Whole-exome sequencing identifies rare genetic variants associated with human plasma metabolites

Pleiotropic and sex-specific genetic mechanisms of circulating metabolic markers

Metabolites in plasma form biosignatures of a range of common complex human diseases. Discovering variants with pleiotropic effects across metabolites can reveal underlying biological mechanisms. We therefore performed uni- and multivariate genome-wide association studies (GWAS) on 249 circulating metabolic markers across 328,006 UK Biobank and Estonian Biobank participants. We investigated rare variation through whole exome sequencing gene burden tests, analysed the role of body mass index through Mendelian randomization, and performed genome-wide interaction analyses with sex. We discovered 15,585 loci summed over the univariate GWAS, with high pleiotropy across markers, linked to a wide range of disorders. Findings from common and rare variant gene tests converged on lipid homeostasis pathways. 31 loci interacted with sex, mapped to genes involved in cholesterol processing. The findings offer insights into the genetic architecture of circulating metabolites, revealing pleiotropic loci, highlighting the role of rare variation, and uncovering sex-specific molecular mechanisms of lipid metabolism.

Plasma Metabolic Outliers Identified in Estonian Human Knockouts

Background/Objectives: Metabolomics, in combination with genetic data, is a powerful approach to study the biochemical consequences of genetic variation. We assessed the impact of human gene knockouts (KOs) on the metabolite levels of Estonia Biobank (EstBB) participants and integrated the results with electronic health record data. Methods: In 150,000 EstBB genotyped participants, we identified 723 KOs with 152 different predicted loss of function (pLoF) variants in 115 genes. For those KOs and 258 controls, 1387 metabolites were profiled using ultra-high-performance liquid chromatography-tandem mass spectrometry. Results: We identified 48 associations linking rare pLoF variants in 22 genes to 43 metabolites. Out of 48 associations, 27 (56%) were found in genes that cause inborn errors of metabolism. The top associations identified in our analysis included genes and metabolites involved in the degradation pathway of the pyrimidine bases uracil and thymine (DPYD and UPB1). We found DPYD gene KOs to be associated with elevated levels of Uracil, confirming that DPD-deficiency is a leading cause of severe 5-Fluorouracil toxicity. Overall, 54% of reported associations are gene targets of approved drugs or bioactive drug-like compounds. Conclusions: Our findings contribute to assessing the impact of human KOs on metabolite levels and offer insights into gene functions, disease mechanism, and drug target validation.

Evaluating the Causal Relationship Between Human Blood Metabolites and the Susceptibility to Alopecia Areata

BACKGROUND

Alopecia areata, a common autoimmune disease, is not fully understood in terms of its cause. However, research suggests that an imbalance in specific blood metabolites may trigger immune system dysfunction, leading to an attack on hair follicles and ultimately resulting in alopecia areata.

METHODS

Two-sample MR analysis was conducted to investigate the causal relationship between plasma metabolites and alopecia areata using various methods. Heterogeneity and pleiotropy were assessed, robustness of findings evaluated, and reverse MR performed for effect analysis.

RESULTS

The MR analysis found a positive causal relationship between alpha-ketoglutarate, propionylcarnitine (c3) and other metabolites with alopecia areata risk. Conversely, xylose 3-(3-hydroxyphenyl)propionate, glycochenodeoxycholate glucuronide (1) along with other metabolites, showed a protective effect against alopecia areata development. Both BWMR and MR-PRESSO confirmed the accuracy of the above results. Reverse MR revealed no reverse causality between plasma metabolites and AA. The robustness of the results was confirmed using the leave-one-out method, which demonstrated no influential instrumental variables affecting the outcomes while accounting for heterogeneity and eliminating horizontal gene pleiotropy effects on estimating causal effects.

CONCLUSION

This study establishes a causal relationship between plasma metabolism and alopecia areata, enhancing our understanding of its underlying mechanisms. These findings also provide valuable references for future screening and prevention strategies.

On the analysis of metabolite quantitative trait loci: Impact of different data transformations and study designs

Metabolomic genome-wide association studies (mGWASs), or metabolomic quantitative trait locus (metQTL) analyses, are gaining growing attention. However, robust methods and analysis guidelines, vital to address the complexity of metabolomic data, remain to be established. Here, we use whole-genome sequencing and metabolomic data from two independent studies to compare different approaches. We adopted three popular data transformation methods for metabolite levels-(i) log10 transformation, (ii) rank inverse normal transformation, and (iii) a fully adjusted two-step procedure-and compared population-based versus family-based analysis approaches. For validation, we performed permutation-based testing, Huber regression, and independent replication analysis. Simulation studies were used to illustrate the observed differences between data transformations. We demonstrate the advantages and limitations of popular analytic strategies used in mGWASs where especially low-frequency variants in combination with a skewed metabolite measurement distribution can lead to potentially false-positive metQTL findings. We recommend the rank inverse normal transformation or robust test statistics such as in family-based association tests as reliable approaches for mGWASs.

Application of copy number variation sequencing combined with whole exome sequencing in prenatal left-right asymmetry disorders

BACKGROUND

Left-right (LR) asymmetry disorders present a complex etiology, with genetic factors emerging as a primary contributor. This study aims to explore the genetic underpinnings of chromosomal variants and individual genes in fetuses afflicted with prenatal LR asymmetry disorder.

METHODS

Through a retrospective analysis conducted between 2020 and 2023 at Tongji Hospital, Huazhong University of Science and Technology, genetic outcomes of LR asymmetric disorder were scrutinized utilizing copy number variation sequencing (CNV-seq) and whole exome sequencing (WES) methodologies.

RESULTS

With a combination of CNV-seq and WES, 5 fetuses in 17 patients with LR asymmetry had chromosomal or genetic variants. CNV-seq revealed a 16p11.2 microdeletion syndrome in a situs inversus fetus presenting pathogenic and a 2q36.3 microduplication syndrome in a fetus with Heterotaxy presenting a variant of uncertain significance (VUS). WES identified NM_198075.4:c.755del in the LRRC56 gene and NM_001454.4:c.865_868dup in the FOXJ1 gene in two situs inversus cases, along with two variants in DNAH5 in two other fetuses. Further bioinformatics scrutiny was conducted to assess the protein structure and function prediction of these variants, ultimately indicating their potential pathogenicity.

CONCLUSION

The study highlights that fetuses with LR asymmetric disorders may have copy number variants, underscoring the significance of mutations in LRRC56 and FOXJ1 in the development of LR asymmetry disorders.

Coupling metabolomics and exome sequencing reveals graded effects of rare damaging heterozygous variants on gene function and human traits

Genetic studies of the metabolome can uncover enzymatic and transport processes shaping human metabolism. Using rare variant aggregation testing based on whole-exome sequencing data to detect genes associated with levels of 1,294 plasma and 1,396 urine metabolites, we discovered 235 gene-metabolite associations, many previously unreported. Complementary approaches (genetic, computational (in silico gene knockouts in whole-body models of human metabolism) and one experimental proof of principle) provided orthogonal evidence that studies of rare, damaging variants in the heterozygous state permit inferences concordant with those from inborn errors of metabolism. Allelic series of functional variants in transporters responsible for transcellular sulfate reabsorption (SLC13A1, SLC26A1) exhibited graded effects on plasma sulfate and human height and pinpointed alleles associated with increased odds of diverse musculoskeletal traits and diseases in the population. This integrative approach can identify new players in incompletely characterized human metabolic reactions and reveal metabolic readouts informative of human traits and diseases.

Transcriptomics and metabolomics insights into the seasonal dynamics of meat quality in yak on the Qinghai-Tibetan Plateau

BACKGROUND

Meat quality in yak is influenced by the fluctuation of nutritional composition in different grazing seasons on the Qinghai-Tibetan Plateau. However, the molecular mechanism underlying in yak meat remains unknown. Therefore, this study aimed to investigate the seasonal dynamics of meat quality in yak by transcriptomics and metabolomics techniques. Twelve healthy female yaks with a similar weight were divided into two groups, including the warm season group (WS) and cold season group (CS). After slaughter, samples of longissimus lumborum were collected and subjected to transcriptomics and metabolomics to explore the effects of different seasons on meat quality.

RESULTS

Yak in the WS group had higher contents of n-3 Polyunsaturated fatty acid (PUFA), n-6 PUFA, threonine, and valine compared to the CS group, but the pH45min and b* values were lower. A total of 75 differentially expressed metabolites in the longissimus lumborum muscle were identified, with 23 metabolites upregulated and 52 metabolites downregulated in the WS group. These metabolites were mainly enriched in the pathway of glycine, serine and threonine metabolism, tryptophan metabolism, and carbohydrate digestion and absorption. In comparison, the WS group exhibited 262 upregulated genes in the longissimus lumborum muscle and 81 downregulated genes relatives to the CS group, which were enriched in the fat deposition of TGF-beta, ECM-receptor interaction, MAPK, and PPAR signaling pathway.

CONCLUSIONS

Among these, downregulated genes NPNT, GADL1, SESN3, and CPXM1 were associated with lipid metabolism and fat deposition in grazing yaks. It was found that DDC, DHTKD1, CCBL1, GCDH, and AOC1 involved in the tryptophan metabolism played an important role in the regulation of energy metabolism in yak.

Multimodal AI/ML for discovering novel biomarkers and predicting disease using multi-omics profiles of patients with cardiovascular diseases

Cardiovascular diseases (CVDs) are complex, multifactorial conditions that require personalized assessment and treatment. Advancements in multi-omics technologies, namely RNA sequencing and whole-genome sequencing, have provided translational researchers with a comprehensive view of the human genome. The efficient synthesis and analysis of this data through integrated approach that characterizes genetic variants alongside expression patterns linked to emerging phenotypes, can reveal novel biomarkers and enable the segmentation of patient populations based on personalized risk factors. In this study, we present a cutting-edge methodology rooted in the integration of traditional bioinformatics, classical statistics, and multimodal machine learning techniques. Our approach has the potential to uncover the intricate mechanisms underlying CVD, enabling patient-specific risk and response profiling. We sourced transcriptomic expression data and single nucleotide polymorphisms (SNPs) from both CVD patients and healthy controls. By integrating these multi-omics datasets with clinical demographic information, we generated patient-specific profiles. Utilizing a robust feature selection approach, we identified a signature of 27 transcriptomic features and SNPs that are effective predictors of CVD. Differential expression analysis, combined with minimum redundancy maximum relevance feature selection, highlighted biomarkers that explain the disease phenotype. This approach prioritizes both biological relevance and efficiency in machine learning. We employed Combination Annotation Dependent Depletion scores and allele frequencies to identify variants with pathogenic characteristics in CVD patients. Classification models trained on this signature demonstrated high-accuracy predictions for CVD. The best performing of these models was an XGBoost classifier optimized via Bayesian hyperparameter tuning, which was able to correctly classify all patients in our test dataset. Using SHapley Additive exPlanations, we created risk assessments for patients, offering further contextualization of these predictions in a clinical setting. Across the cohort, RPL36AP37 and HBA1 were scored as the most important biomarkers for predicting CVDs. A comprehensive literature review revealed that a substantial portion of the diagnostic biomarkers identified have previously been associated with CVD. The framework we propose in this study is unbiased and generalizable to other diseases and disorders.

A roadmap to the molecular human linking multiomics with population traits and diabetes subtypes

In-depth multiomic phenotyping provides molecular insights into complex physiological processes and their pathologies. Here, we report on integrating 18 diverse deep molecular phenotyping (omics-) technologies applied to urine, blood, and saliva samples from 391 participants of the multiethnic diabetes Qatar Metabolomics Study of Diabetes (QMDiab). Using 6,304 quantitative molecular traits with 1,221,345 genetic variants, methylation at 470,837 DNA CpG sites, and gene expression of 57,000 transcripts, we determine (1) within-platform partial correlations, (2) between-platform mutual best correlations, and (3) genome-, epigenome-, transcriptome-, and phenome-wide associations. Combined into a molecular network of > 34,000 statistically significant trait-trait links in biofluids, our study portrays "The Molecular Human". We describe the variances explained by each omics in the phenotypes (age, sex, BMI, and diabetes state), platform complementarity, and the inherent correlation structures of multiomics data. Further, we construct multi-molecular network of diabetes subtypes. Finally, we generated an open-access web interface to "The Molecular Human" ( http://comics.metabolomix.com ), providing interactive data exploration and hypotheses generation possibilities.

Chinese carrier of the HNF1A p.Gln444fs variant exhibits enhanced response to sulfonylureas

Background

We assessed the response to sulfonylureas and the functional characteristics of HNF1A mutations in patients with maturity-onset diabetes of the young type 3 (MODY3).

Methods

We recruited a family with suspected MODY in this study, and gene sequencing (whole-exome sequencing) was used to screen germline mutations. Luciferase reporter assays were used to evaluate the activity of the mutated genes.

Results

Heterozygous HNF1A variant (NM_000545.8:c.1330_1331del, p.Gln444fs) was identified in the proband and was not found in his father, grandmother, and nonrelated healthy controls. The mutant protein had 552 amino acids, 110 fewer than the wild type protein. Furthermore, the amino acid sequence was completely different between the mutant protein and the wild type protein starting from the 444th amino acid. Luciferase reporter assays revealed that the variant had impaired HNF4A promoter-regulation activity. The patient did not achieve good hypoglycemic effects during long-term treatment with insulin and metformin. The effect of hypoglycemic treatment was highly significant after the addition of sulfonylurea drugs.

Conclusions

The HNF1A p.Gln444fs variant associated with MODY3, and most likely a truncated protein, impaired HNF1A transcriptional activity. The variant carrier experienced an enhanced response to sulfonylureas.

Human Genetics and Genomics for Drug Target Identification and Prioritization: Open Targets' Perspective

Open Targets, a consortium among academic and industry partners, focuses on using human genetics and genomics to provide insights to key questions that build therapeutic hypotheses. Large-scale experiments generate foundational data, and open-source informatic platforms systematically integrate evidence for target-disease relationships and provide dynamic tooling for target prioritization. A locus-to-gene machine learning model uses evidence from genome-wide association studies (GWAS Catalog, UK BioBank, and FinnGen), functional genomic studies, epigenetic studies, and variant effect prediction to predict potential drug targets for complex diseases. These predictions are combined with genetic evidence from gene burden analyses, rare disease genetics, somatic mutations, perturbation assays, pathway analyses, scientific literature, differential expression, and mouse models to systematically build target-disease associations (https://platform.opentargets.org). Scored target attributes such as clinical precedence, tractability, and safety guide target prioritization. Here we provide our perspective on the value and impact of human genetics and genomics for generating therapeutic hypotheses.

Effect of the PCSK9 R46L genetic variant on plasma insulin and glucose levels, risk of diabetes mellitus and cardiovascular disease: A meta-analysis

BACKGROUND AND AIM

The impact of the loss-of-function (LOF) genetic variant PCSK9 R46L on glucose homeostasis and cardiovascular disease (CVD) remains uncertain, despite its established correlation with diminished blood cholesterol levels. This meta-analysis aimed at exploring the effect of the PCSK9 R46L genetic variant on plasma insulin and glucose levels, risk of diabetes mellitus and CVD.

METHODS AND RESULTS

PubMed, Embase, and the Cochrane Library were searched for cohort and case-control studies published until October 1, 2023. The studies should report the association of the PCSK9 R46L genetic variant with one of the following: fasting plasma insulin, blood glucose levels, diabetes mellitus, and CVD risk. A dominant model of the PCSK9 R46L genetic variant was employed to statistical analysis. The meta-analyses were performed for continuous variables with standard mean difference (SMD), categorical variables with odds ratio (OR) using a random-effects model. A total of 17 articles with 20 studies engaging 1,186,861 population were identified and mobilized for these analyses. The overall results indicated that, compared with non-carriers of the PCSK9 R46L genetic variant, carriers of the PCSK9 R46L genetic variant did not increase or decrease the levels of fasting plasma insulin (3 studies with 7277 population; SMD, 0.08; 95% CI, -0.04 to 0.19; P = 0.270), and the levels of fasting plasma glucose (7 studies with 9331 population; SMD, 0.03; 95% CI, -0.08 to 0.13; P = 0.610). However, carriers of the PCSK9 R46L genetic variant indeed had 17% reduction in the risk of CVD (11 studies with 558,263 population; OR, 0.83; 95% CI, 0.71 to 0.98; P = 0.030), and 9% increase in the risk of diabetes mellitus (10 studies with 744,466 population; OR, 1.09; 95% CI, 1.04 to 1.14; P < 0.01). Meta-regression analyses indicated that the increased risk of diabetes mellitus and the reduced risk of CVD were positively correlated with reduction in LDL-C (P = 0.004 and 0.033, respectively).

CONCLUSIONS

PCSK9 R46L genetic variant exhibited an elevated susceptibility to diabetes mellitus alongside a reduced vulnerability to CVD.

Association between human blood metabolome and the risk of gastrointestinal tumors

BACKGROUND

The prevalence of gastrointestinal tumors continues to be significant. To uncover promising therapeutic targets for these tumors, we rigorously executed a Mendelian randomization (MR) study to comprehensively screen the blood metabolomes for potential causal mediators of five frequently encountered gastrointestinal tumors (Liver Cancer, Colorectal Cancer, Esophageal Cancer, Gastric Cancer and Pancreatic Cancer).

METHODS

We selected a comprehensive set of 137 distinct blood metabolites derived from three large-scale genome-wide association studies (GWASs) involving a total of 147827 participants of European ancestry. The gastrointestinal tumors-related data were obtained from a GWAS conducted within the Finnish study. Through meticulous MR analyses, we thoroughly assessed the associations between blood metabolites and gastrointestinal tumors. Additionally, a phenome-wide MR (Phe-MR) analysis was employed to investigate the potential on-target side effects of metabolite interventions.

RESULTS

We have identified 1 blood metabolites, namely isovalerylcarnitine (ORlog10: 1.01; 95%CI, 1.01-1.02; P = 1.81×10-7), as the potential causal mediators for liver cancer. However, no potential pathogenic mediators were detected for the other four tumors.

CONCLUSIONS

The current systematic MR analysis elucidated the potential role of isovalerylcarnitine as a causal mediator in the development of liver cancer. Leveraging the power of Phe-MR study facilitated the identification of potential adverse effects associated with drug targets for liver cancer prevention. Considering the weighing of pros and cons, isovalerylcarnitine emerges as a promising candidate for targeted drug interventions in the realm of liver cancer prevention.

Phosphoserine aminotransferase deficiency diagnosed by whole-exome sequencing and LC-MS/MS reanalysis: A case report and review of literature

BACKGROUND

Phosphoserine aminotransferase deficiency (PSATD) is an autosomal recessive disorder associated with hypertonia, psychomotor retardation, and acquired microcephaly. Patients with PSATD have low concentrations of serine in plasma and cerebrospinal fluid.

METHODS

We reported a 2-year-old female child with developmental delay, dyskinesia, and microcephaly. LC-MS/MS was used to detect amino acid concentration in the blood and whole-exome sequencing (WES) was used to identify the variants. PolyPhen-2 web server and PyMol were used to predict the pathogenicity and changes in the 3D model molecular structure of protein caused by variants.

RESULTS

WES demonstrated compound heterozygous variants in PSAT1, which is associated with PSATD, with a paternal likely pathogenic variant (c.235G>A, Gly79Arg) and a maternal likely pathogenic variant (c.43G>C, Ala15Pro). Reduced serine concentration in LC-MS/MS further confirmed the diagnosis of PSATD in this patient.

CONCLUSIONS

Our findings demonstrate the importance of WES combined with LC-MS/MS reanalysis in the diagnosis of genetic diseases and expand the PSAT1 variant spectrum in PSATD. Moreover, we summarize all the cases caused by PSAT1 variants in the literature. This case provides a vital reference for the diagnosis of future cases.

Lessons and Applications of Omics Research in Diabetes Epidemiology

PURPOSE OF REVIEW

Recent advances in genomic technology and molecular techniques have greatly facilitated the identification of disease biomarkers, advanced understanding of pathogenesis of different common diseases, and heralded the dawn of precision medicine. Much of these advances in the area of diabetes have been made possible through deep phenotyping of epidemiological cohorts, and analysis of the different omics data in relation to detailed clinical information. In this review, we aim to provide an overview on how omics research could be incorporated into the design of current and future epidemiological studies.

RECENT FINDINGS

We provide an up-to-date review of the current understanding in the area of genetic, epigenetic, proteomic and metabolomic markers for diabetes and related outcomes, including polygenic risk scores. We have drawn on key examples from the literature, as well as our own experience of conducting omics research using the Hong Kong Diabetes Register and Hong Kong Diabetes Biobank, as well as other cohorts, to illustrate the potential of omics research in diabetes. Recent studies highlight the opportunity, as well as potential benefit, to incorporate molecular profiling in the design and set-up of diabetes epidemiology studies, which can also advance understanding on the heterogeneity of diabetes. Learnings from these examples should facilitate other researchers to consider incorporating research on omics technologies into their work to advance the field and our understanding of diabetes and its related co-morbidities. Insights from these studies would be important for future development of precision medicine in diabetes.

Rare variants in pharmacogenes influence clozapine metabolism in individuals with schizophrenia

Clozapine is the only licensed medication for treatment-resistant schizophrenia (TRS). Few predictors for variation in response to clozapine have been identified, but clozapine metabolism is known to influence therapeutic response and adverse side effects. Here, we expand on genome-wide studies of clozapine metabolism, previously focused on common genetic variation, by analysing whole-exome sequencing data from 2062 individuals with schizophrenia taking clozapine in the UK. We investigated whether rare genomic variation in genes and gene sets involved in the clozapine metabolism pathway influences plasma concentrations of clozapine metabolites, assessed through the longitudinal analysis of 6585 pharmacokinetic assays. We observed a statistically significant association between the burden of rare damaging coding variants (MAF ≤ 1 %) in gene sets broadly related to drug pharmacokinetics and lower clozapine (β = -0.054, SE = 0.019, P-value = 0.005) concentrations in plasma. We estimate that the effects in clozapine plasma concentrations of a single damaging allele in this gene set are akin to reducing the clozapine dose by about 35 mg/day. The gene-based analysis identified rare variants in CYP1A2, which encodes the enzyme responsible for converting clozapine to norclozapine, as having the strongest effects of any gene on clozapine metabolism (β = 0.324, SE = 0.124, P = 0.009). Our findings support the hypothesis that rare genetic variants in known drug-metabolising enzymes and transporters can markedly influence clozapine plasma concentrations; these results suggest that pharmacogenomic efforts trying to predict clozapine metabolism and personalise drug therapy could benefit from the inclusion of rare damaging variants in pharmacogenes beyond those already identified and catalogued as PGx star alleles.

Genetic influences on circulating retinol and its relationship to human health

Retinol is a fat-soluble vitamin that plays an essential role in many biological processes throughout the human lifespan. Here, we perform the largest genome-wide association study (GWAS) of retinol to date in up to 22,274 participants. We identify eight common variant loci associated with retinol, as well as a rare-variant signal. An integrative gene prioritisation pipeline supports novel retinol-associated genes outside of the main retinol transport complex (RBP4:TTR) related to lipid biology, energy homoeostasis, and endocrine signalling. Genetic proxies of circulating retinol were then used to estimate causal relationships with almost 20,000 clinical phenotypes via a phenome-wide Mendelian randomisation study (MR-pheWAS). The MR-pheWAS suggests that retinol may exert causal effects on inflammation, adiposity, ocular measures, the microbiome, and MRI-derived brain phenotypes, amongst several others. Conversely, circulating retinol may be causally influenced by factors including lipids and serum creatinine. Finally, we demonstrate how a retinol polygenic score could identify individuals more likely to fall outside of the normative range of circulating retinol for a given age. In summary, this study provides a comprehensive evaluation of the genetics of circulating retinol, as well as revealing traits which should be prioritised for further investigation with respect to retinol related therapies or nutritional intervention.

Genetics of congenital heart disease

During embryonic development, the cardiovascular system and the central nervous system exhibit a coordinated developmental process through intricate interactions. Congenital heart disease (CHD) refers to structural or functional abnormalities that occur during embryonic or prenatal heart development and is the most common congenital disorder. One of the most common complications in CHD patients is neurodevelopmental disorders (NDD). However, the specific mechanisms, connections, and precise ways in which CHD co-occurs with NDD remain unclear. According to relevant research, both genetic and non-genetic factors are significant contributors to the co-occurrence of sporadic CHD and NDD. Genetic variations, such as chromosomal abnormalities and gene mutations, play a role in the susceptibility to both CHD and NDD. Further research should aim to identify common molecular mechanisms that underlie the co-occurrence of CHD and NDD, possibly originating from shared genetic mutations or shared gene regulation. Therefore, this review article summarizes the current advances in the genetics of CHD co-occurring with NDD, elucidating the application of relevant gene detection techniques. This is done with the aim of exploring the genetic regulatory mechanisms of CHD co-occurring with NDD at the gene level and promoting research and treatment of developmental disorders related to the cardiovascular and central nervous systems.

Ancestry-driven metabolite variation provides insights into disease states in admixed populations

BACKGROUND

Metabolic pathways are related to physiological functions and disease states and are influenced by genetic variation and environmental factors. Hispanics/Latino individuals have ancestry-derived genomic regions (local ancestry) from their recent admixture that have been less characterized for associations with metabolite abundance and disease risk.

METHODS

We performed admixture mapping of 640 circulating metabolites in 3887 Hispanic/Latino individuals from the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Metabolites were quantified in fasting serum through non-targeted mass spectrometry (MS) analysis using ultra-performance liquid chromatography-MS/MS. Replication was performed in 1856 nonoverlapping HCHS/SOL participants with metabolomic data.

RESULTS

By leveraging local ancestry, this study identified significant ancestry-enriched associations for 78 circulating metabolites at 484 independent regions, including 116 novel metabolite-genomic region associations that replicated in an independent sample. Among the main findings, we identified Native American enriched genomic regions at chromosomes 11 and 15, mapping to FADS1/FADS2 and LIPC, respectively, associated with reduced long-chain polyunsaturated fatty acid metabolites implicated in metabolic and inflammatory pathways. An African-derived genomic region at chromosome 2 was associated with N-acetylated amino acid metabolites. This region, mapped to ALMS1, is associated with chronic kidney disease, a disease that disproportionately burdens individuals of African descent.

CONCLUSIONS

Our findings provide important insights into differences in metabolite quantities related to ancestry in admixed populations including metabolites related to regulation of lipid polyunsaturated fatty acids and N-acetylated amino acids, which may have implications for common diseases in populations.

Genetic studies of paired metabolomes reveal enzymatic and transport processes at the interface of plasma and urine

The kidneys operate at the interface of plasma and urine by clearing molecular waste products while retaining valuable solutes. Genetic studies of paired plasma and urine metabolomes may identify underlying processes. We conducted genome-wide studies of 1,916 plasma and urine metabolites and detected 1,299 significant associations. Associations with 40% of implicated metabolites would have been missed by studying plasma alone. We detected urine-specific findings that provide information about metabolite reabsorption in the kidney, such as aquaporin (AQP)-7-mediated glycerol transport, and different metabolomic footprints of kidney-expressed proteins in plasma and urine that are consistent with their localization and function, including the transporters NaDC3 (SLC13A3) and ASBT (SLC10A2). Shared genetic determinants of 7,073 metabolite-disease combinations represent a resource to better understand metabolic diseases and revealed connections of dipeptidase 1 with circulating digestive enzymes and with hypertension. Extending genetic studies of the metabolome beyond plasma yields unique insights into processes at the interface of body compartments.

Effects of protein-coding variants on blood metabolite measurements and clinical biomarkers in the UK Biobank

Genome-wide association studies (GWASs) have established the contribution of common and low-frequency variants to metabolic blood measurements in the UK Biobank (UKB). To complement existing GWAS findings, we assessed the contribution of rare protein-coding variants in relation to 355 metabolic blood measurements-including 325 predominantly lipid-related nuclear magnetic resonance (NMR)-derived blood metabolite measurements (Nightingale Health Plc) and 30 clinical blood biomarkers-using 412,393 exome sequences from four genetically diverse ancestries in the UKB. Gene-level collapsing analyses were conducted to evaluate a diverse range of rare-variant architectures for the metabolic blood measurements. Altogether, we identified significant associations (p < 1 × 10^-8) for 205 distinct genes that involved 1,968 significant relationships for the Nightingale blood metabolite measurements and 331 for the clinical blood biomarkers. These include associations for rare non-synonymous variants in PLIN1 and CREB3L3 with lipid metabolite measurements and SYT7 with creatinine, among others, which may not only provide insights into novel biology but also deepen our understanding of established disease mechanisms. Of the study-wide significant clinical biomarker associations, 40% were not previously detected on analyzing coding variants in a GWAS in the same cohort, reinforcing the importance of studying rare variation to fully understand the genetic architecture of metabolic blood measurements.

Genomic atlas of the plasma metabolome prioritizes metabolites implicated in human diseases

Metabolic processes can influence disease risk and provide therapeutic targets. By conducting genome-wide association studies of 1,091 blood metabolites and 309 metabolite ratios, we identified associations with 690 metabolites at 248 loci and associations with 143 metabolite ratios at 69 loci. Integrating metabolite-gene and gene expression information identified 94 effector genes for 109 metabolites and 48 metabolite ratios. Using Mendelian randomization (MR), we identified 22 metabolites and 20 metabolite ratios having estimated causal effect on 12 traits and diseases, including orotate for estimated bone mineral density, α-hydroxyisovalerate for body mass index and ergothioneine for inflammatory bowel disease and asthma. We further measured the orotate level in a separate cohort and demonstrated that, consistent with MR, orotate levels were positively associated with incident hip fractures. This study provides a valuable resource describing the genetic architecture of metabolites and delivers insights into their roles in common diseases, thereby offering opportunities for therapeutic targets.

Rare and common genetic determinants of metabolic individuality and their effects on human health

Garrod's concept of 'chemical individuality' has contributed to comprehension of the molecular origins of human diseases. Untargeted high-throughput metabolomic technologies provide an in-depth snapshot of human metabolism at scale. We studied the genetic architecture of the human plasma metabolome using 913 metabolites assayed in 19,994 individuals and identified 2,599 variant-metabolite associations (P < 1.25 × 10-11) within 330 genomic regions, with rare variants (minor allele frequency ≤ 1%) explaining 9.4% of associations. Jointly modeling metabolites in each region, we identified 423 regional, co-regulated, variant-metabolite clusters called genetically influenced metabotypes. We assigned causal genes for 62.4% of these genetically influenced metabotypes, providing new insights into fundamental metabolite physiology and clinical relevance, including metabolite-guided discovery of potential adverse drug effects (DPYD and SRD5A2). We show strong enrichment of inborn errors of metabolism-causing genes, with examples of metabolite associations and clinical phenotypes of non-pathogenic variant carriers matching characteristics of the inborn errors of metabolism. Systematic, phenotypic follow-up of metabolite-specific genetic scores revealed multiple potential etiological relationships.

Precision therapy for three Chinese families with maturity-onset diabetes of the young (MODY12)

Maturity-onset diabetes of the young (MODY) is rare monogenic diabetes. However, MODY is often undiagnosed or misdiagnosed. In this study, we aimed to investigate the pathogenic gene for diabetes and provide precise treatment for diabetes patients in three families. Three families with suspected MODY were enrolled and screened for germline mutations using Whole exome sequencing (WES). Candidate pathogenic variants were validated in other family members and non-related healthy controls. Three heterozygous missense mutations in the ABCC8 gene (NM_001287174), c.1555 C>T (p.R519C), c.3706 A>G (p.I1236V), and c.2885 C>T (p.S962L) were found in families A, B, and C, respectively. All mutation sites cosegregated with diabetes, were predicted to be harmful by bioinformatics and were not found in non-related healthy controls. Two probands (onset ages, 8 and 12 years) were sensitive to glimepiride. However, an insufficient dose (2 mg/day) led to ketoacidosis. When the dosage of glimepiride was increased to 4 mg/day, blood sugar remained under control. A dose of 4 mg glimepiride daily also effectively controlled blood sugar in an adult patient 25-year-old. In addition, all patients were sensitive to liraglutide, which could control blood sugar better. These data suggest that ABCC8 was the pathogenic gene in three families with diabetes. Glimepiride (2 mg/day) was not effective in controlling blood sugar in children with ABCC8 mutations, however, 4 mg/daily glimepiride was effective in both adults and children. Moreover, liraglutide was effective in controlling blood sugar in both adults and children with ABCC8 mutations.