Whole-genome sequencing study of serum peptide levels: the Atherosclerosis Risk in Communities study

Identification of blood metabolites associated with risk of Alzheimer's disease by integrating genomics and metabolomics data

Specific metabolites have been reported to be potentially associated with Alzheimer's disease (AD) risk. However, the comprehensive understanding of roles of metabolite biomarkers in AD etiology remains elusive. We performed a large AD metabolome-wide association study (MWAS) by developing blood metabolite genetic prediction models. We evaluated associations between genetically predicted levels of metabolites and AD risk in 39,106 clinically diagnosed AD cases, 46,828 proxy AD and related dementia (proxy-ADD) cases, and 401,577 controls. We further conducted analyses to determine microbiome features associated with the detected metabolites and characterize associations between predicted microbiome feature levels and AD risk. We identified fourteen metabolites showing an association with AD risk. Five microbiome features were further identified to be potentially related to associations of five of the metabolites. Our study provides new insights into the etiology of AD that involves blood metabolites and gut microbiome, which warrants further investigation.

Whole-Genome Sequencing Analysis of Human Metabolome in Multi-Ethnic Populations

Circulating metabolite levels may reflect the state of the human organism in health and disease, however, the genetic architecture of metabolites is not fully understood. We have performed a whole-genome sequencing association analysis of both common and rare variants in up to 11,840 multi-ethnic participants from five studies with up to 1666 circulating metabolites. We have discovered 1985 novel variant-metabolite associations, and validated 761 locus-metabolite associations reported previously. Seventy-nine novel variant-metabolite associations have been replicated, including three genetic loci located on the X chromosome that have demonstrated its involvement in metabolic regulation. Gene-based analysis have provided further support for seven metabolite-replicated loci pairs and their biologically plausible genes. Among those novel replicated variant-metabolite pairs, follow-up analyses have revealed that 26 metabolites have colocalized with 21 tissues, seven metabolite-disease outcome associations have been putatively causal, and 7 metabolites might be regulated by plasma protein levels. Our results have depicted the genetic contribution to circulating metabolite levels, providing additional insights into understanding human disease.

An atlas of genetic scores to predict multi-omic traits

The use of omic modalities to dissect the molecular underpinnings of common diseases and traits is becoming increasingly common. But multi-omic traits can be genetically predicted, which enables highly cost-effective and powerful analyses for studies that do not have multi-omics1. Here we examine a large cohort (the INTERVAL study2; n = 50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, n = 3,175; Olink, n = 4,822), plasma metabolomics (Metabolon HD4, n = 8,153), serum metabolomics (Nightingale, n = 37,359) and whole-blood Illumina RNA sequencing (n = 4,136), and use machine learning to train genetic scores for 17,227 molecular traits, including 10,521 that reach Bonferroni-adjusted significance. We evaluate the performance of genetic scores through external validation across cohorts of individuals of European, Asian and African American ancestries. In addition, we show the utility of these multi-omic genetic scores by quantifying the genetic control of biological pathways and by generating a synthetic multi-omic dataset of the UK Biobank3 to identify disease associations using a phenome-wide scan. We highlight a series of biological insights with regard to genetic mechanisms in metabolism and canonical pathway associations with disease; for example, JAK-STAT signalling and coronary atherosclerosis. Finally, we develop a portal ( https://www.omicspred.org/ ) to facilitate public access to all genetic scores and validation results, as well as to serve as a platform for future extensions and enhancements of multi-omic genetic scores.

Associations between genetically predicted levels of blood metabolites and pancreatic cancer risk

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive solid malignancies, which is featured by systematic metabolism. Thus, a better understanding of metabolic dysregulation in PDAC is important to better characterize its etiology. Here, we performed a large metabolome-wide association study (MWAS) to systematically explore associations between genetically predicted metabolite levels in blood and PDAC risk. Using data from 881 subjects of European descent in the TwinsUK Project, comprehensive genetic models were built to predict serum metabolite levels. These prediction models were applied to the genetic data of 8275 cases and 6723 controls included in the PanScan (I, II and III) and PanC4 consortia. After assessing the metabolite-PDAC risk associations by a slightly modified TWAS/FUSION framework, we identified five metabolites (including two dipeptides) showing significant associations with PDAC risk at false discovery rate (FDR) <0.05. Integrated with gut microbial information, two-sample Mendelian randomization (MR) analyses were further performed to investigate the relationship among serum metabolites, gut microbiome features and PDAC. The flavonoid-degrading bacteria Flavonifractor sp90199495 was found to be associated with metabolite X-21849 and it was also shown to be associated with PDAC risk. Collectively, our study identified novel candidate metabolites for PDAC risk, which could lead to new insights into the etiology of PDAC and improved treatment options.

Extensive metabolic consequences of human glycosyltransferase gene knockouts in prostate cancer

BACKGROUND

Naturally occurring germline gene deletions (KO) represent a unique setting to interrogate gene functions. Complete deletions and differential expression of the human glycosyltransferase UGT2B17 and UGT2B28 genes are linked to prostate cancer (PCa) risk and progression, leukaemia, autoimmune and other diseases.

METHODS

The systemic metabolic consequences of UGT deficiencies were examined using untargeted and targeted mass spectrometry-based metabolomics profiling of carefully matched, treatment-naive PCa cases.

RESULTS

Each UGT KO differentially affected over 5% of the 1545 measured metabolites, with divergent metabolic perturbations influencing the same pathways. Several of the perturbed metabolites are known to promote PCa growth, invasion and metastasis, including steroids, ceramides and kynurenine. In UGT2B17 KO, reduced levels of inactive steroid-glucuronides were compensated by sulfated derivatives that constitute circulating steroid reservoirs. UGT2B28 KO presented remarkably lower levels of oxylipins paralleled by reduced inflammatory mediators, but higher ceramides unveiled as substrates of the enzyme in PCa cells.

CONCLUSION

The distinctive and broad metabolic rewiring caused by UGT KO reinforces the need to examine their unique and divergent functions in PCa biology.

Dynamic patterns of postprandial metabolic responses to three dietary challenges

Food intake triggers extensive changes in the blood metabolome. The kinetics of these changes depend on meal composition and on intrinsic, health-related characteristics of each individual, making the assessment of changes in the postprandial metabolome an opportunity to assess someone's metabolic status. To enable the usage of dietary challenges as diagnostic tools, profound knowledge about changes that occur in the postprandial period in healthy individuals is needed. In this study, we characterize the time-resolved changes in plasma levels of 634 metabolites in response to an oral glucose tolerance test (OGTT), an oral lipid tolerance test (OLTT), and a mixed meal (SLD) in healthy young males (n = 15). Metabolite levels for samples taken at different time points (20 per individual) during the challenges were available from targeted (132 metabolites) and non-targeted (502 metabolites) metabolomics. Almost half of the profiled metabolites (n = 308) showed a significant change in at least one challenge, thereof 111 metabolites responded exclusively to one particular challenge. Examples include azelate, which is linked to ω-oxidation and increased only in OLTT, and a fibrinogen cleavage peptide that has been linked to a higher risk of cardiovascular events in diabetes patients and increased only in OGTT, making its postprandial dynamics a potential target for risk management. A pool of 89 metabolites changed their plasma levels during all three challenges and represents the core postprandial response to food intake regardless of macronutrient composition. We used fuzzy c-means clustering to group these metabolites into eight clusters based on commonalities of their dynamic response patterns, with each cluster following one of four primary response patterns: (i) “decrease-increase” (valley-like) with fatty acids and acylcarnitines indicating the suppression of lipolysis, (ii) “increase-decrease” (mountain-like) including a cluster of conjugated bile acids and the glucose/insulin cluster, (iii) “steady decrease” with metabolites reflecting a carryover from meals prior to the study, and (iv) “mixed” decreasing after the glucose challenge and increasing otherwise. Despite the small number of subjects, the diversity of the challenges and the wealth of metabolomic data make this study an important step toward the characterization of postprandial responses and the identification of markers of metabolic processes regulated by food intake.

Integrative genetic and immune cell analysis of plasma proteins in healthy donors identifies novel associations involving primary immune deficiency genes

Background

Blood plasma proteins play an important role in immune defense against pathogens, including cytokine signaling, the complement system, and the acute-phase response. Recent large-scale studies have reported genetic (i.e., protein quantitative trait loci, pQTLs) and non-genetic factors, such as age and sex, as major determinants to inter-individual variability in immune response variation. However, the contribution of blood-cell composition to plasma protein heterogeneity has not been fully characterized and may act as a mediating factor in association studies.

Methods

Here, we evaluated plasma protein levels from 400 unrelated healthy individuals of western European ancestry, who were stratified by sex and two decades of life (20–29 and 60–69 years), from the Milieu Intérieur cohort. We quantified 229 proteins by Luminex in a clinically certified laboratory and their levels of variation were analyzed together with 5.2 million single-nucleotide polymorphisms. With respect to non-genetic variables, we included 254 lifestyle and biochemical factors, as well as counts of seven circulating immune cell populations measured by hemogram and standardized flow cytometry.

Results

Collectively, we found 152 significant associations involving 49 proteins and 20 non-genetic variables. Consistent with previous studies, age and sex showed a global, pervasive impact on plasma protein heterogeneity, while body mass index and other health status variables were among the non-genetic factors with the highest number of associations. After controlling for these covariates, we identified 100 and 12 pQTLs acting in cis and trans, respectively, collectively associated with 87 plasma proteins and including 19 novel genetic associations. Genetic factors explained the largest fraction of the variability of plasma protein levels, as compared to non-genetic factors. In addition, blood-cell fractions, including leukocytes, lymphocytes, monocytes, neutrophils, eosinophils, basophils, and platelets, had a larger contribution to inter-individual variability than age and sex and appeared as confounders of specific genetic associations. Finally, we identified new genetic associations with plasma protein levels of five monogenic Mendelian disease genes including two primary immunodeficiency genes (Ficolin-3 and FAS).

Conclusions

Our study identified novel genetic and non-genetic factors associated to plasma protein levels which may inform health status and disease management.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-022-01032-y.

Advances in integrative African genomics

There has been a rapid increase in human genome sequencing in the past two decades, resulting in the identification of millions of previously unknown genetic variants. However, African populations are under-represented in sequencing efforts. Additional sequencing from diverse African populations and the construction of African-specific reference genomes is needed to better characterize the full spectrum of variation in humans. However, sequencing alone is insufficient to address the molecular and cellular mechanisms underlying variable phenotypes and disease risks. Determining functional consequences of genetic variation using multi-omics approaches is a fundamental post-genomic challenge. We discuss approaches to close the knowledge gaps about African genomic diversity and review advances in African integrative genomic studies and their implications for precision medicine.

Polygenic Risk Scores for Kidney Function and Their Associations with Circulating Proteome, and Incident Kidney Diseases

BACKGROUND

Genome-wide association studies (GWAS) have revealed numerous loci for kidney function (eGFR). The relationship between polygenic predictors of eGFR, risk of incident adverse kidney outcomes, and the plasma proteome is not known.

METHODS

We developed a genome-wide polygenic risk score (PRS) for eGFR by applying the LDpred algorithm to summary statistics generated from a multiethnic meta-analysis of CKDGen Consortium GWAS ( n =765,348) and UK Biobank GWAS (90% of the cohort; n =451,508), followed by best-parameter selection using the remaining 10% of UK Biobank data ( n =45,158). We then tested the association of the PRS in the Atherosclerosis Risk in Communities (ARIC) study ( n =8866) with incident CKD, ESKD, kidney failure, and AKI. We also examined associations between the PRS and 4877 plasma proteins measured at middle age and older adulthood and evaluated mediation of PRS associations by eGFR.

RESULTS

The developed PRS showed a significant association with all outcomes. Hazard ratios per 1 SD lower PRS ranged from 1.06 (95% CI, 1.01 to 1.11) to 1.33 (95% CI, 1.28 to 1.37). The PRS was significantly associated with 132 proteins at both time points. The strongest associations were with cystatin C, collagen α -1(XV) chain, and desmocollin-2. Most proteins were higher at lower kidney function, except for five proteins, including testican-2. Most correlations of the genetic PRS with proteins were mediated by eGFR.

CONCLUSIONS

A PRS for eGFR is now sufficiently strong to capture risk for a spectrum of incident kidney diseases and broadly influences the plasma proteome, primarily mediated by eGFR.

Advances and Utility of the Human Plasma Proteome

The study of proteins circulating in blood offers tremendous opportunities to diagnose, stratify, or possibly prevent diseases. With recent technological advances and the urgent need to understand the effects of COVID-19, the proteomic analysis of blood-derived serum and plasma has become even more important for studying human biology and pathophysiology. Here we provide views and perspectives about technological developments and possible clinical applications that use mass-spectrometry(MS)- or affinity-based methods. We discuss examples where plasma proteomics contributed valuable insights into SARS-CoV-2 infections, aging, and hemostasis and the opportunities offered by combining proteomics with genetic data. As a contribution to the Human Proteome Organization (HUPO) Human Plasma Proteome Project (HPPP), we present the Human Plasma PeptideAtlas build 2021-07 that comprises 4395 canonical and 1482 additional nonredundant human proteins detected in 240 MS-based experiments. In addition, we report the new Human Extracellular Vesicle PeptideAtlas 2021-06, which comprises five studies and 2757 canonical proteins detected in extracellular vesicles circulating in blood, of which 74% (2047) are in common with the plasma PeptideAtlas. Our overview summarizes the recent advances, impactful applications, and ongoing challenges for translating plasma proteomics into utility for precision medicine.

Sequencing-based genome-wide association studies reporting standards

Genome sequencing has recently become a viable genotyping technology for use in genome-wide association studies (GWASs), offering the potential to analyze a broader range of genome-wide variation, including rare variants. To survey current standards, we assessed the content and quality of reporting of statistical methods, analyses, results, and datasets in 167 exome- or genome-wide-sequencing-based GWAS publications published from 2014 to 2020; 81% of publications included tests of aggregate association across multiple variants, with multiple test models frequently used. We observed a lack of standardized terms and incomplete reporting of datasets, particularly for variants analyzed in aggregate tests. We also find a lower frequency of sharing of summary statistics compared with array-based GWASs. Reporting standards and increased data sharing are required to ensure sequencing-based association study data are findable, interoperable, accessible, and reusable (FAIR). To support that, we recommend adopting the standard terminology of sequencing-based GWAS (seqGWAS). Further, we recommend that single-variant analyses be reported following the same standards and conventions as standard array-based GWASs and be shared in the GWAS Catalog. We also provide initial recommended standards for aggregate analyses metadata and summary statistics.

The Propagation of Racial Disparities in Cardiovascular Genomics Research

Genomics research has improved our understanding of the genetic basis for human traits and diseases. This progress is now being translated into clinical care as we move toward a future of precision medicine. Many hope that expanded use of genomic testing will improve disease screening, diagnosis, risk stratification, and treatment. In many respects, cardiovascular medicine is leading this charge. However, most cardiovascular genomics research has been conducted in populations of primarily European ancestry. This bias has critical downstream effects. Here, we review the current disparities in cardiovascular genomics research, and we outline how these disparities propagate forward through all phases of the translational pipeline. If not adequately addressed, biases in genomics research will further compound the existing health disparities that face underrepresented and marginalized populations.

Exome sequence association study of levels and longitudinal change of cardiovascular risk factor phenotypes in European Americans and African Americans from the Atherosclerosis Risk in Communities Study

Cardiovascular disease (CVD) is responsible for 31% of all deaths worldwide. Among CVD risk factors are age, race, increased systolic blood pressure (BP), and dyslipidemia. Both BP and blood lipids levels change with age, with a dose-dependent relationship between the cumulative exposure to hyperlipidemia and the risk of CVD. We performed an exome sequence association study using longitudinal data with up to 7805 European Americans (EAs) and 3171 African Americans (AAs) from the Atherosclerosis Risk in Communities (ARIC) study. We assessed associations of common (minor allele frequency > 5%) nonsynonymous and splice-site variants and gene-based sets of rare variants with levels and with longitudinal change of seven CVD risk factor phenotypes (BP traits: systolic BP, diastolic BP, pulse pressure; lipids traits: triglycerides, total cholesterol, high-density lipoprotein cholesterol [HDL-C], low-density lipoprotein cholesterol [LDL-C]). Furthermore, we investigated the relationship of the identified variants and genes with select CVD endpoints. We identified two novel genes: DCLK3 associated with the change of HDL-C levels in AAs and RAB7L1 associated with the change of LDL-C levels in EAs. RAB7L1 is further associated with an increased risk of heart failure in ARIC EAs. Investigation of the contribution of genetic factors to the longitudinal change of CVD risk factor phenotypes promotes our understanding of the etiology of CVD outcomes, stressing the importance of incorporating the longitudinal structure of the cohort data in future analyses.

Whole-genome sequencing of African Americans implicates differential genetic architecture in inflammatory bowel disease

Whether or not populations diverge with respect to the genetic contribution to risk of specific complex diseases is relevant to understanding the evolution of susceptibility and origins of health disparities. Here, we describe a large-scale whole-genome sequencing study of inflammatory bowel disease encompassing 1,774 affected individuals and 1,644 healthy control Americans with African ancestry (African Americans). Although no new loci for inflammatory bowel disease are discovered at genome-wide significance levels, we identify numerous instances of differential effect sizes in combination with divergent allele frequencies. For example, the major effect at PTGER4 fine maps to a single credible interval of 22 SNPs corresponding to one of four independent associations at the locus in European ancestry individuals but with an elevated odds ratio for Crohn disease in African Americans. A rare variant aggregate analysis implicates Ca²⁺-binding neuro-immunomodulator CALB2 in ulcerative colitis. Highly significant overall overlap of common variant risk for inflammatory bowel disease susceptibility between individuals with African and European ancestries was observed, with 41 of 241 previously known lead variants replicated and overall correlations in effect sizes of 0.68 for combined inflammatory bowel disease. Nevertheless, subtle differences influence the performance of polygenic risk scores, and we show that ancestry-appropriate weights significantly improve polygenic prediction in the highest percentiles of risk. The median amount of variance explained per locus remains the same in African and European cohorts, providing evidence for compensation of effect sizes as allele frequencies diverge, as expected under a highly polygenic model of disease.

A Genome-wide Association Study Discovers 46 Loci of the Human Metabolome in the Hispanic Community Health Study/Study of Latinos

Variation in levels of the human metabolome reflect changes in homeostasis, providing a window into health and disease. The genetic impact on circulating metabolites in Hispanics, a population with high cardiometabolic disease burden, is largely unknown. We conducted genome-wide association analyses on 640 circulating metabolites in 3,926 Hispanic Community Health Study/Study of Latinos participants. The estimated heritability for 640 metabolites ranged between 0%-54% with a median at 2.5%. We discovered 46 variant-metabolite pairs (p value < 1.2 × 10-10, minor allele frequency ≥ 1%, proportion of variance explained [PEV] mean = 3.4%, PEVrange = 1%-22%) with generalized effects in two population-based studies and confirmed 301 known locus-metabolite associations. Half of the identified variants with generalized effect were located in genes, including five nonsynonymous variants. We identified co-localization with the expression quantitative trait loci at 105 discovered and 151 known loci-metabolites sets. rs5855544, upstream of SLC51A, was associated with higher levels of three steroid sulfates and co-localized with expression levels of SLC51A in several tissues. Mendelian randomization (MR) analysis identified several metabolites associated with coronary heart disease (CHD) and type 2 diabetes. For example, two variants located in or near CYP4F2 (rs2108622 and rs79400241, respectively), involved in vitamin E metabolism, were associated with the levels of octadecanedioate and vitamin E metabolites (gamma-CEHC and gamma-CEHC glucuronide); MR analysis showed that genetically high levels of these metabolites were associated with lower odds of CHD. Our findings document the genetic architecture of circulating metabolites in an underrepresented Hispanic/Latino community, shedding light on disease etiology.

Profile of genetic variations in severely calcified carotid plaques by whole-exome sequencing

Background:

The precise mechanisms of carotid calcification and its clinical significance have not been established.

Methods:

We classified ten plaques from carotid endarterectomy patients into high- and low-calcified plaques based on the Agatston calcium scores. We performed whole-exome sequencing for genetic profiles with single nucleotide variations (SNVs), insertions, and deletions. Bioinformatic data mining was then conducted to disclose specific gene variations to either high- or low-calcified carotid plaques.

Results:

In the carotid plaques, G:C>A:T/C:G>T:A transitions as SNVs, insT after C/insC after A as insertions, and delA after G/delT after C as deletions were most frequently observed, but no significant difference was observed between the high- and low-calcified plaque groups in their proportion of base-pair substitution types. In the bioinformatic analysis, SNVs of ATP binding cassette subfamily C member 6 (ADCC6) were more commonly found in high-calcified plaques and SNVs of KLKB1 were more commonly found in low-calcified plaques compared to the other group. No new genetic variants related to calcification or atherosclerosis among those not registered in dbSNP was detected.

Conclusion:

Our findings clarified the features of base-pair substitutions in carotid plaques, showing no relation to calcification. However, genetic variants in ADCC6 relating to vascular calcification for high-calcified plaques, and in KLKB1 encoding kallikrein associated with vascular regulation of atherosclerosis for low-calcified plaques were more specifically extracted. These results contribute to a better understanding of the genetic basis of molecular activity and calcium formation in carotid plaques.

Genetics meets proteomics: perspectives for large population-based studies

Proteomic analysis of cells, tissues and body fluids has generated valuable insights into the complex processes influencing human biology. Proteins represent intermediate phenotypes for disease and provide insight into how genetic and non-genetic risk factors are mechanistically linked to clinical outcomes. Associations between protein levels and DNA sequence variants that colocalize with risk alleles for common diseases can expose disease-associated pathways, revealing novel drug targets and translational biomarkers. However, genome-wide, population-scale analyses of proteomic data are only now emerging. Here, we review current findings from studies of the plasma proteome and discuss their potential for advancing biomedical translation through the interpretation of genome-wide association analyses. We highlight the challenges faced by currently available technologies and provide perspectives relevant to their future application in large-scale biobank studies.

Characterization of differentially expressed plasma proteins in patients with acute myocardial infarction

Acute myocardial infarction (AMI) remains a leading cause of morbidity and mortality worldwide. Novel biomarkers are needed to identify NSTEMI in AMI patients. The study objective was to use proteomics to identify novel plasma biomarkers for STEMI and NSTEMI patients. iTRAQ analysis was performed on pooled samples from 8 healthy controls and 12 STEMI and 12 NSTEMI patients. Bioinformatics analysis identified 95 differentially expressed proteins that were differentially expressed in the plasma of AMI patients and healthy controls; 28 of these proteins were found in STEMI/Con (22 upregulated and 6 downregulated), 48 in NSTEMI/Con (12 upregulated and 36 downregulated), and 44 in NSTEMI/STEMI (11 upregulated and 33 downregulated). Protein network analysis was then performed using STRING software. Functional analysis revealed that the identified plasma proteins were mainly involved with carbon metabolism, toll-like receptor signaling pathway, and hypertrophic cardiomyopathy. Nine of the proteins (SSA1, MDH1, FCN2, GPI, S100A8, LBP, vinculin, VDBP, and RBP4) that changed levels during AMI progression were further validated by ELISA. The constructed plasma proteome could reflect the AMI pathogenesis molecular mechanisms and provide a method for the early identification of NSTEMI in AMI patients. SIGNIFICANCE: The aim of this study was to use proteomics to identify novel predictive plasma biomarkers for patients with acute myocardial infarction (AMI), which would allow for either identification of individuals at risk of an infarction, and early identification of NSTEMI in patients with AMI. Using an approach that combined iTRAQ with LC-MS/MS, we found 95 proteins that showed significant differences in expression levels among the AMI patients and healthy controls. The proteins SSA1, MDH1, FCN2, GPI, S100A8, LBP, vinculin, VDBP, and RBP4 were found to play crucial roles in the pathogenesis of AMI. Using bioinformatics analysis, we found that dysregulation of carbon metabolism, toll-like receptor signaling pathway, and hypertrophic cardiomyopathy may be the major driving forces for cardiac damage during myocardial infarction. However, further investigations are needed to verify the mechanisms involved in the development of AMI especially NSTEMI. Taken together, our findings lay the foundation for understanding the molecular mechanisms underlying the pathogenic processes of AMI, and suggest potential applications for specific biomarkers in early diagnosis and determination of prognosis.

The promise and reality of therapeutic discovery from large cohorts

Technological advances in rapid data acquisition have transformed medical biology into a data mining field, where new data sets are routinely dissected and analyzed by statistical models of ever-increasing complexity. Many hypotheses can be generated and tested within a single large data set, and even small effects can be statistically discriminated from a sea of noise. On the other hand, the development of therapeutic interventions moves at a much slower pace. They are determined from carefully randomized and well-controlled experiments with explicitly stated outcomes as the principal mechanism by which a single hypothesis is tested. In this paradigm, only a small fraction of interventions can be tested, and an even smaller fraction are ultimately deemed therapeutically successful. In this Review, we propose strategies to leverage large-cohort data to inform the selection of targets and the design of randomized trials of novel therapeutics. Ultimately, the incorporation of big data and experimental medicine approaches should aim to reduce the failure rate of clinical trials as well as expedite and lower the cost of drug development.

Improved power and precision with whole genome sequencing data in genome-wide association studies of inflammatory biomarkers

Genome-wide association studies (GWAS) have identified associations between thousands of common genetic variants and human traits. However, common variants usually explain a limited fraction of the heritability of a trait. A powerful resource for identifying trait-associated variants is whole genome sequencing (WGS) data in cohorts comprised of families or individuals from a limited geographical area. To evaluate the power of WGS compared to imputations, we performed GWAS on WGS data for 72 inflammatory biomarkers, in a kinship-structured cohort. When using WGS data, we identified 18 novel associations that were not detected when analyzing the same biomarkers with genotyped or imputed SNPs. Five of the novel top variants were low frequency variants with a minor allele frequency (MAF) of <5%. Our results suggest that, even when applying a GWAS approach, we gain power and precision using WGS data, presumably due to more accurate determination of genotypes. The lack of a comparable dataset for replication of our results is a limitation in our study. However, this further highlights that there is a need for more genetic epidemiological studies based on WGS data.

Genomic atlas of the human plasma proteome

Although plasma proteins have important roles in biological processes and are the direct targets of many drugs, the genetic factors that control inter-individual variation in plasma protein levels are not well understood. Here we characterize the genetic architecture of the human plasma proteome in healthy blood donors from the INTERVAL study. We identify 1,927 genetic associations with 1,478 proteins, a fourfold increase on existing knowledge, including trans associations for 1,104 proteins. To understand the consequences of perturbations in plasma protein levels, we apply an integrated approach that links genetic variation with biological pathway, disease, and drug databases. We show that protein quantitative trait loci overlap with gene expression quantitative trait loci, as well as with disease-associated loci, and find evidence that protein biomarkers have causal roles in disease using Mendelian randomization analysis. By linking genetic factors to diseases via specific proteins, our analyses highlight potential therapeutic targets, opportunities for matching existing drugs with new disease indications, and potential safety concerns for drugs under development.

Sequence-Based Analysis of Lipid-Related Metabolites in a Multiethnic Study

Small molecule lipid-related metabolites are important components of fatty acid and steroid metabolism-two important contributors to human health. This study investigated the extent to which rare and common genetic variants spanning the human genome influence the lipid-related metabolome. Sequence data from 1552 European-Americans (EA) and 1872 African-Americans (AA) were analyzed to examine the impact of common and rare variants on the levels of 102 circulating lipid-related metabolites measured by a combination of chromatography and mass spectroscopy. We conducted single variant tests [minor allele frequency (MAF) > 5%, statistical significance P-value ≤ 2.45 × 10^-10] and tests aggregating rare variants (MAF ≤ 5%) across multiple genomic motifs, such as coding regions and regulatory domains, and sliding windows. Multiethnic meta-analyses detected 53 lipid-related metabolites-locus pairs, which were inspected for evidence of consistent signal between the two ethnic groups. Thirty-eight lipid-related metabolite-genomic region associations were consistent across ethnicities, among which seven were novel. The regions contain genes that are related to metabolite transport (SLC10A1) and metabolism (SCD, FDX1, UGT2B15, and FADS2). Six of the seven novel findings lie in expression quantitative trait loci affecting the expression levels of 14 surrounding genes in multiple tissues. Imputed expression levels of 10 of the affected genes were associated with four corresponding lipid-related traits in at least one tissue. Our findings offer valuable insight into circulating lipid-related metabolite regulation in a multiethnic population.