Identification of cis-regulatory variation influencing protein abundance levels in human plasma

Crossing the Halfway Point: Aptamer-Based, Highly Multiplexed Assay for the Assessment of the Proteome

Measuring responses in the proteome to various perturbations improves our understanding of biological systems. The value of information gained from such studies is directly proportional to the number of proteins measured. To overcome technical challenges associated with highly multiplexed measurements, we developed an affinity reagent-based method that uses aptamers with protein-like side chains along with an assay that takes advantage of their unique properties. As hybrid affinity reagents, modified aptamers are fully comparable to antibodies in terms of binding characteristics toward proteins, including epitope size, shape complementarity, affinity and specificity. Our assay combines these intrinsic binding properties with serial kinetic proofreading steps to allow highly effective partitioning of stable specific complexes from unstable nonspecific complexes. The use of these orthogonal methods to enhance specificity effectively overcomes the severe limitation to multiplexing inherent to the use of sandwich-based methods. Our assay currently measures half of the unique proteins encoded in the human genome with femtomolar sensitivity, broad dynamic range and exceptionally high reproducibility. Using machine learning to identify patterns of change, we have developed tests based on measurement of multiple proteins predictive of current health states and future disease risk to guide a holistic approach to precision medicine.

A genome-wide association study of mass spectrometry proteomics using the Seer Proteograph platform

Genome-wide association studies (GWAS) with proteomics are essential tools for drug discovery. To date, most studies have used affinity proteomics platforms, which have limited discovery to protein panels covered by the available affinity binders. Furthermore, it is not clear to which extent protein epitope changing variants interfere with the detection of protein quantitative trait loci (pQTLs). Mass spectrometry-based (MS) proteomics can overcome some of these limitations. Here we report a GWAS using the MS-based Seer Proteograph™ platform with blood samples from a discovery cohort of 1,260 American participants and a replication in 325 individuals from Asia, with diverse ethnic backgrounds. We analysed 1,980 proteins quantified in at least 80% of the samples, out of 5,753 proteins quantified across the discovery cohort. We identified 252 and replicated 90 pQTLs, where 30 of the replicated pQTLs have not been reported before. We further investigated 200 of the strongest associated cis-pQTLs previously identified using the SOMAscan and the Olink platforms and found that up to one third of the affinity proteomics pQTLs may be affected by epitope effects, while another third were confirmed by MS proteomics to be consistent with the hypothesis that genetic variants induce changes in protein expression. The present study demonstrates the complementarity of the different proteomics approaches and reports pQTLs not accessible to affinity proteomics, suggesting that many more pQTLs remain to be discovered using MS-based platforms.

Protein-metabolite association studies identify novel proteomic determinants of metabolite levels in human plasma

Although many novel gene-metabolite and gene-protein associations have been identified using high-throughput biochemical profiling, systematic studies that leverage human genetics to illuminate causal relationships between circulating proteins and metabolites are lacking. Here, we performed protein-metabolite association studies in 3,626 plasma samples from three human cohorts. We detected 171,800 significant protein-metabolite pairwise correlations between 1,265 proteins and 365 metabolites, including established relationships in metabolic and signaling pathways such as the protein thyroxine-binding globulin and the metabolite thyroxine, as well as thousands of new findings. In Mendelian randomization (MR) analyses, we identified putative causal protein-to-metabolite associations. We experimentally validated top MR associations in proof-of-concept plasma metabolomics studies in three murine knockout strains of key protein regulators. These analyses identified previously unrecognized associations between bioactive proteins and metabolites in human plasma. We provide publicly available data to be leveraged for studies in human metabolism and disease.

Genetics of circulating inflammatory proteins identifies drivers of immune-mediated disease risk and therapeutic targets

Circulating proteins have important functions in inflammation and a broad range of diseases. To identify genetic influences on inflammation-related proteins, we conducted a genome-wide protein quantitative trait locus (pQTL) study of 91 plasma proteins measured using the Olink Target platform in 14,824 participants. We identified 180 pQTLs (59 cis, 121 trans). Integration of pQTL data with eQTL and disease genome-wide association studies provided insight into pathogenesis, implicating lymphotoxin-α in multiple sclerosis. Using Mendelian randomization (MR) to assess causality in disease etiology, we identified both shared and distinct effects of specific proteins across immune-mediated diseases, including directionally discordant effects of CD40 on risk of rheumatoid arthritis versus multiple sclerosis and inflammatory bowel disease. MR implicated CXCL5 in the etiology of ulcerative colitis (UC) and we show elevated gut CXCL5 transcript expression in patients with UC. These results identify targets of existing drugs and provide a powerful resource to facilitate future drug target prioritization.

CD33 polymorphisms and Parkinson's disease Parkinson's disease in northern Chinese Han population: A case-control study

OBJECTIVE

Parkinson's disease (PD) represents the multisystem illness involving immunological and neuroinflammatory dysfunction. The present work focused on evaluating link of CD33 single nucleotide polymorphisms (SNPs) with PD vulnerability of the northern Chinese Han people, considering CD33's role as a critical immunoregulatory receptor in neuroinflammatory responses.

METHODS

The present case-control study included 475 PD cases together with 475 normal controls. A further division of PD patients into two categories was made: 74 patients with early-onset PD (EOPD; onset age ≤ 50 years) and 401 patients with late-onset PD (LOPD; onset age > 50 years). DNA extraction was conducted, followed by genotyping for 2SNPs of CD33 polymorphisms with polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP).

RESULTS

Alleles (G vs. A, P = 0.028) and AA genotypes (P = 0.042) of rs12985029 were significantly different between the groups. Distinctions were observed between the two groups in the recessive, co-dominant, and additive models (nominal P = 0.030, nominal P = 0.045, and P = 0.032). AA genotype frequency among male PD was higher compared to corresponding male controls (P = 0.034), and in the male group allele A was a factor causing the disease (P = 0.026). The rs12985029 genotypes and allele frequency were different in EOPD compared with LOPD (P = 0.002, P = 0.002, respectively), and in LOPD group relative to healthy control group (P = 0.020 and P = 0.004, separately). Regarding the rs3826656 polymorphism, the frequency of GA genotype was higher in the control group than in the case group (nominal P = 0.036). Overdominance and co-dominant models were different between these groups (P = 0.026, nominal P = 0.030). Subgroup analysis revealed genotype frequency differences between rs3826656 LOPD group and control group (P = 0.018). Furthermore, relationship between rs3826656 and rs12985029 (D' = 0.162, r2 = 0.021) did not reach a complete level of linkage disequilibrium (LD) of northern Chinese Han people.

CONCLUSION

This study establishes an association between CD33 rs12985029 and rs3826656 polymorphisms and PD risk among the selected northern Chinese Han people. The GA genotype, rs3826656, may act as a protective factor against PD, while the A allele, rs12985029,could be genetic risk factor related to PD. Future research should include larger sample sizes and other human populations to further investigate how CD33 polymorphisms contribute to PD.

Proteome and genome integration analysis of obesity

ABSTRACT

The prevalence of obesity has increased worldwide in recent decades. Genetic factors are now known to play a substantial role in the predisposition to obesity and may contribute up to 70% of the risk for obesity. Technological advancements during the last decades have allowed the identification of many hundreds of genetic markers associated with obesity. However, the transformation of current genetic variant-obesity associations into biological knowledge has been proven challenging. Genomics and proteomics are complementary fields, as proteomics extends functional analyses. Integrating genomic and proteomic data can help to bridge a gap in knowledge regarding genetic variant-obesity associations and to identify new drug targets for the treatment of obesity. We provide an overview of the published papers on the integrated analysis of proteomic and genomic data in obesity and summarize four mainstream strategies: overlap, colocalization, Mendelian randomization, and proteome-wide association studies. The integrated analyses identified many obesity-associated proteins, such as leptin, follistatin, and adenylate cyclase 3. Despite great progress, integrative studies focusing on obesity are still limited. There is an increased demand for large prospective cohort studies to identify and validate findings, and further apply these findings to the prevention, intervention, and treatment of obesity. In addition, we also discuss several other potential integration methods.

Integrative Post-Genome-Wide Association Study Analyses Relevant to Psychiatric Disorders: Imputing Transcriptome and Proteome Signals

Background

The genome-wide association study (GWAS) is a common tool to identify genetic variants associated with complex traits, including psychiatric disorders (PDs). However, post-GWAS analyses are needed to extend the statistical inference to biologically relevant entities, e.g., genes, proteins, and pathways. To achieve this goal, researchers developed methods that incorporate biologically relevant intermediate molecular phenotypes, such as gene expression and protein abundance, which are posited to mediate the variant-trait association. Transcriptome-wide association study (TWAS) and proteome-wide association study (PWAS) are commonly used methods to test the association between these molecular mediators and the trait.

Summary

In this review, we discuss the most recent developments in TWAS and PWAS. These methods integrate existing "omic" information with the GWAS summary statistics for trait(s) of interest. Specifically, they impute transcript/protein data and test the association between imputed gene expression/protein level with phenotype of interest by using (i) GWAS summary statistics and (ii) reference transcriptomic/proteomic/genomic datasets. TWAS and PWAS are suitable as analysis tools for (i) primary association scan and (ii) fine-mapping to identify potentially causal genes for PDs.

Key Messages

As post-GWAS analyses, TWAS and PWAS have the potential to highlight causal genes for PDs. These prioritized genes could indicate targets for the development of novel drug therapies. For researchers attempting such analyses, we recommend Mendelian randomization tools that use GWAS statistics for both trait and reference datasets, e.g., summary Mendelian randomization (SMR). We base our recommendation on (i) being able to use the same tool for both TWAS and PWAS, (ii) not requiring the pre-computed weights (and thus easier to update for larger reference datasets), and (iii) most larger transcriptome reference datasets are publicly available and easy to transform into a compatible format for SMR analysis.

Ensemble Modified Aptamer Based Pattern Recognition for Adaptive Target Identification

The difficulty of the molecular design and chemical synthesis of artificial sensing receptors restricts their diagnostic and proteomic applications. Herein, we report a concept of "ensemble modified aptamers" (EMAmers) that exploits the collective recognition abilities of a small set of protein-like side-chain-modified nucleic acid ligands for discriminative identification of molecular or cellular targets. Different types and numbers of hydrophobic functional groups were incorporated at designated positions on nucleic acid scaffolds to mimic amino acid side chains. We successfully assayed 18 EMAmer probes with differential binding affinities to seven proteins. We constructed an EMAmer-based chemical nose sensor and demonstrated its application in blinded unknown protein identification, giving a 92.9% accuracy. Additionally, the sensor is generalizable to the detection of blinded unknown bacterial and cellular samples, which enabled identification accuracies of 96.3% and 94.8%, respectively. This sensing platform offers a discriminative means for adaptive target identification and holds great potential for diverse applications.

Proteomic biomarkers of Kleine-Levin syndrome

STUDY OBJECTIVES

Kleine-Levin syndrome (KLS) is characterized by relapsing-remitting episodes of hypersomnia, cognitive impairment, and behavioral disturbances. We quantified cerebrospinal fluid (CSF) and serum proteins in KLS cases and controls.

METHODS

SomaScan was used to profile 1133 CSF proteins in 30 KLS cases and 134 controls, while 1109 serum proteins were profiled in serum from 26 cases and 65 controls. CSF and serum proteins were both measured in seven cases. Univariate and multivariate analyses were used to find differentially expressed proteins (DEPs). Pathway and tissue enrichment analyses (TEAs) were performed on DEPs.

RESULTS

Univariate analyses found 28 and 141 proteins differentially expressed in CSF and serum, respectively (false discovery rate <0.1%). Upregulated CSF proteins included IL-34, IL-27, TGF-b, IGF-1, and osteonectin, while DKK4 and vWF were downregulated. Pathway analyses revealed microglial alterations and disrupted blood-brain barrier permeability. Serum profiles show upregulation of Src-family kinases (SFKs), proteins implicated in cellular growth, motility, and activation. TEA analysis of up- and downregulated proteins revealed changes in brain proteins (p < 6 × 10-5), notably from the pons, medulla, and midbrain. A multivariate machine-learning classifier performed robustly, achieving a receiver operating curve area under the curve of 0.90 (95% confidence interval [CI] = 0.78-1.0, p = 0.0006) in CSF and 1.0 (95% CI = 1.0-1.0, p = 0.0002) in serum in validation cohorts, with some commonality across tissues, as the model trained on serum sample also discriminated CSF samples of controls versus KLS cases.

CONCLUSIONS

Our study identifies proteomic KLS biomarkers with diagnostic potential and provides insight into biological mechanisms that will guide future research in KLS.

Genetic Profile of Endotoxemia Reveals an Association With Thromboembolism and Stroke

Background Translocation of lipopolysaccharide from gram-negative bacteria into the systemic circulation results in endotoxemia. In addition to acute infections, endotoxemia is detected in cardiometabolic disorders, such as cardiovascular diseases and obesity. Methods and Results We performed a genome-wide association study of serum lipopolysaccharide activity in 11 296 individuals from 6 different Finnish study cohorts. Endotoxemia was measured by limulus amebocyte lysate assay in the whole population and by 2 other techniques (Endolisa and high-performance liquid chromatography/tandem mass spectrometry) in subpopulations. The associations of the composed genetic risk score of endotoxemia and thrombosis-related clinical end points for 195 170 participants were analyzed in FinnGen. Lipopolysaccharide activity had a genome-wide significant association with 741 single-nucleotide polymorphisms in 5 independent loci, which were mainly located at genes affecting the contact activation of the coagulation cascade and lipoprotein metabolism and explained 1.5% to 9.2% of the variability in lipopolysaccharide activity levels. The closest genes included KNG1, KLKB1, F12, SLC34A1, YPEL4, CLP1, ZDHHC5, SERPING1, CBX5, and LIPC. The genetic risk score of endotoxemia was associated with deep vein thrombosis, pulmonary embolism, pulmonary heart disease, and venous thromboembolism. Conclusions The biological activity of lipopolysaccharide in the circulation (ie, endotoxemia) has a small but highly significant genetic component. Endotoxemia is associated with genetic variation in the contact activation pathway, vasoactivity, and lipoprotein metabolism, which play important roles in host defense, lipopolysaccharide neutralization, and thrombosis, and thereby thromboembolism and stroke.

Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders

Understanding the tissue-specific genetic controls of protein levels is essential to uncover mechanisms of post-transcriptional gene regulation. In this study, we generated a genomic atlas of protein levels in three tissues relevant to neurological disorders (brain, cerebrospinal fluid and plasma) by profiling thousands of proteins from participants with and without Alzheimer's disease. We identified 274, 127 and 32 protein quantitative trait loci (pQTLs) for cerebrospinal fluid, plasma and brain, respectively. cis-pQTLs were more likely to be tissue shared, but trans-pQTLs tended to be tissue specific. Between 48.0% and 76.6% of pQTLs did not co-localize with expression, splicing, DNA methylation or histone acetylation QTLs. Using Mendelian randomization, we nominated proteins implicated in neurological diseases, including Alzheimer's disease, Parkinson's disease and stroke. This first multi-tissue study will be instrumental to map signals from genome-wide association studies onto functional genes, to discover pathways and to identify drug targets for neurological diseases.

Association of IL1R1 Coding Variant With Plasma-Level Soluble ST2 and Risk of Aortic Dissection

Objective: Aortic dissection (AD) is characterized by an acute onset, rapid progress, and high mortality. Levels of soluble ST2 (sST2) on presentation are elevated in patients with acute AD, which can be used to discriminate AD patients from patients with chest pain. sST2 concentrations were found to be highly heritable in the general population. The aim of this study was to investigate the associations of variations in ST2-related gene expression with sST2 concentrations and AD risk. Methods: This case-control study involving a total of 2,277 participants were conducted, including 435 AD patients and age- and sex-matched 435 controls in the discovery stage, and 464 patients and 943 controls in the validation stage. Eight ST2-related genes were selected by systematic review. Tag single-nucleotide polymorphisms (SNPs) were screened out from the Chinese population of the 1,000 Genomes Database. Twenty-one ST2-related SNPs were genotyped, and plasma sST2 concentrations were measured. Results: In the discovery stage, rs13019803 located in IL1R1 was significantly associated with AD after Bonferroni correction (p = 0.0009) and was correlated with circulating sST2 levels in patients with type A AD(AAD) [log-sST2 per C allele increased by 0.180 (95%) CI: 0.002 - 0.357] but not in type B. Combining the two stages together, rs13019803C was associated with plasma sST2 level in AAD patients [log-sST2 increased by 0.141 (95% CI: 0.055-0.227) for per C allele]. Odds ratio of rs13019803 on the risk of AAD is 1.67 (95% CI: 1.33-2.09). Conclusions: The IL1R1 SNP rs13019803C is associated with higher sST2 levels and increased risk of AAD.

Proteomic biomarkers of sleep apnea

STUDY OBJECTIVES

Obstructive sleep apnea (OSA) is characterized by recurrent partial to complete upper airway obstructions during sleep, leading to repetitive arousals and oxygen desaturations. Although many OSA biomarkers have been reported individually, only a small subset have been validated through both cross-sectional and intervention studies. We sought to profile serum protein biomarkers in OSA in unbiased high throughput assay.

METHODS

A highly multiplexed aptamer array (SomaScan) was used to profile 1300 proteins in serum samples from 713 individuals in the Stanford Sleep Cohort, a patient-based registry. Outcome measures derived from overnight polysomnography included Obstructive Apnea Hypopnea Index (OAHI), Central Apnea Index (CAI), 2% Oxygen Desaturation index, mean and minimum oxygen saturation indices during sleep. Additionally, a separate intervention-based cohort of 16 individuals was used to assess proteomic profiles pre- and post-intervention with positive airway pressure.

RESULTS

OAHI was associated with 65 proteins, predominantly pathways of complement, coagulation, cytokine signaling, and hemostasis which were upregulated. CAI was associated with two proteins including Roundabout homolog 3 (ROBO3), a protein involved in bilateral synchronization of the pre-Bötzinger complex and cystatin F. Analysis of pre- and post intervention samples revealed IGFBP-3 protein to be increased while LEAP1 (Hepicidin) to be decreased with intervention. An OAHI machine learning classifier (OAHI >=15 vs OAHI<15) trained on SomaScan protein measures alone performed robustly, achieving 76% accuracy in a validation dataset.

CONCLUSIONS

Multiplex protein assays offer diagnostic potential and provide new insights into the biological basis of sleep disordered breathing.

Revealing the role of the human blood plasma proteome in obesity using genetic drivers

Blood circulating proteins are confounded readouts of the biological processes that occur in different tissues and organs. Many proteins have been linked to complex disorders and are also under substantial genetic control. Here, we investigate the associations between over 1000 blood circulating proteins and body mass index (BMI) in three studies including over 4600 participants. We show that BMI is associated with widespread changes in the plasma proteome. We observe 152 replicated protein associations with BMI. 24 proteins also associate with a genome-wide polygenic score (GPS) for BMI. These proteins are involved in lipid metabolism and inflammatory pathways impacting clinically relevant pathways of adiposity. Mendelian randomization suggests a bi-directional causal relationship of BMI with LEPR/LEP, IGFBP1, and WFIKKN2, a protein-to-BMI relationship for AGER, DPT, and CTSA, and a BMI-to-protein relationship for another 21 proteins. Combined with animal model and tissue-specific gene expression data, our findings suggest potential therapeutic targets further elucidating the role of these proteins in obesity associated pathologies.

Cerebrospinal fluid metabolomics identifies 19 brain-related phenotype associations

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

Proteome-wide Systems Genetics to Identify Functional Regulators of Complex Traits

Proteomic technologies now enable the rapid quantification of thousands of proteins across genetically diverse samples. Integration of these data with systems-genetics analyses is a powerful approach to identify new regulators of economically important or disease-relevant phenotypes in various populations. In this review, we summarize the latest proteomic technologies and discuss technical challenges for their use in population studies. We demonstrate how the analysis of correlation structure and loci mapping can be used to identify genetic factors regulating functional protein networks and complex traits. Finally, we provide an extensive summary of the use of proteome-wide systems genetics throughout fungi, plant, and animal kingdoms and discuss the power of this approach to identify candidate regulators and drug targets in large human consortium studies.

ANMerge: A Comprehensive and Accessible Alzheimer's Disease Patient-Level Dataset

BACKGROUND

Accessible datasets are of fundamental importance to the advancement of Alzheimer's disease (AD) research. The AddNeuroMed consortium conducted a longitudinal observational cohort study with the aim to discover AD biomarkers. During this study, a broad selection of data modalities was measured including clinical assessments, magnetic resonance imaging, genotyping, transcriptomic profiling, and blood plasma proteomics. Some of the collected data were shared with third-party researchers. However, this data was incomplete, erroneous, and lacking in interoperability.

OBJECTIVE

To provide the research community with an accessible, multimodal, patient-level AD cohort dataset.

METHODS

We systematically addressed several limitations of the originally shared resources and provided additional unreleased data to enhance the dataset.

RESULTS

In this work, we publish and describe ANMerge, a new version of the AddNeuroMed dataset. ANMerge includes multimodal data from 1,702 study participants and is accessible to the research community via a centralized portal.

CONCLUSION

ANMerge is an information rich patient-level data resource that can serve as a discovery and validation cohort for data-driven AD research, such as, for example, machine learning and artificial intelligence approaches.

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.

Multiple functional variants in the IL1RL1 region are pretransplant markers for risk of GVHD and infection deaths

Graft-versus-host disease (GVHD) and infections are the 2 main causes of death without relapse after allogeneic hematopoietic cell transplantation (HCT). Elevated soluble serum simulation-2 (sST2), the product of IL1RL1 in plasma/serum post-HCT, is a validated GVHD biomarker. Hundreds of SNPs at 2q12.1 have been shown to be strongly associated with sST2 concentrations in healthy populations. We therefore hypothesized that the donor genetic variants in IL1RL1 correlate with sST2 protein levels associated with patient survival outcomes after HCT. We used DISCOVeRY-BMT (Determining the Influence of Susceptibility Conveying Variants Related to 1-Year Mortality after Blood and Marrow Transplantation), a genomic study of >3000 donor-recipient pairs, to inform our hypothesis. We first measured pre-HCT plasma/serum sST2 levels in a subset of DISCOVeRY-BMT donors (n = 757) and tested the association of donor sST2 levels with donor single nucleotide polymorphisms (SNPs) in the 2q12.1 region. Donor SNPs associated with sST2 levels were then tested for association with recipient death caused by acute GVHD (aGVHD)-, infection-, and transplant-related mortality in cohorts 1 and 2. Meta-analyses of cohorts 1 and 2 were performed using fixed-effects inverse variance weighting, and P values were corrected for multiple comparisons. Donor risk alleles in rs22441131 (P _meta = .00026) and rs2310241 (P _meta = .00033) increased the cumulative incidence of aGVHD death up to fourfold and were associated with high sST2 levels. Donor risk alleles at rs4851601 (P _meta = 9.7 × 10^-7), rs13019803 (P _meta = 8.9 × 10^-6), and rs13015714 (P _meta = 5.3 × 10^-4) increased cumulative incidence of infection death to almost sevenfold and were associated with low sST2 levels. These functional variants are biomarkers of infection or aGVHD death and could facilitate donor selection, prophylaxis, and a conditioning regimen to reduce post-HCT mortality.

Population-scale proteome variation in human induced pluripotent stem cells

Human disease phenotypes are driven primarily by alterations in protein expression and/or function. To date, relatively little is known about the variability of the human proteome in populations and how this relates to variability in mRNA expression and to disease loci. Here, we present the first comprehensive proteomic analysis of human induced pluripotent stem cells (iPSC), a key cell type for disease modelling, analysing 202 iPSC lines derived from 151 donors, with integrated transcriptome and genomic sequence data from the same lines. We characterised the major genetic and non-genetic determinants of proteome variation across iPSC lines and assessed key regulatory mechanisms affecting variation in protein abundance. We identified 654 protein quantitative trait loci (pQTLs) in iPSCs, including disease-linked variants in protein-coding sequences and variants with trans regulatory effects. These include pQTL linked to GWAS variants that cannot be detected at the mRNA level, highlighting the utility of dissecting pQTL at peptide level resolution.

Genome-wide association study of rate of cognitive decline in Alzheimer's disease patients identifies novel genes and pathways

INTRODUCTION

Variability exists in the disease trajectories of Alzheimer's disease (AD) patients. We performed a genome-wide association study to examine rate of cognitive decline (ROD) in patients with AD.

METHODS

We tested for interactions between genetic variants and time since diagnosis to predict the ROD of a composite cognitive score in 3946 AD cases and performed pathway analysis on the top genes.

RESULTS

Suggestive associations (P < 1.0 × 10-6 ) were observed on chromosome 15 in DNA polymerase-γ (rs3176205, P = 1.11 × 10-7 ), chromosome 7 (rs60465337,P = 4.06 × 10-7 ) in contactin-associated protein-2, in RP11-384F7.1 on chromosome 3 (rs28853947, P = 5.93 × 10-7 ), family with sequence similarity 214 member-A on chromosome 15 (rs2899492, P = 5.94 × 10-7 ), and intergenic regions on chromosomes 16 (rs4949142, P = 4.02 × 10-7 ) and 4 (rs1304013, P = 7.73 × 10-7 ). Significant pathways involving neuronal development and function, apoptosis, memory, and inflammation were identified.

DISCUSSION

Pathways related to AD, intelligence, and neurological function determine AD progression, while previously identified AD risk variants, including the apolipoprotein (APOE) ε4 and ε2 variants, do not have a major impact.

Epigenetics meets proteomics in an epigenome-wide association study with circulating blood plasma protein traits

DNA methylation and blood circulating proteins have been associated with many complex disorders, but the underlying disease-causing mechanisms often remain unclear. Here, we report an epigenome-wide association study of 1123 proteins from 944 participants of the KORA population study and replication in a multi-ethnic cohort of 344 individuals. We identify 98 CpG-protein associations (pQTMs) at a stringent Bonferroni level of significance. Overlapping associations with transcriptomics, metabolomics, and clinical endpoints suggest implication of processes related to chronic low-grade inflammation, including a network involving methylation of NLRC5, a regulator of the inflammasome, and associated pQTMs implicating key proteins of the immune system, such as CD48, CD163, CXCL10, CXCL11, LAG3, FCGR3B, and B2M. Our study links DNA methylation to disease endpoints via intermediate proteomics phenotypes and identifies correlative networks that may eventually be targeted in a personalized approach of chronic low-grade inflammation.

Profiling of the plasma proteome across different stages of human heart failure

Heart failure (HF) is a major public health problem characterized by inability of the heart to maintain sufficient output of blood. The systematic characterization of circulating proteins across different stages of HF may provide pathophysiological insights and identify therapeutic targets. Here we report application of aptamer-based proteomics to identify proteins associated with prospective HF incidence in a population-based cohort, implicating modulation of immunological, complement, coagulation, natriuretic and matrix remodeling pathways up to two decades prior to overt disease onset. We observe further divergence of these proteins from the general population in advanced HF, and regression after heart transplantation. By leveraging coronary sinus samples and transcriptomic tools, we describe likely cardiac and specific cellular origins for several of the proteins, including Nt-proBNP, thrombospondin-2, interleukin-18 receptor, gelsolin, and activated C5. Our findings provide a broad perspective on both cardiac and systemic factors associated with HF development.

Identification of Common and Rare Genetic Variation Associated With Plasma Protein Levels Using Whole-Exome Sequencing and Mass Spectrometry

BACKGROUND

Identifying genetic variation associated with plasma protein levels, and the mechanisms by which they act, could provide insight into alterable processes involved in regulation of protein levels. Although protein levels can be affected by genetic variants, their estimation can also be biased by missense variants in coding exons causing technical artifacts. Integrating genome sequence genotype data with mass spectrometry-based protein level estimation could reduce bias, thereby improving detection of variation that affects RNA or protein metabolism.

METHODS

Here, we integrate the blood plasma protein levels of 664 proteins from 165 participants of the Tromsø Study, measured via tandem mass tag mass spectrometry, with whole-exome sequencing data to identify common and rare genetic variation associated with peptide and protein levels (protein quantitative trait loci [pQTLs]). We additionally use literature and database searches to prioritize putative functional variants for each pQTL.

RESULTS

We identify 109 independent associations (36 protein and 73 peptide) and use genotype data to exclude 49 (4 protein and 45 peptide) as technical artifacts. We describe 2 particular cases of rare variation: 1 associated with the complement pathway and 1 with platelet degranulation. We identify putative functional variants and show that pQTLs act through diverse molecular mechanisms that affect both RNA and protein metabolism.

CONCLUSIONS

We show that although the majority of pQTLs exert their effects by modulating RNA metabolism, many affect protein levels directly. Our work demonstrates the extent by which pQTL studies are affected by technical artifacts and highlights how prioritizing the functional variant in pQTL studies can lead to insights into the molecular steps by which a protein may be regulated.

Comprehensive Cis-Regulation Analysis of Genetic Variants in Human Lymphoblastoid Cell Lines

Genetic variants can influence the expression of mRNA and protein. Genetic regulatory loci such as expression quantitative trait loci (eQTLs) and protein quantitative trait loci (pQTLs) exist in several species. However, it remains unclear how human genetic variants regulate mRNA and protein expression. Here, we characterized six mechanistic models for the genetic regulatory patterns of single-nucleotide polymorphisms (SNPs) and their actions on post-transcriptional expression. Data from Yoruba HapMap lymphoblastoid cell lines were analyzed to identify human cis-eQTLs and pQTLs, as well as protein-specific QTLs (psQTLs). Our results indicated that genetic regulatory loci primarily affected mRNA and protein abundance in patterns where the two were well-correlated. While this finding was observed in both humans and mice (57.5% and 70.3%, respectively), the genetic regulatory patterns differed between species, implying evolutionary differences. Mouse SNPs generally targeted changes in transcript expression (51%), whereas in humans, they largely regulated protein abundance, independent of transcription levels (55.9%). The latter independent function can be explained by psQTLs. Our analysis suggests that local functional genetic variants in the human genome mainly modulate protein abundance independent of mRNA levels through post-transcriptional mechanisms. These findings clarify the impact of genetic variation on phenotype, which is of particular relevance to disease risk and treatment response.

Genome and epigenome wide studies of neurological protein biomarkers in the Lothian Birth Cohort 1936

Although plasma proteins may serve as markers of neurological disease risk, the molecular mechanisms responsible for inter-individual variation in plasma protein levels are poorly understood. Therefore, we conduct genome- and epigenome-wide association studies on the levels of 92 neurological proteins to identify genetic and epigenetic loci associated with their plasma concentrations (n = 750 healthy older adults). We identify 41 independent genome-wide significant (P < 5.4 × 10^-10) loci for 33 proteins and 26 epigenome-wide significant (P < 3.9 × 10^-10) sites associated with the levels of 9 proteins. Using this information, we identify biological pathways in which putative neurological biomarkers are implicated (neurological, immunological and extracellular matrix metabolic pathways). We also observe causal relationships (by Mendelian randomisation analysis) between changes in gene expression (DRAXIN, MDGA1 and KYNU), or DNA methylation profiles (MATN3, MDGA1 and NEP), and altered plasma protein levels. Together, this may help inform causal relationships between biomarkers and neurological diseases.

Association of IL1RAP-related genetic variation with cerebrospinal fluid concentration of Alzheimer-associated tau protein

A possible involvement of the gene IL1RAP (interleukin-1 receptor-associated protein) in the pathogenesis of Alzheimer’s disease (AD) has been suggested in GWASs of cerebrospinal fluid (CSF) tau levels and longitudinal change in brain amyloid burden. The aim of this study was to examine previously implicated genetic markers in and near IL1RAP in relation to AD risk, CSF tau and Aβ biomarkers, as well as cognitive decline, in a case (AD)-control study and an age homogenous population-based cohort. Genotyping of IL1RAP-related single nucleotide polymorphisms (SNPs), selected based on previous GWAS results, was performed. 3446 individuals (1154 AD cases and 2292 controls) were included in the analyses of AD risk, 1400 individuals (cognitively normal = 747, AD = 653) in the CSF biomarker analyses, and 861 individuals in the analyses of cognitive decline. We found no relation between IL1RAP-related SNPs and AD risk. However, CSF total-tau and phospho-tau were associated with the SNP rs9877502 (p = 6 × 10⁻³ and p = 5 × 10⁻⁴). Further, nominal associations (p = 0.03–0.05) were found between three other SNPs and CSF biomarker levels, or levels of cognitive performance and decline in a sub-sample from the general population. These results support previous studies suggesting an association of IL1RAP with disease intensity of AD.

Identifying Multi-Omics Causers and Causal Pathways for Complex Traits

The central dogma of molecular biology delineates a unidirectional causal flow, i.e., DNA → RNA → protein → trait. Genome-wide association studies, next-generation sequencing association studies, and their meta-analyses have successfully identified ~12,000 susceptibility genetic variants that are associated with a broad array of human physiological traits. However, such conventional association studies ignore the mediate causers (i.e., RNA, protein) and the unidirectional causal pathway. Such studies may not be ideally powerful; and the genetic variants identified may not necessarily be genuine causal variants. In this article, we model the central dogma by a mediate causal model and analytically prove that the more remote an omics level is from a physiological trait, the smaller the magnitude of their correlation is. Under both random and extreme sampling schemes, we numerically demonstrate that the proteome-trait correlation test is more powerful than the transcriptome-trait correlation test, which in turn is more powerful than the genotype-trait association test. In conclusion, integrating RNA and protein expressions with DNA data and causal inference are necessary to gain a full understanding of how genetic causal variants contribute to phenotype variations.

Deep molecular phenotypes link complex disorders and physiological insult to CpG methylation

Epigenetic regulation of cellular function provides a mechanism for rapid organismal adaptation to changes in health, lifestyle and environment. Associations of cytosine-guanine di-nucleotide (CpG) methylation with clinical endpoints that overlap with metabolic phenotypes suggest a regulatory role for these CpG sites in the body's response to disease or environmental stress. We previously identified 20 CpG sites in an epigenome-wide association study (EWAS) with metabolomics that were also associated in recent EWASs with diabetes-, obesity-, and smoking-related endpoints. To elucidate the molecular pathways that connect these potentially regulatory CpG sites to the associated disease or lifestyle factors, we conducted a multi-omics association study including 2474 mass-spectrometry-based metabolites in plasma, urine and saliva, 225 NMR-based lipid and metabolite measures in blood, 1124 blood-circulating proteins using aptamer technology, 113 plasma protein N-glycans and 60 IgG-glyans, using 359 samples from the multi-ethnic Qatar Metabolomics Study on Diabetes (QMDiab). We report 138 multi-omics associations at these CpG sites, including diabetes biomarkers at the diabetes-associated TXNIP locus, and smoking-specific metabolites and proteins at multiple smoking-associated loci, including AHRR. Mendelian randomization suggests a causal effect of metabolite levels on methylation of obesity-associated CpG sites, i.e. of glycerophospholipid PC(O-36: 5), glycine and a very low-density lipoprotein (VLDL-A) on the methylation of the obesity-associated CpG loci DHCR24, MYO5C and CPT1A, respectively. Taken together, our study suggests that multi-omics-associated CpG methylation can provide functional read-outs for the underlying regulatory response mechanisms to disease or environmental insults.

ProGeM: a framework for the prioritization of candidate causal genes at molecular quantitative trait loci

Quantitative trait locus (QTL) mapping of molecular phenotypes such as metabolites, lipids and proteins through genome-wide association studies represents a powerful means of highlighting molecular mechanisms relevant to human diseases. However, a major challenge of this approach is to identify the causal gene(s) at the observed QTLs. Here, we present a framework for the 'Prioritization of candidate causal Genes at Molecular QTLs' (ProGeM), which incorporates biological domain-specific annotation data alongside genome annotation data from multiple repositories. We assessed the performance of ProGeM using a reference set of 227 previously reported and extensively curated metabolite QTLs. For 98% of these loci, the expert-curated gene was one of the candidate causal genes prioritized by ProGeM. Benchmarking analyses revealed that 69% of the causal candidates were nearest to the sentinel variant at the investigated molecular QTLs, indicating that genomic proximity is the most reliable indicator of 'true positive' causal genes. In contrast, cis-gene expression QTL data led to three false positive candidate causal gene assignments for every one true positive assignment. We provide evidence that these conclusions also apply to other molecular phenotypes, suggesting that ProGeM is a powerful and versatile tool for annotating molecular QTLs. ProGeM is freely available via GitHub.

IL6/IL6R genetic diversity and plasma IL6 levels in bipolar disorder: An Indo-French study

Reports of association of genetic variants of IL6 and its receptor (IL6R) with psychiatric disorders are inconsistent, and there are few population-based studies thus far in bipolar disorder (BD). We genotyped the IL6 rs1800795 and IL6R rs2228145 polymorphisms in two independent sets of patients exposed to different environmental stimuli such as climatic conditions or specific infectious burden - a French sample and a south Indian Tamil sample of BD with quantitation of circulating plasma IL-6 levels in the latter sub-sample. In both populations, allele and genotype frequencies did not differ significantly between cases and controls for either polymorphism. Upon stratifying based on age at onset, we found no associations with the IL6 rs1800795 variant. However, the IL6R rs2228145 C allele and CC genotype were associated with early onset of disease in the French sample when compared to late onset BD. A similar trend was observed in the Indian population where we also found that plasma IL-6 levels were significantly higher in BD and also in patients who were in residual phase or remission both as compared to controls. Our findings are in favour of a possible trans-ethnic implication of the IL6R genetic diversity in BD and reinforce the notion that IL-6 is an important marker of the operating inflammatory processes in the disease.

Trait Loci Mapping and CSF Proteome

Recent advance in high-throughput proteome analysis has enabled genome-wide and proteome-wide analyses of associations between single nucleotide polymorphisms and protein expression levels. Protein quantitative trait locus (pQTL) studies using cerebrospinal fluid (CSF) and DNA samples may provide valuable insights into the genetic basis and molecular mechanisms regulating protein expression in the central nervous system. In this chapter, we describe a step-by-step procedures of CSF collection and pQTL analysis, by using PLINK and R software.

Defining the molecular signatures of Achilles tendinopathy and anterior cruciate ligament ruptures: A whole-exome sequencing approach

Musculoskeletal soft tissue injuries are complex phenotypes with genetics being one of many proposed risk factors. Case-control association studies using the candidate gene approach have predominately been used to identify risk loci for these injuries. However, the ability to identify all risk conferring variants using this approach alone is unlikely. Therefore, this study aimed to further define the genetic profile of these injuries using an integrated omics approach involving whole exome sequencing and a customised analyses pipeline. The exomes of ten exemplar asymptomatic controls and ten exemplar cases with Achilles tendinopathy were individually sequenced using a platform that included the coverage of the untranslated regions and miRBase miRNA genes. Approximately 200 000 variants were identified in the sequenced samples. Previous research was used to guide a targeted analysis of the genes encoding the tenascin-C (TNC) glycoprotein and the α1 chain of type XXVII collagen (COL27A1) located on chromosome 9. Selection of variants within these genes were; however, not predetermined but based on a tiered filtering strategy. Four variants in TNC (rs1061494, rs1138545, rs2104772 and rs1061495) and three variants in the upstream COL27A1 gene (rs2567706, rs2241671 and rs2567705) were genotyped in larger Achilles tendinopathy and anterior cruciate ligament (ACL) rupture sample groups. The CC genotype of TNC rs1061494 (C/T) was associated with the risk of Achilles tendinopathy (p = 0.018, OR: 2.5 95% CI: 1.2-5.1). Furthermore, the AA genotype of the TNC rs2104772 (A/T) variant was significantly associated with ACL ruptures in the female subgroup (p = 0.035, OR: 2.3 95% CI: 1.1-5.5). An inferred haplotype in the TNC gene was also associated with the risk of Achilles tendinopathy. These results provide a proof of concept for the use of a customised pipeline for the exploration of a larger genomic dataset. This approach, using previous research to guide a targeted analysis of the data has generated new genetic signatures in the biology of musculoskeletal soft tissue injuries.

A gender-specific association of the polymorphism Ile197Met in the kininogen 1 gene with plasma irbesartan concentrations in Chinese patients with essential hypertension

This study was conducted to explore interactions in the association of the kininogen (KNG1) Ile197Met polymorphism and gender with plasma concentrations of irbesartan in Chinese patients with essential hypertension. A total of 1100 subjects with essential hypertension received a daily oral dose of 150 mg irbesartan for twenty-eight consecutive days. High-performance liquid chromatography-fluorescence (HPLC) was used to detect plasma irbesartan concentrations on day 28. The KNG1 Ile197Met gene polymorphism was determined using high-throughput TaqMan technology. The frequency distribution of KNG1 Ile197Met genotype conformed to the Hardy-Weinberg equilibrium. After 28 days of treatment, patients with the GG genotype had significantly lower irbesartan concentrations (P = 0.033) compared to homozygous TT genotype carriers. After stratifying by gender, male G allele carriers had significantly lower irbesartan concentrations (GG, P = 0.015; TG, P = 0.015, respectively) relative to TT genotype after adjusting for age, region, body mass index (BMI), smoking, and alcohol consumption. However, there was no significant difference in female subjects. A further test for a multiplicative interaction between the KNG1 Ile197Met polymorphism and gender in association with ln-plasma irbesartan concentrations in a multiple linear regression model was also significant (P for interaction = 0.033). This is the first study to suggest that gender may influence the association of the Ile197Met variant of KNG1 with ln-plasma irbesartan concentration. This finding may indicate that the interaction of gender and the KNG1 Ile197Met gene polymorphism can influence plasma trough irbesartan concentrations, which may contribute to a better development of personalized hypertensive treatment in Chinese patients.

Genome-wide mapping of plasma protein QTLs identifies putatively causal genes and pathways for cardiovascular disease

Identifying genetic variants associated with circulating protein concentrations (protein quantitative trait loci; pQTLs) and integrating them with variants from genome-wide association studies (GWAS) may illuminate the proteome's causal role in disease and bridge a knowledge gap regarding SNP-disease associations. We provide the results of GWAS of 71 high-value cardiovascular disease proteins in 6861 Framingham Heart Study participants and independent external replication. We report the mapping of over 16,000 pQTL variants and their functional relevance. We provide an integrated plasma protein-QTL database. Thirteen proteins harbor pQTL variants that match coronary disease-risk variants from GWAS or test causal for coronary disease by Mendelian randomization. Eight of these proteins predict new-onset cardiovascular disease events in Framingham participants. We demonstrate that identifying pQTLs, integrating them with GWAS results, employing Mendelian randomization, and prospectively testing protein-trait associations holds potential for elucidating causal genes, proteins, and pathways for cardiovascular disease and may identify targets for its prevention and treatment.

A genome-wide association study on photic sneeze syndrome in a Japanese population

Photic sneeze syndrome (PSS) is characterized by a tendency to sneeze when the eye is exposed to bright light. Recent genome-wide association studies (GWASs) have identified single-nucleotide polymorphisms (SNPs) associated with PSS in Caucasian populations. We performed a GWAS on PSS in Japanese individuals who responded to a web-based survey and provided saliva samples. After quality control, genotype data of 210,086 SNPs in 11,409 individuals were analyzed. The overall prevalence of PSS was 3.2%. Consistent with previous reports, SNPs at 3p12.1 were associated with PSS at genome-wide significance (p < 5.0 × 10^-8). Furthermore, two novel loci at 9q34.2 and 4q35.2 reached suggestive significance (p < 5.0 × 10^-6). Our data also provided evidence supporting the two additional SNPs on 2q22.3 and 9q33.2 reportedly associated with PSS. Our study reproduced previous findings in Caucasian populations and further suggested novel PSS loci in the Japanese population.

Co-occurring expression and methylation QTLs allow detection of common causal variants and shared biological mechanisms

Inherited genetic variation affects local gene expression and DNA methylation in humans. Most expression quantitative trait loci (cis-eQTLs) occur at the same genomic location as a methylation QTL (cis-meQTL), suggesting a common causal variant and shared mechanism. Using DNA and RNA from peripheral blood of Bangladeshi individuals, here we use co-localization methods to identify eQTL-meQTL pairs likely to share a causal variant. We use partial correlation and mediation analyses to identify >400 of these pairs showing evidence of a causal relationship between expression and methylation (i.e., shared mechanism) with many additional pairs we are underpowered to detect. These co-localized pairs are enriched for SNPs showing opposite associations with expression and methylation, although many SNPs affect multiple CpGs in opposite directions. This work demonstrates the pervasiveness of co-regulated expression and methylation in the human genome. Applying this approach to other types of molecular QTLs can enhance our understanding of regulatory mechanisms.

Genetic Architecture of the Cardiovascular Risk Proteome

BACKGROUND

We recently identified 156 proteins in human plasma that were each associated with the net Framingham Cardiovascular Disease Risk Score using an aptamer-based proteomic platform in Framingham Heart Study Offspring participants. Here we hypothesized that performing genome-wide association studies and exome array analyses on the levels of each of these 156 proteins might identify genetic determinants of risk-associated circulating factors and provide insights into early cardiovascular pathophysiology.

METHODS

We studied the association of genetic variants with the plasma levels of each of the 156 Framingham Cardiovascular Disease Risk Score-associated proteins using linear mixed-effects models in 2 population-based cohorts. We performed discovery analyses on plasma samples from 759 participants of the Framingham Heart Study Offspring cohort, an observational study of the offspring of the original Framingham Heart Study and their spouses, and validated these findings in plasma samples from 1421 participants of the MDCS (Malmö Diet and Cancer Study). To evaluate the utility of this strategy in identifying new biological pathways relevant to cardiovascular disease pathophysiology, we performed studies in a cell-model system to experimentally validate the functional significance of an especially novel genetic association with circulating apolipoprotein E levels.

RESULTS

We identified 120 locus-protein associations in genome-wide analyses and 41 associations in exome array analyses, the majority of which have not been described previously. These loci explained up to 66% of interindividual plasma protein-level variation and, on average, accounted for 3 times the amount of variation explained by common clinical factors, such as age, sex, and diabetes mellitus status. We described overlap among many of these loci and cardiovascular disease genetic risk variants. Finally, we experimentally validated a novel association between circulating apolipoprotein E levels and the transcription factor phosphatase 1G. Knockdown of phosphatase 1G in a human liver cell model resulted in decreased apolipoprotein E transcription and apolipoprotein E protein levels in cultured supernatants.

CONCLUSIONS

We identified dozens of novel genetic determinants of proteins associated with the Framingham Cardiovascular Disease Risk Score and experimentally validated a new role for phosphatase 1G in lipoprotein biology. Further, genome-wide and exome array data for each protein have been made publicly available as a resource for cardiovascular disease research.

Novel Tenascin-C Haplotype Modifies the Risk for a Failure to Heal After Rotator Cuff Repair

BACKGROUND

Several single-nucleotide polymorphisms (SNPs) in the TNC gene have recently been found to be associated with degenerative rotator cuff tears.

HYPOTHESIS

Exonic SNPs in the TNC gene are related to the risk for a failure to heal after rotator cuff repair.

STUDY DESIGN

Case-control study; Level of evidence, 3.

METHODS

A total of 302 patients from the Vienna area and European Caucasian ancestry underwent mini-open rotator cuff repair for a full-thickness superior or posterosuperior tear and were assessed for the integrity of the repair 1 year postoperatively with a real-time 7.5- to 10-MHz ultrasound linear array transducer. Outcomes were classified as intact (complete footprint coverage), small (<200 mm²), or large (≥200 mm²) recurrent defect. Patients were genotyped for 15 previously identified risk SNPs within a 49-kbp segment of the TNC gene with the KASP genotyping technology or the Ion-Torrent Personal Genome Machine System.

RESULTS

All recurrent defects were atraumatic failures, and the overall failure rate was 39.7%. Of the traditional risk factors, only the initial tear size was significantly associated with a failure to heal. In a multinomial logistic regression model, the T allele at rs1138545 [C>T] was protective for a large recurrent defect (odds ratio = 0.16; 95% CI, 0.09-0.31). The role of rs1138545 was further backed by haplotype analysis, which showed that the combination of the C allele at rs1138545 [C>T], the A allele at rs2104772 [A>T], and the G allele at rs10759752 [A>G] formed the risk-related haplotype [CAG]. The CAG haplotype was associated with large recurrent defects ( P < .0001; haplotype frequency, 0.394; haplotype score, 4.518). Exonic marker rs1138545 transcribed into all isoforms of the TNC protein, whereas exonic marker rs2104772, which has been associated with Achilles tendinopathy before, transcribed only into large isoforms of the TNC protein.

CONCLUSION

Recurrent defects after rotator cuff repairs are clinically relevant, and a heritable component of the disorder is plausible on the basis of a genetic association with 8 TNC variants. Characterization of TNC sequences that favor tendon healing will help engineer new products in regenerative medicine.

Genome-wide quantitative trait loci mapping of the human cerebrospinal fluid proteome

Cerebrospinal fluid (CSF) is virtually the only one accessible source of proteins derived from the central nervous system (CNS) of living humans and possibly reflects the pathophysiology of a variety of neuropsychiatric diseases. However, little is known regarding the genetic basis of variation in protein levels of human CSF. We examined CSF levels of 1,126 proteins in 133 subjects and performed a genome-wide association analysis of 514,227 single nucleotide polymorphisms (SNPs) to detect protein quantitative trait loci (pQTLs). To be conservative, Spearman's correlation was used to identify an association between genotypes of SNPs and protein levels. A total of 421 cis and 25 trans SNP-protein pairs were significantly correlated at a false discovery rate (FDR) of less than 0.01 (nominal P < 7.66 × 10-9). Cis-only analysis revealed additional 580 SNP-protein pairs with FDR < 0.01 (nominal P < 2.13 × 10-5). pQTL SNPs were more likely, compared to non-pQTL SNPs, to be a disease/trait-associated variants identified by previous genome-wide association studies. The present findings suggest that genetic variations play an important role in the regulation of protein expression in the CNS. The obtained database may serve as a valuable resource to understand the genetic bases for CNS protein expression pattern in humans.

Emerging Affinity-Based Proteomic Technologies for Large-Scale Plasma Profiling in Cardiovascular Disease

Plasma biomarkers that reflect molecular states of the cardiovascular system are central for clinical decision making. Routinely used plasma biomarkers include troponins, natriuretic peptides, and lipoprotein particles, yet interrogate only a modest subset of pathways relevant to cardiovascular disease. Systematic profiling of a larger portion of circulating plasma proteins (the plasma proteome) will provide opportunities for unbiased discovery of novel markers to improve diagnostic or predictive accuracy. In addition, proteomic profiling may inform pathophysiological understanding and point to novel therapeutic targets. Obstacles for comprehensive proteomic profiling include the immense size and structural heterogeneity of the proteome, and the broad range of abundance levels, as well. Proteome-wide, untargeted profiling can be performed in tissues and cells with tandem mass spectrometry. However, applications to plasma are limited by the need for complex preanalytical sample preparation stages limiting sample throughput. Multiplexing of targeted methods based on capture and detection of specific proteins are therefore receiving increasing attention in plasma proteomics. Immunoaffinity assays are the workhorse for measuring individual proteins but have been limited for proteomic applications by long development times, cross-reactivity preventing multiplexing, specificity issues, and incomplete sensitivity to detect proteins in the lower range of the abundance spectrum (below picograms per milliliter). Emerging technologies to address these issues include nucleotide-labeled immunoassays and aptamer reagents that can be automated for efficient multiplexing of thousands of proteins at high sample throughput, coupling of affinity capture methods to mass spectrometry for improved specificity, and ultrasensitive detection systems to measure low-abundance proteins. In addition, proteomics can now be integrated with modern genomics tools to comprehensively relate proteomic profiles to genetic variants, which may both influence binding of affinity reagents and serve to validate the target specificity of affinity assays. The application of deep quantitative proteomic profiling to large cohorts has thus become increasingly feasible with emerging affinity methods. The aims of this article are to provide the broad readership of Circulation with a timely overview of emerging methods for affinity proteomics and recent progress in cardiovascular medicine based on such methods.

Candidate gene approach identifies six SNPs in tenascin-C (TNC) associated with degenerative rotator cuff tears

Evidence for a heritable predisposition to rotator cuff tears (RCTs) is growing. Unrelated Caucasian individuals with surgically diagnosed full thickness RCTs (cases) and elderly Caucasian controls with intact rotator cuffs were screened for differences at the candidate genes: TNC, Col5A1, TIMP-1, MMP-1, MMP-2, MMP-3, MMP-9, and MMP-13. A first cohort (59 cases; 32 controls) was genotyped with the Sequenom MassARRAY iPLEX system. Of 142 SNPs within about 67-kbp of the TNC gene, 30 were tested for differences in proportions between cases and controls. A second, matched cohort (96 patients; 44 controls) was also genotyped for the same 30 SNPs, but with the KASP™ genotyping technology. Combining the two cohorts and after Bonferroni correction, six SNPs were significantly associated with RCT. Compared to controls, RCT patients showed a significantly higher rate of homozygosity at rs72758637, rs7021589, and rs1138545; a significantly higher rate of heterozygosity at rs10759753, rs3789870, and rs7035322 and a higher minor allele frequency at rs3789870. Rs1138545, a missense SNP in exon10 might be of biological significance because it varies the amino acid sequence close to the TNC-FNIII5 domain. The FNIII5 domain binds multiple growth factors and co-ligates with integrins during tendon healing. © 2016 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:894-901, 2017.

The druggable genome and support for target identification and validation in drug development

Target identification (determining the correct drug targets for a disease) and target validation (demonstrating an effect of target perturbation on disease biomarkers and disease end points) are important steps in drug development. Clinically relevant associations of variants in genes encoding drug targets model the effect of modifying the same targets pharmacologically. To delineate drug development (including repurposing) opportunities arising from this paradigm, we connected complex disease- and biomarker-associated loci from genome-wide association studies to an updated set of genes encoding druggable human proteins, to agents with bioactivity against these targets, and, where there were licensed drugs, to clinical indications. We used this set of genes to inform the design of a new genotyping array, which will enable association studies of druggable genes for drug target selection and validation in human disease.

Connecting genetic risk to disease end points through the human blood plasma proteome

Genome-wide association studies (GWAS) with intermediate phenotypes, like changes in metabolite and protein levels, provide functional evidence to map disease associations and translate them into clinical applications. However, although hundreds of genetic variants have been associated with complex disorders, the underlying molecular pathways often remain elusive. Associations with intermediate traits are key in establishing functional links between GWAS-identified risk-variants and disease end points. Here we describe a GWAS using a highly multiplexed aptamer-based affinity proteomics platform. We quantify 539 associations between protein levels and gene variants (pQTLs) in a German cohort and replicate over half of them in an Arab and Asian cohort. Fifty-five of the replicated pQTLs are located in trans. Our associations overlap with 57 genetic risk loci for 42 unique disease end points. We integrate this information into a genome-proteome network and provide an interactive web-tool for interrogations. Our results provide a basis for novel approaches to pharmaceutical and diagnostic applications.

Individual genetic variation can affect the levels of protein in blood, but detailed data sets linking these two types of data are rare. Here, the authors carry out a genome-wide association study of levels of over a thousand different proteins, and describe many new SNP-protein interactions.

Common Genetic Polymorphisms Influence Blood Biomarker Measurements in COPD

Implementing precision medicine for complex diseases such as chronic obstructive lung disease (COPD) will require extensive use of biomarkers and an in-depth understanding of how genetic, epigenetic, and environmental variations contribute to phenotypic diversity and disease progression. A meta-analysis from two large cohorts of current and former smokers with and without COPD [SPIROMICS (N = 750); COPDGene (N = 590)] was used to identify single nucleotide polymorphisms (SNPs) associated with measurement of 88 blood proteins (protein quantitative trait loci; pQTLs). PQTLs consistently replicated between the two cohorts. Features of pQTLs were compared to previously reported expression QTLs (eQTLs). Inference of causal relations of pQTL genotypes, biomarker measurements, and four clinical COPD phenotypes (airflow obstruction, emphysema, exacerbation history, and chronic bronchitis) were explored using conditional independence tests. We identified 527 highly significant (p < 8 X 10-10) pQTLs in 38 (43%) of blood proteins tested. Most pQTL SNPs were novel with low overlap to eQTL SNPs. The pQTL SNPs explained >10% of measured variation in 13 protein biomarkers, with a single SNP (rs7041; p = 10-392) explaining 71%-75% of the measured variation in vitamin D binding protein (gene = GC). Some of these pQTLs [e.g., pQTLs for VDBP, sRAGE (gene = AGER), surfactant protein D (gene = SFTPD), and TNFRSF10C] have been previously associated with COPD phenotypes. Most pQTLs were local (cis), but distant (trans) pQTL SNPs in the ABO blood group locus were the top pQTL SNPs for five proteins. The inclusion of pQTL SNPs improved the clinical predictive value for the established association of sRAGE and emphysema, and the explanation of variance (R2) for emphysema improved from 0.3 to 0.4 when the pQTL SNP was included in the model along with clinical covariates. Causal modeling provided insight into specific pQTL-disease relationships for airflow obstruction and emphysema. In conclusion, given the frequency of highly significant local pQTLs, the large amount of variance potentially explained by pQTL, and the differences observed between pQTLs and eQTLs SNPs, we recommend that protein biomarker-disease association studies take into account the potential effect of common local SNPs and that pQTLs be integrated along with eQTLs to uncover disease mechanisms. Large-scale blood biomarker studies would also benefit from close attention to the ABO blood group.

Associations Between Common and Rare Exonic Genetic Variants and Serum Levels of 20 Cardiovascular-Related Proteins: The Tromsø Study

Supplemental Digital Content is available in the text.

Background—

Genetic variation can be used to study causal relationships between biomarkers and diseases. Here, we identify new common and rare genetic variants associated with cardiovascular-related protein levels (protein quantitative trait loci [pQTLs]). We functionally annotate these pQTLs, predict and experimentally confirm a novel molecular interaction, and determine which pQTLs are associated with diseases and physiological phenotypes.

Methods and Results—

As part of a larger case–control study of venous thromboembolism, serum levels of 51 proteins implicated in cardiovascular diseases were measured in 330 individuals from the Tromsø Study. Exonic genetic variation near each protein’s respective gene (cis) was identified using sequencing and arrays. Using single site and gene-based tests, we identified 27 genetic associations between pQTLs and the serum levels of 20 proteins: 14 associated with common variation in cis, of which 6 are novel (ie, not previously reported); 7 associations with rare variants in cis, of which 4 are novel; and 6 associations in trans. Of the 20 proteins, 15 were associated with single sites and 7 with rare variants. cis-pQTLs for kallikrein and F12 also show trans associations for proteins (uPAR, kininogen) known to be cleaved by kallikrein and with NTproBNP. We experimentally demonstrate that kallikrein can cleave proBNP (NTproBNP precursor) in vitro. Nine of the pQTLs have previously identified associations with 17 disease and physiological phenotypes.

Conclusions—

We have identified cis and trans genetic variation associated with the serum levels of 20 proteins and utilized these pQTLs to study molecular mechanisms underlying disease and physiological phenotypes.

Signatures of Evolutionary Adaptation in Quantitative Trait Loci Influencing Trace Element Homeostasis in Liver

Essential trace elements possess vital functions at molecular, cellular, and physiological levels in health and disease, and they are tightly regulated in the human body. In order to assess variability and potential adaptive evolution of trace element homeostasis, we quantified 18 trace elements in 150 liver samples, together with the expression levels of 90 genes and abundances of 40 proteins involved in their homeostasis. Additionally, we genotyped 169 single nucleotide polymorphism (SNPs) in the same sample set. We detected significant associations for 8 protein quantitative trait loci (pQTL), 10 expression quantitative trait loci (eQTLs), and 15 micronutrient quantitative trait loci (nutriQTL). Six of these exceeded the false discovery rate cutoff and were related to essential trace elements: 1) one pQTL for GPX2 (rs10133290); 2) two previously described eQTLs for HFE (rs12346) and SELO (rs4838862) expression; and 3) three nutriQTLs: The pathogenic C282Y mutation at HFE affecting iron (rs1800562), and two SNPs within several clustered metallothionein genes determining selenium concentration (rs1811322 and rs904773). Within the complete set of significant QTLs (which involved 30 SNPs and 20 gene regions), we identified 12 SNPs with extreme patterns of population differentiation (FST values in the top 5% percentile in at least one HapMap population pair) and significant evidence for selective sweeps involving QTLs at GPX1, SELENBP1, GPX3, SLC30A9, and SLC39A8. Overall, this detailed study of various molecular phenotypes illustrates the role of regulatory variants in explaining differences in trace element homeostasis among populations and in the human adaptive response to environmental pressures related to micronutrients.

Genetic studies of plasma analytes identify novel potential biomarkers for several complex traits

Genome-wide association studies of 146 plasma protein levels in 818 individuals revealed 56 genome-wide significant associations (28 novel) with 47 analytes. Loci associated with plasma levels of 39 proteins tested have been previously associated with various complex traits such as heart disease, inflammatory bowel disease, Type 2 diabetes and multiple sclerosis. These data suggest that these plasma protein levels may constitute informative endophenotypes for these complex traits. We found three potential pleiotropic genes: ABO for plasma SELE and ACE levels, FUT2 for CA19-9 and CEA plasma levels and APOE for ApoE and CRP levels. We also found multiple independent signals in loci associated with plasma levels of ApoH, CA19-9, FetuinA, IL6r and LPa. Our study highlights the power of biological traits for genetic studies to identify genetic variants influencing clinically relevant traits, potential pleiotropic effects and complex disease associations in the same locus.