Identification of seven novel loci associated with amino acid levels using single-variant and gene-based tests in 8545 Finnish men from the METSIM study

Dissecting genetic regulation of metabolic coordination

Understanding genetic regulation of metabolism is critical for gaining insights into the causes of metabolic diseases. Traditional metabolome-based genome-wide association studies (mGWAS) focus on static associations between single nucleotide polymorphisms (SNPs) and metabolite levels, overlooking the changing relationships caused by genotypes within the metabolic network. Notably, some metabolites exhibit changes in correlation patterns with other metabolites under certain physiological conditions while maintaining their overall abundance level. In this manuscript, we develop Metabolic Differential-coordination GWAS (mdGWAS), an innovative framework that detects SNPs associated with the changing correlation patterns between metabolites and metabolic pathways. This approach transcends and complements conventional mean-based analyses by identifying latent regulatory factors that govern the system-level metabolic coordination. Through comprehensive simulation studies, mdGWAS demonstrated robust performance in detecting SNP-metabolite-metabolite associations. Applying mdGWAS to genotyping and mass spectrometry (MS)-based metabolomics data of the METabolic Syndrome In Men (METSIM) Study revealed novel SNPs and genes potentially involved in the regulation of the coordination between metabolic pathways.

Abundant repressor binding sites in human enhancers are associated with the fine-tuning of gene regulation

The regulation of gene expression relies on the coordinated action of transcription factors (TFs) at enhancers, including both activator and repressor TFs. We employed deep learning (DL) to dissect HepG2 enhancers into positive (PAR), negative (NAR), and neutral activity regions. Sharpr-MPRA and STARR-seq highlight the dichotomy impact of NARs and PARs on modulating and catalyzing the activity of enhancers, respectively. Approximately 22% of HepG2 enhancers, termed "repressive impact enhancers" (RIEs), are predominantly populated by NARs and transcriptional repression motifs. Genes flanking RIEs exhibit a stage-specific decline in expression during late development, suggesting RIEs' role in trimming enhancer activities. About 16.7% of human NARs emerge from neutral rhesus macaque DNA. This gain of repressor binding sites in RIEs is associated with a 30% decrease in the average expression of flanking genes in humans compared to rhesus macaque. Our work reveals modulated enhancer activity and adaptable gene regulation through the evolutionary dynamics of TF binding sites.

Genome-wide characterization of circulating metabolic biomarkers

Genome-wide association analyses using high-throughput metabolomics platforms have led to novel insights into the biology of human metabolism1-7. This detailed knowledge of the genetic determinants of systemic metabolism has been pivotal for uncovering how genetic pathways influence biological mechanisms and complex diseases8-11. Here we present a genome-wide association study for 233 circulating metabolic traits quantified by nuclear magnetic resonance spectroscopy in up to 136,016 participants from 33 cohorts. We identify more than 400 independent loci and assign probable causal genes at two-thirds of these using manual curation of plausible biological candidates. We highlight the importance of sample and participant characteristics that can have significant effects on genetic associations. We use detailed metabolic profiling of lipoprotein- and lipid-associated variants to better characterize how known lipid loci and novel loci affect lipoprotein metabolism at a granular level. We demonstrate the translational utility of comprehensively phenotyped molecular data, characterizing the metabolic associations of intrahepatic cholestasis of pregnancy. Finally, we observe substantial genetic pleiotropy for multiple metabolic pathways and illustrate the importance of careful instrument selection in Mendelian randomization analysis, revealing a putative causal relationship between acetone and hypertension. Our publicly available results provide a foundational resource for the community to examine the role of metabolism across diverse diseases.

Bidirectional two-sample mendelian randomization analysis identifies causal associations of MRI-based cortical thickness and surface area relation to NAFLD

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) patients have exhibited extra-hepatic neurological changes, but the causes and mechanisms remain unclear. This study investigates the causal effect of NAFLD on cortical structure through bidirectional two-sample Mendelian randomization analysis.

METHODS

Genetic data from 778,614 European individuals across four NAFLD studies were used to determine genetically predicted NAFLD. Abdominal MRI scans from 32,860 UK Biobank participants were utilized to evaluate genetically predicted liver fat and volume. Data from the ENIGMA Consortium, comprising 51,665 patients, were used to evaluate the associations between genetic susceptibility, NAFLD risk, liver fat, liver volume, and alterations in cortical thickness (TH) and surface area (SA). Inverse-variance weighted (IVW) estimation, Cochran Q, and MR-Egger were employed to assess heterogeneity and pleiotropy.

RESULTS

Overall, NAFLD did not significantly affect cortical SA or TH. However, potential associations were noted under global weighting, relating heightened NAFLD risk to reduced parahippocampal SA and decreased cortical TH in the caudal middle frontal, cuneus, lingual, and parstriangularis regions. Liver fat and volume also influenced the cortical structure of certain regions, although no Bonferroni-adjusted p-values reached significance. Two-step MR analysis revealed that liver fat, AST, and LDL levels mediated the impact of NAFLD on cortical structure. Multivariable MR analysis suggested that the impact of NAFLD on the cortical TH of lingual and parstriangularis was independent of BMI, obesity, hyperlipidemia, and diabetes.

CONCLUSION

This study provides evidence that NAFLD causally influences the cortical structure of the brain, suggesting the existence of a liver-brain axis in the development of NAFLD.

A Genome-Wide Association Study of Serum Metabolite Profiles in Septic Shock Patients

OBJECTIVES

We sought to assess whether genetic associations with metabolite concentrations in septic shock patients could be used to identify pathways of potential importance for understanding sepsis pathophysiology.

DESIGN

Retrospective multicenter cohort studies of septic shock patients.

SETTING

All participants who were admitted to 27 participating hospital sites in three countries (Australia, New Zealand, and the United Kingdom) were eligible for inclusion.

PATIENTS

Adult, critically ill, mechanically ventilated patients with septic shock (n = 230) who were a subset of the Adjunctive Corticosteroid Treatment in Critically Ill Patients with Septic Shock trial (ClinicalTrials.gov number: NCT01448109).

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

A genome-wide association study was conducted for a range of serum metabolite levels for participants. Genome-wide significant associations (p ≤ 5 × 10-8) were found for the two major ketone bodies (3-hydroxybutyrate [rs2456680] and acetoacetate [rs2213037] and creatinine (rs6851961). One of these single-nucleotide polymorphisms (SNPs) (rs2213037) was located in the alcohol dehydrogenase cluster of genes, which code for enzymes related to the metabolism of acetoacetate and, therefore, presents a plausible association for this metabolite. None of the three SNPs showed strong associations with risk of sepsis, 28- or 90-day mortality, or Acute Physiology and Chronic Health Evaluation score (a measure of sepsis severity).

CONCLUSIONS

We suggest that the genetic associations with metabolites may reflect a starvation response rather than processes involved in sepsis pathophysiology. However, our results require further investigation and replication in both healthy and diseased cohorts including those of different ancestry.

Plasma Metabolite Signatures in Male Carriers of Genetic Variants Associated with Non-Alcoholic Fatty Liver Disease

Both genetic and non-genetic factors are important in the pathophysiology of non-alcoholic fatty liver disease (NAFLD). The aim of our study was to identify novel metabolites and pathways associated with NAFLD by including both genetic and non-genetic factors in statistical analyses. We genotyped six genetic variants in the PNPLA3, TM6SF2, MBOAT7, GCKR, PPP1R3B, and HSD17B13 genes reported to be associated with NAFLD. Non-targeted metabolomic profiling was performed from plasma samples. We applied a previously validated fatty liver index to identify participants with NAFLD. First, we associated the six genetic variants with 1098 metabolites in 2 339 men without NAFLD to determine the effects of the genetic variants on metabolites, and then in 2 535 men with NAFLD to determine the joint effects of genetic variants and non-genetic factors on metabolites. We identified several novel metabolites and metabolic pathways, especially for PNPLA3, GCKR, and PPP1R38 variants relevant to the pathophysiology of NAFLD. Importantly, we showed that each genetic variant for NAFLD had a specific metabolite signature. The plasma metabolite signature was unique for each genetic variant, suggesting that several metabolites and different pathways are involved in the risk of NAFLD. The FLI index reliably identifies metabolites for NAFLD in large population-based studies.

Integrative analysis of metabolite GWAS illuminates the molecular basis of pleiotropy and genetic correlation

Pleiotropy and genetic correlation are widespread features in genome-wide association studies (GWAS), but they are often difficult to interpret at the molecular level. Here, we perform GWAS of 16 metabolites clustered at the intersection of amino acid catabolism, glycolysis, and ketone body metabolism in a subset of UK Biobank. We utilize the well-documented biochemistry jointly impacting these metabolites to analyze pleiotropic effects in the context of their pathways. Among the 213 lead GWAS hits, we find a strong enrichment for genes encoding pathway-relevant enzymes and transporters. We demonstrate that the effect directions of variants acting on biology between metabolite pairs often contrast with those of upstream or downstream variants as well as the polygenic background. Thus, we find that these outlier variants often reflect biology local to the traits. Finally, we explore the implications for interpreting disease GWAS, underscoring the potential of unifying biochemistry with dense metabolomics data to understand the molecular basis of pleiotropy in complex traits and diseases.

Extent to which array genotyping and imputation with large reference panels approximate deep whole-genome sequencing

Understanding the genetic basis of human diseases and traits is dependent on the identification and accurate genotyping of genetic variants. Deep whole-genome sequencing (WGS), the gold standard technology for SNP and indel identification and genotyping, remains very expensive for most large studies. Here, we quantify the extent to which array genotyping followed by genotype imputation can approximate WGS in studies of individuals of African, Hispanic/Latino, and European ancestry in the US and of Finnish ancestry in Finland (a population isolate). For each study, we performed genotype imputation by using the genetic variants present on the Illumina Core, OmniExpress, MEGA, and Omni 2.5M arrays with the 1000G, HRC, and TOPMed imputation reference panels. Using the Omni 2.5M array and the TOPMed panel, ≥90% of bi-allelic single-nucleotide variants (SNVs) are well imputed (r2 > 0.8) down to minor-allele frequencies (MAFs) of 0.14% in African, 0.11% in Hispanic/Latino, 0.35% in European, and 0.85% in Finnish ancestries. There was little difference in TOPMed-based imputation quality among the arrays with >700k variants. Individual-level imputation quality varied widely between and within the three US studies. Imputation quality also varied across genomic regions, producing regions where even common (MAF > 5%) variants were consistently not well imputed across ancestries. The extent to which array genotyping and imputation can approximate WGS therefore depends on reference panel, genotype array, sample ancestry, and genomic location. Imputation quality by variant or genomic region can be queried with our new tool, RsqBrowser, now deployed on the Michigan Imputation Server.

Crosstalk between Host Genome and Metabolome among People with HIV in South Africa

Genome-wide association studies (GWAS) of circulating metabolites have revealed the role of genetic regulation on the human metabolome. Most previous investigations focused on European ancestry, and few studies have been conducted among populations of African descent living in Africa, where the infectious disease burden is high (e.g., human immunodeficiency virus (HIV)). It is important to understand the genetic associations of the metabolome in diverse at-risk populations including people with HIV (PWH) living in Africa. After a thorough literature review, the reported significant gene−metabolite associations were tested among 490 PWH in South Africa. Linear regression was used to test associations between the candidate metabolites and genetic variants. GWAS of 154 plasma metabolites were performed to identify novel genetic associations. Among the 29 gene−metabolite associations identified in the literature, we replicated 10 in South Africans with HIV. The UGT1A cluster was associated with plasma levels of biliverdin and bilirubin; SLC16A9 and CPS1 were associated with carnitine and creatine, respectively. We also identified 22 genetic associations with metabolites using a genome-wide significance threshold (p-value < 5 × 10−8). In a GWAS of plasma metabolites in South African PWH, we replicated reported genetic associations across ancestries, and identified novel genetic associations using a metabolomics approach.

Genome-wide association studies of metabolites in Finnish men identify disease-relevant loci

Few studies have explored the impact of rare variants (minor allele frequency < 1%) on highly heritable plasma metabolites identified in metabolomic screens. The Finnish population provides an ideal opportunity for such explorations, given the multiple bottlenecks and expansions that have shaped its history, and the enrichment for many otherwise rare alleles that has resulted. Here, we report genetic associations for 1391 plasma metabolites in 6136 men from the late-settlement region of Finland. We identify 303 novel association signals, more than one third at variants rare or enriched in Finns. Many of these signals identify genes not previously implicated in metabolite genome-wide association studies and suggest mechanisms for diseases and disease-related traits.

The WWOX/HIF1A Axis Downregulation Alters Glucose Metabolism and Predispose to Metabolic Disorders

Recent reports indicate that the hypoxia-induced factor (HIF1α) and the Warburg effect play an initiating role in glucotoxicity, which underlies disorders in metabolic diseases. WWOX has been identified as a HIF1α regulator. WWOX downregulation leads to an increased expression of HIF1α target genes encoding glucose transporters and glycolysis’ enzymes. It has been proven in the normoglycemic mice cells and in gestational diabetes patients. The aim of the study was to determine WWOX’s role in glucose metabolism regulation in hyperglycemia and hypoxia to confirm its importance in the development of metabolic disorders. For this purpose, the WWOX gene was silenced in human normal fibroblasts, and then cells were cultured under different sugar and oxygen levels. Thereafter, it was investigated how WWOX silencing alters the genes and proteins expression profile of glucose transporters and glycolysis pathway enzymes, and their activity. In normoxia normoglycemia, higher glycolysis genes expression, their activity, and the lactate concentration were observed in WWOX KO fibroblasts in comparison to control cells. In normoxia hyperglycemia, it was observed a decrease of insulin-dependent glucose uptake and a further increase of lactate. It likely intensifies hyperglycemia condition, which deepen the glucose toxic effect. Then, in hypoxia hyperglycemia, WWOX KO caused weaker glucose uptake and elevated lactate production. In conclusion, the WWOX/HIF1A axis downregulation alters glucose metabolism and probably predispose to metabolic disorders.

Association of BCAT2 and BCKDH polymorphisms with clinical, anthropometric and biochemical parameters in young adults

BACKGROUND AND AIM

Circulating amino acids are modified by sex, body mass index (BMI) and insulin resistance (IR). However, whether the presence of genetic variants in branched-chain amino acid (BCAA) catabolic enzymes modifies circulating amino acids is still unknown. Thus, we determined the frequency of two genetic variants, one in the branched-chain aminotransferase 2 (BCAT2) gene (rs11548193), and one in the branched-chain ketoacid dehydrogenase (BCKDH) gene (rs45500792), and elucidated their impact on circulating amino acid levels together with clinical, anthropometric and biochemical parameters.

METHODS AND RESULTS

We performed a cross-sectional comparative study in which we recruited 1612 young adults (749 women and 863 men) aged 19.7 ± 2.1 years and with a BMI of 24.9 ± 4.7 kg/m². Participants underwent clinical evaluation and provided blood samples for DNA extraction and biochemical analysis. The single nucleotide polymorphisms (SNPs) were determined by allelic discrimination using real-time polymerase chain reaction (PCR). The frequencies of the less common alleles were 15.2 % for BCAT2 and 9.83 % for BCKDH. The subjects with either the BCAT2 or BCKDH SNPs displayed no differences in the evaluated parameters compared with subjects homozygotes for the most common allele at each SNP. However, subjects with both SNPs had higher body weight, BMI, blood pressure, glucose, and circulating levels of aspartate, isoleucine, methionine, and proline than the subjects homozygotes for the most common allele (P < 0.05, One-way ANOVA).

CONCLUSION

Our findings suggest that the joint presence of both the BCAT2 rs11548193 and BCKDH rs45500792 SNPs induces metabolic alterations that are not observed in subjects without either SNP.

Genome-wide analysis of blood lipid metabolites in over 5000 South Asians reveals biological insights at cardiometabolic disease loci

BACKGROUND

Genetic, lifestyle, and environmental factors can lead to perturbations in circulating lipid levels and increase the risk of cardiovascular and metabolic diseases. However, how changes in individual lipid species contribute to disease risk is often unclear. Moreover, little is known about the role of lipids on cardiovascular disease in Pakistan, a population historically underrepresented in cardiovascular studies.

METHODS

We characterised the genetic architecture of the human blood lipidome in 5662 hospital controls from the Pakistan Risk of Myocardial Infarction Study (PROMIS) and 13,814 healthy British blood donors from the INTERVAL study. We applied a candidate causal gene prioritisation tool to link the genetic variants associated with each lipid to the most likely causal genes, and Gaussian Graphical Modelling network analysis to identify and illustrate relationships between lipids and genetic loci.

RESULTS

We identified 253 genetic associations with 181 lipids measured using direct infusion high-resolution mass spectrometry in PROMIS, and 502 genetic associations with 244 lipids in INTERVAL. Our analyses revealed new biological insights at genetic loci associated with cardiometabolic diseases, including novel lipid associations at the LPL, MBOAT7, LIPC, APOE-C1-C2-C4, SGPP1, and SPTLC3 loci.

CONCLUSIONS

Our findings, generated using a distinctive lipidomics platform in an understudied South Asian population, strengthen and expand the knowledge base of the genetic determinants of lipids and their association with cardiometabolic disease-related loci.

Serum amino acid concentrations are modified by age, insulin resistance, and BCAT2 rs11548193 and BCKDH rs45500792 polymorphisms in subjects with obesity

BACKGROUND & AIMS

The amino acid profile of young adults is modified by sex, body mass index (BMI) and insulin resistance (IR). However, we do not know if age or the presence of specific polymorphisms in the genes of BCAT2 and BCKDH contribute to changes in the amino acid profile, especially in subjects with obesity. Therefore, we have evaluated the effect of age, the presence of IR and the polymorphisms of BCAT2 rs11548193 and BCKDH rs45500792 on the concentration of amino acids in subjects with obesity.

METHODS

This was a cross-sectional study conducted with 487 subjects with obesity. Participants underwent a physical examination in which their clinical history was obtained and a blood sample was taken for biochemical, hormonal, and DNA analysis.

RESULTS

Adults <30 years old with obesity had higher levels of alanine, arginine, aspartate, histidine, leucine, lysine, methionine, phenylalanine, proline, serine and valine than adults ≥30 years old. Interestingly, regardless of age, we found that arginine, aspartate, serine decreased, while proline and tyrosine increased in the presence of IR; tyrosine and sum of branched-chain amino acids (∑BCAA) were the amino acids that increased in the presence of BCAT2 rs11548193 and BCKDH rs45500792 polymorphisms.

CONCLUSIONS

We found that the amino acid profiles of subjects with obesity are differentially modified by age, the presence of IR, and the presence of the BCAT2 rs11548193 and BCKDH rs45500792 polymorphisms.

The influence of rare variants in circulating metabolic biomarkers

Circulating metabolite levels are biomarkers for cardiovascular disease (CVD). Here we studied, association of rare variants and 226 serum lipoproteins, lipids and amino acids in 7,142 (discovery plus follow-up) healthy participants. We leveraged the information from multiple metabolite measurements on the same participants to improve discovery in rare variant association analyses for gene-based and gene-set tests by incorporating correlated metabolites as covariates in the validation stage. Gene-based analysis corrected for the effective number of tests performed, confirmed established associations at APOB, APOC3, PAH, HAL and PCSK (p<1.32x10-7) and identified novel gene-trait associations at a lower stringency threshold with ACSL1, MYCN, FBXO36 and B4GALNT3 (p<2.5x10-6). Regulation of the pyruvate dehydrogenase (PDH) complex was associated for the first time, in gene-set analyses also corrected for effective number of tests, with IDL and LDL parameters, as well as circulating cholesterol (pMETASKAT<2.41x10-6). In conclusion, using an approach that leverages metabolite measurements obtained in the same participants, we identified novel loci and pathways involved in the regulation of these important metabolic biomarkers. As large-scale biobanks continue to amass sequencing and phenotypic information, analytical approaches such as ours will be useful to fully exploit the copious amounts of biological data generated in these efforts.

Inborn errors of enzymes in glutamate metabolism

Glutamate is involved in a variety of metabolic pathways. We reviewed the literature on genetic defects of enzymes that directly metabolise glutamate, leading to inborn errors of glutamate metabolism. Seventeen genetic defects of glutamate metabolising enzymes have been reported, of which three were only recently identified. These 17 defects affect the inter-conversion of glutamine and glutamate, amino acid metabolism, ammonia detoxification, and glutathione metabolism. We provide an overview of the clinical and biochemical phenotypes of these rare defects in an effort to ease their recognition. By categorising these by biochemical pathway, we aim to create insight into the contributing role of deviant glutamate and glutamine levels to the pathophysiology. For those disorders involving the inter-conversion of glutamine and glutamate, these deviant levels are postulated to play a pivotal pathophysiologic role. For the other IEM however-with the exception of urea cycle defects-abnormal glutamate and glutamine concentrations were rarely reported. To create insight into the clinical consequences of disturbed glutamate metabolism-rather than individual glutamate and glutamine levels-the prevalence of phenotypic abnormalities within the 17 IEM was compared to their prevalence within all Mendelian disorders and subsequently all disorders with metabolic abnormalities notated in the Human Phenotype Ontology (HPO) database. For this, a hierarchical database of all phenotypic abnormalities of the 17 defects in glutamate metabolism based on HPO was created. A neurologic phenotypic spectrum of developmental delay, ataxia, seizures, and hypotonia are common in the inborn errors of enzymes in glutamate metabolism. Additionally, ophthalmologic and skin abnormalities are often present, suggesting that disturbed glutamate homeostasis affects tissues of ectodermal origin: brain, eye, and skin. Reporting glutamate and glutamine concentrations in patients with inborn errors of glutamate metabolism would provide additional insight into the pathophysiology.

A comprehensive study of metabolite genetics reveals strong pleiotropy and heterogeneity across time and context

Genetic studies of metabolites have identified thousands of variants, many of which are associated with downstream metabolic and obesogenic disorders. However, these studies have relied on univariate analyses, reducing power and limiting context-specific understanding. Here we aim to provide an integrated perspective of the genetic basis of metabolites by leveraging the Finnish Metabolic Syndrome In Men (METSIM) cohort, a unique genetic resource which contains metabolic measurements, mostly lipids, across distinct time points as well as information on statin usage. We increase effective sample size by an average of two-fold by applying the Covariates for Multi-phenotype Studies (CMS) approach, identifying 588 significant SNP-metabolite associations, including 228 new associations. Our analysis pinpoints a small number of master metabolic regulator genes, balancing the relative proportion of dozens of metabolite levels. We further identify associations to changes in metabolic levels across time as well as genetic interactions with statin at both the master metabolic regulator and genome-wide level.

Effect of non-normality and low count variants on cross-phenotype association tests in GWAS

Many complex human diseases, such as type 2 diabetes, are characterized by multiple underlying traits/phenotypes that have substantially shared genetic architecture. Multivariate analysis of correlated traits has the potential to increase the power of detecting underlying common genetic loci. Several cross-phenotype association methods have been proposed-some require individual-level data on traits and genotypes, while the others require only summary-level data. In this article, we explore whether non-normality of multivariate trait distribution affects the inference from some of the existing multi-trait methods and how that effect is dependent on the allele count of the genetic variant being tested. We find that most of these tests are susceptible to biases that lead to spurious association signals. Even after controlling for confounders that may contribute to non-normality and then applying inverse normal transformation on the residuals of each trait, these tests may have inflated type I errors for variants with low minor allele counts (MACs). A likelihood ratio test of association based on the ordinal regression of individual-level genotype conditional on the traits seems to be the least biased and can maintain type I error when the MAC is reasonably large (e.g., MAC > 30). Application of these methods to publicly available summary statistics of eight amino acid traits on European samples seem to exhibit systematic inflation (especially for variants with low MAC), which is consistent with our findings from simulation experiments.

Metabolomics in genetic testing

Metabolomics is an intriguing field of study providing a new readout of the biochemical activities taking place at the moment of sampling within a subject's biofluid or tissue. Metabolite concentrations are influenced by several factors including disease, environment, drugs, diet and, importantly, genetics. Metabolomics signatures, which describe a subject's phenotype, are useful for disease diagnosis and prognosis, as well as for predicting and monitoring the effectiveness of treatments. Metabolomics is conventionally divided into targeted (i.e., the quantitative analysis of a predetermined group of metabolites) and untargeted studies (i.e., analysis of the complete set of small-molecule metabolites contained in a biofluid without a pre-imposed metabolites-selection). Both approaches have demonstrated high value in the investigation and understanding of several monogenic and multigenic conditions. Due to low costs per sample and relatively short analysis times, metabolomics can be a useful and robust complement to genetic sequencing.

Exome sequencing of Finnish isolates enhances rare-variant association power

Exome-sequencing studies have generally been underpowered to identify deleterious alleles with a large effect on complex traits as such alleles are mostly rare. Because the population of northern and eastern Finland has expanded considerably and in isolation following a series of bottlenecks, individuals of these populations have numerous deleterious alleles at a relatively high frequency. Here, using exome sequencing of nearly 20,000 individuals from these regions, we investigate the role of rare coding variants in clinically relevant quantitative cardiometabolic traits. Exome-wide association studies for 64 quantitative traits identified 26 newly associated deleterious alleles. Of these 26 alleles, 19 are either unique to or more than 20 times more frequent in Finnish individuals than in other Europeans and show geographical clustering comparable to Mendelian disease mutations that are characteristic of the Finnish population. We estimate that sequencing studies of populations without this unique history would require hundreds of thousands to millions of participants to achieve comparable association power.

Genetic Determinants of Circulating Glycine Levels and Risk of Coronary Artery Disease

Background Recent studies have revealed sexually dimorphic associations between the carbamoyl-phosphate synthase 1 locus, intermediates of the metabolic pathway leading from choline to urea, and risk of coronary artery disease ( CAD ) in women. Based on evidence from the literature, the atheroprotective association with carbamoyl-phosphate synthase 1 could be mediated by the strong genetic effect of this locus on increased circulating glycine levels. Methods and Results We sought to identify additional genetic determinants of circulating glycine levels by carrying out a meta-analysis of genome-wide association study data in up to 30 118 subjects of European ancestry. Mendelian randomization and other analytical approaches were used to determine whether glycine-associated variants were associated with CAD and traditional risk factors. Twelve loci were significantly associated with circulating glycine levels, 7 of which were not previously known to be involved in glycine metabolism ( ACADM , PHGDH , COX 18- ADAMTS 3, PSPH , TRIB 1, PTPRD , and ABO ). Glycine-raising alleles at several loci individually exhibited directionally consistent associations with decreased risk of CAD . However, these effects could not be attributed directly to glycine because of associations with other CAD -related traits. By comparison, genetic models that only included the 2 variants directly involved in glycine degradation and for which there were no other pleiotropic associations were not associated with risk of CAD or blood pressure, lipid levels, and obesity-related traits. Conclusions These results provide additional insight into the genetic architecture of glycine metabolism, but do not yield conclusive evidence for a causal relationship between circulating levels of this amino acid and risk of CAD in humans.

Assessing the causal association of glycine with risk of cardio-metabolic diseases

Circulating levels of glycine have previously been associated with lower incidence of coronary heart disease (CHD) and type 2 diabetes (T2D) but it remains uncertain if glycine plays an aetiological role. We present a meta-analysis of genome-wide association studies for glycine in 80,003 participants and investigate the causality and potential mechanisms of the association between glycine and cardio-metabolic diseases using genetic approaches. We identify 27 genetic loci, of which 22 have not previously been reported for glycine. We show that glycine is genetically associated with lower CHD risk and find that this may be partly driven by blood pressure. Evidence for a genetic association of glycine with T2D is weaker, but we find a strong inverse genetic effect of hyperinsulinaemia on glycine. Our findings strengthen evidence for a protective effect of glycine on CHD and show that the glycine-T2D association may be driven by a glycine-lowering effect of insulin resistance.

Multi-SKAT: General framework to test for rare-variant association with multiple phenotypes

In genetic association analysis, a joint test of multiple distinct phenotypes can increase power to identify sets of trait-associated variants within genes or regions of interest. Existing multiphenotype tests for rare variants make specific assumptions about the patterns of association with underlying causal variants, and the violation of these assumptions can reduce power to detect association. Here, we develop a general framework for testing pleiotropic effects of rare variants on multiple continuous phenotypes using multivariate kernel regression (Multi-SKAT). Multi-SKAT models affect sizes of variants on the phenotypes through a kernel matrix and perform a variance component test of association. We show that many existing tests are equivalent to specific choices of kernel matrices with the Multi-SKAT framework. To increase power of detecting association across tests with different kernel matrices, we developed a fast and accurate approximation of the significance of the minimum observed P value across tests. To account for related individuals, our framework uses random effects for the kinship matrix. Using simulated data and amino acid and exome-array data from the METabolic Syndrome In Men (METSIM) study, we show that Multi-SKAT can improve power over single-phenotype SKAT-O test and existing multiple-phenotype tests, while maintaining Type I error rate.

The impact of common genetic variants in the mitochondrial glycine cleavage system on relevant metabolites

The glycine cleavage system (GCS) is a complex of four enzymes enabling glycine to serve as a source of one-carbon units to the cell. We asked whether concentrations of glycine, dimethylglycine, formate, and serine in blood are influenced by variation within GCS genes in a sample of young, healthy individuals. Fifty-two variants tagging (r² < 0.9) the four GCS genes were tested; one variant, GLDC rs2297442-G, was significantly associated (p = .0007) with decreased glycine concentrations in serum.

Circulating amino acids and the risk of macrovascular, microvascular and mortality outcomes in individuals with type 2 diabetes: results from the ADVANCE trial

AIMS/HYPOTHESES

We aimed to quantify the association of individual circulating amino acids with macrovascular disease, microvascular disease and all-cause mortality in individuals with type 2 diabetes.

METHODS

We performed a case-cohort study (N = 3587), including 655 macrovascular events, 342 microvascular events (new or worsening nephropathy or retinopathy) and 632 all-cause mortality events during follow-up, in a secondary analysis of the Action in Diabetes and Vascular Disease: Preterax and Diamicron Modified Release Controlled Evaluation (ADVANCE) study. For this study, phenylalanine, isoleucine, glutamine, leucine, alanine, tyrosine, histidine and valine were measured in stored plasma samples by proton NMR metabolomics. Hazard ratios were modelled per SD increase in each amino acid.

RESULTS

In models investigating associations and potential mechanisms, after adjusting for age, sex and randomised treatment, phenylalanine was positively, and histidine inversely, associated with macrovascular disease risk. These associations were attenuated to the null on further adjustment for extended classical risk factors (including eGFR and urinary albumin/creatinine ratio). After adjustment for extended classical risk factors, higher tyrosine and alanine levels were associated with decreased risk of microvascular disease (HR 0.78; 95% CI 0.67, 0.91 and HR 0.86; 95% CI 0.76, 0.98, respectively). Higher leucine (HR 0.79; 95% CI 0.69, 0.90), histidine (HR 0.89; 95% CI 0.81, 0.99) and valine (HR 0.79; 95% CI 0.70, 0.88) levels were associated with lower risk of mortality. Investigating the predictive ability of amino acids, addition of all amino acids to a risk score modestly improved classification of participants for macrovascular (continuous net reclassification index [NRI] +35.5%, p < 0.001) and microvascular events (continuous NRI +14.4%, p = 0.012).

CONCLUSIONS/INTERPRETATION

We report distinct associations between circulating amino acids and risk of different major complications of diabetes. Low tyrosine appears to be a marker of microvascular risk in individuals with type 2 diabetes independently of fundamental markers of kidney function.