we assessed the association of these variants with VTE in a population-based case-control study (the MEGA study) with 4,291 cases and 4,866 controls.

RESULTS

Six variants remained as putative disease-risk candidates. These variants are located in 6 genes spread among 3 different loci: 2p21 (PLEKHH2 NM_172069:c.3105T>C, LRPPRC rs372371276, SRBD1 rs34959371), 5q35.2 (UNC5A NM_133369.2:c.1869+23C>A), and 17q25.1 (GPRC5C rs142232982, RAB37 rs556450784). In GIFT, additional carriers were identified only for the variants located in the 2p21 locus. In MEGA, additional carriers for several of these variants were identified in both cases and controls, without a difference in prevalence; no carrier of the UNC5A variant was present.

CONCLUSION

Despite sequencing of several individuals from two thrombophilic families resulting in 6 candidate variants, we were unable to confirm their relevance as novel thrombophilic defects.

Intracoronary Imaging, Cholesterol Efflux, and Transcriptomics after Intensive Statin Treatment in Diabetes

Residual atherothrombotic risk remains higher in patients with versus without diabetes mellitus (DM) despite statin therapy. The underlying mechanisms are unclear. This is a retrospective post-hoc analysis of the YELLOW II trial, comparing patients with and without DM (non-DM) who received rosuvastatin 40 mg for 8–12 weeks and underwent intracoronary multimodality imaging of an obstructive nonculprit lesion, before and after therapy. In addition, blood samples were drawn to assess cholesterol efflux capacity (CEC) and changes in gene expression in peripheral blood mononuclear cells (PBMC). There was a significant reduction in low density lipoprotein-cholesterol (LDL-C), an increase in CEC and beneficial changes in plaque morphology including increase in fibrous cap thickness and decrease in the prevalence of thin cap fibro-atheroma by optical coherence tomography in DM and non-DM patients. While differential gene expression analysis did not demonstrate differences in PBMC transcriptome between the two groups on the single-gene level, weighted gene coexpression network analysis revealed two modules of coexpressed genes associated with DM, Collagen Module and Platelet Module, related to collagen catabolism and platelet function respectively. Bayesian network analysis revealed key driver genes within these modules. These transcriptomic findings might provide potential mechanisms responsible for the higher cardiovascular risk in DM patients.

Induced Pluripotent Stem Cell Differentiation Enables Functional Validation of GWAS Variants in Metabolic Disease

Genome-wide association studies (GWAS) have highlighted a large number of genetic variants with potential disease association, but functional analysis remains a challenge. Here we describe an approach to functionally validate identified variants through differentiation of induced pluripotent stem cells (iPSCs) to study cellular pathophysiology. We collected peripheral blood cells from Framingham Heart Study participants and reprogrammed them to iPSCs. We then differentiated 68 iPSC lines into hepatocytes and adipocytes to investigate the effect of the 1p13 rs12740374 variant on cardiometabolic disease phenotypes via transcriptomics and metabolomic signatures. We observed a clear association between rs12740374 and lipid accumulation and gene expression in differentiated hepatocytes, in particular, expression of SORT1, CELSR2, and PSRC1, consistent with previous analyses of this variant using other approaches. Initial investigation of additional SNPs also highlighted correlations with gene expression. These findings suggest that iPSC-based population studies hold promise as tools for the functional validation of GWAS variants.

Population and allelic variation of A-to-I RNA editing in human transcriptomes

Background

A-to-I RNA editing is an important step in RNA processing in which specific adenosines in some RNA molecules are post-transcriptionally modified to inosines. RNA editing has emerged as a widespread mechanism for generating transcriptome diversity. However, there remain significant knowledge gaps about the variation and function of RNA editing.

Results

In order to determine the influence of genetic variation on A-to-I RNA editing, we integrate genomic and transcriptomic data from 445 human lymphoblastoid cell lines by combining an RNA editing QTL (edQTL) analysis with an allele-specific RNA editing (ASED) analysis. We identify 1054 RNA editing events associated with cis genetic polymorphisms. Additionally, we find that a subset of these polymorphisms is linked to genome-wide association study signals of complex traits or diseases. Finally, compared to random cis polymorphisms, polymorphisms associated with RNA editing variation are located closer spatially to their respective editing sites and have a more pronounced impact on RNA secondary structure.

Conclusions

Our study reveals widespread cis variation in RNA editing among genetically distinct individuals and sheds light on possible phenotypic consequences of such variation on complex traits and diseases.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-017-1270-7) contains supplementary material, which is available to authorized users.

Genome-wide association study for feed efficiency and growth traits in U.S. beef cattle

Background

Single nucleotide polymorphism (SNP) arrays for domestic cattle have catalyzed the identification of genetic markers associated with complex traits for inclusion in modern breeding and selection programs. Using actual and imputed Illumina 778K genotypes for 3887 U.S. beef cattle from 3 populations (Angus, Hereford, SimAngus), we performed genome-wide association analyses for feed efficiency and growth traits including average daily gain (ADG), dry matter intake (DMI), mid-test metabolic weight (MMWT), and residual feed intake (RFI), with marker-based heritability estimates produced for all traits and populations.

Results

Moderate and/or large-effect QTL were detected for all traits in all populations, as jointly defined by the estimated proportion of variance explained (PVE) by marker effects (PVE ≥ 1.0%) and a nominal P-value threshold (P ≤ 5e-05). Lead SNPs with PVE ≥ 2.0% were considered putative evidence of large-effect QTL (n = 52), whereas those with PVE ≥ 1.0% but < 2.0% were considered putative evidence for moderate-effect QTL (n = 35). Identical or proximal lead SNPs associated with ADG, DMI, MMWT, and RFI collectively supported the potential for either pleiotropic QTL, or independent but proximal causal mutations for multiple traits within and between the analyzed populations. Marker-based heritability estimates for all investigated traits ranged from 0.18 to 0.60 using 778K genotypes, or from 0.17 to 0.57 using 50K genotypes (reduced from Illumina 778K HD to Illumina Bovine SNP50). An investigation to determine if QTL detected by 778K analysis could also be detected using 50K genotypes produced variable results, suggesting that 50K analyses were generally insufficient for QTL detection in these populations, and that relevant breeding or selection programs should be based on higher density analyses (imputed or directly ascertained).

Conclusions

Fourteen moderate to large-effect QTL regions which ranged from being physically proximal (lead SNPs ≤ 3Mb) to fully overlapping for RFI, DMI, ADG, and MMWT were detected within and between populations, and included evidence for pleiotropy, proximal but independent causal mutations, and multi-breed QTL. Bovine positional candidate genes for these traits were functionally conserved across vertebrate species.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3754-y) contains supplementary material, which is available to authorized users.

Genome-wide analysis identifies an african-specific variant in SEMA4D associated with body mass index

OBJECTIVE

The prevalence of obesity varies between ethnic groups. No genome-wide association study (GWAS) for body mass index (BMI) has been conducted in continental Africans.

METHODS

We performed a GWAS for BMI in 1,570 West Africans (WA). Replication was conducted in independent samples of WA (n = 1,411) and African Americans (AA) (n = 9,020).

RESULTS

We identified a novel genome-wide significant African-specific locus for BMI (SEMA4D, rs80068415; minor allele frequency = 0.008, P = 2.10 × 10-8 ). This finding was replicated in independent samples of WA (P = 0.013) and AA (P = 0.017). Individuals with obesity had higher serum SEMA4D levels compared to those without obesity (P < 0.0001), and elevated levels of serum SEMA4D were associated with increased obesity risk (OR = 4.2, P < 1 × 10-4 ). The prevalence of obesity was higher in individuals with the CT versus TT genotypes (55.6% vs. 22.9%).

CONCLUSIONS

A novel variant in SEMA4D was significantly associated with BMI. Carriers of the C allele were 4.6 BMI units heavier than carriers of the T allele (P = 0.0007). This variant is monomorphic in Europeans and Asians, highlighting the importance of studying diverse populations. While there is evidence for the involvement of SEMA4D in inflammatory processes, this study is the first to implicate SEMA4D in obesity pathophysiology.

Common and rare exonic MUC5B variants associated with type 2 diabetes in Han Chinese

Genome-wide association studies have identified over one hundred common genetic risk variants associated with type 2 diabetes (T2D). However, most of the heritability of T2D has not been accounted for. In this study, we investigated the contribution of rare and common variants to T2D susceptibility by analyzing exome array data in 1,908 Han Chinese genotyped with Affymetrix Axiom® Exome Genotyping Arrays. Based on the joint common and rare variants analysis of 57,704 autosomal SNPs within 12,244 genes using Sequence Kernel Association Tests (SKAT), we identified significant associations between T2D and 25 variants (9 rare and 16 common) in MUC5B, p-value 1.01×10⁻¹⁴. This finding was replicated (p = 0.0463) in an independent sample that included 10,401 unrelated individuals. Sixty-six of 1,553 possible haplotypes based on 25 SNPs within MUC5B showed significant association with T2D (Bonferroni corrected p values < 3.2×10⁻⁵). The expression level of MUC5B is significantly higher in pancreatic tissues of persons with T2D compared to those without T2D (p-value = 5×10⁻⁵). Our findings suggest that dysregulated MUC5B expression may be involved in the pathogenesis of T2D. As a strong candidate gene for T2D, MUC5B may play an important role in the mechanisms underlying T2D etiology and its complications.

Age at menarche and age at natural menopause in East Asian women: a genome-wide association study

Age at menarche (AM) and age at natural menopause (ANM) are complex traits with a high heritability. Abnormal timing of menarche or menopause is associated with a reduced span of fertility and risk for several age-related diseases including breast, endometrial and ovarian cancer, cardiovascular disease, and osteoporosis. To identify novel genetic loci for AM or ANM in East Asian women and to replicate previously identified loci primarily in women of European ancestry by genome-wide association studies (GWASs), we conducted a two-stage GWAS. Stage I aimed to discover promising novel AM and ANM loci using GWAS data of 8073 women from Shanghai, China. The Stage II replication study used the data from another Chinese GWAS (n = 1230 for AM and n = 1458 for ANM), a Korean GWAS (n = 4215 for AM and n = 1739 for ANM), and de novo genotyping of 2877 additional Chinese women. Previous GWAS-identified loci for AM and ANM were also evaluated. We identified two suggestive menarcheal age loci tagged by rs79195475 at 10q21.3 (beta = -0.118 years, P = 3.4 × 10^-6) and rs1023935 at 4p15.1 (beta = -0.145 years, P = 4.9 × 10^-6) and one menopausal age locus tagged by rs3818134 at 22q12.2 (beta = -0.276 years, P = 8.8 × 10^-6). These suggestive loci warrant a further validation in independent populations. Although limited by low statistical power, we replicated 19 of the 98 menarche loci and 5 of the 20 menopause loci previously identified in women of European ancestry in East Asian women, suggesting a shared genetic architecture for these two traits across populations.

Influence of early life exposure, host genetics and diet on the mouse gut microbiome and metabolome

Although the gut microbiome plays important roles in host physiology, health and disease¹, we lack understanding of the complex interplay between host genetics and early life environment on the microbial and metabolic composition of the gut. We used the genetically diverse Collaborative Cross mouse system² to discover that early life history impacts the microbiome composition, whereas dietary changes have only a moderate effect. By contrast, the gut metabolome was shaped mostly by diet, with specific non-dietary metabolites explained by microbial metabolism. Quantitative trait analysis identified mouse genetic trait loci (QTL) that impact the abundances of specific microbes. Human orthologues of genes in the mouse QTL are implicated in gastrointestinal cancer. Additionally, genes located in mouse QTL for Lactobacillales abundance are implicated in arthritis, rheumatic disease and diabetes. Furthermore, Lactobacillales abundance was predictive of higher host T-helper cell counts, suggesting an important link between Lactobacillales and host adaptive immunity.

Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases

Complex and dynamic networks of molecules are involved in human diseases. High-throughput technologies enable omics studies interrogating thousands to millions of makers with similar biochemical properties (eg, transcriptomics for RNA transcripts). However, a single layer of "omics" can only provide limited insights into the biological mechanisms of a disease. In the case of genome-wide association studies, although thousands of single nucleotide polymorphisms have been identified for complex diseases and traits, the functional implications and mechanisms of the associated loci are largely unknown. Additionally, the genomic variants alone are not able to explain the changing disease risk across the life span. DNA, RNA, protein, and metabolite often have complementary roles to jointly perform a certain biological function. Such complementary effects and synergistic interactions between omic layers in the life course can only be captured by integrative study of multiple molecular layers. Building upon the success in single-omics discovery research, population studies started adopting the multi-omics approach to better understanding the molecular function and disease etiology. Multi-omics approaches integrate data obtained from different omic levels to understand their interrelation and combined influence on the disease processes. Here, we summarize major omics approaches available in population research, and review integrative approaches and methodologies interrogating multiple omic layers, which enhance the gene discovery and functional analysis of human diseases. We seek to provide analytical recommendations for different types of multi-omics data and study designs to guide the emerging multi-omic research, and to suggest improvement of the existing analytical methods.

The Relevance of Genomic Signatures at Adhesion GPCR Loci in Humans

Adhesion G protein-coupled receptors (aGPCRs) have a long evolutionary history dating back to very basal unicellular eukaryotes. Almost every vertebrate is equipped with a set of different aGPCRs. Genomic sequence data of several hundred extinct and extant species allows for reconstruction of aGPCR phylogeny in vertebrates and non-vertebrates in general but also provides a detailed view into the recent evolutionary history of human aGPCRs. Mining these sequence sources with bioinformatic tools can unveil many facets of formerly unappreciated aGPCR functions. In this review, we extracted such information from the literature and open public sources and provide insights into the history of aGPCR in humans. This includes comprehensive analyses of signatures of selection, variability of human aGPCR genes, and quantitative traits at human aGPCR loci. As indicated by a large number of genome-wide genotype-phenotype association studies, variations in aGPCR contribute to specific human phenotypes. Our survey demonstrates that aGPCRs are significantly involved in adaptation processes, phenotype variations, and diseases in humans.

Genetics of common forms of heart failure: challenges and potential solutions

PURPOSE OF REVIEW

In contrast to many other human diseases, the use of genome-wide association studies (GWAS) to identify genes for heart failure (HF) has had limited success. We will discuss the underlying challenges as well as potential new approaches to understanding the genetics of common forms of HF.

RECENT FINDINGS

Recent research using intermediate phenotypes, more detailed and quantitative stratification of HF symptoms, founder populations and novel animal models has begun to allow researchers to make headway toward explaining the genetics underlying HF using GWAS techniques.

SUMMARY

By expanding analyses of HF to improved clinical traits, additional HF classifications and innovative model systems, the intractability of human HF GWAS should be ameliorated significantly.

Biochemical insights from population studies with genetics and metabolomics

Genome-wide association studies with concentrations of hundreds of small molecules in samples collected from thousands of individuals (mGWAS) access otherwise inaccessible natural genetic experiments and their influence on the metabolic capacities of the human body. By sampling the natural metabolic and genetic variability that is present in the general population, mGWAS identified over 150 associations between genetic variants and variation in the metabolic composition of human body fluids. Many of these genetic variants were found to be located in enzyme or transporter coding genes, whose functions match the biochemical nature of the associated metabolites. Associations identified by mGWAS can reveal novel biochemical knowledge, such as the function of uncharacterized genes, the biochemical identity of small molecules, and the structure of entire biochemical pathways. Here we review findings of recent mGWAS and discuss concrete examples of how their results can be interpreted in a biochemical context. We describe online resources that are available for mining mGWAS results. In this context, we present two concepts that also find more general applications in the field of metabolomics: strengthening of associations by looking at ratios between metabolite pairs and reconstruction of metabolic pathways by Gaussian graphical modeling.

Plasma metabolomic profiles enhance precision medicine for volunteers of normal health

Precision medicine, taking account of human individuality in genes, environment, and lifestyle for early disease diagnosis and individualized therapy, has shown great promise to transform medical care. Nontargeted metabolomics, with the ability to detect broad classes of biochemicals, can provide a comprehensive functional phenotype integrating clinical phenotypes with genetic and nongenetic factors. To test the application of metabolomics in individual diagnosis, we conducted a metabolomics analysis on plasma samples collected from 80 volunteers of normal health with complete medical records and three-generation pedigrees. Using a broad-spectrum metabolomics platform consisting of liquid chromatography and GC coupled with MS, we profiled nearly 600 metabolites covering 72 biochemical pathways in all major branches of biosynthesis, catabolism, gut microbiome activities, and xenobiotics. Statistical analysis revealed a considerable range of variation and potential metabolic abnormalities across the individuals in this cohort. Examination of the convergence of metabolomics profiles with whole-exon sequences (WESs) provided an effective approach to assess and interpret clinical significance of genetic mutations, as shown in a number of cases, including fructose intolerance, xanthinuria, and carnitine deficiency. Metabolic abnormalities consistent with early indications of diabetes, liver dysfunction, and disruption of gut microbiome homeostasis were identified in several volunteers. Additionally, diverse metabolic responses to medications among the volunteers may assist to identify therapeutic effects and sensitivity to toxicity. The results of this study demonstrate that metabolomics could be an effective approach to complement next generation sequencing (NGS) for disease risk analysis, disease monitoring, and drug management in our goal toward precision care.

PVT1: a rising star among oncogenic long noncoding RNAs

It is becoming increasingly clear that short and long noncoding RNAs critically participate in the regulation of cell growth, differentiation, and (mis)function. However, while the functional characterization of short non-coding RNAs has been reaching maturity, there is still a paucity of well characterized long noncoding RNAs, even though large studies in recent years are rapidly increasing the number of annotated ones. The long noncoding RNA PVT1 is encoded by a gene that has been long known since it resides in the well-known cancer risk region 8q24. However, a couple of accidental concurrent conditions have slowed down the study of this gene, that is, a preconception on the primacy of the protein-coding over noncoding RNAs and the prevalent interest in its neighbor MYC oncogene. Recent studies have brought PVT1 under the spotlight suggesting interesting models of functioning, such as competing endogenous RNA activity and regulation of protein stability of important oncogenes, primarily of the MYC oncogene. Despite some advancements in modelling the PVT1 role in cancer, there are many questions that remain unanswered concerning the precise molecular mechanisms underlying its functioning.

Genome-wide association studies in Africans and African Americans: expanding the framework of the genomics of human traits and disease

Genomic research is one of the tools for elucidating the pathogenesis of diseases of global health relevance and paving the research dimension to clinical and public health translation. Recent advances in genomic research and technologies have increased our understanding of human diseases, genes associated with these disorders, and the relevant mechanisms. Genome-wide association studies (GWAS) have proliferated since the first studies were published several years ago and have become an important tool in helping researchers comprehend human variation and the role genetic variants play in disease. However, the need to expand the diversity of populations in GWAS has become increasingly apparent as new knowledge is gained about genetic variation. Inclusion of diverse populations in genomic studies is critical to a more complete understanding of human variation and elucidation of the underpinnings of complex diseases. In this review, we summarize the available data on GWAS in recent African ancestry populations within the western hemisphere (i.e. African Americans and peoples of the Caribbean) and continental African populations. Furthermore, we highlight ways in which genomic studies in populations of recent African ancestry have led to advances in the areas of malaria, HIV, prostate cancer, and other diseases. Finally, we discuss the advantages of conducting GWAS in recent African ancestry populations in the context of addressing existing and emerging global health conditions.

Disturbed flow induces systemic changes in metabolites in mouse plasma: a metabolomics study using ApoE⁻/⁻ mice with partial carotid ligation

Disturbed blood flow (d-flow) occurring in branched and curved arteries promotes endothelial dysfunction and atherosclerosis, in part, by altering gene expression and epigenomic profiles in endothelial cells. While a systemic metabolic change is known to play a role in atherosclerosis, it is unclear whether it can be regulated by local d-flow. Here, we tested this hypothesis by carrying out a metabolomics study using blood plasma samples obtained from ApoE(-/-) mice that underwent a partial carotid ligation surgery to induce d-flow. Mice receiving sham ligation were used as a control. To study early metabolic changes, samples collected from 1 wk after partial ligation when endothelial dysfunction occurs, but before atheroma develops, were analyzed by high-resolution mass spectrometry. A metabolome-wide association study showed that 128 metabolites were significantly altered in the ligated mice compared with the sham group. Of these, sphingomyelin (SM; m/z 703.5747), a common mammalian cell membrane sphingolipid, was most significantly increased in the ligated mice. Of the 128 discriminatory metabolites, 18 and 41 were positively and negatively correlated with SM, respectively. The amino acids methionine and phenylalanine were increased by d-flow, while phosphatidylcholine and phosphatidylethanolamine were decreased by d-flow, and these metabolites were correlated with SM. Other significantly affected metabolites included dietary and environmental agents. Pathway analysis showed that the metabolic changes of d-flow impacted broad functional networks. These results suggest that signaling from d-flow occurring in focal regions induces systemic metabolic changes associated with atherosclerosis.