Metabolite Quantitative Trait Loci Research Articles

Systematic investigation of genetically determined plasma and urinary metabolites to discover potential interventional targets for colorectal cancer.

We aimed to identify plasma and urinary metabolites related to colorectal cancer (CRC) risk and elucidate their mediator role in the associations between modifiable risk factors and CRC. Metabolite quantitative trait loci were derived from two published metabolomics genome-wide association studies (GWASs), and summary-level data were extracted for 651 plasma metabolites and 208 urinary metabolites. Genetic associations with CRC were obtained from a large-scale GWAS meta-analysis (100,204 cases; 154,587 controls) and the FinnGen cohort (4,957 cases; 304,197 controls). Mendelian randomization (MR) and colocalization analyses were performed to evaluate the causal roles of metabolites in CRC. Druggability evaluation was employed to prioritize potential therapeutic targets. Multivariable MR and mediation estimation were conducted to elucidate the mediating effects of metabolites on the associations between modifiable risk factors and CRC. The study identified 30 plasma metabolites and four urinary metabolites for CRC. Plasma sphingomyelin and urinary lactose, which were positively associated with CRC risk, could be modulated by drug interventions (ie, Olipudase alfa, Tilactase). Thirteen modifiable risk factors were associated with nine metabolites and eight of these modifiable risk factors were associated with CRC risk. These nine metabolites mediated the effect of modifiable risk factors (Actinobacteria, BMI, waist-hip ratio, fasting insulin, smoking initiation) on CRC. This study identified key metabolite biomarkers associated with CRC and elucidated their mediator roles in the associations between modifiable risk factors and CRC. These findings provide new insights into the etiology and potential therapeutic targets for CRC and the etiological pathways of modifiable environmental factors with CRC.

Abstract 4443: Trans-population two-sample Mendelian randomization study of circulating metabolites and prostate cancer risk

Abstract While prostate cancer (PCa) is highly heritable, mechanisms underlying disease risk and PCa disparities are not well understood. Here, we conducted a two-sample Mendelian randomization (MR) to assess whether serum metabolites are causally associated with PCa risk in European, African, and Hispanic populations. MR analyses were performed on metabolite quantitative trait loci (mQTLs) for 250 metabolites quantified by untargeted mass spectroscopy via the Metabolon platform for 1,498 European and 1,740 African ancestry individuals from the Atherosclerosis Risk in Communities (ARIC) cohort and for 711 metabolites measured in 3,166 Hispanic individuals from the Hispanic Community Health Study/Study of Latinos (SOL). PCa GWAS summary statistics were obtained for men from European (122,188 cases, 604,640 controls), African (19,391 cases, 61,608 controls) and Hispanic (3,931 cases, 26,405 controls) populations from the PRACTICAL Consortium. Within each population, QTLs associated with metabolites at the genome-wide significance level (P<5x10-8) were included in instruments upon removing rare (minor allele frequency≤0.01) or correlated (R2≥0.2) SNPs calculated in ancestry-matched TOPMed populations. Inverse variance weighted (IVW) random effect models are presented as primary results. Sensitivity analyses were utilized to assess assumption violations (weighted mode, weighted median and MR egger). MR were conducted separately in each population, and fixed effect meta-analyses were conducted across population-specific MR results to identify trans-population associations. A false discovery rate was implemented to account for multiple testing. In total, 22, 4 and 1 metabolites significantly associated with PCa risk in Hispanic, European and African populations, respectively. Of these, 13 metabolites had a MR instrument in ≥1 population, 12 of which were significant following a trans-population meta-analysis, including 5 fatty acids, 3 lysophospholipids, 1 amino acid, 1 carbohydrate, 1 nucleotide and 1 xenobiotic. All fatty acids were associated with decreased PCa odds (2%-10%), as were 4 other metabolites (amino acid 3-methyoxytyrosine, nucleotide 5-methyluridine and lysophospholipids 1-archidonoyl-GPC (20:4n6) and 1-archidonoyl-GPE (20:4n6), 4%-9%). The 3 remaining metabolites, erythritol, 1-linoleoyl-GPE (18:2) and mannose, were found to increase PCa odds by 10%, 6% and 4%, respectively. Additional metabolite harmonization efforts are underway to conduct trans-population analyses metabolome-wide. This study provides evidence of associations between metabolites, such as fatty acids and lysophysopholipids, and PCa across diverse populations. These findings point to mechanisms that could inform preventive or therapeutic strategies pending functional investigations. Multivariable and bidirectional MR are ongoing to further assess findings. Citation Format: Harriett Fuller, Rebecca Rohde, Heather Highland, Jiayi Shen, Bing Yu, Eric Boerwinkle, Megan Grove, Kari E. North, David V. Conti, Christopher A. Haiman, Kristin Young, Burcu Darst. Trans-population two-sample Mendelian randomization study of circulating metabolites and prostate cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4443.

Dynamic metabolite QTL analyses provide novel biochemical insights into kernel development and nutritional quality improvement in common wheat

Despite recent advances in crop metabolomics, the genetic control and molecular basis of the wheat kernel metabolome at different developmental stages remain largely unknown. Here, we performed widely targeted metabolite profiling of kernels from three developmental stages (grain-filling kernels [FKs], mature kernels [MKs], and germinating kernels [GKs]) using a population of 159 recombinant inbred lines. We detected 625 annotated metabolites and mapped 3173, 3143, and 2644 metabolite quantitative trait loci (mQTLs) in FKs, MKs, and GKs, respectively. Only 52 mQTLs were mapped at all three stages, indicating the high stage specificity of the wheat kernel metabolome. Four candidate genes were functionally validated by in vitro enzymatic reactions and/or transgenic approaches in wheat, three of which mediated the tricin metabolic pathway. Metabolite flux efficiencies within the tricin pathway were evaluated, and superior candidate haplotypes were identified, comprehensively delineating the tricin metabolism pathway in wheat. Finally, additional wheat metabolic pathways were re-constructed by updating them to incorporate the 177 candidate genes identified in this study. Our work provides new information on variations in the wheat kernel metabolome and important molecular resources for improvement of wheat nutritional quality.

Genomic atlas of human cerebrospinal fluid and brain metabolomics provides in‐depth understanding of cellular mechanism for neurodegeneration

AbstractBackgroundBrain metabolism is critical for the pathological mechanism of neurodegeneration. However, most metabolite quantitative trait loci (MQTL) studies have used tissues other than brain or cerebrospinal fluid (CSF) and are not ideal for neurodegeneration. We therefore pursued a large‐scale brain and CSF study to identify metabolites contributing to 27 brain‐related diseases.MethodWe obtained 440 metabolites in 2311 CSF samples and 962 metabolites in 1016 brain samples (WashU, ROSMAP, MAYO). We identified MQTL and performed metabolome‐wide association study (MWAS) to identify metabolites affecting diseases. Mendelian randomization (MR) and colocalization was performed for causality and shared genetic regulations.ResultWe identified 192 associations for 144 CSFmetabolites at 102 loci. Of those, 122 associations and 24 loci were novel, indicating tissue specificity. The brain study found 35 associations at 27 loci. Through MWAS, we identified 57 metabolites associated with 17 traits, in which 21 colocalized. Of the eight metabolites associated with AD, succinylcarnitine and adenine were causal. Succinylcarnitine was associated with AD risk gene LACTB, which product regulates lipid metabolism in mitochondrial, where succinylcarnitine generates energy. Both succinylcarnitine and LACTB have been implicated in obesity, a risk factor for AD. For Parkinson disease (PD), low galactosylglycerol level was causal, with signal colocalized at GALC with PD. The knockout of GALC gene prevented alpha‐synuclein accumulation in PD mice model, indicating that galactosylceramidase contributed to the development of PD. For cognitive performance, high levels of three metabolites (6‐oxopiperidine‐2‐carboxylate, 3‐hydroxyisobutyrate and argininosuccinate) were causal for low cognition, with additional support from colocalization and literature evidences. Pyridoxine‐dependent epilepsy patients, often with impaired cognition, had increased level of 6‐oxopiperidine‐2‐carboxylate. AD patients who underwent medium chain triglycerides treatment and improved cognition had changed level of beta‐hydroxybutyrate, related to 3‐hydroxyisobutyrate. Moreover, argininosuccinate lyase deficiency patients often develop severe cognitive impairment and seizures.ConclusionOur large‐scale CSF and brain MQTL study identified tissue‐specific MQTLs and multiple metabolites contributing to neurodegenerative disorders. Our results expand the knowledge on neurodegeneration, providing insights to neurodegeneration etiology, including AD, PD, and cognitive function.

Regulation of kynurenine metabolism by blood donor genetics and biology impacts red cell hemolysis in vitro and in vivo

AbstractIn the field of transfusion medicine, the clinical relevance of the metabolic markers of the red blood cell (RBC) storage lesion is incompletely understood. Here, we performed metabolomics of RBC units from 643 donors enrolled in the Recipient Epidemiology and Donor Evaluation Study, REDS RBC Omics. These units were tested on storage days 10, 23, and 42 for a total of 1929 samples and also characterized for end-of-storage hemolytic propensity after oxidative and osmotic insults. Our results indicate that the metabolic markers of the storage lesion poorly correlated with hemolytic propensity. In contrast, kynurenine was not affected by storage duration and was identified as the top predictor of osmotic fragility. RBC kynurenine levels were affected by donor age and body mass index and were reproducible within the same donor across multiple donations from 2 to 12 months apart. To delve into the genetic underpinnings of kynurenine levels in stored RBCs, we thus tested kynurenine levels in stored RBCs on day 42 from 13 091 donors from the REDS RBC Omics study, a population that was also genotyped for 879 000 single nucleotide polymorphisms. Through a metabolite quantitative trait loci analysis, we identified polymorphisms in SLC7A5, ATXN2, and a series of rate-limiting enzymes (eg, kynurenine monooxygenase, indoleamine 2,3-dioxygenase, and tryptophan dioxygenase) in the kynurenine pathway as critical factors affecting RBC kynurenine levels. By interrogating a donor-recipient linkage vein-to-vein database, we then report that SLC7A5 polymorphisms are also associated with changes in hemoglobin and bilirubin levels, suggestive of in vivo hemolysis in 4470 individuals who were critically ill and receiving single-unit transfusions.

Association between Neuroligin-1 polymorphism and plasma glutamine levels in individuals with autism spectrum disorder

Unravelling the relationships between candidate genes and autism spectrum disorder (ASD) phenotypes remains an outstanding challenge. Endophenotypes, defined as inheritable, measurable quantitative traits, might provide intermediary links between genetic risk factors and multifaceted ASD phenotypes. In this study, we sought to determine whether plasma metabolite levels could serve as endophenotypes in individuals with ASD and their family members. We employed an untargeted, high-resolution metabolomics platform to analyse 14,342 features across 1099 plasma samples. These samples were collected from probands and their family members participating in the Autism Genetic Resource Exchange (AGRE) (N=658), compared with neurotypical individuals enrolled in the PrecisionLink Health Discovery (PLHD) program at Boston Children's Hospital (N=441). We conducted a metabolite quantitative trait loci (mQTL) analysis using whole-genome genotyping data from each cohort in AGRE and PLHD, aiming to prioritize significant mQTL and metabolite pairs that were exclusively observed in AGRE. Within the AGRE group, we identified 54 significant associations between genotypes and metabolite levels (P<5.27×10-11), 44 of which were not observed in the PLHD group. Plasma glutamine levels were found to be associated with variants in the NLGN1 gene, a gene that encodes post-synaptic cell-adhesion molecules in excitatory neurons. This association was not detected in the PLHD group. Notably, a significant negative correlation between plasma glutamine and glutamate levels was observed in the AGRE group, but not in the PLHD group. Furthermore, plasma glutamine levels showed a negative correlation with the severity of restrictive and repetitive behaviours (RRB) in ASD, although no direct association was observed between RRB severity and the NLGN1 genotype. Our findings suggest that plasma glutamine levels could potentially serve as an endophenotype, thus establishing a link between the genetic risk associated with NLGN1 and the severity of RRB in ASD. This identified association could facilitate the development of novel therapeutic targets, assist in selecting specific cohorts for clinical trials, and provide insights into target symptoms for future ASD treatment strategies. This work was supported by the National Institute of Health (grant numbers: R01MH107205, U01TR002623, R24OD024622, OT2OD032720, and R01NS129188) and the PrecisionLink Biobank for Health Discovery at Boston Children's Hospital.

Characterization of novel loci controlling seed oil content in Brassica napus by marker metabolite-based multi-omics analysis

BackgroundSeed oil content is an important agronomic trait of Brassica napus (B. napus), and metabolites are considered as the bridge between genotype and phenotype for physical traits.ResultsUsing a widely targeted metabolomics analysis in a natural population of 388 B. napus inbred lines, we quantify 2172 metabolites in mature seeds by liquid chromatography mass spectrometry, in which 131 marker metabolites are identified to be correlated with seed oil content. These metabolites are then selected for further metabolite genome-wide association study and metabolite transcriptome-wide association study. Combined with weighted correlation network analysis, we construct a triple relationship network, which includes 21,000 edges and 4384 nodes among metabolites, metabolite quantitative trait loci, genes, and co-expression modules. We validate the function of BnaA03.TT4, BnaC02.TT4, and BnaC05.UK, three candidate genes predicted by multi-omics analysis, which show significant impacts on seed oil content through regulating flavonoid metabolism in B. napus.ConclusionsThis study demonstrates the advantage of utilizing marker metabolites integrated with multi-omics analysis to dissect the genetic basis of agronomic traits in crops.

Identification of genetic regulators of human metabolites in CSF

AbstractBackgroundBrain metabolism perturbation in dementia is not well understood. Metabolite levels are quantitative traits that have been linked to genetic loci. Existing metabolite quantitative trait loci (MQTL) were found mostly using tissues other than cerebrospinal fluid (CSF) and brain, which being less than ideal for studying neurodegenerative diseases. The first CSF MQTL study was published last year, but with limited power. Therefore, we hope to expand our knowledge on MQTL in the central nervous system to better understand neurological disorders.MethodWe performed metabolome‐wide genome‐wide association study (M‐GWAS). More than 400 metabolites passed QC in CSF from 2329 participants recruited from five cohorts (CASTLE project). We performed a three‐stage study design: discovery (1224 CASTLE participants), replication (meta‐analysis of results from 1087 CASTLE participants and 291 CSF participants (Panyard, D.J. et. al 2021)) and meta‐analyses. Linear regression model includes age, sex, genetics principal components, genotyping array, and cohort information as covariates. We defined an association region to be 1 Mb interval centered on the index SNP (lowest study‐wide P value) by performing clumping of signals using plink1.9. We merged association regions if they share overlapping chromosome location to identify genetic locus associated with metabolite level traits. Future plan includes association study including rare variants, functional annotation of each locus, conditional analysis to identify independent signals, replication of identified signals, M‐GWAS for Brain metabolomics datasets (1169 samples in total), comparing features of identified associations amongst multi‐tissue (CSF, brain and blood) using mashr in R. In addition, colocalization and Mendelian randomization analysis will be performed to discover metabolites contributing to neurological disorders.ResultThe preliminary CSF M‐GWAS study jointing five cohorts identified 165 associations in 125 metabolites from 90 loci. Meta‐analysis on the discovery and replication results will be utilized for the downstream functional annotation, tissue comparison, and disease mechanistic exploration.ConclusionWe present the largest CSF M‐GWAS study so far and showed a substantial number of genetic association findings for metabolites. We not only expect to replicate a number of our findings using studies with blood and CSF tissues, but also expect to discover novel signals that has potential for being unique to CSF.

Comprehensive characterization of putative genetic influences on plasma metabolome in a pediatric cohort.

The human exposome is composed of diverse metabolites and small chemical compounds originated from endogenous and exogenous sources, respectively. Genetic and environmental factors influence metabolite levels, while the extent of genetic contributions across metabolic pathways is not yet known. Untargeted profiling of human metabolome using high-resolution mass spectrometry (HRMS) combined with genome-wide genotyping allows comprehensive identification of genetically influenced metabolites. As such previous studies of adults discovered and replicated genotype-metabotype associations. However, these associations have not been characterized in children. We conducted the largest genome by metabolome-wide association study to date of children (N = 441) using 619,688 common genetic variants and 14,342 features measured by HRMS. Narrow-sense heritability (h2) estimates of plasma metabolite concentrations using genomic relatedness matrix restricted maximum likelihood (GREML) method showed a bimodal distribution with high h2 (> 0.8) for 15.9% of features and low h2 (< 0.2) for most of features (62.0%). The features with high h2 were enriched for amino acid and nucleic acid metabolism, while carbohydrate and lipid concentrations showed low h2. For each feature, a metabolite quantitative trait loci (mQTL) analysis was performed to identify genetic variants that were potentially associated with plasma levels. Fifty-four associations among 29 features and 43 genetic variants were identified at a genome-wide significance threshold p < 3.5 × 10-12 (= 5 × 10-8/14,342 features). Previously reported associations such as UGT1A1 and bilirubin; PYROXD2 and methyl lysine; and ACADS and butyrylcarnitine were successfully replicated in our pediatric cohort. We found potential candidates for novel associations including CSMD1 and a monostearyl alcohol triglyceride (m/z 781.7483, retention time (RT) 89.3s); CALN1 and Tridecanol (m/z 283.2741, RT 27.6). A gene-level enrichment analysis using MAGMA revealed highly interconnected modules for dADP biosynthesis, sterol synthesis, and long-chain fatty acid transport in the gene-feature network. Comprehensive profiling of plasma metabolome across age groups combined with genome-wide genotyping revealed a wide range of genetic influence on diverse chemical species and metabolic pathways. The developmental trajectory of a biological system is shaped by gene-environment interaction especially in early life. Therefore, continuous efforts on generating metabolomics data in diverse human tissue types across age groups are required to understand gene-environment interaction toward healthy aging trajectories.

Metabolite QTL analysis of ROSMAP and ADNI prioritized sequencing data identifies C14:2 genetic locus on Chr 2

AbstractBackgroundPeripheral metabolic disturbance is a known component of Alzheimer’s Disease (AD), however predictive genetic markers, as well as the associated pathways, remain to be determined. Two AD studies, ROSMAP and ADNI, have generated Whole Genome Sequencing (WGS) and ante‐mortem serum metabolic biomarker samples. A previous study utilizing ROSMAP data has identified the metabolite tetradecadienylcarnitine, C14:2, as protective against incident AD in both brain and serum data (Brain and blood metabolome for Alzheimer’s dementia: Findings from a targeted metabolomics analysis; Huo et. al).MethodAfter standard QC procedure, WGS data from ROSMAP participants was prioritized to include only variants within +/‐ 1000 bp of 584 AD prioritized genes identified on the AD Knowledge Portal’s Agora Platform (https://agora.adknowledgeportal.org/genes/(genes‐router:genes‐list)), resulting in a subset of 401,415 variants. A metabolite quantitative trait locus (mQTL) study was performed (N = 67) investigating associations between these variants of interest and the C14:2 metabolite measurements collected from the ROSMAP cohort by Biocrates p180 flow injection analysis. A subsequent single‐variant targeted mQTL analysis (N = 464) utilizing ADNI‐GO/2 WGS and C14:2 metabolic data (Biocrates p180 flow injection analysis) was performed. Age, gender and education were used as covariates in both analyses.Result: A novel locus, rs147667251, showed significance to the C14:2 metabolic biomarker in both ROSMAP and ADNI serum mQTL analyses. In ROSMAP data, rs147667251 was identified as most significant to the C14:2 biomarker with a p‐value of 5.507e‐10 (threshold = 1.2e‐7). In a single test mQTL of ADNI C14:2 data, the rs147667251 locus had a significant p‐value of 0.02415 (threshold = 0.05). The locus lies on chromosome two and is an intron variant of the DPP10 gene. The DPP10 gene encodes a type II membrane protein and is known to have altered transcript and protein expression linked to AD.ConclusionIn this AD prioritized mQTL study, a single genetic locus, rs147667251, within the AD nominated gene DPP10 has been identified as associated with serum levels of C14:2. DPP10 is known to be dysregulated in AD and a previous study has identified the C14:2 metabolite as protective against AD, warranting further investigation into the regulatory impact of rs147667251 on DPP10 expression and the C14:2 metabolic pathway.

Genome-wide metabolite quantitative trait loci analysis (mQTL) in red blood cells from volunteer blood donors

The Red Blood Cell (RBC)-Omics study, part of the larger NHLBI-funded Recipient Epidemiology and Donor Evaluation Study (REDS-III), aims to understand the genetic contribution to blood donor RBC characteristics. Previous work identified donor demographic, behavioral, genetic, and metabolic underpinnings to blood donation, storage, and (to a lesser extent) transfusion outcomes, but none have yet linked the genetic and metabolic bodies of work. We performed a Genome-Wide Association (GWA) analysis using RBC-Omics study participants with generated untargeted metabolomics data to identify metabolite quantitative trait loci (mQTL) in RBCs. We performed GWA analyses of 382 metabolites in 243 individuals imputed using the 1000 Genomes Project phase 3 all-ancestry reference panel. Analyses were conducted using ProbABEL and adjusted for sex, age, donation center, number of whole blood donations in the past two years, and first ten principal components of ancestry. Our results identified 423 independent genetic loci associated with 132 metabolites (p < 5x10-8). Potentially novel locus-metabolite associations were identified for the region encoding heme transporter FLVCR1 and choline, and for lysophosphatidylcholine acetyltransferase LPCAT3 and lysophosphatidylserine 16.0, 18.0, 18.1, and 18.2; these associations are supported by published rare disease and mouse studies. We also confirmed previous metabolite GWA results for associations including N(6)-Methyl-L-lysine and protein PYROXD2, and various carnitines and transporter SLC22A16. Association between pyruvate levels and G6PD polymorphisms was validated in an independent cohort and novel murine models of G6PD deficiency (African and Mediterranean variants). We demonstrate that it is possible to perform metabolomics-scale GWA analyses with a modest, trans-ancestry sample size.

Genome-wide scan of Alzheimer disease cohort identifies genetic loci associated with human brain metabolite levels.

Although metabolome-wide association study (MWAS) has been performed in a wide range of tissue types, such as serum, plasma, urine, saliva, and cerebrospinal fluid (CSF), it has not been conducted on any brain tissue. Here we seek to expand our knowledge on the genetic influence of brain metabolism and on the contribution of metabolism abnormality to AD with the metabolomics approach. We aim to identify metabolite quantitative trait loci (metabQTLs) with the largest AD brain cohort available. We performed the first brain MWAS with 460 parietal cortex brains from the Knight-ADRC. 880 metabolites were measured by the powerful non-targeted Metabolon platform (HD4). European individuals were selected, and the analyses were adjusted for age at death, sex, genotype array methods, and genetic components. We identified 31 locus-metabolite associations in 30 metabolites with genome-wide significance. 7 associations were significant after study-wide Bonferroni correction. We are performing functional characterization and replication leveraging datasets from ROSMAP brain cohort, WADRC CSF cohort, and other tissues' cohorts. The strongest locus-metabolite association (p=5.2e-104), found in N6-methyllysine in the discovery phase, was first identified in CSF MWAS (Panyard et al., biorxiv, 2020). The first brain metabolome-wide association study discovered the importance of genetic influence on brain metabolite levels. Following expectation, brain metabQTLs can be replicated in the CSF study, implicating that brain and CSF share genetic features in modulating metabolism.

Integrative Analysis of the Inflammatory Bowel Disease Serum Metabolome Improves Our Understanding of Genetic Etiology and Points to Novel Putative Therapeutic Targets

Polygenic and environmental factors are underlying causes of inflammatory bowel disease (IBD). We hypothesized that integration of the genetic loci controlling a metabolite's abundance, with known IBD genetic susceptibility loci, may help resolve metabolic drivers of IBD. We measured the levels of 1300 metabolites in the serum of 484 patients with ulcerative colitis (UC) and 464 patients with Crohn's disease (CD) and 365 controls. Differential metabolite abundance was determined for disease status, subtype, clinical and endoscopic disease activity, as well as IBD phenotype including disease behavior, location, and extent. To inform on the genetic basis underlying metabolic diversity, we integrated metabolite and genomic data. Genetic colocalization and Mendelian randomization analyses were performed using known IBD risk loci to explore whether any metabolite was causally associated with IBD. We found 173 genetically controlled metabolites (metabolite quantitative trait loci, 9 novel) within 63 non-overlapping loci (7 novel). Furthermore, several metabolites significantly associated with IBD disease status and activity as defined using clinical and endoscopic indexes. This constitutes a resource for biomarker discovery and IBD biology insights. Using this resource, we show that a novel metabolite quantitative trait locus for serum butyrate levels containing ACADS was not supported as causal for IBD; replicate the association of serum omega-6 containing lipids with the fatty acid desaturase 1/2 locus and identify these metabolites as causal for CD through Mendelian randomization; and validate a novel association of serum plasmalogen and TMEM229B, which was predicted as causal for CD. An exploratory analysis combining genetics and unbiased serum metabolome surveys can reveal novel biomarkers of disease activity and potential mediators of pathology in IBD.

Discovering metabolite quantitative trait loci in asthma using an isolated population

Integration of metabolomics with genetics may advance understanding of disease pathogenesis but has been underused in asthma genetic studies. We sought to discover new genetic effects in asthma and to characterize the molecular consequences of asthma genetic risk through integration with the metabolome in a homogeneous population. From fasting serum samples collected on 348 Tangier Island residents, we quantified 2612 compounds using untargeted metabolomics. Genotyping was performed using Illumina's MEGA array imputed to the TOPMed reference panel. To prioritize metabolites for genome-wide association analysis, we performed a metabolome-wide association study with asthma, selecting asthma-associated metabolites with heritability q value less than 0.01 for genome-wide association analysis. We also tested the association between all metabolites and 8451 candidate asthma single nucleotide polymorphisms previously associated with asthma in the UK Biobank. We followed up significant associations by characterizing shared genetic signal for metabolites and asthma using colocalization analysis. For detailed Methods, please see this article's Online Repository at www.jacionline.org. A total of 60 metabolites were associated with asthma (P< .01), including 40 heritable metabolites tested in genome-wide association analysis. We observed a strong association peak for the endocannabinoid linoleoyl ethanolamide on chromosome 6 in VNN1 (P< 2.7×10-9). We found strong evidence (colocalization posterior probability >75%) for a shared causal variant between 3 metabolites and asthma, including the polyamine acisoga and variants in LPP, and derivative leukotriene B4 and intergenic variants in chr10p14. We identified novel metabolite quantitative trait loci with asthma associations. Identification and characterization of these genetically driven metabolites may provide insight into the functional consequences of genetic risk factors for asthma.

Integrating genomics and multiplatform metabolomics enables metabolite quantitative trait loci detection in breeding-relevant apple germplasm.

Apple (Malus × domestica) has commercial and nutritional value, but breeding constraints of tree crops limit varietal improvement. Marker-assisted selection minimises these drawbacks, but breeders lack applications for targeting fruit phytochemicals. To understand genotype-phytochemical associations in apples, we have developed a high-throughput integration strategy for genomic and multiplatform metabolomics data. Here, 124 apple genotypes, including members of three pedigree-connected breeding families alongside diverse cultivars and wild selections, were genotyped and phenotyped. Metabolite genome-wide association studies (mGWAS) were conducted with c.10000 single nucleotide polymorphisms and phenotypic data acquired via LC-MS and 1 H NMR untargeted metabolomics. Putative metabolite quantitative trait loci (mQTL) were then validated via pedigree-based analyses (PBA). Using our developed method, 519, 726 and 177 putative mQTL were detected in LC-MS positive and negative ionisation modes, and NMR, respectively. mQTL were indicated on each chromosome, with hotspots on linkage groups 16 and 17. A chlorogenic acid mQTL was discovered on chromosome 17 via mGWAS and validated with a two-step PBA, enabling discovery of novel candidate gene-metabolite relationships. Complementary data from three metabolomics approaches and dual genomics analyses increased confidence in validity of compound annotation and mQTL detection. Our platform demonstrates the utility of multiomic integration to advance data-driven, phytochemical-based plant breeding.

Genomic basis underlying the metabolome-mediated drought adaptation of maize

BackgroundDrought is a major environmental disaster that causes crop yield loss worldwide. Metabolites are involved in various environmental stress responses of plants. However, the genetic control of metabolomes underlying crop environmental stress adaptation remains elusive.ResultsHere, we perform non-targeted metabolic profiling of leaves for 385 maize natural inbred lines grown under well-watered as well as drought-stressed conditions. A total of 3890 metabolites are identified and 1035 of these are differentially produced between well-watered and drought-stressed conditions, representing effective indicators of maize drought response and tolerance. Genetic dissections reveal the associations between these metabolites and thousands of single-nucleotide polymorphisms (SNPs), which represented 3415 metabolite quantitative trait loci (mQTLs) and 2589 candidate genes. 78.6% of mQTLs (2684/3415) are novel drought-responsive QTLs. The regulatory variants that control the expression of the candidate genes are revealed by expression QTL (eQTL) analysis of the transcriptomes of leaves from 197 maize natural inbred lines. Integrated metabolic and transcriptomic assays identify dozens of environment-specific hub genes and their gene-metabolite regulatory networks. Comprehensive genetic and molecular studies reveal the roles and mechanisms of two hub genes, Bx12 and ZmGLK44, in regulating maize metabolite biosynthesis and drought tolerance.ConclusionOur studies reveal the first population-level metabolomes in crop drought response and uncover the natural variations and genetic control of these metabolomes underlying crop drought adaptation, demonstrating that multi-omics is a powerful strategy to dissect the genetic mechanisms of crop complex traits.

A computationally efficient Bayesian seemingly unrelated regressions model for high-dimensional quantitative trait loci discovery.

Our work is motivated by the search for metabolite quantitative trait loci (QTL) in a cohort of more than 5000 people. There are 158 metabolites measured by NMR spectroscopy in the 31-year follow-up of the Northern Finland Birth Cohort 1966 (NFBC66). These metabolites, as with many multivariate phenotypes produced by high-throughput biomarker technology, exhibit strong correlation structures. Existing approaches for combining such data with genetic variants for multivariate QTL analysis generally ignore phenotypic correlations or make restrictive assumptions about the associations between phenotypes and genetic loci. We present a computationally efficient Bayesian seemingly unrelated regressions model for high-dimensional data, with cell-sparse variable selection and sparse graphical structure for covariance selection. Cell sparsity allows different phenotype responses to be associated with different genetic predictors and the graphical structure is used to represent the conditional dependencies between phenotype variables. To achieve feasible computation of the large model space, we exploit a factorisation of the covariance matrix. Applying the model to the NFBC66 data with 9000 directly genotyped single nucleotide polymorphisms, we are able to simultaneously estimate genotype–phenotype associations and the residual dependence structure among the metabolites. The R package BayesSUR with full documentation is available at https://cran.r-project.org/web/packages/BayesSUR/

Genome-Wide Approach to Identify Quantitative Trait Loci for Drought Tolerance in Tetraploid Potato (Solanum tuberosum L.).

Drought represents a major abiotic stress factor negatively affecting growth, yield and tuber quality of potatoes. Quantitative trait locus (QTL) analyses were performed in cultivated potatoes for drought tolerance index DRYM (deviation of relative starch yield from the experimental median), tuber starch content, tuber starch yield, tuber fresh weight, selected transcripts and metabolites under control and drought stress conditions. Eight genomic regions of major interest for drought tolerance were identified, three representing standalone DRYM QTL. Candidate genes, e.g., from signaling pathways for ethylene, abscisic acid and brassinosteroids, and genes encoding cell wall remodeling enzymes were identified within DRYM QTL. Co-localizations of DRYM QTL and QTL for tuber starch content, tuber starch yield and tuber fresh weight with underlying genes of the carbohydrate metabolism were observed. Overlaps of DRYM QTL with metabolite QTL for ribitol or galactinol may indicate trade-offs between starch and compatible solute biosynthesis. Expression QTL confirmed the drought stress relevance of selected transcripts by overlaps with DRYM QTL. Bulked segregant analyses combined with next-generation sequencing (BSAseq) were used to identify mutations in genes under the DRYM QTL on linkage group 3. Future analyses of identified genes for drought tolerance will give a better insight into drought tolerance in potatoes.

Genomic-Metabolomic Associations Support the Role of LIPC and Glycerophospholipids in Age-Related Macular Degeneration.

Purpose:Large-scale genome-wide association studies (GWAS) have reported important single nucleotide polymorphisms (SNPs) with significant associations with age-related macular degeneration (AMD). However, their role in disease development remains elusive. This study aimed to assess SNP–metabolite associations (i.e., metabolite quantitative trait loci [met-QTL]) and to provide insights into the biological mechanisms of AMD risk SNPs.Design:Cross-sectional multicenter study (Boston, Massachusetts, and Coimbra, Portugal).Participants:Patients with AMD (n = 388) and control participants (n = 98) without any vitreoretinal disease (> 50 years).Methods:Age-related macular degeneration grading was performed using color fundus photographs according to the Age-Related Eye Disease Study classification scheme. Fasting blood samples were collected and evaluated with mass spectrometry for metabolomic profiling and Illumina OmniExpress for SNPs profiling. Analyses of met-QTL of endogenous metabolites were conducted using linear regression models adjusted for age, gender, smoking, 10 metabolite principal components (PCs), and 10 SNP PCs. Additionally, we analyzed the cumulative effect of AMD risk SNPs on plasma metabolites by generating genetic risk scores and assessing their associations with metabolites using linear regression models, accounting for the same covariates. Modeling was performed first for each cohort, and then combined by meta-analysis. Multiple comparisons were accounted for using the false discovery rate (FDR).Main Outcome Measures:Plasma metabolite levels associated with AMD risk SNPs.Results:After quality control, data for 544 plasma metabolites were included. Meta-analysis of data from all individuals (AMD patients and control participants) identified 28 significant met-QTL (β = 0.016−0.083; FDR q-value < 1.14 × 10−2), which corresponded to 5 metabolites and 2 genes: ASPM and LIPC. Polymorphisms in the LIPC gene were associated with phosphatidylethanolamine metabolites, which are glycerophospholipids, and polymorphisms in the ASPM gene with branched-chain amino acids. Similar results were observed when considering only patients with AMD. Genetic risk score–metabolite associations further supported a global impact of AMD risk SNPs on the plasma metabolome.Conclusions:This study demonstrated that genomic–metabolomic associations can provide insights into the biological relevance of AMD risk SNPs. In particular, our results support that the LIPC gene and the glycerophospholipid metabolic pathway may play an important role in AMD, thus offering new potential therapeutic targets for this disease.

Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations

The study of metabolomics and disease has enabled the discovery of new risk factors, diagnostic markers, and drug targets. For neurological and psychiatric phenotypes, the cerebrospinal fluid (CSF) is of particular importance. However, the CSF metabolome is difficult to study on a large scale due to the relative complexity of the procedure needed to collect the fluid. Here, we present a metabolome-wide association study (MWAS), which uses genetic and metabolomic data to impute metabolites into large samples with genome-wide association summary statistics. We conduct a metabolome-wide, genome-wide association analysis with 338 CSF metabolites, identifying 16 genotype-metabolite associations (metabolite quantitative trait loci, or mQTLs). We then build prediction models for all available CSF metabolites and test for associations with 27 neurological and psychiatric phenotypes, identifying 19 significant CSF metabolite-phenotype associations. Our results demonstrate the feasibility of MWAS to study omic data in scarce sample types.