Global ageing poses a substantial economic burden on health and social care costs. Enabling a greater proportion of older people to stay healthy for longer is key to the future sustainability of health, social and economic policy. Frailty and associated decrease in resilience plays a central role in poor health in later life. In this study, we present a population level assessment of the metabolic phenotype associated with frailty. Analysis of serum from 1191 older individuals (aged between 56 and 84 years old) and subsequent longitudinal validation (on 786 subjects) was carried out using liquid and gas chromatography-mass spectrometry metabolomics and stratified across a frailty index designed to quantitatively summarize vulnerability. Through multivariate regression and network modelling and mROC modeling we identified 12 significant metabolites (including three tocotrienols and six carnitines) that differentiate frail and non-frail phenotypes. Our study provides evidence that the dysregulation of carnitine shuttle and vitamin E pathways play a role in the risk of frailty. Risk of age-related chronic disorders and decrease in resilience is associated with ageing. Here the authors analyse the human blood metabolome and identify metabolites associated with frailty. As a consequence, it is estimated that between 2015 and 2050 the global proportion of over 60-year olds will increase from 12 to 22%2, while the number of over-65s is forecast to triple by 20503, even though recent evidence suggests that maximum lifespan could be fixed5.A consequence of ageing is the decline in biological function that will eventually lead to a progressive deterioration of physiological performance, a decline in the ability to respond to stress and an associated increase in vulnerability. Since 1950 life expectancy has been rising at a rate of more than three years per decade, and since the onset of the millennium this has risen to five years per decade6) increasing the risk of exposure to chronic age-related disorders7. So, in 2015 the global healthy life expectancy at birth (HALE) was calculated to be 63.1 years8, which indicates a substantial burden of later life morbidity, even though rises in HALE are a testament to continuing global healthcare advancements including medical diagnosis and treatment of cardiovascular disease9, immunization10, smoking cessation11, healthy diet12 and an increased understanding of social determinants of health13. Such demographic shifts and resulting population ageing are leading public health practitioners and policy makers to actively push for global innovations that can positively shape the health of future elderly populations16. Indeed, healthy ageing in later life has enormous potential to affect society as a whole. Consequently, to mitigate the potential economic and social strains that flow from population ageing brings call for direct government initiatives to develop appropriate public health interventions to reduce the impact of functional decline associated with frailty.While increasing life expectancies are a positive development, all individuals are still currently subject to natural constraints and face decline in health status with age 22. Although these methods have acceptable performance in identification of frailty status, they have some limitations, including subjectivity of individual answers, resource utilisation costs and lack of linkage to underlying biological mechanisms23.Clinically, the effects of frailty are defined as a multimodal syndrome emphasized by a loss of internal reserves  that gives rise to biological vulnerability within an individual. This inherent complexity means that there is no single diagnostic tool available to identify the presence and extent of frailty. However, a myriad of scoring instruments are reported within the literature. These typically assess a number of negative outcomes, or distal phenotypes, and are validated by stratifying the scoring aspects of large patient cohorts such as surgical prognosis25, dysregulation of transcriptional networks and age-dependent decline26, alongside metabolic signatures of biological ageing in young27 and older people28. By contrast, no such large scale, population level comparison of frailty identified using a validated measure versus metabolism has been undertaken. Yet the ability to uncover downstream biochemical relationships by applying metabolomics based approaches hold great potential for the development of public health strategies to reduce the risk of frailty and its adverse consequences.Orthogonal to such procedures, biochemical assessments of frailty in the context of biomarker detection are sought after as they hold the potential to identify biological pathways that contribute to a frailty phenotype, offering opportunities in developing strategies for the identification and management of frailty. A focus for the bio-gerontology community within this area has been the chemistry of life contained within the central dogma of molecular biology. Several large cohort studies have demonstrated correlation between mitochondrial DNA and energy co-factors alongside mortalitywww.micra.manchester.ac.uk/research/fraill/) takes an interdisciplinary approach to examine the causal processes relating to frailty and wellbeing at older ages. Within this work, we performed high-throughput untargeted mass spectrometry-based metabolic profiling coupled with pathway and network analysis on longitudinal serum samples, taken four years apart, from a cohort of well-phenotyped ageing (≥58 years old) subjects from the English Longitudinal Study of Ageing (http://www.elsa-project.ac.uk/). The aim of this approach is to stratify metabolic phenotypes (metabotypes29) over a FI derived from over 60 measured indicators and assess whether associated biochemical networks relate to biological degeneration associated with frailty. From this work we have identified a panel of 12 metabolites associated with the FI and from these, two chemical classes (tocotrienols and carnitines) exhibit significantly modulated under-expression when overlaid across the FI. Subsequent network enrichment analysis and statistical modelling has identified carnitine shuttle and vitamin E metabolism as two modulated pathways that are related to a higher energy metabolic phenotype. The ability of the combined metabolic model to predict frailty has also been confirmed by a cross validated ROC model. In conjunction with these results, Mendelian Randomization analysis has been performed on previously collected GWAS data to determine the causal relationship of frailty to carnitine levels. This indicates a level of significant association between decreased levels of carnitine and frailty.As the complex links between age-related frailty and the underlying life-course, social, psychological, genetic and metabolic processes remain unclear, the fRaill project  and Wave 6 (n = 1753) of the ELSA 6. These items covered a broad range of attributes such as cognitive function, falls and fractures, vision, hearing, chronic diseases and depression. Due to the complexity in index-coding and cut-off point determination, the presence of each item attributed varying predetermined amounts to an individual’s FI score30 which had an overall range of 0.04–0.698 over the full sample cohort. Subsequent plotting of the FI distribution produced a unimodal right skewed distribution of data from both waves , 0.1–0.2 (48.4%), 0.2–0.3 (17.5%), >0.3 (7.9%).In the first stage of our analysis we performed cross-sectional stratification of a continuous FI over mass spectrometry-derived metabolite data within the fRaill project. The aim was to identify underlying chemical determinants of frailty (or resilience) and build an associated metabolic pathway model. Sample members participated in a face-to-face interview, a nurse assessment of physical function, anthropometric measurements and collection of blood samples. A full list of questionnaire characteristics and summary metadata can be found here—ve 4 n = 846 and W31. To investigate this, a robust linear regression (RLR) model was developed to determine the level of correlation between FI and age  is equal to FI = 0.119xAge + 0.004092. Derived from this the SSE (sum of squares of error) = 5.7017 and R squared (coefficient of determination) = 0.3357, where 1 = total correlation and 0 = non-correlation. As a measure of the discrepancy between the observed data and the estimation made by the linear regression model, the R value of 0.34 explains a reasonable proportion of variance in frailty explained by age which is to be expected given the measure over a large age range. But, the sum-of-square-error of estimates (SSE) still indicates there is still a large amount of unexplained variance that is present across all ages. Similar models for each FI category were also calculated  were women and other major characteristics are represented in Fig. 32) to enable the assessment of all data point for all subjects. This completed dataset was subsequently tested by further sensitivity analysis and all biochemistry measures were then re-stratified over the FI as a whole and stratified in to male and female subgroups. Pearson’s correlation scores were calculated for non-standardized biomarker values and only significantly correlated biomarkers were used for standardisation purposes. Individuals were then grouped into four classes based on FI and at the same time, male vs. female stratification was also performed  principal component-discriminant function analysis (PC-DFA) modelling allowed for the stratification of the mass spectrometry metabolite data (x-input) over the FI. This PC-DFA approach was used to cluster the UHPLC-MS and GC-MS datasets and the scores plot . 1846 subjects were used to generate the FI and a combined total of 1191 serum samples were selected for untargeted multiplatform metabolomics analysis based on availability, quality of sample and metadata inclusiveness. Within the complete sample set 57.9%  on the training set only and permutation testing was used to generate null distributions33. This method indicated a strong separation between >0.2 and <0.2 on the FI and a moderate level separation between <0.1 and 0.1–0.2, thus supporting similar clustering within the PC-DFA. This was also supported by the subsequent null-distribution classification rate. Subsequent univariate analysis was carried out to determine the statistical significance of individual metabolites modulated by the frailty metabotype. Non-parametric t-tests cross-validated by false discovery rates were used to assess metabolite significance between samples that lay at <0.2 and >0.2 on the FI. Spearman based correlation was also performed to determine between metabolite association and develop associated clusters as indicated by the heatmap in Supplementary Fig. The development of the combined PC-DFA and Random Under Sampling boosting Classification and Regression Tree analyses (RUSBoost-CART) models were performed to reduce the dimensionality of the multivariate LCMS and GCMS datasets while simultaneously investigating the presence of metabolic differentiation between the four levels of scoring within the frail index Fig. . The RUS34 to predict network activity and identify biochemical pathways modulated by the frailty metabotype. This method has successfully been applied to a diverse range of clinical areas such as liver damage36 and T cell activation37. The general analysis pipeline of using XCMS deconvolution in conjunction with the mummichog pathway method to develop an integrated systems approach has already been documented38. By applying this methodology and mapping m/z clustering differences on to an integrated metabolic network containing data from the UCSD BiGG39, KEGG http://www.genome.jp/kegg/kegg1.html and Edinburgh Human Metabolic Network40 resources, metabolic differences between frail versus non-frail sample classes were identified. The generation of subsequent metabolic activity networks highlighted in Fig. Having established the presence of separation in FI level using the PC-DFA approach, we subsequently used the mummichog based pathway enrichment enrichment41 , with major components from the carnitine shuttle and vitamin E pathways still present  was undertaken to provide biological validation of the significant metabolites identified. A FI was calculated from metadata belonging to the 1753 subjects (238 lower than Wave 4 due to subject attrition) and as before a unimodal right skewed distribution of data was observed , derived from two studies44. We found that Odds Ratio of frailty per 10% decrease in carnitine was 1.53 , providing significant evidence for the causal relationship.To investigate the causal effect of carnitine levels on frailty we conducted Mendelian Randomization analysisTo investigate the effects of model adjustment fully, we performed a global analysis of a range of co-morbidities and biochemistry factors a using multidimensional scaling analysis approach. This tool was used to measure the level of similarity between factors that are closely correlated (or anti-correlated) to the frailty indices. Through this method five different factors identified as being highly correlated to frailty were  but does not account for all variation.•hsCRP—The pathogenic mechanisms of C-reactive protein (CRP) in ageing involves binding to FcγRII and activation of the TGF-β/Smad3 and non-TGF-β/Smad3 signalling pathways. These are directly and indirectly used to induce inflammation and fibrosis thus impairing the ability of a cell to proliferate and ultimately contributing to the process of ageing.Cfib—Plasma fibrinogen levels were noted to increase over the FI and this relationship has been noted in ageing studies looking at both male and female subjects.HbA1c/Fasting-glucose—Within the literature, there is a wealth of evidence that demonstrates HbA1c levels increase as non-diabetic subjects age. Our similar observation of the association of frailty to HbA1C fits well with this theory due to the high level of correlation to frailty to age.45 and mitochondrial DNA47 brought about by environmental stressors. Ensuing dysfunction can be translated back to physiological status and contribute to an ageing phenotype. Indeed, population studies have already examined metabolic baseline levels in human health48 and longevity28, showing that metabolomics combined with symptom, biochemical or demographic data can successfully identify distinct biochemical models that were not previously been associated with lifespan in humans. These studies not only indicate modulation of various metabolic pathways such as those within the TCA cycle28 and lipid biosynthesis50, but also suggest that large sample sizes (n > 600)48 and precise analytical methodologies, such as those performed within the HUSERMET study51, are essential for robust analysis of the data generated. Yet, metabolic studies directly investigating frailty have previously focused primarily on the influence of specific disease states, such as breast cancer52 (n = 79) and sarcopenia53 (n = 139), and have not specifically analysed the broad underlying causal processes relating to the frailty-ageing condition. With the aim of expanding knowledge in this area, our goal was to identify the presence of a potential frailty metabolic phenotype and link it to associated physiological and pathophysiological processes. Using a validated assessment of frailty status in conjunction with standard biochemistry analysis and high-throughput metabolic profiling, we generated a metabolic network that highlights significant areas of metabolism that are associated with the clinically assessed FI. This multi-level approach developed a mROC model that identified 12 metabolites as being highly significant in the differentiation of subjects exhibiting frail and non-frail phenotypes as indicated by their position on the FI. Ultimately, our studies show that global lipid metabolism is changed under the frailty phenotype and down regulation of the carnitine shuttle and vitamin E metabolism show potential in playing a role in modulating cellular energy production. These biochemical observations in turn mirror the reduced state of physical activity observed clinically in frail subjects.At the biochemical level, ageing is a continuous and dynamic remodelling process of metabolism and cell function. This chemical reconditioning is heavily influenced by unrepaired accumulation of DNA mutational damage occurring within nuclear DNA6  levels were also evaluated over the index range, the decline in which was correlated with a higher FI. This correlation is in agreement with previous studies reporting a widely-recognised association between decreasing androgen levels and ageing66.Steadily increasing fibrinogen and white blood cell levels were also noted across the FI Fig. . Fibrinon = 1664)63 in which multiple regression models indicated decreased triglyceride levels predicted to serve as an indicator of longevity in females.All clinical biochemistry data were also analysed controlling for sex. As a result, an interesting observation was noted within the measured triglyceride levels. Upon stratification, male vs. female triglyceride levels act in a divergent manner  (rs12356193) and SLC22A4 (solute carrier family 22 member 4) (rs419291) genes. These SNPs were strongly associated with carnitine levels in a study of human metabolites 43. Although our results do not survive strict correction for multiple testing, they are firmly supported by the literature. A recent study measuring common variants  using healthy ageing as outcome reported the possible involvement of the SLC22A4 gene, represented by multiple variants, including rs41929167.Using the two main pathways identified within pathway analysis, two of the four genetic instruments used in the Mendelian Randomization analysis showed evidence for the causal effect of carnitine levels in frailty. Our instrument SNPs represent the 68. However, they are also noted for their ability to scavenge reactive nitrogen species, inhibit cyclooxygenase- and 5-lipoxygenase-catalyzed eicosanoids, and suppress pro-inflammatory signalling, such as NF-κB69. This reduction of free radical-mediated oxidative damage alongside general inflammatory suppression is vital to the maintenance of a healthy lifestyle over time. Breakdown of the endogenous antioxidant system can lead to the accumulation of oxidative damage from lipids that has been linked to ageing, cancer and many other co-morbidities70. The ability of the carnitine shuttle to generate acetyl-CoA is vital for the successful generation of FADH2 and the regeneration of ATP at the end of the electron transport chain. Breakdown of this mechanism is terminal to the cell. We found that a decrease in the levels of several carnitines at higher levels of frailty could be potential indications that general cell-based lipid metabolism is deteriorating, but it is essential that further experimentation needs to be performed to confirm and validate this hypothesis. The importance of the kynurenine pathway provides a conduit for the consumption of over 99% of ingested tryptophan that is unused in protein synthesis72. With an upregulation of tryptophan noted within the frail metabotype, and with age-related sarcopenia known to be an underlying phenotype within frailty, this observation suggests that muscle protein breakdown is a potential contributor to frailty metabolic output. Further along the kynurenine pathway, a bottleneck in the biogenesis of the vital energy co-factor NAD and its associated dysregulation has also been associated to mitochondrial disturbances73, activation in times of stress and immune activation74 alongside links to neurodegenerative diseases75. Conversely, the tryptophan kynurenine pathway is also the starting point for the biosynthesis of two related neurotransmitters; serotonin and melatonin. Previous work has indicated that an over activation of this pathway can lead to activation of the immune system and downstream accumulation of potentially neurotoxic intermediates such as quinolinic acid72 and kynurenic acid76. These metabolites are currently considered to be involved in some way in Alzheimer’s disease, Parkinson’s disease, Huntington’s disease and amyotrophic lateral sclerosis and future works in frailty metabolism should consider them as interesting mechanistic targeted76.Vitamin E analogues, in this case detected tocotrienols, are well-documented due to their lipoperoxyl radical-scavenging abilities in the termination of lipid peroxidation via proton transfer on to lipid free radicalsThis work exemplifies the high suitability for combined metabolic and pathway analysis to explore and uncover significantly modulated biological pathways within biogerontology. The longitudinal nature of the study, alongside the unselected aspect of the sample cohort are strengths that increases the external validity of the findings. However, several limitations also exist, and these should be considered. While providing results that are consistent with data from previous experimental literature, our findings should be considered hypothesis generating in nature.This fact, tied to the restricted geography of the cohort , requires that further validation from a range of independent cohorts is essential to test the conserved nature of the results.78 and humans living extremely long lives80 and these observations need to be related to RNA, protein and metabolite expression. Candidate gene-association studies on data from the same wave of ELSA have indicated genetic changes effecting lipoprotein receptor-related protein 1 (LRP1) gene on chromosome 1281. This multi ligand receptor has previously been reported to be involved in lipid homeostasis including cholesterol transport; thus, supporting our theory of global lipid imbalance in frailty.Also, to understand fully the complex biological processes that are dysregulated as a component of frailty, a comprehensive systems-based approach is needed to model all dimensions of the process. Further work is needed link metabolic profiles to genotypic expression. Several genetic mutations and markers have already been identified in model organismsThere are also several limitations that need to be considered within the Mendelian Randomization work. Firstly, as frailty is a complex condition and as such is likely to involve multiple genetic variants. The genetic variants typically explain only a small fraction of the total variance in traits; therefore, MR studies require very large sample sizes for sufficient statistical power. Although we chose the two-sample approach to achieve greater power, our sample size with 1500 cases and 3500 controls may not be powerful enough.82. The MR-Egger regression method provides valid causal estimates in the presence of some violations of the MR assumptions. However, as this method requires more than two genetic variants assigned to the same exposure, we could not test for this and assumed no pleiotropic effects. Also, in order to obtain more conclusive evidence on the effect of carnitine levels on frailty, studies with sufficiently large sample size are required. While our results should be interpreted with caution, this is an important exercise towards identifying causal relationships.Secondly, the instruments we employed may be considered weak, as indicated by the large confidence intervals of the causal estimates. Finally, for individual polymorphisms the variance explained is usually <1%; therefore, it is advisable to combine multiple polymorphisms into a single allele score to maximize the explanatory power of the instrument. However, due to lack of reported significant SNP-metabolite associations, the available number of genetic instruments was a single or maximum two SNPs for the exposures. The lack of multiple genetic instruments also prevented us from carrying out a pleiotropy test. One of the assumptions of MR analysis is that there is no horizontal pleiotropy, i.e., when a genetic variant affecting multiple traits via separate pathways21, is multifactorial and subsequently recognisable by a distinct biochemical phenotype. Our results, primarily imply that a deterioration in lipid metabolism is present within those who clinically present as frail: The downstream set of metabolic observations detected within this study (primarily linked to energy dysregulation) are directly linked with the primary clinical description for frailty: a reduction in physiological reserve. Metabolic frailty measurement has the potential to contribute greatly to the standardisation of frailty assessment. In addition, the application of metabolomics in combination with other -omics based technologies (such as we have with Mendelian Randomization) offers the potential for a greater understanding of the biologic basis and complexity of frailty. Knowledge of frailty risk factors and biomarkers offers the scope to yield effective early stage interventions that can be incorporated into standard of care practices and ultimately contribute to healthy ageing.In summary, our work reveals that the presence of frailty, with an associated increased risk of negative health outcomes in later life, is not only just identifiable through symptomatic presentation but, as predicted by Fried and colleagues83. Body weight was measured using Tanita electronic scales without shoes and in light clothing, and height was measured using a Stadiometer with the Frankfort plane in the horizontal position. Body mass index (BMI) was calculated by the standard equation—weight (kilograms)/height (metres) squared. Detailed information on biochemical blood analysis, the internal quality control, and the external quality assessment for the laboratory are summarised in the Supplementary Information.The English Longitudinal Study of Ageing (ELSA) is a continuing cohort study that contains a nationally representative sample of men and women born on or before February 1952 living within England. Data collected at Wave 4 (2008–09) were used as the data source and serum sample source for this study. Data collected at Wave 6 (2012–13) were used for the longitudinal validation of the model (786 samples). This study was performed in compliance with all relevant ethical regulations and guidelines for work with human participants. Participants gave informed written consent to participate in the study and ethical approval was obtained from the London Multi-Centre Research Ethics Committee. Clinical Measurements: Nurses collected anthropometric data , blood pressure (BP), and non-fasting blood samples using standard protocols developed within the Health Survey for England83 and stored at −80 °C prior to analysis. Ultra-High Performance Liquid Chromatography Mass Spectrometry (UHPLC-MS) and gas chromatography mass spectrometry (GC-MS) were performed in tandem on each sample using the Dunn51 and Begley84 protocols with some minor alterations and is briefly summarised as follows:Untargeted metabolite profiling was performed on serum samples that were collected from participants using standard serum collection techniquesg) yielded a metabolite rich supernatant that was split in to two aliquots and lyophilised for 12 h to yield a metabolite pellet that was stored at −80 °C prior to analysis. A pooled QC standard was also generated by combining 30 µL aliquots of each sample in to a pooled vial with subsequent 330 µL portions being extracted identical to each sample.Metabolites were extracted from the 1191 serum samples from Waves 4 and 100 samples from Wave 6 by individually adding 900 µL of an organic solvent mixture of 80% methanol/15 water/5% acetonitrile to 330 µL of serum. Subsequent vortexing and centrifugation  held at a constant temperature of 50 °C while a solvent flow rate of 400 µL min−1 was used to drive the chromatographic separation.Processed metabolite pellets were defrosted at 4 °C and subsequently reconstituted in 100 µL of mobile phase A. UHPLC-MS analysis was performed using an Accela UHPLC auto sampler system coupled to an electrospray LTQ-Orbitrap XL hybrid mass spectrometer . Analysis was carried out in positive ESI mode while samples in each run were completely randomised to negate for any bias. A gradient type UHPLC method was used during each run with 95% water/5% methanol/0.1% formic acid as mobile phase A and 95% water/5% methanol/0.1% formic acid as mobile phase B. 5 µL of the extract was injected onto a Hypersil GOLD UHPLC C−1), the tetrapeptide MRFA (1 µg ml−1) and Ultramark 1621 (0.001%) in an aqueous solution of acetonitrile (50%), methanol (25%) and acetic acid (1%). Acquisition settings for initial profiling were carried out at 30,000 resolution in centroid and ran at 1 µ-scan per 400 ms in the 100–1000 m/z range with source gases set at sheath gas = 40 arbitrary units, aux gas = 0 arbitrary units, sweep gas = 5 arbitrary units. The ESI source voltage was set to 3.5 V, and capillary ion transfer tube temperature set at 275 °C.Mass calibration was carried out in accordance with the manufacturer’s guidelines using caffeine  was used as the operating system for the Thermo LTQ-Orbitrap XL MS system. Data processing was initiated by the conversion of the standard UHPLC raw files in to the NetCDF format via the software conversion tool within Xcaliber.www.rstudio.com) using the XCMS algorithm (http://masspec.scripps.edu/xcms/xcms.php). The output yielded a data matrix of mass spectral features with related accurate m/z and retention time pairs. Data from the internally pooled QC samples were then used to align for instrument drift and quality control . The data matrix was also signal corrected to remove peaks that crossed the 20% RSD threshold within QC samples across the analytical run.Peak picking was carried out in R-Studio . The output yielded a data matrix similar to the retention time and quant mass values contained within an internal GC standard library containing over 1600 pure reference compounds run under identical conditions.GCMS Analysis was carried out on a Leco Pegasus 3 Time-of-Flight mass spectrometer coupled to an Agilent 6890 GC oven and Gerstel-MPS autosampler. Derivatization and instrument conditions were identical to those used by the Begley protocol41. Where available, pure reference standards were purchased  and used to confirm the highest level of metabolite identification—Level 1. Where no standard was available, matching of measured MS/MS spectra against those from within the METLIN metabolite database (http://metlin.scripps.edu/) was performed to give a Level 2 annotation confirmed by appropriate secondary ion m/z values. All scoring is available in Supplementary Table All metabolites identified as significant within the analysis were assessed and scored according to rules set out by the Chemical Analysis Working Group of the Metabolite Standards Initiativen analysis was performed on chemical standards using a LTQ-Orbitrap XL hybrid mass spectrometer . Precursor ion full scan was performed followed by an additional scan where the ion of interest for trapped within the linear ion trap for 1000 ms and subsequently subjected to CID of 50 au, following which the fragment ions were detected. A minimum of three scans were recorded for both precursor and product ions. A combined spectrum of both FTMS and ion-trap data was used to generate product ion lists and intensity. The same tuning method, injection volume, CID and activation energy were applied to QC and standard sample to standardise the comparison. For all metabolites analysed, retention time was matched within 20 s or below  using groups determined by FI value. In this process, each data set was split in to training/tests sets and resampled85. Sex stratified PC-DFA plots are also documented in Supplementary Fig. t-tests, cross validation, heat-map correlation curves and ROC curves were performed using MetaboAnalyst 3.086http://www.metaboanalyst.ca/. Due to the unbalanced nature of the sample classes (non-frail vs. frail) a by Monte-Carlo cross validation (MCCV) was use to balance the groups. In each MCCV, two thirds (2/3) of the samples were used to evaluate the feature importance. The top 12 (Wave 4) and top 11 (Wave 6) important features were then used to build classification models which were validated on the 1/3 the samples that were left out. The procedure was repeated 500 times to calculate the performance and confidence interval of each model. Classification and feature ranking were performed using aPLS-DA algorithms using seven latent variables as input to determine the final ROC curves.PC-DFA, RUSBoost-CART and robust spline alignment analysis were carried out using MATLAB 2012a . Prior to chemometric analysis data matrices were log34 was used offline in Python (Version 3.5.2) to predict network activity from pre-processed UHPLC-MS metabolomics data. [M + H]+ was selected as the force primary ion (z) alongside an evidence cut-off score of 3 to include a metabolite within an activity network (e). The full metabolite data set was used as an input and 554 extracted features were determined as significant (p < 0.0001) from the associated t-test yielding 263 potential metabolites. From this, 22 network modules were generated using an activity network of 23 annotated and statistically significant metabolites. Output files were visualised in Cytoscape (Version 3.4.0)  pathway analysis87 were the same.A text-mining approach using the keyword ‘carnitine’ yielded four possible exposures and five genetic instruments (single nucleotide polymorphisms—SNPs). Instruments were assigned into the same exposure if the reported direction of effect and the study43.Exposure 1: Blood metabolite levels (unit increase) (carnitine), SNPs: rs12356193, rs41929143.Exposure 2: Blood metabolite levels (unit decrease) (carnitine), SNP: rs146678844.Exposure 3: Acylcarnitine levels (unit increase) (Carnitine), SNP: rs117160687.Exposure 4: Metabolic traits (unit decrease) (carnitine), SNP: rs7094971r2 = 0.87). For Exposure 2 (rs1466788) and Exposure 3 (rs1171606) the direction of association for the outcome and the exposure was the same, against the expectations. MR analysis results of causal estimates are summarised in Supplementary Table Rs7094971 (Exposure 4) was excluded from further analyses, as it was in high linkage disequilibrium with rs12356193 (Further information on research design is available in the Supplementary InformationPeer Review FileReporting SummarySource Data


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. Genome-wide association studies of metabolites have revealed hundreds of genetic associations using univariate analyses. Here, the authors use a multivariate approach to perform association analyses for 158 serum metabolites, followed by fine mapping and GxE interaction tests with statin use and age. Because metabolites affect or are affected by a diverse set of biological processes, lifestyle and environmental exposures, and disease states2, they are routinely used biomarkers3. Thanks to the recent technological advances, diverse components of the metabolome are being measured in large human cohorts, offering new opportunities to improve our understanding of the molecular mechanisms underlying metabolism and corresponding human traits and diseases4. For example, previous work has highlighted the role of metabolites in diseases such as Type 2 Diabetes6, cardiovascular, and heart diseases9, and obesity10.The human metabolome includes over 100,000 small molecules, ranging from peptides and lipids, to drugs and pollutants11, provided estimation of the heritability of multiple metabolites12, and highlighted the biological and clinical relevance of some of these findings13. All of these studies relied on standard univariate analyses and assessed marginal additive effects of genetic variants only. Despite a number of advantages, univariate approaches have limitations and the implementation of new integrative approaches are needed to further reconstruct the complex genetic network of gene–metabolite associations and its dependence on the context of each individual.A number of genome-wide association studies (GWAS) of metabolites have also been performed. These studies identified hundreds of genetic variant–metabolite associations14 cohort. To improve the detection of metabolite-associated variants and infer complex mechanisms underlying the genetics of metabolites, we perform a series of analyses using recently developed multivariate approaches as well as existing methods. First, unlike previous metabolite GWAS20, we leverage the high correlation structure between metabolites to increase the power of association tests via the Covariates for Multi-phenotype Studies (CMS) method21. Second, we produce an integrated view of the genetic–metabolite network, highlighting genes with strong pleiotropic effects while showing how integrated analysis of such genes can be leveraged to identify likely causal variants. Third, we examine variants with effects dependent on statin treatment and age, two established modifiers of metabolite profiles22 and disease risk, using bivariate heritability and interaction analyses. Overall, our analysis provides a step towards richer understanding of genetic regulation of metabolites as a function of environmental factors.Here, we explore the genetics of 158 serum metabolites measured with nuclear magnetic resonance (NMR), including mostly serum lipids, in 6263 unrelated men from the Finnish Metabolic Syndrome In Men (METSIM)21, a powerful method we recently developed for the analysis of multivariate data sets (Online Methods). For both methods, we tested association between each SNP and each metabolite while adjusting for potential confounding factors, including age and medical treatments . To approximate the number of independent associations identified, we grouped significant SNPs in independent linkage disequilibrium (LD) blocks23, denoted further as regions (Online Methods). We obtained 588 region-metabolite associations involving a total of 54 independent regions , 7 IDL, 21 low-density lipoprotein (LDL) and 28 high-density lipoprotein (HDL)), 9 amino acids, 16 fatty acids, and 35 other molecules  achieved by decreasing the overall residual variance , confirming the absence of bias owing to the adjustment for covariatesCMS Fig. . Second,CMS Fig. . As expeNeff = 8,768). However, for the 588 significant associations, CMS leads to an average Neff of 14,000, which corresponds to a 2.2-fold increase as compared with the baseline sample size of 6263 individuals. The maximum gain in power we observed was equivalent to the analysis of 96,108 individuals . Interestingly, that variant, which was only border-line nominally significant in the standard marginal model (P = 0.058), was reported to be associated with HDL, LDL, TG, and TC  in the Willer et al.28 study that included 188,577 individuals , the only independent study with available summary statistics for the SNP-metabolites pair we report. Out of the 228 region-metabolite pairs, 88 were available for in silico replication (39%). Among those, 60 (68.2%) were replicated at a nominal threshold of 5%. Non-replication of the remaining 28 associations is likely explained by limited power in the replication dataset. Indeed, we observed a strong correlation (ρ = 0.63) between the effect sizes of top SNP per region derived from METSIM and the strength of signal for the same variant in the Kettunen et al.11 study  with previous GWAS on coronary heart disease (CHD)16, body mass index (BMI)17, and type 2 diabetes (T2D)18, we observed substantial enrichment for nominally significant association. Given a false discovery rate (FDR) at 10%, we observed 30 significant genes for CHD, 5 for BMI, and 4 for T2D  capture over 75% (N = 457) of all associations .The extensive pleiotropic effects in this regions are illustrated in Fig. LIPC, it required 19 and 12 PCs to explain 99% of the variance of the 75 metabolites, and the 43 lipoproteins, respectively.To better understand the role of these master regulators we performed two additional analyses. First, we appreciate that the observed pleiotropy for these genes is relative, because of the strong correlation across phenotypes. To approximate the number of independent components associated with each gene, we derived the number of principal components (PCs) necessary to explain percentages of the total variance of the corresponding associated metabolites  TRIB1, LPL, GCKR, GALNT2, and APOA5 are mostly associated with VLDL of average size; and (iii) PCSK9, LDLR, CELSR2, and APOC1 are associated primarily with large and lipoprotein LDL. Looking at other lipoprotein associated genes, we found that some might fit in the second categories, but a majority appears to have more-targeted effects, being associated with specific types and sizes . Conversely, there were nine top SNPs for the 75 metabolites associated with LIPC. Part of this heterogeneity might be explained by LD in these regions, but also by the presence of multiple causal variants affecting different metabolites. To investigate this possibility, we applied the FINEMAP33 algorithm using the example of the latter LIPC region after performing additional genotype imputation in that region  is composed only of SNP rs10468017, which was previously found associated with age-related macular degeneration (AMD)38 and with LIPC expression in human liver tissue39. The second site (B) includes four SNPs in complete LD that were previously associated with hypertension40 and AMD42. Among the four SNPs, rs2070895 is the strongest candidate in our data with a potential association path through a regulation by USF1, a gene with a record of association with lipids46. Finally, the last site (C) included two SNPs, among which rs113298164 clearly harbored the highest number of relevant bio-features. It is a rare missense mutation, which has been reported to be involved in hepatic lipase deficiency47. Additional details on the functional annotation analysis are provided in the Supplementary Note We cross-referenced top variants of these three sites with GWAS of common human diseasesDeciphering the posterior probability across all SNP-metabolite pair would be challenging because of the dimensionality of the fine-mapping results. However, some global patterns were observed . Based on the q value distribution48, there were 35 significant interactions at a 10% FDR, showing that at least some of the identified individual SNP-metabolite effects depends on statin use status. Most of these interactions were observed for APOC1 and TRIM1 genes, whereas other genes  show interaction with a single metabolite. We also checked statin interaction in follow-up data (see Online methods), and found limited interaction values, except for APOC1 region, in which 90% of interaction signals found in baseline data were replicated at the 5% significance threshold.An important component of the METSIM cohort is the collection of statin use among participants. To examine changes in genetic regulation of metabolites when taking statins, we performed an interaction test between SNPs and statin for each of the 588 region-metabolite associations, including the 457 associations with the 13 regulator genes. Although no interaction test passed a Bonferroni correction for multiple testing , TRIB1 , and LDLR , and nominal significance for CETP . Interestingly, previous work showed association between APOC1 and statin-mediated lipid response49, and statin was also shown to be associated with an upregulation of the expression of LDLR50. Overall, our analysis suggests that the effect of statin on metabolites might be modified by only a few core genes, and that these interactions do not only impact LDL, but also a range of other lipids.Several of the 13 identified master metabolic regulator genes showed enrichment for negative interaction effects Fig. . To acco−8p value threshold with either STD or CMS . The four new signals were observed for S_HDL_TG, VLDL_C, LA_FA and Faw6_FA. We also performed age interaction test on the linear regression between p values was significant.Another unique aspect of the METSIM cohort is a second measurement of the same metabolites, using the same technology, ~ 5 years after the baseline (Online methods) for 3351 unrelated individuals. We used these data to screen for genetic variants associated with an intra-individual change in metabolites level across time. We applied the same strategy as for our primary analysis but using the difference between follow-up and baseline data divided by age difference as outcome (mentclasspt{minimaAPOA5 (P = 4.6 × 10−6) and LDLR (P = 3.0 × 10−5). The three genes unaffected were LIPC, LPL, PCSK9, suggesting that the effect of these genes remain persistent with aging, although the relative importance of LIPC across some associated metabolites might be affected. For example, LIPC showed strong positive association with P = 1.6 × 10−7) and a decrease in Triglycerides in medium HDL (P = 2.4 × 10−6).As for the SNP-by-statin interaction analysis, we observed strong concordant effects for the 13 master metabolic regulator genes Fig. , and per52. Figure p value < 2e-9) having higher heritability at follow-up. The sample size was not large enough to estimate genetic correlation with low standard error, but the average estimate of 0.92, and the strong correlation of fixed effect sizes between time points . In the most extreme case, the gain in power was equivalent to the analysis of > 96,000 individuals, thus demonstrating the strong potential for this approach in future studies. We also performed a large-scale genome-wide study exploring genetic effect on change in circulating metabolites between two time points, providing both individual SNPs GWAS and co-heritability results derived using a bivariate linear mixed model applied to individual-level data.LIPC, LDLR, and PCSK9 showed global effects, and were associated with all types and sizes of metabolites. On the other hand, some equally pleiotropic genes , such as APOA5 and GCKR, appear to affect specific types of lipids, and VLDL in particular. Many of the master regulator variants also display different top associated variants depending on the metabolites analyzed. Although some of this variability might be owing to LD in these regions, our fine mapping of the LIPC region showed that the pleiotropic effect of at least some of these genes is likely owing to multiple genetic variants with heterogeneous effects on the associated metabolites.To better understand the role of the master regulators in the etiology of lipoprotein components, which contributed the vast majority of the reported associations, we performed a series of analyses that highlighted a limited number of association patterns. Overall, three genes 54. Moreover, the fine-mapping analysis we performed for LIPC demonstrated how multi-trait associations might help identify likely causal variants. However, we used a naive approach that simply aggregates univariate fine-mapping results. More advanced methodologies integrating all information into a single framework could provide more-accurate posterior probabilities on likely causal variants.More systematic fine mapping of all identified associations is out of the scope of this work as it would require high-density SNPs data including rare variants. Future fine-mapping studies may further improve resolution by leveraging functional annotations55 that statin effects are non-uniform on secondary phenotypes such as fasting glucose across individuals. Although the current study is not sufficiently powered to examine these questions directly, it does identify relevant genes to examine for pharmacogenomic studies of statin in properly designed cohorts. The age interactions are also a unique aspect of this work and raise the possibility that genetics can impact trajectories of metabolism over an individual’s life span. Although speculative, the most intriguing possibility is that genetic variants could mitigate metabolic disease risk by slowing the natural alteration of metabolic profiles across time.Finally, the observation of age and statin interactions further highlights the utility of obtaining extensive clinical phenotype data as well as collecting multiple time points, which are much better powered to identify age effects than cohort studies. The statin interactions suggest that genetic variation may influence the effectiveness and impact of the drug at a given dose, and may underlie our recent observation56 were not available for many of the core metabolic genes, but they could become feasible as larger RNA-seq data sets across more tissues are produced. Third, our metabolite panel included mostly serum lipids, and the presence of a limited number of master regulators only apply to those phenotypes. Whether other sets of related metabolites have a similar genetic architecture, or even share the same regulators, would have to be determined in cohort with a broader range of metabolites. Fourth, we mapped associated variants with the closest gene. However, extended fine-mapping analysis, as mentioned above, as well as gene expression analysis across multiple tissues might demonstrate a mode of action through other genes.There are several other shortcomings of this work. First, the CMS approach is currently limited to the analysis of unrelated individuals, and has a much higher computational cost than standard linear regression. As a result, we had to remove related individuals from our analysis and limit the screening to the 600 K genotyped variants. We are currently developing an improved implementation of CMS addressing these limitations. This will allow for further increases in power thanks to the addition of related individuals, and the analysis of imputed genotypes that might help refine signal at associated regions. Second, TWAS analyses would be of high interest to further explain the link between genetic variants and circulating metabolites. However, TWAS estimates57, demonstrated the importance of developing and implementing novel approaches and analytical strategies that allow for a more extensive use of the data and to move toward a more integrated perspective on multivariate molecular phenotypes.In conclusion, our study shows that the genetics of lipid metabolites is strongly interconnected, harboring core regulator genes with strong pleiotropic effects, and that other metabolite-associated factors might interfere with some of these core genes, decreasing or increasing their overall effect on metabolites. Further characterizing such global effects would be of particularly high interest in the assessment of drug treatments targeting metabolites. Finally, with the increasing amount of genomic data available, our study as well as previous work14 is composed of 10,197 Finnish men from 45 to 73 years old and aimed at investigating non-genetic and genetic factors associated with Type 2 Diabetes and cardiovascular diseases. Participants were recruited and examined between 2005 and 2010 in Kuopio town in Eastern Finland. The study was approved by the ethics committee of the University of Kuopio and Kuopio University Hospital, in accordance with the Helsinki Declaration. All study participants gave written informed consent. For each sample, 228 serum metabolites  measurements were made with NMR at baseline. A follow-up study was conducted ~ 5 years after the baseline study. In all, 6496 participants (64%) were reexamined with the same protocol and metabolites were measured a second time using the same technology. In our study, we considered 158 variables, including 150 raw measurements and eight ratios. Other available variables, which were mostly percentages, were not included in the study. Besides metabolic measurements, several variables were also available including drug treatment and large group of other phenotypes. All samples were genotyped for 665,478 SNPs using the Illumina OmniExpress chip. Genotype data went through standard quality control, filtering individuals with missing rate below 5%, and SNPs with missing rate below 5% or with P < 10−5 in Hardy–Weinberg test.The METSIM cohort59 and it has been widely applied in genetic and epidemiological studies61. This refined targeted metabolomics panel of > 100 serum metabolic phenotypes, includes lipoprotein subclass and lipoprotein lipids, fatty acids, and amino acids assessed by NMR from serum samples. Overall, 14 lipoprotein classes varying in sizes were analyzed including six classes of VLDLs, one class of IDLs, three classes LDLs, and four classes of HDLs. Within each lipoprotein particle, the concentrations of the following lipids were measured: total lipids, phospholipids, total cholesterol, cholesterol ester, free cholesterol, and triglycerides.We used a high-throughput serum NMR platform for metabolic profiling. Details of this platform have been published previouslyrntransform function in R package GenABEL62. Because of potential confounding effect of statins use on metabolites, we excluded all statins users  when analyzing LDL, IDL, Apolipoprotein B and cholesterol. We also excluded fibrates users  when analyzing VLDL, IDL, triglycerides, and chylomicron for similar reason. Finally, we removed all individuals with a genetic relationship coefficient larger than 0.05 and used only unrelated individuals. After filtering, there remained 6263 samples available for analysis. For SNP data, we filtered variants with a minor allele frequency lower than 1%. In all, 609,262 SNPs remained after filtering.In order to remove outliers without reducing sample size, we first applied inverse normal rank-transformation on every analyzed metabolite. This was done using the Y) and each SNP (G), adjusted for established confounding factors (C): age and medical treatments :We used two different models in the analysis. First, we performed an STD between each metabolite  l includes a subset of candidate covariates (see next paragraph), and (iii) G and all G does not display association with G. The last step consists in a linear regression between predictor and outcome, adjusted for the selected covariates (YL):Then, we used the CMS algorithm to select additional covariates for each SNP-metabolite pair tested. Consider a metabolite Yk a pre-filtering of candidate covariates To address some of the limitations of CMS, we also applied for each outcome p values, whether for marginal genetic effect, or interaction effect have been derived using a Wald test, –i.e.,t follows a chi-squared distribution with one degree of freedom.All reported −8 by the number of effective tests accounting for all variants tested and all metabolites. To estimate the number of effective tests, we first did a PC analysis of our 158 metabolites. Then, we calculated the number of PCs that explained 99% of the total variance. We obtained 39 effective tests. The significance threshold was then 1.28 × 10−9.The threshold used to determine significant SNPs was calculated by dividing the standard genome-wide significant threshold of 5 × 1023. These regions are 10 kb to 26 Mb long, with an average size of 1.6 Mb. For each region, we kept the SNP with the best p-value obtained by either STD or CMS. We then used the UCSC database to assign the closest gene to each SNP, with a maximum distance of 100 kb.Because of the great number of signals, we chose to summarize our results by genomic regions, corresponding to approximately independent LD blocks. We sliced the genome in 1703 independent regions based on a recombination map recently described by Berisa et al.f and b are metabolite measurements at follow-up and baseline, respectively. As for baseline data analysis, we used STD and CMS approaches, with covariates pre-selection based on AIC. Confounding factors used for the baseline analysis were also included as covariate in all We used data from baseline and follow-up studies to perform GWAS of the difference between the two time points, divided by the age difference. We called that variable We performed two follow-up interaction analyses for subset of SNP-metabolite associations identified in the GWAS. First, we assessed SNP-by-age interaction effect in both baseline and follow-up analyses for the subset of SNP showing significant effects on p value). The regression was adjusted by confounding factors and included the interaction term z score statistics to form a vector k is the number of metabolites analyzed. We then derived the following multivariate statistics:k metabolites analyzed jointly. Under the null hypothesis of no association between the variant tested and any of the k metabolites, T follows a central chi-square distribution with k degree of freedom.To assess the significance of enrichment for positive or negative effects observed for the identified core regulator genes, we implemented a multivariate test of all associations that accounted for the correlation between metabolites analyzed jointly. For each gene, we selected the top associated variants across all metabolites  and there were minimal differences in variance explained (< 5%). Heritability estimates at the two time points were plotted using circlize R package64, whereas the complete GCTA output, including genetic and environmental variance estimates, genetic and environmental covariances, and LRT p values for genetic correlation are provided in Supplementary Data We first took a set of 3342 individuals corresponding to the intersection between baseline and follow-up data. The baseline and follow-up phenotypes were combined, normalized, and separated into baseline and follow-up series, so the normalized phenotypes at baseline and follow-up were directly comparable . We used GCTA's bivariate REMLFurther information on research design is available in the Supplementary InformationSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6Supplementary Data 7Supplementary Data 8Supplementary Data 9Supplementary Data 10Supplementary Data 11Supplementary Data 12Supplementary Data 13Peer Review FileDescription of Additional Supplementary FilesReporting Summary


In both data sets, our single-CpG-marker results using kernels and linear regression were in good agreement. In the real data, we investigated the introns of the CPT1A gene previously reported as associated with TG levels as separate ROIs, and were able to find hints of an association of cg17058475 and cg00574958 with post-treatment TG levels. In the simulated data, we investigated a total of 10 regions, in which the 5 causal and 5 non-causal markers lie, respectively, with increased methylation variances, yielding plausible results for the 3 window sizes. Overall, this indicates that kernels for CpG markers are feasible. An interaction regression model for the causal SNP with the nearest CpG marker identified an effect for the SNPs with the three greatest heritabilities simulated. The simulation model assumed full SNP effect only for unmethylated regions decreasing to zero in the case of full methylation. Thus, in the context of a clear candidate setting, interaction between epigenetic and genetic data may enhance information, albeit nominally, even with small sample sizes. Relieving the burden of multiple testing, developing kernels further to analyze data from multiple omics jointly is well warranted.In GAW20, we investigated the association of specific genetic regions of interest (ROIs) with log-transformed triglyceride (TG) levels following lipid-lowering medication using epigenetic and genetic markers. The goal was to incorporate kernels for cytosine-phosphate-guanine (CpG) markers and compare the kernels to a purely parametric model. Post-treatment TG levels were investigated for post-methylation data at CpG sites and region-specific SNPs and adjusted for pre-treatment TG levels and age, in independent individuals only (real data:  The human genome is a highly intricate system comprising various genic and gene regulatory elements. Epigenetic intervention turns it into a jungle. High-throughput technologies have been used to profile phenotypes in multiple omics dimensions. In order to dissect complex genomic traits, statistical tools need to handle a multitude of markers both within and across such dimensions. The kernel score test (KST) enables us to test a set of markers for an overall association with a phenotype , such asOur previous research , 4 focusThe GAW20 data were provided by the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study , a longiGAW20 also provided simulated data using a family structure and genotypes identical to those in the original data. The answer sheet we were provided described the simulation model. Post-treatment methylation levels were modeled based on pre-methylation with a higher variation at ten 10 CpG cytosine-phosphate-guanine (CpG) markers than for all others. TGs were influenced by five 5 causal SNPs with decreasing heritability and several polygenes. However, the influence of each of the 5 causal SNPs on log TG change decreased with increasing degree of methylation in that particular ROI. Five of the 10 CpG markers were those close to the causal SNPs. We defined the corresponding region as causal ROI and the other mentioned regions as non-causal ROIs.Since we were focusing only on independent individuals with available phenotypic and genetic/epigenetic information, we only included exactly one member from each pedigree. Hence, we included 150 individuals from the real data and 111 individuals from the simulated data of replicate 84.CPT1A gene defined by intronic boundaries. All the genomic information is in build hg18. After this we began to investigate genetic markers and used the knowledge of the model for effect simulation.We first investigated models with and without kernels with post-treatment methylation data. To define ROIs for the simulated data, we consulted the GAW20 answers for the 5 causal and 5 noncausal CpG markers and associated SNPs to define 10 ROIs with the boundaries lying 0 kilobase pairs (kbp), 15 kbp, and 3 kbp upstream and downstream of each of these 5 + 5 CpG markers. In the real data, we formulated sets of CpG markers in the To investigate the association of specific ROIs with post-lnTGs, we employed linear and semi-parametric kernel regressions, all adjusted for age and pre-lnTG.K. This is calculated as K = ZZt, where Z is a n × mu matrix for n individuals and mu markers of region u. It models a linear effect of the considered markers on the response Y. Let Y =  denote the post-lnTGs. Y is modeled as:X is the matrix for known fixed covariates, including age and pre-lnTG; b is the vector of corresponding regression parameters; and ε denotes the usual residuals. The non-parametric function h(Z) depends on the n × n dimensional kernel matrix K (for more details refer to Schaid [h(Z) equals zero or not. It is computed from maximum likelihood estimates for the parameters of the null model. The p values were calculated using Davies’ exact method [In the KST, we used a linear kernel to transform the available epigenetic (or genetic) information of the n individuals into a similarity matrix Let Y = Y,…,Yn dent method .x1 with its corresponding/nearest CpG marker x2 and their interaction for the 5 causal SNP markers:To investigate CpG markers only, we employed a linear regression that included the marker itself, as well as a kernel regression including a kernel for the ROI. For this kernel, we used three different windows, all of which included the CpG marker itself and windows of sizes ±0 kbp, ±15 kbp, and ± 3 kbp. After investigating CpG markers only, we applied a linear regression including the causal SNP marker p Values are not adjusted for multiple testing. The significance level is set to 5%. All analyses were performed in R.p values for the kernel with the candidate CpG marker and windows of sizes ±15 kbp, ±3 kbp, and ± 0 kbp, and for single-marker regression analysis. Other than ROI-1, the results for the KST with several CpG markers  are comparable. Furthermore, the results for a single CpG marker  are also similar. As to be expected from the simulation, no significant associations were found.We performed region-based KST and linear regression to analyze the association of the post-treatment methylation in the 10 candidate ROIs with post-lnTG adjusted for pre-lnTG and age. Table Subsequently, we performed the KST that incorporated genetic information by employing the causal SNPs nearest to the CpG markers considered above. As expected, this did not yield significant associations (data not shown). Lastly, for the 5 causal ROIs, we employed the regression model defined by eq.  with cauCPT1A gene. Table We analyzed the introns of the CPT1A gene with TGs in the real data by employing KST and the linear regression model. The GOLDN study also reports cg17058475 and cg00574958 are also reported by GOLDN study to be as associated with TGs and cg00574958 as correlated with CPT1A expression [In this analysis, the small sample size that results from using only independent individuals limits our power. Nevertheless, we were still able to detect nominally significant associations for 3 of the 5 causal SNPs from the simulation employing a model of interaction with their nearest CpG marker. We also found hints for association of cg17058475 and cg00574958 in intron 1 of the ZcT in Eq.  + ε . As we wanted to see the behavior of kernels and regression in this general context, we opted to focus on unrelated individuals. Our strategy can be adapted for family data. In GAW19, we used KST on family data by introducing a design matrix—n et al. ). SeveraOur analysis with multi-omics data in a linear regression interaction model in comparison to single omics data in KST and linear regression framework emphasizes that careful integration of multi-omics data might enable researchers to explain a greater proportion of the variance in complex traits, even in small samples. Consequently, it would seem highly warranted to extend kernels to incorporate multiple types of omics data.


R2 for predicting power of association analysis using imputation results. Our results show that (1) GIGI outperforms Merlin; (2) family-based imputation outperforms population-based imputation for rare variants but not for common ones; (3) combining family- and population-based imputation outperforms all imputation approaches for all minor allele frequencies; (4) GIGI-Pick gives the best selection strategy based on the R2 criterion; and (5) R2 is the best measure of imputation accuracy. Our study is the first to extensively evaluate the imputation performance of many available family- and population-based tools on the same family data and provides guidelines for future studies.Genotype imputation is widely used in genome-wide association studies to boost variant density, allowing increased power in association testing. Many studies currently include pedigree data due to increasing interest in rare variants coupled with the availability of appropriate analysis tools. The performance of population-based (subjects are unrelated) imputation methods is well established. However, the performance of family- and population-based imputation methods on family data has been subject to much less scrutiny. Here, we extensively compare several family- and population-based imputation methods on family data of large pedigrees with both European and African ancestry. Our comparison includes many widely used family- and population-based tools and another method, Ped_Pop, which combines family- and population-based imputation results. We also compare four subject selection strategies for full sequencing to serve as the reference panel for imputation: GIGI-Pick, ExomePicks, PRIMUS, and random selection. Moreover, we compare two imputation accuracy metrics: the Imputation Quality Score and Pearson's correlation  Genome-wide association studies (GWAS) have led to the discovery of hundreds of loci associated with complex diseases . Large sTo efficiently impute rare variants, imputation approaches that work well in general pedigrees are needed. To date, only a few methods have been proposed for family-based imputation designs, including Merlin , GIGI C coupled https://bioinformatics.qcri.org/ped_pop) approach . For common variants, these metrics provide similar accuracies, but for rare variants, this is not the case. For instance, the CR yields overestimated accuracy because common alleles are easily imputed , the imputation quality score (IQS) , and Pea imputed . There i and IQS  but ignohttp://genome.sph.umich.edu/wiki/ExomePicks), and PRIMUS  intervals with respect to the power of association analysis using a linear mixed model. We ignored the concordance rate because of the well-known limitation mentioned earlier , MaCH, minimac, IMPUTE, Beagle, and Ped_Pop. To run Merlin, we split all pedigrees into smaller subpedigrees that can fit in the memory and then combined the subpedigree results. We also compared the effect of four subject selection strategies—GIGI-Pick, ExomePicks, PRIMUS, and random selection—on the imputation accuracy. Finally, we compared the imputation accuracy measures  earlier . Our papR2 values were estimated between the imputed and true observed dosages. Here, the dosage is the estimated (or observed) fraction of minor alleles in the genotype. This computation was performed for all SNPs except the observed GWAS SNPs , which were not imputed. The results of mean R2 for random selection are summarized in Supplemental Fig. S1). Zooming in on the  MAF interval, Supplemental Figure S2 shows how the different methods behave for rare variants and how the clear improvement of population-based approaches starts to be apparent between . This process yielded the following number of tests within the respective MAF bins: 2,497,900, 1,708,680, 955,460, 911,850, 766,810, 652,040, 461,260 for EUR and 2,621,320, 3,333,930, 1,646,340, 1,384,900, 840,620, 582,290, and 481,600 for AFR. We used three values of α: 0.05, 0.01, and 0.001. Note that more stringent thresholds could be applied, which will likely yield lower power. For the sake of comparing the different imputation approaches and not evaluating the statistical power per se, the α thresholds we used would be enough to reach our main conclusions.For each SNP and each genetic data set, we simulated 10 quantitative traits for the In all approaches evaluated, imputation relies on inferring missing genotypes in study subjects using a reference data set of fully sequenced subjects. The study subjects are genotyped on a sparse map of SNPs. All imputation methods are based on two steps: phasing and imputation. For family-based imputation, the reference data set only needs to contain subjects from the pedigrees under study. For population-based imputation methods on pedigree data, the reference data set was formed by combining all sequenced subjects across pedigrees. In all the imputation analyses, we selected subjects based on the pedigree structure from which we simulated sequence data in order to obtain the reference data set. In all tools we compared, the default parameters suggested by their respective developers were used.http://genome.sph.umich.edu/wiki/ExomePicks), PRIMUS . However, IMPUTE phasing and imputation algorithms did not change in the new version. The new phasing and imputation versions that are being proposed are mainly aiming at handling larger data sets.For phasing, we used the following programs: SHAPEIT (v2) , duoHMM https://bioinformatics.qcri.org/ped_pop) .To benefit from both IBD and LD information, we combined family- and population-based imputation results using Ped_Pop (ped_pop) . Ped_PopR2, the imputation quality score (IQS)  in all scenarios. Note that we chose not to use the CR in our comparison because of its known limitations.Several measures to compute imputation accuracy have been proposed. Examples include Pearson's correlation re (IQS)  based onvariants . HoweverY = βjXj + ε where Xj is the variable representing the genotype of the jth SNP coded as the number of copies of the minor alleles, βj is the corresponding regression coefficient, and https://cran.r-project.org/web/packages/kinship2/index.html).We constructed the following linear mixed model to test for association between SNPs and quantitative traits: Finally, in our study, we evaluated several combinations of phasing and imputation methods in family- and population-based designs in both EUR and AFR. These combinations (Phasing+Imputation) were the following: MaCH+minimac, SHAPEIT+minimac, duoHMM+minimac, Eagle+minimac, IMPUTE+IMPUTE, SHAPEIT+IMPUTE, duoHMM+IMPUTE, Eagle+IMPUTE, Beagle+Beagle, GIGI (using gl_auto), Merlin, and Ped_Pop (combining GIGI and duoHMM+minimac). All approaches were performed for the four selection strategies: GIGI-Pick, ExomePicks, PRIMUS, and random selection. Note that Merlin was run for the subpedigrees only with the GIGI-Pick and random selection strategies. To have a fair comparison with GIGI, GIGI was run on the same subpedigrees and also on the full, large pedigrees.https://zenodo.org/record/1485558) and on our site at https://bioinformatics.qcri.org/IRD.Simulated genetic sequence data and pedigree structures used in our study are available at Zenodo with DOI: 10.5281/zenodo. 1485558 (


Triglycerides are an important measure of heart health. Although more than 90 genes have been found to be associated to lipids, they only explain 12 to 15% of the variance in lipid levels. Evidence suggests that age may interact with the genetic effect on lipid levels. Existing methods to detect the main effect of rare variants cannot be readily applied for testing the gene environment interaction effect of rare variants, as those methods either have unstable results or inflated Type I error rates when the main effect exists. To overcome these difficulties, we developed two statistical methods: testing of optimally weighted combination of single-nucleotide polymorphism (SNP) environment interaction (TOW-SE) and a variable weight TOW-SE (VW-TOW-SE) to test the gene environment interaction effect of rare variants by grouping SNPs into biologically meaningful SNP-sets (SNPs in a gene or pathway) to improve power and interpretability. The proposed methods can be applied to either continuous or binary environmental variables, and to either continuous or binary outcomes. Simulation studies show that Type I error rates of the proposed methods are under control. Comparing the two methods with the existing interaction sequence kernel association test (iSKAT), the VW-TOW-SE is the most powerful test and the TOW-SE is the second most powerful test when gene environment interaction effect exists for both rare and common variants. The three tests were applied to the GAW20 simulated data, among the five regions in which the main effect of common SNPs was simulated and the gene–age interaction effect was not included. As expected, none of the tests indicated positive results. Highly heritable triglycerides (TG)  are an iClear evidence shows that lipids vary by age. A handful of lipid loci with age-dependent effects were identified from candidate gene studies and genome-wide association study (GWAS) , 5. HoweDue to the allelic heterogeneity and the extreme rarity of individual variants , most exn = 246) to test TG susceptible to gene–age interactions on the five imputed genes. The main effect of common SNP was simulated in the five regions. However, the gene–age interaction effect was not included. As expected, using the proposed methods, none of the regions indicated significantly gene–age interaction effects on TG.The Genetic Analysis Workshop (GAW) 20 “SimulationBestOneRepresentative” data includes TG levels before and after treatment with fenofibrate and genotyped genome-wide SNPs from the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) study . We impun individuals. Each individual has been genotyped at M variants in a genomic region (a gene or a pathway). For the ith individual, denote yi as the trait value (continuous or binary); Ei as the environmental variable (continuous or binary); Gi =  as the genotypic scores at M variants, where gim ∈ {0, 1, 2} is the number of minor alleles the ith individual has at the mth variant. Zi denotes the potential confounder covariates.Consider a sample of We use the generalized linear model (GLM):EiGi, where f (∙) is a monotone “link” function. For a quantitative trait, f (∙) will be an identity link function. For a binary trait, a logit link function will be used. Coefficients of each term in Eq. . We divide variants into rare and common. Let ELAVL4 is associated with body mass index , rs1012116, rs10828412, and rs4399565 [HS3ST3A1]) of TG reside on chromosomes 1, 6, 8, 9, 10, and 17, respectively. The 1000 Genomes project haplotypes integrated phase I served as the reference panel. It includes 1092 individuals. Our analysis was based on 246 unrelated individuals. Both pre-treatment and post-treatment TG values were provided for two visits. We used the log ratio of the pre-treatment mean and the post-treatment mean as the phenotype trait in our G × E interaction analysis. For individuals who did not have two visits, we just used the existing value. The median age is 64 years (range: 28–83 years). We used the centered age in our analysis. The median of the TG ratio is 1.54 (range: 0.72–4.24). We excluded 8 individuals from our analysis because of completely missing post-treatment TG values.We applied TOW-SE, VW-TOW-SE, and iSKAT to the GAW20 “SimulationBestOneRepresentative” data, which includes TG levels before and after treatment with fenofibrate and genotyped genome-wide SNPs from the GOLDN project . We impusoftware . Five maWe evaluated the performance of TOW-SE, VW-TOW-SE, and iSKAT by testing G × E interaction effect on TG for the aforementioned 5 regions. Each region consists of 10 SNPs, and the fifth SNP in each region is the major main effect causal SNP of TG. Additionally, we assessed the effect of region size on the power of the tests using the region of rs4399565.Table The computation time for TOW-SE and VW-TOW-SE using 10,000 permutations for analyzing 1000 individuals in a region that includes 50 SNPs is 9 s and 20 s, respectively. Suppose a whole genome sequencing data with 13,498,188 SNPs, TOW-SE will take 674 h (28 days) to conduct a whole genome analysis. The effect analysis of the region size suggests that the three methods will perform better when the region size is larger. However, the larger the region size, the higher chance for collinearity to appear in the region, which makes the computation more complex. To minimize the problem of collinearity, we recommend a region size between 10 SNPs and 30 SNPs when we apply the proposed methods to a genome-wide scan.In summary, we developed two novel statistical methods: TOW-SE and VW-TOW-SE by grouping SNPs into biologically meaningful SNP-sets, which improved power and interpretability. Simulation studies show that the proposed methods yielded well-controlled Type I error rates under all study conditions. When gene environment interaction effect exists for both rare and common variants, VW-TOW-SE is the most powerful test, TOW-SE is the second most powerful test, and iSKAT is the least powerful test.


NFATC1, SOX5, NBAS, and TCF7L1. These results provide evidence that normal variation in facial asymmetry may be impacted by common genetic variants and further motivate the development of automated summaries of complex phenotypes.Human facial asymmetry is due to a complex interaction of genetic and environmental factors. To identify genetic influences on facial asymmetry, we developed a method for automated scoring that summarizes local morphology features and their spatial distribution. A genome-wide association study using asymmetry scores from two local symmetry features was conducted and significant genetic associations were identified for one asymmetry feature, including genes thought to play a role in craniofacial disorders and development:  The ability to make connections between genetic and phenotypic variation, hinges on phenotypic descriptions that are sufficiently detailed to capture the traits of interest. Biomedical imaging creates very high dimensional datasets that can be analyzed and used to extract phenotype descriptions. Traditional phenotyping from images consists of 2D and 3D measurements of landmarks manually placed on the image. Landmark data is typically sparse and is likely insufficient to capture the complexity necessary for an association with genetic data. A recent study, testing the relationship of facial asymmetry, estimated from nine mid-facial landmarks, with genetic variation at 102 single nucleotide polymorphism (SNP) loci, recently associated with facial shape variation, was unable to identify any SNP relating to asymmetry . These commercial stereo-photography systems incorporate multiple camera viewpoints to provide a 3D mesh of the human face, at no risk to the subject, with the high level of anatomical integrity required for medical research. Several recent studies have assessed the amount of noise or variability that may be present in 3D meshes acquired using the 3dMD system, compared to alternative methods such as direct anthropometry and digital photogrammetry .A standard set of 24 facial landmarks was collected for each 3D facial mesh. In this study, a subset of 18 landmarks was selected to minimize the number of subjects excluded, due to missing the landmark points. A diagram of the landmarks used for analysis is shown in Figure The genotype data consists of 964,193 SNPs on the Illumina  OmniExpress+Exome v1.2 array plus 4,322 custom SNPs chosen in regions of interest based on previous studies of the genetics of facial variation.Most attempts to summarize image characteristics rely on global features that describe the image as a whole, or local features calculated point-wise across the image. Previous work evaluating asymmetry in facial images has tended toward a local, point-wise approach  were classified as incoherent. An example of a feature heat map and coherent regions extracted from the histogram is shown in Figure where b as in Birchfield and Rangarajan  and j and j. This spatial weighting term represents average error between feature maps, measured in standard deviations, for each coherent region. To achieve a symmetric distance measure, the total distance between h and h′ was defined as:where ρ was applied to assess the similarity of each individual feature heat map to the average feature heat map. This provided a hybrid local-plus-global summary of the abnormality of asymmetry of an individual and was assigned as our score of asymmetry. Average feature heat maps for subjects with the lowest (lower 10 percent of the data set) and highest (upper 10 percent of the data set) asymmetry scores for the angle of surface orientation feature are shown in Figure This distance p-values globally over both phenotype scores in order to control the false discovery rate (FDR) , which made it possible to detect the significant associations.Each phenotype score was tested for associations with age and sex. The Pearson correlation coefficient was used to test for an association with age. An association with sex was tested using the Kendall rank correlation coefficient (tau). The Kendall test does not rely on the assumption of normally distributed data and so is more appropriate for dichotomous data such as sex. The correlations found between the asymmetry scores, age and sex were weak, though the correlations had high levels of significance in terms of the p-value < 5 × 10−8) are listed in Table NFATC1, SOX5, and NBAS) and one (SNX6) is on a gene with a potential link. NFATC1 encodes a transcription factor that plays a role in mandibular development and the Wnt signaling pathway, which is instrumental to facial morphogenesis , and were generated by projects U01DE020078 and U01DE020054. The FaceBase Data Management Hub (U01DE020057) and the FaceBase Consortium are funded by the National Institute of Dental and Craniofacial Research. The phenotype scores developed for this work will be made available on request.SR carried out the main efforts on the research including developing the theory behind the angle of surface orientation score and the angle of deformation score as well also carrying out all the experiments, and writing the paper. S-IL acted as a consultant on the analysis of the GWAS results and helped to write the Results and Discussion sections of the paper. LS served as the primary adviser to SR in this work.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.