Common Genetic Polymorphisms Influence Blood Biomarker Measurements in COPD

Proteome-wide association study using cis and trans variants and applied to blood cell and lipid-related traits in the Women's Health Initiative study

In most Proteome-Wide Association Studies (PWAS), variants near the protein-coding gene (±1 Mb), also known as cis single nucleotide polymorphisms (SNPs), are used to predict protein levels, which are then tested for association with phenotypes. However, proteins can be regulated through variants outside of the cis region. An intermediate GWAS step to identify protein quantitative trait loci (pQTL) allows for the inclusion of trans SNPs outside the cis region in protein-level prediction models. Here, we assess the prediction of 540 proteins in 1002 individuals from the Women's Health Initiative (WHI), split equally into a GWAS set, an elastic net training set, and a testing set. We compared the testing r2 between measured and predicted protein levels using this proposed approach, to the testing r2 using only cis SNPs. The two methods usually resulted in similar testing r2, but some proteins showed a significant increase in testing r2 with our method. For example, for cartilage acidic protein 1, the testing r2 increased from 0.101 to 0.351. We also demonstrate reproducible findings for predicted protein association with lipid and blood cell traits in WHI participants without proteomics data and in UK Biobank utilizing our PWAS weights.

Disease Modules Associated with Unfavorable Sleep Patterns and Their Genetic Determinants: A Prospective Cohort Study of the UK Biobank

Despite the established associations between sleep-related traits and major diseases, comprehensive assessment on affected disease modules and their genetic determinants is lacking. Using multiple correspondence analysis and the k-means clustering algorithm, 235,826 eligible participants were clustered into distinct unfavorable sleep patterns [short sleep duration (n = 10,073), snoring (22,419), insomnia (102,771), insomnia and snoring (62,909)] and favorable sleep pattern groups (37,654). The associations of unfavorable sleep patterns with 134 diseases were estimated using Cox regression models; and comorbidity network analyses were applied for disease module identification. Genetic determinants associated with each disease module were identified by genome-wide association studies (GWAS). During an average follow-up of 10.80 years, unfavorable sleep patterns featured by 'short sleep duration', 'snoring', 'insomnia', and 'insomnia and snoring' were associated with increased risk of 0, 9, 10, and 19 diseases, respectively. Furthermore, comorbidity network analyses categorized these affected diseases into three disease modules, characterized by predominant diseases related to digestive system, circulatory and endocrine systems (snoring-related patterns only), and musculoskeletal system (insomnia-related patterns only). Using the number of affected diseases, as an index of a person's susceptibility to each disease module [i.e., susceptible score (SS)], GWAS analyses identified five, one, and three significant loci associated with the residual SS of these aforementioned disease modules, respectively, which mapped to several potential biological pathways, including those related to hormone regulation and catecholamine uptake. In conclusion, individuals with unfavorable sleep patterns, particularly snoring and insomnia, had increased risk of multiple diseases. The identification of three major disease modules with their relevant genetic determinants may facilitate strategy development for precision prevention of future health decline.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43657-023-00144-8.

Proteomic networks and related genetic variants associated with smoking and chronic obstructive pulmonary disease

BACKGROUND

Studies have identified individual blood biomarkers associated with chronic obstructive pulmonary disease (COPD) and related phenotypes. However, complex diseases such as COPD typically involve changes in multiple molecules with interconnections that may not be captured when considering single molecular features.

METHODS

Leveraging proteomic data from 3,173 COPDGene Non-Hispanic White (NHW) and African American (AA) participants, we applied sparse multiple canonical correlation network analysis (SmCCNet) to 4,776 proteins assayed on the SomaScan v4.0 platform to derive sparse networks of proteins associated with current vs. former smoking status, airflow obstruction, and emphysema quantitated from high-resolution computed tomography scans. We then used NetSHy, a dimension reduction technique leveraging network topology, to produce summary scores of each proteomic network, referred to as NetSHy scores. We next performed a genome-wide association study (GWAS) to identify variants associated with the NetSHy scores, or network quantitative trait loci (nQTLs). Finally, we evaluated the replicability of the networks in an independent cohort, SPIROMICS.

RESULTS

We identified networks of 13 to 104 proteins for each phenotype and exposure in NHW and AA, and the derived NetSHy scores significantly associated with the variable of interests. Networks included known (sRAGE, ALPP, MIP1) and novel molecules (CA10, CPB1, HIS3, PXDN) and interactions involved in COPD pathogenesis. We observed 7 nQTL loci associated with NetSHy scores, 4 of which remained after conditional analysis. Networks for smoking status and emphysema, but not airflow obstruction, demonstrated a high degree of replicability across race groups and cohorts.

CONCLUSIONS

In this work, we apply state-of-the-art molecular network generation and summarization approaches to proteomic data from COPDGene participants to uncover protein networks associated with COPD phenotypes. We further identify genetic associations with networks. This work discovers protein networks containing known and novel proteins and protein interactions associated with clinically relevant COPD phenotypes across race groups and cohorts.

Ancestral origins and post-admixture adaptive evolution of highland Tajiks

It remains debatable how many genes and how various the mechanisms are behind human adaptation to extreme environments, such as high altitudes. Despite extensive studies on Tibetans, Andeans and Ethiopians, new insights are expected to be provided with careful analysis of underrepresented highlanders living in a different geographical region, such as the Tajiks, who reside on the Pamir Plateau at an average altitude exceeding 4000 meters. Moreover, genetic admixture, as we observed in the current whole-genome deep-sequencing study of Xinjiang Tajiks (XJT), offers a unique opportunity to explore how admixture may facilitate adaptation to high-altitude environments. Compared with other extensively studied highlanders, XJT showed pronounced admixture patterns: most of their ancestry are derived from West Eurasians (34.5%-48.3%) and South Asians (21.4%-40.0%), and some minor ancestry from East Asians and Siberians (3.62%-17.5%). The greater genetic diversity in XJT than in their ancestral source populations provides a genetic basis for their adaptation to high-altitude environments. The admixture gain of functional adaptive components from ancestral populations could facilitate adaptation to high-altitude environments. Specifically, admixture-facilitated adaptation was strongly associated with skin-related candidate genes that respond to UV radiation (e.g. HERC2 and BNC2) and cardiovascular-system-related genes (e.g. MPI and BEST1). Notably, no adaptive variants of genes showing outstanding natural selection signatures in the Tibetan or Andean highlanders were identified in XJT, including EPAS1 and EGLN1, indicating that a different set of genes contributed to XJT's survival on the Pamir Plateau, although some genes underlying natural selection in XJT have been previously reported in other highlanders. Our results highlight the unique genetic adaptations in XJT and propose that admixture may play a vital role in facilitating high-altitude adaptation. By introducing and elevating diversity, admixture likely induces novel genetic factors that contribute to the survival of populations in extreme environments like the highlands.

Whole genome sequencing based analysis of inflammation biomarkers in the Trans-Omics for Precision Medicine (TOPMed) consortium

Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38 465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program (with varying sample size by trait, where the minimum sample size was n = 737 for MMP-1). We identified 22 distinct single-variant associations across 6 traits-E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin-that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.

Common variation in a long non-coding RNA gene modulates variation of circulating TGF-β2 levels in metastatic colorectal cancer patients (Alliance)

BACKGROUND

Herein, we report results from a genome-wide study conducted to identify protein quantitative trait loci (pQTL) for circulating angiogenic and inflammatory protein markers in patients with metastatic colorectal cancer (mCRC). The study was conducted using genotype, protein marker, and baseline clinical and demographic data from CALGB/SWOG 80405 (Alliance), a randomized phase III study designed to assess outcomes of adding VEGF or EGFR inhibitors to systemic chemotherapy in mCRC patients. Germline DNA derived from blood was genotyped on whole-genome array platforms. The abundance of protein markers was quantified using a multiplex enzyme-linked immunosorbent assay from plasma derived from peripheral venous blood collected at baseline. A robust rank-based method was used to assess the statistical significance of each variant and protein pair against a strict genome-wide level. A given pQTL was tested for validation in two external datasets of prostate (CALGB 90401) and pancreatic cancer (CALGB 80303) patients. Bioinformatics analyses were conducted to further establish biological bases for these findings.

RESULTS

The final analysis was carried out based on data from 540,021 common typed genetic variants and 23 protein markers from 869 genetically estimated European patients with mCRC. Correcting for multiple testing, the analysis discovered a novel cis-pQTL in LINC02869, a long non-coding RNA gene, for circulating TGF-β2 levels (rs11118119; AAF = 0.11; P-value < 1.4e-14). This finding was validated in a cohort of 538 prostate cancer patients from CALGB 90401 (AAF = 0.10, P-value < 3.3e-25). The analysis also validated a cis-pQTL we had previously reported for VEGF-A in advanced pancreatic cancer, and additionally identified trans-pQTLs for VEGF-R3, and cis-pQTLs for CD73.

CONCLUSIONS

This study has provided evidence of a novel cis germline genetic variant that regulates circulating TGF-β2 levels in plasma of patients with advanced mCRC and prostate cancer. Moreover, the validation of previously identified pQTLs for VEGF-A, CD73, and VEGF-R3, potentiates the validity of these associations.

Proteomic Networks and Related Genetic Variants Associated with Smoking and Chronic Obstructive Pulmonary Disease

Background

Studies have identified individual blood biomarkers associated with chronic obstructive pulmonary disease (COPD) and related phenotypes. However, complex diseases such as COPD typically involve changes in multiple molecules with interconnections that may not be captured when considering single molecular features.

Methods

Leveraging proteomic data from 3,173 COPDGene Non-Hispanic White (NHW) and African American (AA) participants, we applied sparse multiple canonical correlation network analysis (SmCCNet) to 4,776 proteins assayed on the SomaScan v4.0 platform to derive sparse networks of proteins associated with current vs. former smoking status, airflow obstruction, and emphysema quantitated from high-resolution computed tomography scans. We then used NetSHy, a dimension reduction technique leveraging network topology, to produce summary scores of each proteomic network, referred to as NetSHy scores. We next performed genome-wide association study (GWAS) to identify variants associated with the NetSHy scores, or network quantitative trait loci (nQTLs). Finally, we evaluated the replicability of the networks in an independent cohort, SPIROMICS.

Results

We identified networks of 13 to 104 proteins for each phenotype and exposure in NHW and AA, and the derived NetSHy scores significantly associated with the variable of interests. Networks included known (sRAGE, ALPP, MIP1) and novel molecules (CA10, CPB1, HIS3, PXDN) and interactions involved in COPD pathogenesis. We observed 7 nQTL loci associated with NetSHy scores, 4 of which remained after conditional analysis. Networks for smoking status and emphysema, but not airflow obstruction, demonstrated a high degree of replicability across race groups and cohorts.

Conclusions

In this work, we apply state-of-the-art molecular network generation and summarization approaches to proteomic data from COPDGene participants to uncover protein networks associated with COPD phenotypes. We further identify genetic associations with networks. This work discovers protein networks containing known and novel proteins and protein interactions associated with clinically relevant COPD phenotypes across race groups and cohorts.

Common variation in a long non-coding RNA gene modulates variation of circulating TGF-β2 levels in metastatic colorectal cancer patients (Alliance)

Background

Herein, we report results from a genome-wide study conducted to identify protein quantitative trait loci (pQTL) for circulating angiogenic and inflammatory protein markers in patients with metastatic colorectal cancer (mCRC).The study was conducted using genotype, protein marker, and baseline clinical and demographic data from CALGB/SWOG 80405 (Alliance), a randomized phase III study designed to assess outcomes of adding VEGF or EGFR inhibitors to systemic chemotherapy in mCRC patients. Germline DNA derived from blood was genotyped on whole-genome array platforms. The abundance of protein markers was quantified using a multiplex enzyme-linked immunosorbent assay from plasma derived from peripheral venous blood collected at baseline. A robust rank-based method was used to assess the statistical significance of each variant and protein pair against a strict genome-wide level. A given pQTL was tested for validation in two external datasets of prostate (CALGB 90401) and pancreatic cancer (CALGB 80303) patients. Bioinformatics analyses were conducted to further establish biological bases for these findings.

Results

The final analysis was carried out based on data from 540,021 common typed genetic variants and 23 protein markers from 869 genetically estimated European patients with mCRC. Correcting for multiple testing, the analysis discovered a novel cis-pQTL in LINC02869, a long non-coding RNA gene, for circulating TGF-β2 levels (rs11118119; AAF = 0.11; P-value < 1.4e-14). This finding was validated in a cohort of 538 prostate cancer patients from CALGB 90401 (AAF = 0.10, P-value < 3.3e-25). The analysis also validated a cis-pQTL we had previously reported for VEGF-A in advanced pancreatic cancer, and additionally identified trans-pQTLs for VEGF-R3, and cis-pQTLs for CD73.

Conclusions

This study has provided evidence of a novel cis germline genetic variant that regulates circulating TGF-β2 levels in plasma of patients with advanced mCRC and prostate cancer. Moreover, the validation of previously identified pQTLs for VEGF-A, CD73, and VEGF-R3, potentiates the validity of these associations.

A novel multi-ancestry proteome-wide Mendelian randomization study implicates extracellular proteins, tubular cells, and fibroblasts in estimated glomerular filtration rate regulation

Estimated glomerular filtration rate (eGFR) impacts the concentration of plasma biomarkers confounding biomarker association studies of eGFR with reverse causation. To identify biomarkers causally associated with eGFR, we performed a proteome-wide Mendelian randomization study. Genetic variants nearby biomarker coding genes were tested for association with plasma concentration of 1,161 biomarkers in a multi-ancestry sample of 12,066 participants from the Prospective Urban and Rural Epidemiological (PURE) study. Using two-sample Mendelian randomization, individual variants' effects on biomarker concentration were correlated with their effects on eGFR and kidney traits from published genome-wide association studies (GWAS). Genetically altered concentrations of 22 biomarkers were associated with eGFR above a Bonferroni-corrected significance threshold. Five biomarkers were previously identified by GWAS (UMOD, FGF5, LGALS7, NINJ1, COL18A1). Nine biomarkers were within 1 Mb of the lead GWAS variant but the gene for the biomarker was unidentified as the candidate for the GWAS signal (INHBC, TNFRSF11A, TCN2, PXN1, PRTN3, PSMD9, TFPI, ITGB6, CA3). Single-cell transcriptomic data indicated the 22 biomarkers are expressed in kidney tubules, collecting duct, fibroblasts, and immune cells. Pathway analysis showed significant enrichment of identified biomarkers in the extracellular kidney parenchyma. Thus, using genetic regulators of biomarker concentration via proteome-wide Mendelian randomization, we identified 22 biomarkers that appear to causally impact eGFR in either a beneficial or adverse manner. The current study provides rationale for novel therapeutic targets for eGFR and emphasized a role for extracellular proteins produced by tubular cells and fibroblasts for impacting eGFR.

Leveraging molecular quantitative trait loci to comprehend complex diseases/traits from the omics perspective

Comprehending the molecular basis of quantitative genetic variation is a principal goal for complex diseases or traits. Molecular quantitative trait loci (molQTLs) have made it possible to investigate the effects of genetic variants hiding behind large-scale omics data. A deeper understanding of molQTL is urgently required in light of the multi-dimensionalization of omics data to more fully elucidate the pertinent biological mechanisms. Herein, we reviewed molQTLs with the corresponding resource from the omics perspective and further discussed the integrative strategy of GWAS-molQTL to infer their causal effects. Subsequently, we described the opportunities and challenges encountered by molQTL. The case studies showed that molQTL is essential for complex diseases and traits, whether single- or multi-omics QTLs. Overall, we highlighted the functional significance of genetic variants to employ the discovery of molQTL in complex diseases and traits.

Whole Genome Sequencing Based Analysis of Inflammation Biomarkers in the Trans-Omics for Precision Medicine (TOPMed) Consortium

Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38,465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program. We identified 22 distinct single-variant associations across 6 traits - E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin - that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.

Large scale proteomic studies create novel privacy considerations

Privacy protection is a core principle of genomic but not proteomic research. We identified independent single nucleotide polymorphism (SNP) quantitative trait loci (pQTL) from COPDGene and Jackson Heart Study (JHS), calculated continuous protein level genotype probabilities, and then applied a naïve Bayesian approach to link SomaScan 1.3K proteomes to genomes for 2812 independent subjects from COPDGene, JHS, SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) and Multi-Ethnic Study of Atherosclerosis (MESA). We correctly linked 90-95% of proteomes to their correct genome and for 95-99% we identify the 1% most likely links. The linking accuracy in subjects with African ancestry was lower (~ 60%) unless training included diverse subjects. With larger profiling (SomaScan 5K) in the Atherosclerosis Risk Communities (ARIC) correct identification was > 99% even in mixed ancestry populations. We also linked proteomes-to-proteomes and used the proteome only to determine features such as sex, ancestry, and first-degree relatives. When serial proteomes are available, the linking algorithm can be used to identify and correct mislabeled samples. This work also demonstrates the importance of including diverse populations in omics research and that large proteomic datasets (> 1000 proteins) can be accurately linked to a specific genome through pQTL knowledge and should not be considered unidentifiable.

Genetic regulators of sputum mucin concentration and their associations with COPD phenotypes

Hyper-secretion and/or hyper-concentration of mucus is a defining feature of multiple obstructive lung diseases, including chronic obstructive pulmonary disease (COPD). Mucus itself is composed of a mixture of water, ions, salt and proteins, of which the gel-forming mucins, MUC5AC and MUC5B, are the most abundant. Recent studies have linked the concentrations of these proteins in sputum to COPD phenotypes, including chronic bronchitis (CB) and acute exacerbations (AE). We sought to determine whether common genetic variants influence sputum mucin concentrations and whether these variants are also associated with COPD phenotypes, specifically CB and AE. We performed a GWAS to identify quantitative trait loci for sputum mucin protein concentration (pQTL) in the Sub-Populations and InteRmediate Outcome Measures in COPD Study (SPIROMICS, n = 708 for total mucin, n = 215 for MUC5AC, MUC5B). Subsequently, we tested for associations of mucin pQTL with CB and AE using regression modeling (n = 822-1300). Replication analysis was conducted using data from COPDGene (n = 5740) and by examining results from the UK Biobank. We identified one genome-wide significant pQTL for MUC5AC (rs75401036) and two for MUC5B (rs140324259, rs10001928). The strongest association for MUC5B, with rs140324259 on chromosome 11, explained 14% of variation in sputum MUC5B. Despite being associated with lower MUC5B, the C allele of rs140324259 conferred increased risk of CB (odds ratio (OR) = 1.42; 95% confidence interval (CI): 1.10-1.80) as well as AE ascertained over three years of follow up (OR = 1.41; 95% CI: 1.02-1.94). Associations between rs140324259 and CB or AE did not replicate in COPDGene. However, in the UK Biobank, rs140324259 was associated with phenotypes that define CB, namely chronic mucus production and cough, again with the C allele conferring increased risk. We conclude that sputum MUC5AC and MUC5B concentrations are associated with common genetic variants, and the top locus for MUC5B may influence COPD phenotypes, in particular CB.

A blood and bronchoalveolar lavage protein signature of rapid FEV1 decline in smoking-associated COPD

Accelerated progression of chronic obstructive pulmonary disease (COPD) is associated with increased risks of hospitalization and death. Prognostic insights into mechanisms and markers of progression could facilitate development of disease-modifying therapies. Although individual biomarkers exhibit some predictive value, performance is modest and their univariate nature limits network-level insights. To overcome these limitations and gain insights into early pathways associated with rapid progression, we measured 1305 peripheral blood and 48 bronchoalveolar lavage proteins in individuals with COPD [n = 45, mean initial forced expiratory volume in one second (FEV1) 75.6 ± 17.4% predicted]. We applied a data-driven analysis pipeline, which enabled identification of protein signatures that predicted individuals at-risk for accelerated lung function decline (FEV1 decline ≥ 70 mL/year) ~ 6 years later, with high accuracy. Progression signatures suggested that early dysregulation in elements of the complement cascade is associated with accelerated decline. Our results propose potential biomarkers and early aberrant signaling mechanisms driving rapid progression in COPD.

Biological and Genetic Mechanisms of COPD, Its Diagnosis, Treatment, and Relationship with Lung Cancer

Chronic obstructive pulmonary disease (COPD) is one of the most prevalent chronic adult diseases, with significant worldwide morbidity and mortality. Although long-term tobacco smoking is a critical risk factor for this global health problem, its molecular mechanisms remain unclear. Several phenomena are thought to be involved in the evolution of emphysema, including airway inflammation, proteinase/anti-proteinase imbalance, oxidative stress, and genetic/epigenetic modifications. Furthermore, COPD is one main risk for lung cancer (LC), the deadliest form of human tumor; formation and chronic inflammation accompanying COPD can be a potential driver of malignancy maturation (0.8-1.7% of COPD cases develop cancer/per year). Recently, the development of more research based on COPD and lung cancer molecular analysis has provided new light for understanding their pathogenesis, improving the diagnosis and treatments, and elucidating many connections between these diseases. Our review emphasizes the biological factors involved in COPD and lung cancer, the advances in their molecular mechanisms' research, and the state of the art of diagnosis and treatments. This work combines many biological and genetic elements into a single whole and strongly links COPD with lung tumor features.

Ethnic disparities in mortality and group-specific risk factors in the UK Biobank

Despite a substantial overall decrease in mortality, disparities among ethnic minorities in developed countries persist. This study investigated mortality disparities and their associated risk factors for the three largest ethnic groups in the United Kingdom: Asian, Black, and White. Study participants were sampled from the UK Biobank (UKB), a prospective cohort enrolled between 2006 and 2010. Genetics, biological samples, and health information and outcomes data of UKB participants were downloaded and data-fields were prioritized based on participants with death registry records. Kaplan-Meier method was used to evaluate survival differences among ethnic groups; survival random forest feature selection followed by Cox proportional-hazard modeling was used to identify and estimate the effects of shared and ethnic group-specific mortality risk factors. The White ethnic group showed significantly worse survival probability than the Asian and Black groups. In all three ethnic groups, endoscopy and colonoscopy procedures showed significant protective effects on overall mortality. Asian and Black women show lower relative risk of mortality than men, whereas no significant effect of sex was seen for the White group. The strongest ethnic group-specific mortality associations were ischemic heart disease for Asians, COVID-19 for Blacks, and cancers of respiratory/intrathoracic organs for Whites. Mental health-related diagnoses, including substance abuse, anxiety, and depression, were a major risk factor for overall mortality in the Asian group. The effect of mental health on Asian mortality, particularly for digestive cancers, was exacerbated by an observed hesitance to answer mental health questions, possibly related to cultural stigma. C-reactive protein (CRP) serum levels were associated with both overall and cause-specific mortality due to COVID-19 and digestive cancers in the Black group, where elevated CRP has previously been linked to psychosocial stress due to discrimination. Our results point to mortality risk factors that are group-specific and modifiable, supporting targeted interventions towards greater health equity.

NetSHy: network summarization via a hybrid approach leveraging topological properties

MOTIVATION

Biological networks can provide a system-level understanding of underlying processes. In many contexts, networks have a high degree of modularity, i.e. they consist of subsets of nodes, often known as subnetworks or modules, which are highly interconnected and may perform separate functions. In order to perform subsequent analyses to investigate the association between the identified module and a variable of interest, a module summarization, that best explains the module's information and reduces dimensionality is often needed. Conventional approaches for obtaining network representation typically rely only on the profiles of the nodes within the network while disregarding the inherent network topological information.

RESULTS

In this article, we propose NetSHy, a hybrid approach which is capable of reducing the dimension of a network while incorporating topological properties to aid the interpretation of the downstream analyses. In particular, NetSHy applies principal component analysis (PCA) on a combination of the node profiles and the well-known Laplacian matrix derived directly from the network similarity matrix to extract a summarization at a subject level. Simulation scenarios based on random and empirical networks at varying network sizes and sparsity levels show that NetSHy outperforms the conventional PCA approach applied directly on node profiles, in terms of recovering the true correlation with a phenotype of interest and maintaining a higher amount of explained variation in the data when networks are relatively sparse. The robustness of NetSHy is also demonstrated by a more consistent correlation with the observed phenotype as the sample size decreases. Lastly, a genome-wide association study is performed as an application of a downstream analysis, where NetSHy summarization scores on the biological networks identify more significant single nucleotide polymorphisms than the conventional network representation.

AVAILABILITY AND IMPLEMENTATION

R code implementation of NetSHy is available at https://github.com/thaovu1/NetSHy.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A Polynesian-specific copy number variant encompassing the MICA gene associates with gout

Gout is of particularly high prevalence in the Māori and Pacific (Polynesian) populations of Aotearoa New Zealand (NZ). Here, we investigated the contribution of common population-specific copy number variation (CNV) to gout in the Aotearoa NZ Polynesian population. Microarray-generated genome-wide genotype data from Aotearoa NZ Polynesian individuals with (n = 1196) and without (n = 1249) gout were analyzed. Comparator population groups were 552 individuals of European ancestry and 1962 of Han Chinese ancestry. Levels of circulating major histocompatibility complex (MHC) class I polypeptide-related sequence A (MICA) were measured by enzyme-linked immunosorbent assay. Fifty-four CNV regions (CNVRs) appearing in at least 10 individuals were detected, of which seven common (>2%) CNVRs were specific to or amplified in Polynesian people. A burden test of these seven revealed associations of insertion/deletion with gout (odds ratio (OR) 95% confidence interval [CI] = 1.80 [1.01; 3.22], P = 0.046). Individually testing of the seven CNVRs for association with gout revealed nominal association of CNVR1 with gout in Western Polynesian (Chr6: 31.36-31.45 Mb, OR = 1.72 [1.03; 2.92], P = 0.04), CNVR6 in the meta-analyzed Polynesian sample sets (Chr1: 196.75-196.92 Mb, OR = 1.86 [1.16; 3.00], P = 0.01) and CNVR9 in Western Polynesian (Chr1: 189.35-189.54 Mb, OR = 2.75 [1.15; 7.13], P = 0.03). Analysis of European gout genetic association data demonstrated a signal of association at the CNVR1 locus that was an expression quantitative trait locus for MICA. The most common CNVR (CNVR1) includes deletion of the MICA gene, encoding an immunomodulatory protein. Expression of MICA was reduced in the serum of individuals with the deletion. In summary, we provide evidence for the association of CNVR1 containing MICA with gout in Polynesian people, implicating class I MHC-mediated antigen presentation in gout.

Chronic air pollution-induced subclinical airway inflammation and polygenic susceptibility

Background

Air pollutants can activate low-grade subclinical inflammation which further impairs respiratory health. We aimed to investigate the role of polygenic susceptibility to chronic air pollution-induced subclinical airway inflammation.

Methods

We used data from 296 women (69–79 years) enrolled in the population-based SALIA cohort (Study on the influence of Air pollution on Lung function, Inflammation and Aging). Biomarkers of airway inflammation were measured in induced-sputum samples at follow-up investigation in 2007–2010. Chronic air pollution exposures at residential addresses within 15 years prior to the biomarker assessments were used to estimate main environmental effects on subclinical airway inflammation. Furthermore, we calculated internally weighted polygenic risk scores based on genome-wide derived single nucleotide polymorphisms. Polygenic main and gene-environment interaction (GxE) effects were investigated by adjusted linear regression models.

Results

Higher exposures to nitrogen dioxide (NO₂), nitrogen oxides (NO_x), particulate matter with aerodynamic diameters of ≤ 2.5 μm, ≤ 10 μm, and 2.5–10 µm significantly increased the levels of leukotriene (LT)B₄ by 19.7% (p-value = 0.005), 20.9% (p = 0.002), 22.1% (p = 0.004), 17.4% (p = 0.004), and 23.4% (p = 0.001), respectively. We found significant effects of NO₂ (25.9%, p = 0.008) and NO_x (25.9%, p-value = 0.004) on the total number of cells. No significant GxE effects were observed. The trends were mostly robust in sensitivity analyses.

Conclusions

While this study confirms that higher chronic exposures to air pollution increase the risk of subclinical airway inflammation in elderly women, we could not demonstrate a significant role of polygenic susceptibility on this pathway. Further studies are required to investigate the role of polygenic susceptibility.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-022-02179-3.

Genetically regulated gene expression and proteins revealed discordant effects

Background

Although gene-expression (GE) and protein levels are typically strongly genetically regulated, their correlation is known to be low. Here we investigate this phenomenon by focusing on the genetic background of this correlation in order to understand the similarities and differences in the genetic regulation of these omics layers.

Methods and results

We performed locus-wide association studies of 92 protein levels measured in whole blood for 2,014 samples of European ancestry and found that 66 are genetically regulated. Three female- and one male-specific effects were detected. We estimated the genetically regulated GE for all significant genes in 49 GTEx v8 tissues. A total of 7 proteins showed negative correlations with their respective GE across multiple tissues. Finally, we tested for causal links of GE on protein expression via Mendelian Randomization, and confirmed a negative causal effect of GE on protein level for five of these genes in a total of 63 gene-tissue pairs: BLMH, CASP3, CXCL16, IL6R, and SFTPD. For IL6R, we replicated the negative causal effect on coronary-artery disease (CAD), while its GE was positively linked to CAD.

Conclusion

While total GE and protein levels are only weakly correlated, we found high correlations between their genetically regulated components across multiple tissues. Of note, strong negative causal effects of tissue-specific GE on five protein levels were detected. Causal network analyses revealed that GE effects on CAD risks was in general mediated by protein levels.

Transcriptome and metabolome changes induced by bitter melon (Momordica charantia)- intake in a high-fat diet induced obesity model

Background and aim

Metabolic syndrome (MetS) is a complex disease of physiological imbalances interrelated to abnormal metabolic conditions, such as abdominal obesity, type II diabetes, dyslipidemia and hypertension. In the present pilot study, we investigated the nutraceutical bitter melon (Momordica charantia L) -intake induced transcriptome and metabolome changes and the converging metabolic signaling networks underpinning its inhibitory effects against MetS-associated risk factors.

Experimental procedure

Metabolic effects of lyophilized bitter melon juice (BMJ) extract (oral gavage 200 mg/kg/body weight-daily for 40 days) intake were evaluated in diet-induced obese C57BL/6J male mice [fed-high fat diet (HFD), 60 kcal% fat]. Changes in a) serum levels of biochemical parameters, b) gene expression in the hepatic transcriptome (microarray analysis using Affymetrix Mouse Exon 1.0 ST arrays), and c) metabolite abundance levels in lipid-phase plasma [liquid chromatography mass spectrometry (LC-MS)-based metabolomics] after BMJ intervention were assessed.

Results and conclusion

BMJ-mediated changes showed a positive trend towards enhanced glucose homeostasis, vitamin D metabolism and suppression of glycerophospholipid metabolism. In the liver, nuclear peroxisome proliferator-activated receptor (PPAR) and circadian rhythm signaling, as well as bile acid biosynthesis and glycogen metabolism targets were modulated by BMJ (p < 0.05). Thus, our in-depth transcriptomics and metabolomics analysis suggests that BMJ-intake lowers susceptibility to the onset of high-fat diet associated MetS risk factors partly through modulation of PPAR signaling and its downstream targets in circadian rhythm processes to prevent excessive lipogenesis, maintain glucose homeostasis and modify immune responses signaling.

Genetics of chronic obstructive pulmonary disease: understanding the pathobiology and heterogeneity of a complex disorder

Chronic obstructive pulmonary disease (COPD) is a deadly and highly morbid disease. Susceptibility to and heterogeneity of COPD are incompletely explained by environmental factors such as cigarette smoking. Family-based and population-based studies have shown that a substantial proportion of COPD risk is related to genetic variation. Genetic association studies have identified hundreds of genetic variants that affect risk for COPD, decreased lung function, and other COPD-related traits. These genetic variants are associated with other pulmonary and non-pulmonary traits, demonstrate a genetic basis for at least part of COPD heterogeneity, have a substantial effect on COPD risk in aggregate, implicate early-life events in COPD pathogenesis, and often involve genes not previously suspected to have a role in COPD. Additional progress will require larger genetic studies with more ancestral diversity, improved profiling of rare variants, and better statistical methods. Through integration of genetic data with other omics data and comprehensive COPD phenotypes, as well as functional description of causal mechanisms for genetic risk variants, COPD genetics will continue to inform novel approaches to understanding the pathobiology of COPD and developing new strategies for management and treatment.

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.

Genetic Regulation of Cytokine Response in Patients with Acute Community-Acquired Pneumonia

Background: Community-acquired pneumonia (CAP) is an acute disease condition with a high risk of rapid deteriorations. We analysed the influence of genetics on cytokine regulation to obtain a better understanding of patient’s heterogeneity. Methods: For up to N = 389 genotyped participants of the PROGRESS study of hospitalised CAP patients, we performed a genome-wide association study of ten cytokines IL-1β, IL-6, IL-8, IL-10, IL-12, MCP-1 (MCAF), MIP-1α (CCL3), VEGF, VCAM-1, and ICAM-1. Consecutive secondary analyses were performed to identify independent hits and corresponding causal variants. Results: 102 SNPs from 14 loci showed genome-wide significant associations with five of the cytokines. The most interesting associations were found at 6p21.1 for VEGF (p = 1.58 × 10⁻²⁰), at 17q21.32 (p = 1.51 × 10⁻⁹) and at 10p12.1 (p = 2.76 × 10⁻⁹) for IL-1β, at 10p13 for MIP-1α (CCL3) (p = 2.28 × 10⁻⁹), and at 9q34.12 for IL-10 (p = 4.52 × 10⁻⁸). Functionally plausible genes could be assigned to the majority of loci including genes involved in cytokine secretion, granulocyte function, and cilial kinetics. Conclusion: This is the first context-specific genetic association study of blood cytokine concentrations in CAP patients revealing numerous biologically plausible candidate genes. Two of the loci were also associated with atherosclerosis with probable common or consecutive pathomechanisms.

An Integrative Genomic Strategy Identifies sRAGE as a Causal and Protective Biomarker of Lung Function

BACKGROUND

There are few clinically useful circulating biomarkers of lung function and lung disease. We hypothesized that genome-wide association studies (GWAS) of circulating proteins in conjunction with GWAS of pulmonary traits represents a clinically relevant approach to identifying causal proteins and therapeutically useful insights into mechanisms related to lung function and disease.

STUDY QUESTION

Can an integrative genomic strategy using GWAS of plasma soluble receptor for advanced glycation end-products (sRAGE) levels in conjunction with GWAS of lung function traits identify putatively causal relations of sRAGE to lung function?

STUDY DESIGN AND METHODS

Plasma sRAGE levels were measured in 6,861 Framingham Heart Study participants and GWAS of sRAGE was conducted to identify protein quantitative trait loci (pQTL), including cis-pQTL variants at the sRAGE protein-coding gene locus (AGER). We integrated sRAGE pQTL variants with variants from GWAS of lung traits. Colocalization of sRAGE pQTL variants with lung trait GWAS variants was conducted, and Mendelian randomization was performed using sRAGE cis-pQTL variants to infer causality of sRAGE for pulmonary traits. Cross-sectional and longitudinal protein-trait association analyses were conducted for sRAGE in relation to lung traits.

RESULTS

Colocalization identified shared genetic signals for sRAGE with lung traits. Mendelian randomization analyses suggested protective causal relations of sRAGE to several pulmonary traits. Protein-trait association analyses demonstrated higher sRAGE levels to be cross-sectionally and longitudinally associated with preserved lung function.

INTERPRETATION

sRAGE is produced by type I alveolar cells, and it acts as a decoy receptor to block the inflammatory cascade. Our integrative genomics approach provides evidence for sRAGE as a causal and protective biomarker of lung function, and the pattern of associations is suggestive of a protective role of sRAGE against restrictive lung physiology. We speculate that targeting the AGER/sRAGE axis may be therapeutically beneficial for the treatment and prevention of inflammation-related lung disease.

Genetic Risk and Chronic Obstructive Pulmonary Disease Independently Predict the Risk of Incident Severe COVID-19

Rationale: Both genetic variants and chronic obstructive pulmonary disease (COPD) contribute to the risk of incident severe coronavirus disease (COVID-19). Whether genetic risk of incident severe COVID-19 is the same regardless of preexisting COPD is unknown. Objectives: In this study, we aimed to investigate the potential interaction between genetic risk and COPD in relation to severe COVID-19. Methods: We constructed a polygenic risk score for severe COVID-19 by using 112 single-nucleotide polymorphisms in 430,582 participants from the UK Biobank study. We examined the associations of genetic risk and COPD with severe COVID-19 by using logistic regression models. Results: Of 430,582 participants, 712 developed severe COVID-19 as of February 22, 2021, of whom 19.8% had preexisting COPD. Compared with participants at low genetic risk, those at intermediate genetic risk (odds ratio [OR], 1.34; 95% confidence interval [CI], 1.09-1.66) and high genetic risk (OR, 1.50; 95% CI, 1.18-1.92) had higher risk of severe COVID-19 (P for trend = 0.001), and the association was independent of COPD (P for interaction = 0.76). COPD was associated with a higher risk of incident severe COVID-19 (OR, 1.37; 95% CI, 1.12-1.67; P = 0.002). Participants at high genetic risk and with COPD had a higher risk of severe COVID-19 (OR, 2.05; 95% CI, 1.35-3.04; P < 0.001) than those at low genetic risk and without COPD. Conclusions: The polygenic risk score, which combines multiple risk alleles, can be effectively used in screening for high-risk populations of severe COVID-19. High genetic risk correlates with a higher risk of severe COVID-19, regardless of preexisting COPD.

Association of Systemic Inflammation with Depressive Symptoms in Individuals with COPD

Rationale

Depression is a prevalent comorbidity of chronic obstructive pulmonary disease (COPD) that, along with COPD, has been associated with inflammation. An association between inflammation and depression in COPD has not been validated in a large COPD cohort.

Methods

Individuals from the University of Pittsburgh SCCOR cohort and the COPDGene cohort with tobacco use history and airway obstruction (FEV₁/FVC <0.7) were evaluated using the Beck Depression Inventory II (BDI-II) and the Hospital Anxiety and Depression Scale (HADS), respectively. Participants completed symptom-related questionnaires and plasma IL-6 measurements. T-test, Fisher’s Exact tests and logistic regression were used for statistical analysis.

Results

The SCCOR cohort included 220 obstructed participants: 44% female and 21.4% with elevated depressive symptoms. GOLD staging distribution was predominantly stage I and II. The COPDGene cohort included 745 obstructed participants: 44% female and 13.0% with elevated depressive symptoms. GOLD distribution was predominantly stage II and III. In the SCCOR cohort, correlation between IL-6 and depressive symptoms trended toward significance (p= 0.08). Multivariable modeling adjusted for FEV₁, age, gender and medical comorbidities showed a significant association (OR = 1.70, 95% CI = 1.08–2.69). IL-6 was significantly associated with elevated depressive symptoms in COPDGene in both univariate (p=0.001) and multivariable modeling (OR = 1.52, 95% CI =1.13–2.04).

Conclusion

Elevated plasma IL-6 levels are associated with depressive symptoms in individuals with COPD independent of airflow limitation and comorbid risk factors for depression. Our results suggest that systemic inflammation may play a significant and possibly bidirectional role in depression associated with COPD.

Alteration of immune profiles is associated with pulmonary function and symptoms in patients with chronic obstructive pulmonary disease

Inflammation serves a key role in chronic obstructive pulmonary disease (COPD). However, changes in the immune profiles of patients with COPD remain unclear. The present prospective observational study aimed to determine the expression profiles of immune cells and inflammatory factors of patients with COPD and healthy controls, and to analyze the relationship between immune profiles and smoking history. A total of 140 subjects were enrolled in the present study between September 2018 and April 2019 at West China Hospital of Sichuan University (Chengdu, China). These included 87 patients with stable COPD and 24 patients with acute exacerbated COPD, as well as 29 healthy controls. Baseline characteristics were recorded during the screening period, and levels of immune cells were examined using flow cytometry. In addition, levels of inflammatory factors were measured using ELISAs. The results revealed increased expression of the CD64⁺/CD14⁺ mononuclear phagocyte system (MPS) and CD16⁺CD66⁺ neutrophils, and decreased expression of CD3⁺CD4⁺ T cells and CD3⁺ CD8⁺ T cells (all P<0.05) in the peripheral blood of patients with COPD and smokers relative to non-smoking controls. In addition, significant differences were observed in protein levels of IL-6, IL-1β, TNF-α, TGF-α, IFN-γ, IL-8, IL-17A and CRP among the three groups (all P<0.05). Furthermore, the IL-17A, TNF and NF-κB signaling pathways were found to serve a key role in the inflammatory network of COPD. Pearson's correlation analysis revealed a positive correlation between CD3⁺T lymphocyte counts and pulmonary function, and a negative correlation with smoking and exacerbations. Furthermore, neutrophils and MPS were negatively associated with pulmonary function, and IL-8 was positively associated with cough. There was also a negative association between CRP and IL-17A with pulmonary function but a positive correlation with symptoms and exacerbation. Club cell secretory protein was also negatively associated with emphysema parameters. In conclusion, the present findings revealed significant differences in profiles of immune factors among patients with COPD, smokers and non-smoking controls and their association with clinical characteristics. The clinical trial registration number of the present study is: ChiCTR1800015700 (registered with http://www.chictr.org.cn/index.aspx, 2018/04/16).

Hedgehog interacting protein-expressing lung fibroblasts suppress lymphocytic inflammation in mice

Chronic obstructive pulmonary disease (COPD) is mainly caused by cigarette smoking and characterized by chronic inflammation in vulnerable individuals. However, it is unknown how genetic factors may shape chronic inflammation in COPD. To understand how hedgehog interacting protein, encoded by HHIP gene identified in the genome-wide association study in COPD, plays a role in inflammation, we utilized Hhip^+/– mice that present persistent inflammation and emphysema upon aging similar to that observed in human COPD. By performing single-cell RNA sequencing of the whole lung from mice at different ages, we found that Hhip^+/– mice developed a cytotoxic immune response with a specific increase in killer cell lectin-like receptor G1–positive CD8⁺ T cells with upregulated Ifnγ expression recapitulating human COPD. Hhip expression was restricted to a lung fibroblast subpopulation that had increased interaction with CD8⁺ T lymphocytes in Hhip^+/– compared with Hhip^+/+ during aging. Hhip-expressing lung fibroblasts had upregulated IL-18 pathway genes in Hhip^+/– lung fibroblasts, which was sufficient to drive increased levels of IFN-γ in CD8⁺ T cells ex vivo. Our finding provides insight into how a common genetic variation contributes to the amplified lymphocytic inflammation in COPD.

Protein Biomarkers for COPD Outcomes

COPD is a clinically heterogeneous syndrome characterized by injury to airways, airspaces, and lung vasculature and usually caused by tobacco smoke and/or air pollution exposure. COPD is also independently associated with nonpulmonary comorbidities (eg, cardiovascular disease) and malignancies (eg, GI, bladder), suggesting a role for systemic injury. Since not all those with exposure develop COPD, there has been a search for plasma and lung biomarkers that confer increased cross-sectional and longitudinal risk. This search typically focuses on clinically relevant COPD outcomes such as FEV1, FEV1 decline, CT measurements of emphysema, or exacerbation frequency. The rapid advances in omics technology and the molecular phenotyping of COPD cohorts now permit large-scale evaluation of genetic, transcriptomic, proteomic, and metabolic biomarkers. This review focuses on protein biomarkers associated with clinically relevant COPD outcomes. The prototypic COPD protein biomarker is alpha-1 antitrypsin; however, this biomarker only accounts for 1% to 5% of COPD. This article reviews and summarizes the evidence for other validated biomarkers for each COPD outcome, and discusses their advantages, weaknesses, and required regulatory steps to move the biomarker from the bench into clinic. Although we highlight the emergence of many novel biomarkers (eg, fibrinogen, soluble receptor for advanced glycation, surfactant protein D, club cell secretory protein), there is increasing evidence that individual biomarkers only explain a fraction of the increased COPD risk and that multiple biomarker panels are needed to completely explain clinical variation and risk in individuals and populations.

Future Biomarkers for Infection and Inflammation in Febrile Children

Febrile patients, suffering from an infection, inflammatory disease or autoimmunity may present with similar or overlapping clinical symptoms, which makes early diagnosis difficult. Therefore, biomarkers are needed to help physicians form a correct diagnosis and initiate the right treatment to improve patient outcomes following first presentation or admittance to hospital. Here, we review the landscape of novel biomarkers and approaches of biomarker discovery. We first discuss the use of current plasma parameters and whole blood biomarkers, including results obtained by RNA profiling and mass spectrometry, to discriminate between bacterial and viral infections. Next we expand upon the use of biomarkers to distinguish between infectious and non-infectious disease. Finally, we discuss the strengths as well as the potential pitfalls of current developments. We conclude that the use of combination tests, using either protein markers or transcriptomic analysis, have advanced considerably and should be further explored to improve current diagnostics regarding febrile infections and inflammation. If proven effective when combined, these biomarker signatures will greatly accelerate early and tailored treatment decisions.

Endothelial injury, F-actin and vitamin-D binding protein after hematopoietic stem cell transplant and association with clinical outcomes

Endothelial injury after hematopoietic stem cell transplant is an important initiating factor for early transplant toxicities of thrombotic microangiopathy and acute graft versus host disease. We hypothesized that release of the angiopathic molecule filamentous actin (F-actin) from hematopoietic cells lysed during conditioning prior to stem cell transplant would be associated with clinical outcomes. We detected F-actin in the blood of 52% of stem cell transplant recipients in the first 14 days after transplant, and children with detectable F-actin had a significantly elevated risk of thrombotic microangiopathy (P=0.03) and non-relapse mortality (P=0.04). F-actin is cleared from the circulation by vitamin D binding protein (VDBP) so we expected that higher levels of VDBP would improve outcomes. In a cohort of 190 children receiving an allogeneic transplant, risk of thrombotic microangiopathy was reduced in those with serum concentrations of VDBP above the median at day 30 (10% vs. 31%, P=0.01), and graft versus host disease and non-relapse mortality were reduced in those with levels above the median at day 100 (3% vs. 18%, P=0.04 and 0% vs. 15%, P=0.002). Western blot analyses demonstrated actin-VDBP complexes in the blood, which cleared by day 21-28. Our data support modulation of cytokine secretion and macrophage phenotype by VDBP later after transplant. Taken together, our data identify an association between Factin, a mediator of endothelial damage, and VDBP, an actin scavenger, as modifiers of risk of clinical consequences of endothelial injury.

Joo JW. MARS: leveraging allelic heterogeneity to increase power of association testing

In standard genome-wide association studies (GWAS), the standard association test is underpowered to detect associations between loci with multiple causal variants with small effect sizes. We propose a statistical method, Model-based Association test Reflecting causal Status (MARS), that finds associations between variants in risk loci and a phenotype, considering the causal status of variants, only requiring the existing summary statistics to detect associated risk loci. Utilizing extensive simulated data and real data, we show that MARS increases the power of detecting true associated risk loci compared to previous approaches that consider multiple variants, while controlling the type I error.

Soluble receptor for advanced glycation end products (sRAGE) as a biomarker of COPD

BACKGROUND

Soluble receptor for advanced glycation end products (sRAGE) is a proposed emphysema and airflow obstruction biomarker; however, previous publications have shown inconsistent associations and only one study has investigate the association between sRAGE and emphysema. No cohorts have examined the association between sRAGE and progressive decline of lung function. There have also been no evaluation of assay compatibility, receiver operating characteristics, and little examination of the effect of genetic variability in non-white population. This manuscript addresses these deficiencies and introduces novel data from Pittsburgh COPD SCCOR and as well as novel work on airflow obstruction. A meta-analysis is used to quantify sRAGE associations with clinical phenotypes.

METHODS

sRAGE was measured in four independent longitudinal cohorts on different analytic assays: COPDGene (n = 1443); SPIROMICS (n = 1623); ECLIPSE (n = 2349); Pittsburgh COPD SCCOR (n = 399). We constructed adjusted linear mixed models to determine associations of sRAGE with baseline and follow up forced expiratory volume at one second (FEV1) and emphysema by quantitative high-resolution CT lung density at the 15th percentile (adjusted for total lung capacity).

RESULTS

Lower plasma or serum sRAGE values were associated with a COPD diagnosis (P < 0.001), reduced FEV1 (P < 0.001), and emphysema severity (P < 0.001). In an inverse-variance weighted meta-analysis, one SD lower log10-transformed sRAGE was associated with 105 ± 22 mL lower FEV1 and 4.14 ± 0.55 g/L lower adjusted lung density. After adjusting for covariates, lower sRAGE at baseline was associated with greater FEV1 decline and emphysema progression only in the ECLIPSE cohort. Non-Hispanic white subjects carrying the rs2070600 minor allele (A) and non-Hispanic African Americans carrying the rs2071288 minor allele (A) had lower sRAGE measurements compare to those with the major allele, but their emphysema-sRAGE regression slopes were similar.

CONCLUSIONS

Lower blood sRAGE is associated with more severe airflow obstruction and emphysema, but associations with progression are inconsistent in the cohorts analyzed. In these cohorts, genotype influenced sRAGE measurements and strengthened variance modelling. Thus, genotype should be included in sRAGE evaluations.

The ABO histo-blood group, endothelial activation, and acute respiratory distress syndrome risk in critical illness

BACKGROUNDThe ABO histo-blood group is defined by carbohydrate modifications and is associated with risk for multiple diseases, including acute respiratory distress syndrome (ARDS). We hypothesized that genetically determined blood subtype A1 is associated with increased risk of ARDS and markers of microvascular dysfunction and coagulation.METHODSWe conducted analyses in 3 cohorts of critically ill trauma and sepsis patients (n = 3710) genotyped on genome-wide platforms to determine the association of the A1 blood type genotype with ARDS risk. We subsequently determined whether associations were present in FUT2-defined nonsecretors who lack ABO antigens on epithelium, but not endothelium. In a patient subgroup, we determined the associations of blood type with plasma levels of endothelial glycoproteins and disseminated intravascular coagulation (DIC). Lastly, we tested whether blood type A was associated with less donor lung injury recovery during human ex vivo lung perfusion (EVLP).RESULTSThe A1 genotype was associated with a higher risk of moderate to severe ARDS relative to type O in all 3 populations. In sepsis, this relationship was strongest in nonpulmonary infections. The association persisted in nonsecretors, suggesting a vascular mechanism. The A1 genotype was also associated with higher DIC risk as well as concentrations of thrombomodulin and von Willebrand factor, which in turn were associated with ARDS risk. Blood type A was also associated with less lung injury recovery during EVLP.CONCLUSIONWe identified a replicable association between ABO blood type A1 and risk of ARDS among the critically ill, possibly mediated through microvascular dysfunction and coagulation.FUNDINGNIH HL122075, HL125723, HL137006, HL137915, DK097307, HL115354, HL101779, and the University of Pennsylvania McCabe Fund Fellowship Award.

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.

Integration of SNP Disease Association, eQTL, and Enrichment Analyses to Identify Risk SNPs and Susceptibility Genes in Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) is a complex disease caused by the disturbance of genetic and environmental factors. Single-nucleotide polymorphisms (SNPs) play a vital role in the genetic dissection of complex diseases. In-depth analysis of SNP-related information could recognize disease-associated biomarkers and further uncover the genetic mechanism of complex diseases. Risk-related variants might act on the disease by affecting gene expression and gene function. Through integrating SNP disease association study and expression quantitative trait loci (eQTL) analysis, as well as functional enrichment of containing known causal genes, four risk SNPs and four corresponding susceptibility genes were identified utilizing next-generation sequencing (NGS) data of COPD. Of the four risk SNPs, one could be found in the SNPedia database that stored disease-related SNPs and has been linked to a disease in the literature. Four genes showed significant differences from the perspective of normal/disease or variant/nonvariant samples, as well as the high performance of sample classification. It is speculated that the four susceptibility genes could be used as biomarkers of COPD. Furthermore, three of our susceptibility genes have been confirmed in the literature to be associated with COPD. Among them, two genes had an impact on the significance of expression correlation of known causal genes they interact with, respectively. Overall, this research may present novel insights into the diagnosis and pathogenesis of COPD and susceptibility gene identification of other complex diseases.

Plasma Metabolomic Signatures of Chronic Obstructive Pulmonary Disease and the Impact of Genetic Variants on Phenotype-Driven Modules

Background: Small studies have recently suggested that there are specific plasma metabolic signatures in chronic obstructive pulmonary disease (COPD), but there have been no large comprehensive study of metabolomic signatures in COPD that also integrate genetic variants. Materials and Methods: Fresh frozen plasma from 957 non-Hispanic white subjects in COPDGene was used to quantify 995 metabolites with Metabolon's global metabolomics platform. Metabolite associations with five COPD phenotypes (chronic bronchitis, exacerbation frequency, percent emphysema, post-bronchodilator forced expiratory volume at one second [FEV1]/forced vital capacity [FVC], and FEV1 percent predicted) were assessed. A metabolome-wide association study was performed to find genetic associations with metabolite levels. Significantly associated single-nucleotide polymorphisms were tested for replication with independent metabolomic platforms and independent cohorts. COPD phenotype-driven modules were identified in network analysis integrated with genetic associations to assess gene-metabolite-phenotype interactions. Results: Of metabolites tested, 147 (14.8%) were significantly associated with at least 1 COPD phenotype. Associations with airflow obstruction were enriched for diacylglycerols and branched chain amino acids. Genetic associations were observed with 109 (11%) metabolites, 72 (66%) of which replicated in an independent cohort. For 20 metabolites, more than 20% of variance was explained by genetics. A sparse network of COPD phenotype-driven modules was identified, often containing metabolites missed in previous testing. Of the 26 COPD phenotype-driven modules, 6 contained metabolites with significant met-QTLs, although little module variance was explained by genetics. Conclusion: A dysregulation of systemic metabolism was predominantly found in COPD phenotypes characterized by airflow obstruction, where we identified robust heritable effects on individual metabolite abundances. However, network analysis, which increased the statistical power to detect associations missed previously in classic regression analyses, revealed that the genetic influence on COPD phenotype-driven metabolomic modules was modest when compared with clinical and environmental factors.

Receptor for advanced glycation end-product rs1800624 polymorphism contributes to increase breast cancer risk: Evidence from a meta-analysis

BACKGROUND

Although several studies have identified an association between the receptor for advanced glycation end-product (RAGE) rs1800624 polymorphism and breast cancer, the results have been conflicting. Therefore, we conducted a meta-analysis to assess the relationship between the RAGE rs1800624 polymorphism and breast cancer risk.

METHODS

Studies were searched in the PubMed, Web of Science, Embase, Wanfang Med Online, and China National Knowledge Infrastructure databases until September 20, 2019 to identify all potential literature on this association. Fixed-effect or random-effect models were used to calculate odds ratios (ORs) and their corresponding 95% confidence intervals (95% CIs). Subgroup and sensitivity analyses and tests for publication bias were also performed.

RESULTS

Five eligible studies involving 2823 subjects (1410 patients and 1413 healthy controls) were included in the current meta-analysis. The pooled analysis indicated a positive correlation between the RAGE rs1800624 polymorphism and the risk of breast cancer in a homozygous genetic model (OR = 1.423, 95% CI = 1.043-1.941, P = .026). Ethnicity-based subgroup analysis demonstrated that RAGE rs1800624 polymorphism may increase the risk of breast cancer in the Asian population in homozygous model (OR = 1.661, 95% CI = 1.178-2.342, P = .004).

CONCLUSION

The RAGE rs1800624 polymorphism may increase the risk of breast cancer in the homozygous genetic model, especially in Asian populations. Large-scale and well-designed studies are needed in different populations to further evaluate the role of the RAGE polymorphism in breast cancer.

A general framework for integrative analysis of incomplete multiomics data

There is a tremendous current interest in measuring multiple types of omics features (e.g., DNA sequences, RNA expressions, methylation profiles, metabolic profiles, protein expressions) on a large number of subjects. Although genotypes are typically available for all study subjects, other data types may be measured only on a subset of subjects due to cost or other constraints. In addition, quantitative omics measurements, such as metabolite levels and protein expressions, are subject to detection limits in that the measurements below (or above) certain thresholds are not detectable. In this article, we propose a rigorous and powerful approach to handle missing values and detection limits in integrative analysis of multiomics data. We relate quantitative omics variables to genetic variants and other variables through linear regression models and relate phenotypes to quantitative omics variables and other variables through generalized linear models. We derive the joint-likelihood for the two sets of models by allowing arbitrary patterns of missing values and detection limits for quantitative omics variables. We carry out maximum-likelihood estimation through computationally fast and stable algorithms. The resulting estimators are approximately unbiased and statistically efficient. An application to a major study on chronic obstructive lung disease yielded new biological insights.

Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals

Circulating proteins are vital in human health and disease and are frequently used as biomarkers for clinical decision-making or as targets for pharmacological intervention. Here, we map and replicate protein quantitative trait loci (pQTL) for 90 cardiovascular proteins in over 30,000 individuals, resulting in 451 pQTLs for 85 proteins. For each protein, we further perform pathway mapping to obtain trans-pQTL gene and regulatory designations. We substantiate these regulatory findings with orthogonal evidence for trans-pQTLs using mouse knockdown experiments (ABCA1 and TRIB1) and clinical trial results (chemokine receptors CCR2 and CCR5), with consistent regulation. Finally, we evaluate known drug targets, and suggest new target candidates or repositioning opportunities using Mendelian randomization. This identifies 11 proteins with causal evidence of involvement in human disease that have not previously been targeted, including EGF, IL-16, PAPPA, SPON1, F3, ADM, CASP-8, CHI3L1, CXCL16, GDF15 and MMP-12. Taken together, these findings demonstrate the utility of large-scale mapping of the genetics of the proteome and provide a resource for future precision studies of circulating proteins in human health.

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.

Genome-wide pQTL analysis of protein expression regulatory networks in the human liver

BACKGROUND

Previous expression quantitative trait loci (eQTL) studies have identified thousands of genetic variants to be associated with gene expression at the mRNA level in the human liver. However, protein expression often correlates poorly with mRNA levels. Thus, protein quantitative trait loci (pQTL) study is required to identify genetic variants that regulate protein expression in human livers.

RESULTS

We conducted a genome-wide pQTL study in 287 normal human liver samples and identified 900 local pQTL variants and 4026 distant pQTL variants. We further discovered 53 genome hotspots of pQTL variants. Transcriptional region mapping analysis showed that 1133 pQTL variants are in transcriptional regulatory regions. Genomic region enrichment analysis of the identified pQTL variants revealed 804 potential regulatory interactions among 595 predicted regulators (e.g., non-coding RNAs) and 394 proteins. Moreover, pQTL variants and trait-variant integration analysis implied several novel mechanisms underlying the relationships between protein expression and liver diseases, such as alcohol dependence. Notably, over 2000 of the identified pQTL variants have not been reported in previous eQTL studies, suggesting extensive involvement of genetic polymorphisms in post-transcriptional regulation of protein expression in human livers.

CONCLUSIONS

We have partially established protein expression regulation networks in human livers and generated a wealth of pQTL data that could serve as a valuable resource for the scientific community.

Multi-method genome- and epigenome-wide studies of inflammatory protein levels in healthy older adults

BACKGROUND

The molecular factors which control circulating levels of inflammatory proteins are not well understood. Furthermore, association studies between molecular probes and human traits are often performed by linear model-based methods which may fail to account for complex structure and interrelationships within molecular datasets.

METHODS

In this study, we perform genome- and epigenome-wide association studies (GWAS/EWAS) on the levels of 70 plasma-derived inflammatory protein biomarkers in healthy older adults (Lothian Birth Cohort 1936; n = 876; Olink® inflammation panel). We employ a Bayesian framework (BayesR+) which can account for issues pertaining to data structure and unknown confounding variables (with sensitivity analyses using ordinary least squares- (OLS) and mixed model-based approaches).

RESULTS

We identified 13 SNPs associated with 13 proteins (n = 1 SNP each) concordant across OLS and Bayesian methods. We identified 3 CpG sites spread across 3 proteins (n = 1 CpG each) that were concordant across OLS, mixed-model and Bayesian analyses. Tagged genetic variants accounted for up to 45% of variance in protein levels (for MCP2, 36% of variance alone attributable to 1 polymorphism). Methylation data accounted for up to 46% of variation in protein levels (for CXCL10). Up to 66% of variation in protein levels (for VEGFA) was explained using genetic and epigenetic data combined. We demonstrated putative causal relationships between CD6 and IL18R1 with inflammatory bowel disease and between IL12B and Crohn's disease.

CONCLUSIONS

Our data may aid understanding of the molecular regulation of the circulating inflammatory proteome as well as causal relationships between inflammatory mediators and disease.

Multi-SNP mediation intersection-union test

SUMMARY

Tens of thousands of reproducibly identified GWAS (Genome-Wide Association Studies) variants, with the vast majority falling in non-coding regions resulting in no eventual protein products, call urgently for mechanistic interpretations. Although numerous methods exist, there are few, if any methods, for simultaneously testing the mediation effects of multiple correlated SNPs via some mediator (e.g. the expression of a gene in the neighborhood) on phenotypic outcome. We propose multi-SNP mediation intersection-union test (SMUT) to fill in this methodological gap. Our extensive simulations demonstrate the validity of SMUT as well as substantial, up to 92%, power gains over alternative methods. In addition, SMUT confirmed known mediators in a real dataset of Finns for plasma adiponectin level, which were missed by many alternative methods. We believe SMUT will become a useful tool to generate mechanistic hypotheses underlying GWAS variants, facilitating functional follow-up.

AVAILABILITY AND IMPLEMENTATION

The R package SMUT is publicly available from CRAN at https://CRAN.R-project.org/package=SMUT.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Facets of individual-specific health signatures determined from longitudinal plasma proteome profiling

BACKGROUND

Precision medicine approaches aim to tackle diseases on an individual level through molecular profiling. Despite the growing knowledge about diseases and the reported diversity of molecular phenotypes, the descriptions of human health on an individual level have been far less elaborate.

METHODS

To provide insights into the longitudinal protein signatures of well-being, we profiled blood plasma collected over one year from 101 clinically healthy individuals using multiplexed antibody assays. After applying an antibody validation scheme, we utilized > 700 protein profiles for in-depth analyses of the individuals' short-term health trajectories.

FINDINGS

We found signatures of circulating proteomes to be highly individual-specific. Considering technical and longitudinal variability, we observed that 49% of the protein profiles were stable over one year. We also identified eight networks of proteins in which 11-242 proteins covaried over time. For each participant, there were unique protein profiles of which some could be explained by associations to genetic variants.

INTERPRETATION

This observational and non-interventional study identifyed noticeable diversity among clinically healthy subjects, and facets of individual-specific signatures emerged by monitoring the variability of the circulating proteomes over time. To enable more personal hence precise assessments of health states, longitudinal profiling of circulating proteomes can provide a valuable component for precision medicine approaches.

FUNDING

This work was supported by the Erling Persson Foundation, the Swedish Heart and Lung Foundation, the Knut and Alice Wallenberg Foundation, Science for Life Laboratory, and the Swedish Research Council.

Comparison of Proteomic Assessment Methods in Multiple Cohort Studies

Novel proteomics platforms, such as the aptamer-based SOMAscan platform, can quantify large numbers of proteins efficiently and cost-effectively and are rapidly growing in popularity. However, comparisons to conventional immunoassays remain underexplored, leaving investigators unsure when cross-assay comparisons are appropriate. The correlation of results from immunoassays with relative protein quantification is explored by SOMAscan. For 63 proteins assessed in two chronic obstructive pulmonary disease (COPD) cohorts, subpopulations and intermediate outcome measures in COPD Study (SPIROMICS), and COPDGene, using myriad rules based medicine multiplex immunoassays and SOMAscan, Spearman correlation coefficients range from -0.13 to 0.97, with a median correlation coefficient of ≈0.5 and consistent results across cohorts. A similar range is observed for immunoassays in the population-based Multi-Ethnic Study of Atherosclerosis and for other assays in COPDGene and SPIROMICS. Comparisons of relative quantification from the antibody-based Olink platform and SOMAscan in a small cohort of myocardial infarction patients also show a wide correlation range. Finally, cis pQTL data, mass spectrometry aptamer confirmation, and other publicly available data are integrated to assess relationships with observed correlations. Correlation between proteomics assays shows a wide range and should be carefully considered when comparing and meta-analyzing proteomics data across assays and studies.

IL-1Rahigh-IL-4low-IL-13low: A Novel Plasma Cytokine Signature Associated with Olfactory Dysfunction in Older US Adults

Inflammation has been implicated in physical frailty, but its role in sensory impairment is unclear. Given that olfactory impairment predicts dementia and mortality, determining the role of the immune system in olfactory dysfunction would provide insights mechanisms of neurosensory decline. We analyzed data from the National Social Life, Health and Aging Project, a representative sample of home-dwelling older US adults. Plasma levels of 18 cytokines were measured using standard protocols (Luminex xMAP). Olfactory function was assessed with validated tools (n-butanol sensitivity and odor identification, each via Sniffin' Sticks). We tested the association between cytokine profiles and olfactory function using multivariate ordinal logistic regression, adjusting for age, gender, race/ethnicity, education level, cognitive function, smoking status, and comorbidity. Older adults with the IL-1Rahigh-IL-4low-IL-13low cytokine profile had worse n-butanol odor sensitivity (odds ratio [OR] = 1.61, 95% confidence interval [CI] 1.19-2.17) and worse odor identification (OR = 1.42, 95% CI 1.11-1.80). Proinflammatory, Th1, or Th2 cytokine profiles were not associated with olfactory function. Moreover, accounting for physical frailty did not alter the main findings. In conclusion, we identified a plasma cytokine signature-IL-1Rahigh-IL-4low-IL-13low-that is associated with olfactory dysfunction in older US adults. These data implicate systemic inflammation in age-related olfactory dysfunction and support a role for immune mechanisms in this process, a concept that warrants additional scrutiny.