A genome- and phenome-wide association study to identify genetic variants influencing platelet count and volume and their pleiotropic effects

Ribosomal DNA copy number variation associates with hematological profiles and renal function in the UK Biobank

The phenotypic impact of genetic variation of repetitive features in the human genome is currently understudied. One such feature is the multi-copy 47S ribosomal DNA (rDNA) that codes for rRNA components of the ribosome. Here, we present an analysis of rDNA copy number (CN) variation in the UK Biobank (UKB). From the first release of UKB whole-genome sequencing (WGS) data, a discovery analysis in White British individuals reveals that rDNA CN associates with altered counts of specific blood cell subtypes, such as neutrophils, and with the estimated glomerular filtration rate, a marker of kidney function. Similar trends are observed in other ancestries. A range of analyses argue against reverse causality or common confounder effects, and all core results replicate in the second UKB WGS release. Our work demonstrates that rDNA CN is a genetic influence on trait variance in humans.

Volumetric brain MRI signatures of heart failure with preserved ejection fraction in the setting of dementia

Heart failure with preserved ejection fraction (HFpEF) is an important, emerging risk factor for dementia, but it is not clear whether HFpEF contributes to a specific pattern of neuroanatomical changes in dementia. A major challenge to studying this is the relative paucity of datasets of patients with dementia, with/without HFpEF, and relevant neuroimaging. We sought to demonstrate the feasibility of using modern data mining tools to create and analyze clinical imaging datasets and identify the neuroanatomical signature of HFpEF-associated dementia. We leveraged the bioinformatics tools at Vanderbilt University Medical Center to identify patients with a diagnosis of dementia with and without comorbid HFpEF using the electronic health record. We identified high resolution, clinically-acquired neuroimaging data on 30 dementia patients with HFpEF (age 76.9 ± 8.12 years, 61% female) as well as 301 age- and sex-matched patients with dementia but without HFpEF to serve as comparators (age 76.2 ± 8.52 years, 60% female). We used automated image processing pipelines to parcellate the brain into 132 structures and quantify their volume. We found six regions with significant atrophy associated with HFpEF: accumbens area, amygdala, posterior insula, anterior orbital gyrus, angular gyrus, and cerebellar white matter. There were no regions with atrophy inversely associated with HFpEF. Patients with dementia and HFpEF have a distinct neuroimaging signature compared to patients with dementia only. Five of the six regions identified in are in the temporo-parietal region of the brain. Future studies should investigate mechanisms of injury associated with cerebrovascular disease leading to subsequent brain atrophy.

Development of phenotyping algorithms for hypertensive disorders of pregnancy (HDP) and their application in more than 22,000 pregnant women

Recently, many phenotyping algorithms for high-throughput cohort identification have been developed. Prospective genome cohort studies are critical resources for precision medicine, but there are many hurdles in the precise cohort identification. Consequently, it is important to develop phenotyping algorithms for cohort data collection. Hypertensive disorders of pregnancy (HDP) is a leading cause of maternal morbidity and mortality. In this study, we developed, applied, and validated rule-based phenotyping algorithms of HDP. Two phenotyping algorithms, algorithms 1 and 2, were developed according to American and Japanese guidelines, and applied into 22,452 pregnant women in the Birth and Three-Generation Cohort Study of the Tohoku Medical Megabank project. To precise cohort identification, we analyzed both structured data (e.g., laboratory and physiological tests) and unstructured clinical notes. The identified subtypes of HDP were validated against reference standards. Algorithms 1 and 2 identified 7.93% and 8.08% of the subjects as having HDP, respectively, along with their HDP subtypes. Our algorithms were high performing with high positive predictive values (0.96 and 0.90 for algorithms 1 and 2, respectively). Overcoming the hurdle of precise cohort identification from large-scale cohort data collection, we achieved both developed and implemented phenotyping algorithms, and precisely identified HDP patients and their subtypes from large-scale cohort data collection.

Genomic Variation Affecting MPV and PLT Count in Association with Development of Ischemic Stroke and Its Subtypes

Platelets play a significant role in the pathophysiology of ischemic stroke since they are involved in the formation of intravascular thrombus after erosion or rupture of the atherosclerotic plaques. Platelet (PLT) count and mean platelet volume (MPV) are the two significant parameters that affect the functions of platelets. In the current study, MPV and PLT count was evaluated using flow cytometry and a cell counter. SonoClot analysis was carried out to evaluate activated clot timing (ACT), clot rate (CR), and platelet function (PF). Genotyping was carried out using GSA and Sanger sequencing, and expression analysis was performed using RT-PCR. In silico analysis was carried out using the GROMACS tool and UNAFold. The interaction of significant proteins with other proteins was predicted using the STRING database. Ninety-six genes were analyzed, and a significant association of THPO (rs6141) and ARHGEF3 (rs1354034) was observed with the disease and its subtypes. Altered genotypes were associated significantly with increased MPV, decreased PLT count, and CR. Expression analysis revealed a higher expression in patients bearing the variant genotypes of both genes. In silico analysis revealed that mutation in the THPO gene leads to the reduced compactness of protein structure. mRNA encoded by mutated ARHGEF3 gene increases the half-life of mRNA. The two significant proteins interact with many other proteins, especially the ones involved in platelet activation, aggregation, erythropoiesis, megakaryocyte maturation, and cytoskeleton rearrangements, suggesting that they could be important players in the determination of MPV values. In conclusion, the current study demonstrated the role of higher MPV affected by genetic variation in the development of IS and its subtypes. The results of the current study also indicate that higher MPV can be used as a biomarker for the disease and altered genotypes, and higher MPV can be targeted for better therapeutic outcomes.

Substrate-specific effects of natural genetic variation on proteasome activity

Protein degradation is an essential biological process that regulates protein abundance and removes misfolded and damaged proteins from cells. In eukaryotes, most protein degradation occurs through the stepwise actions of two functionally distinct entities, the ubiquitin system and the proteasome. Ubiquitin system enzymes attach ubiquitin to cellular proteins, targeting them for degradation. The proteasome then selectively binds and degrades ubiquitinated substrate proteins. Genetic variation in ubiquitin system genes creates heritable differences in the degradation of their substrates. However, the challenges of measuring the degradative activity of the proteasome independently of the ubiquitin system in large samples have limited our understanding of genetic influences on the proteasome. Here, using the yeast Saccharomyces cerevisiae, we built and characterized reporters that provide high-throughput, ubiquitin system-independent measurements of proteasome activity. Using single-cell measurements of proteasome activity from millions of genetically diverse yeast cells, we mapped 15 loci across the genome that influence proteasomal protein degradation. Twelve of these 15 loci exerted specific effects on the degradation of two distinct proteasome substrates, revealing a high degree of substrate-specificity in the genetics of proteasome activity. Using CRISPR-Cas9-based allelic engineering, we resolved a locus to a causal variant in the promoter of RPT6, a gene that encodes a subunit of the proteasome's 19S regulatory particle. The variant increases RPT6 expression, which we show results in increased proteasome activity. Our results reveal the complex genetic architecture of proteasome activity and suggest that genetic influences on the proteasome may be an important source of variation in the many cellular and organismal traits shaped by protein degradation.

PIK3R3 is a candidate regulator of platelet count in people of Bangladeshi ancestry

Background

Blood platelets are mediators of atherothrombotic disease and are regulated by complex sets of genes. Association studies in European ancestry populations have already detected informative platelet regulatory loci. Studies in other ancestries can potentially reveal new associations because of different allele frequencies, linkage structures, and variant effects.

Objectives

To reveal new regulatory genes for platelet count (PLT).

Methods

Genome-wide association studies (GWAS) were performed in 20,218 Bangladeshi and 9198 Pakistani individuals from the Genes & Health study. Loci significantly associated with PLT underwent fine-mapping to identify candidate genes.

Results

Of 1588 significantly associated variants (P < 5 × 10-8) at 20 loci in the Bangladeshi analysis, most replicated findings in prior transancestry GWAS and in the Pakistani analysis. However, the Bangladeshi locus defined by rs946528 (chr1:46019890) did not associate with PLT in the Pakistani analysis but was in the same linkage disequilibrium block (r2 ≥ 0.5) as PLT-associated variants in prior East Asian GWAS. The single independent association signal was refined to a 95% credible set of 343 variants spanning 8 coding genes. Functional annotation, mapping to megakaryocyte regulatory regions, and colocalization with blood expression quantitative trait loci identified the likely mediator of the PLT phenotype to be PIK3R3 encoding a regulator of phosphoinositol 3-kinase (PI3K).

Conclusion

Abnormal PI3K activity in the vessel wall is already implicated in the pathogenesis of atherothrombosis. Our identification of a new association between PIK3R3 and PLT provides further mechanistic insights into the contribution of the PI3K pathway to platelet biology.

A transcriptome-wide association study of uterine fibroids to identify potential genetic markers and toxic chemicals

Uterine fibroid is one of the most prevalent benign tumors in women, with high socioeconomic costs. Although genome-wide association studies (GWAS) have identified several loci associated with uterine fibroid risks, they could not successfully interpret the biological effects of genomic variants at the gene expression levels. To prioritize uterine fibroid susceptibility genes that are biologically interpretable, we conducted a transcriptome-wide association study (TWAS) by integrating GWAS data of uterine fibroid and expression quantitative loci data. We identified nine significant TWAS genes including two novel genes, RP11-282O18.3 and KBTBD7, which may be causal genes for uterine fibroid. We conducted functional enrichment network analyses using the TWAS results to investigate the biological pathways in which the overall TWAS genes were involved. The results demonstrated the immune system process to be a key pathway in uterine fibroid pathogenesis. Finally, we carried out chemical–gene interaction analyses using the TWAS results and the comparative toxicogenomics database to determine the potential risk chemicals for uterine fibroid. We identified five toxic chemicals that were significantly associated with uterine fibroid TWAS genes, suggesting that they may be implicated in the pathogenesis of uterine fibroid. In this study, we performed an integrative analysis covering the broad application of bioinformatics approaches. Our study may provide a deeper understanding of uterine fibroid etiologies and informative notifications about potential risk chemicals for uterine fibroid.

The systemic-level repercussions of cancer-associated inflammation mediators produced in the tumor microenvironment

The tumor microenvironment is a dynamic, complex, and redundant network of interactions between tumor, immune, and stromal cells. In this intricate environment, cells communicate through membrane-membrane, ligand-receptor, exosome, soluble factors, and transporter interactions that govern cell fate. These interactions activate the diverse and superfluous signaling pathways involved in tumor promotion and progression and induce subtle changes in the functional activity of infiltrating immune cells. The immune response participates as a selective pressure in tumor development. In the early stages of tumor development, the immune response exerts anti-tumor activity, whereas during the advanced stages, the tumor establishes mechanisms to evade the immune response, eliciting a chronic inflammation process that shows a pro-tumor effect. The deregulated inflammatory state, in addition to acting locally, also triggers systemic inflammation that has repercussions in various organs and tissues that are distant from the tumor site, causing the emergence of various symptoms designated as paraneoplastic syndromes, which compromise the response to treatment, quality of life, and survival of cancer patients. Considering the tumor-host relationship as an integral and dynamic biological system, the chronic inflammation generated by the tumor is a communication mechanism among tissues and organs that is primarily orchestrated through different signals, such as cytokines, chemokines, growth factors, and exosomes, to provide the tumor with energetic components that allow it to continue proliferating. In this review, we aim to provide a succinct overview of the involvement of cancer-related inflammation at the local and systemic level throughout tumor development and the emergence of some paraneoplastic syndromes and their main clinical manifestations. In addition, the involvement of these signals throughout tumor development will be discussed based on the physiological/biological activities of innate and adaptive immune cells. These cellular interactions require a metabolic reprogramming program for the full activation of the various cells; thus, these requirements and the by-products released into the microenvironment will be considered. In addition, the systemic impact of cancer-related proinflammatory cytokines on the liver-as a critical organ that produces the leading inflammatory markers described to date-will be summarized. Finally, the contribution of cancer-related inflammation to the development of two paraneoplastic syndromes, myelopoiesis and cachexia, will be discussed.

Whole genome sequence analysis of platelet traits in the NHLBI Trans-Omics for Precision Medicine (TOPMed) initiative

Platelets play a key role in thrombosis and hemostasis. Platelet count (PLT) and mean platelet volume (MPV) are highly heritable quantitative traits, with hundreds of genetic signals previously identified, mostly in European ancestry populations. We here utilize whole genome sequencing (WGS) from NHLBI's Trans-Omics for Precision Medicine initiative (TOPMed) in a large multi-ethnic sample to further explore common and rare variation contributing to PLT (n = 61 200) and MPV (n = 23 485). We identified and replicated secondary signals at MPL (rs532784633) and PECAM1 (rs73345162), both more common in African ancestry populations. We also observed rare variation in Mendelian platelet-related disorder genes influencing variation in platelet traits in TOPMed cohorts (not enriched for blood disorders). For example, association of GP9 with lower PLT and higher MPV was partly driven by a pathogenic Bernard-Soulier syndrome variant (rs5030764, p.Asn61Ser), and the signals at TUBB1 and CD36 were partly driven by loss of function variants not annotated as pathogenic in ClinVar (rs199948010 and rs571975065). However, residual signal remained for these gene-based signals after adjusting for lead variants, suggesting that additional variants in Mendelian genes with impacts in general population cohorts remain to be identified. Gene-based signals were also identified at several genome-wide association study identified loci for genes not annotated for Mendelian platelet disorders (PTPRH, TET2, CHEK2), with somatic variation driving the result at TET2. These results highlight the value of WGS in populations of diverse genetic ancestry to identify novel regulatory and coding signals, even for well-studied traits like platelet traits.

Nonsynonymous single-nucleotide polymorphisms in the G6PC2 gene affect protein expression, enzyme activity, and fasting blood glucose

G6PC2 encodes a glucose-6-phosphatase (G6Pase) catalytic subunit that modulates the sensitivity of insulin secretion to glucose and thereby regulates fasting blood glucose (FBG). A common single-nucleotide polymorphism (SNP) in G6PC2, rs560887 is an important determinant of human FBG variability. This SNP has a subtle effect on G6PC2 RNA splicing, which raises the question as to whether nonsynonymous SNPs with a major impact on G6PC2 stability or enzyme activity might have a broader disease/metabolic impact. Previous attempts to characterize such SNPs were limited by the very low inherent G6Pase activity and expression of G6PC2 protein in islet-derived cell lines. In this study, we describe the use of a plasmid vector that confers high G6PC2 protein expression in islet cells, allowing for a functional analysis of 22 nonsynonymous G6PC2 SNPs, 19 of which alter amino acids that are conserved in mouse G6PC2 and the human and mouse variants of the related G6PC1 isoform. We show that 16 of these SNPs markedly impair G6PC2 protein expression (>50% decrease). These SNPs have variable effects on the stability of human and mouse G6PC1, despite the high sequence homology between these isoforms. Four of the remaining six SNPs impaired G6PC2 enzyme activity. Electronic health record-derived phenotype analyses showed an association between high-impact SNPs and FBG, but not other diseases/metabolites. While homozygous G6pc2 deletion in mice increases the risk of hypoglycemia, these human data reveal no evidence that the beneficial use of partial G6PC2 inhibitors to lower FBG would be associated with unintended negative consequences.

Early Change in Platelet Count and MPV Levels of Patients Who Received Hemodialysis for the First Time: Mogadishu Somalia Experience

INTRODUCTION

Mean platelet volume (MPV) is a marker used to assess the platelet' size and is also an indicator of platelet reactivity and prothrombotic status.

OBJECTIVE

In this study, we aimed to determine the relationship between MPV and biochemical parameters in patients who had received hemodialysis (HD) for the first time and then in respect of those same patients after their fourth HD.

METHOD

151 HD patients were enrolled in this study. Patients were eligible for inclusion if they had received their first HD session during this study protocol. Prehemodialysis blood samples were taken. Most laboratory values, including mean platelet volume (MPV) level and platelets (PLT) count, were measured before the first HD and after the fourth HD session for each patient.

RESULTS

Among the patients in our study, the mean age profile of the male patients (n = 103; 68.2%) was found to be higher than that of the female patients (n = 48; 31.8%) (53.62 ± 18.19 vs. 46.17 ± 17.9 years) (p = 0.019).In the patients' laboratory results after the fourth HD session, MPV, MPV/Plt, and Na values had increased to those after the first HD session (p < 0.001). When age and gender status were taken into account, the level of weak positive correlation with white blood cell count (WBC), neutrophil, and red cell distribution width (RDW) was found, while the weak negative correlation with platelet to lymphocyte ratio (PLR) was found (p < 0.001).

CONCLUSIONS

In our study, we found that increase in MPV and MPV/PLT levels was significant in the fourth HD session of patients with CKD. It is also debatable that there are findings indicating an increase in platelet reactivity in the first weeks of the onset of HD. This could be an early indicator of the early prevention of cardiovascular diseases.

Secondary analyses for genome-wide association studies using expression quantitative trait loci

Genome-wide association studies (GWAS) have successfully identified thousands of single nucleotide polymorphisms (SNPs) associated with complex traits; however, the identified SNPs account for a fraction of trait heritability, and identifying the functional elements through which genetic variants exert their effects remains a challenge. Recent evidence suggests that SNPs associated with complex traits are more likely to be expression quantitative trait loci (eQTL). Thus, incorporating eQTL information can potentially improve power to detect causal variants missed by traditional GWAS approaches. Using genomic, transcriptomic, and platelet phenotype data from the Genetic Study of Atherosclerosis Risk family-based study, we investigated the potential to detect novel genomic risk loci by incorporating information from eQTL in the relevant target tissues (i.e., platelets and megakaryocytes) using established statistical principles in a novel way. Permutation analyses were performed to obtain family-wise error rates for eQTL associations, substantially lowering the genome-wide significance threshold for SNP-phenotype associations. In addition to confirming the well known association between PEAR1 and platelet aggregation, our eQTL-focused approach identified a novel locus (rs1354034) and gene (ARHGEF3) not previously identified in a GWAS of platelet aggregation phenotypes. A colocalization analysis showed strong evidence for a functional role of this eQTL.

Whole-exome analysis of adolescents with low VWF and heavy menstrual bleeding identifies novel genetic associations

HMB is associated with rare and common variants in genes related to anemias and bleeding disorders.

These are the first exome-sequencing results from patients with HMB, as well as their comparison with control exomes.

Adolescents with low von Willebrand factor (VWF) levels and heavy menstrual bleeding (HMB) experience significant morbidity. There is a need to better characterize these patients genetically and improve our understanding of the pathophysiology of bleeding. We performed whole-exome sequencing on 86 postmenarchal patients diagnosed with low VWF levels (30-50 IU/dL) and HMB and compared them with 660 in-house controls. We compared the number of rare stop-gain/stop-loss and rare ClinVar “pathogenic” variants between cases and controls, as well as performed gene burden and gene-set burden analyses. We found an enrichment in cases of rare stop-gain/stop-loss variants in genes involved in bleeding disorders and an enrichment of rare ClinVar “pathogenic” variants in genes involved in anemias. The 2 most significant genes in the gene burden analysis, CFB and DNASE2, are associated with atypical hemolytic uremia and severe anemia, respectively. VWF also surpassed exome-wide significance in the gene burden analysis (P = 7.31 × 10⁻⁶). Gene-set burden analysis revealed an enrichment of rare nonsynonymous variants in cases in several hematologically relevant pathways. Further, common variants in FERMT2, a gene involved in the regulation of hemostasis and angiogenesis, surpassed genome-wide significance. We demonstrate that adolescents with HMB and low VWF have an excess of rare nonsynonymous and pathogenic variants in genes involved in bleeding disorders and anemia. Variants of variable penetrance in these genes may contribute to the spectrum of phenotypes observed in patients with HMB and could partially explain the bleeding phenotype. By identifying patients with HMB who possess these variants, we may be able to improve risk stratification and patient outcomes.

Genetic variants associated with platelet count are predictive of human disease and physiological markers

Platelets play an important role in hemostasis and other aspects of vascular biology. We conducted a meta-analysis of platelet count GWAS using data on 536,974 Europeans and identified 577 independent associations. To search for mechanisms through which these variants affect platelets, we applied cis-expression quantitative trait locus, DEPICT and IPA analyses and assessed genetic sharing between platelet count and various traits using polygenic risk scoring. We found genetic sharing between platelet count and counts of other blood cells (except red blood cells), in addition to several other quantitative traits, including markers of cardiovascular, liver and kidney functions, height, and weight. Platelet count polygenic risk score was predictive of myeloproliferative neoplasms, rheumatoid arthritis, ankylosing spondylitis, hypertension, and benign prostate hyperplasia. Taken together, these results advance understanding of diverse aspects of platelet biology and how they affect biological processes in health and disease.

Evgenia Mikaelsdottir et al. report a study of variants associated with platelet count among European individuals where they identify 577 associations. They also report a genetic overlap between platelet count and human diseases, including myeloproliferative neoplasms, rheumatoid arthritis, and hypertension, as well as a genetic overlap between platelet count and various physiological markers.

Genetics of Acute Respiratory Distress Syndrome: Pathways to Precision

Clinical risk factors alone fail to fully explain acute respiratory distress syndrome (ARDS) risk or ARDS death, suggesting that individual risk factors contribute. The goals of genomic ARDS studies include better mechanistic understanding, identifying dysregulated pathways that may be amenable to pharmacologic targeting, using genomic causal inference techniques to find measurable traits with meaning, and deconvoluting ARDS heterogeneity by proving reproducible subpopulations that may share a unique biology. This article discusses the latest advances in ARDS genomics, provides historical perspective, and highlights some of the ways that the coronavirus disease 2019 (COVID-19) pandemic is accelerating genomic ARDS research.

JMJD1C knockdown affects myeloid cell lines proliferation, viability, and gemcitabine/carboplatin-sensitivity

OBJECTIVES

Chemotherapy-induced hematological toxicities are potentially life-threatening adverse drug reactions that vary between individuals. Recently, JMJD1C has been associated with gemcitabine/carboplatin-induced thrombocytopenia in non-small-cell lung cancer patients, making it a candidate marker for predicting the risk of toxicity. This study investigates if JMJD1C knockdown affects gemcitabine/carboplatin-sensitivity in cell lines.

METHODS

Lentiviral transduction-mediated shRNA knockdown of JMJD1C in the cell lines K562 and MEG-01 were performed using shRNA#32 and shRNA#33. The knockdown was evaluated using qPCR. Cell proliferation, viability, and gemcitabine/carboplatin-sensitivity were subsequently determined using cell counts, trypan blue, and the MTT assay.

RESULTS

ShRNA#33 resulted in JMJD1C downregulation by 56.24% in K562 and 68.10% in MEG-01. Despite incomplete knockdown, proliferation (reduction of cell numbers by 61-68%, day 7 post-transduction) and viability (reduction by 21-53%, day 7 post-transduction) were impaired in K562 and MEG-01 cells. Moreover, JMJD1C knockdown reduced the gemcitabine IC50-value for K562 cells (P < 0.01) and MEG-01 cells (P < 0.05) compared to scrambled shRNA control transduced cells.

CONCLUSIONS

Our results suggest that JMJD1C is essential for proliferation, survival, and viability of K562 and MEG-01 cells. Further, JMJD1C also potentially affects the cells gemcitabine/carboplatin-sensitivity. Although further research is required, the findings show that JMJD1C could have an influential role for gemcitabine/carboplatin-sensitivity.

Low Intra-Individual Variation in Mean Platelet Volume Over Time in Systemic Lupus Erythematosus

Platelets have recently emerged as important immune modulators in systemic lupus erythematosus (SLE), in addition to their role in thrombosis and cardiovascular disease. However, studies investigating mean platelet volume (MPV) in SLE are often scarce, conflicting and cross-sectional. In this study, MPV was measured in clinical routine throughout a defined time-period to quantify both individual MPV fluctuations and investigate if such variations are associated with disease activity and clinical phenotypes of SLE. Of our 212 patients, 34 patients had only one MPV value reported with the remaining 178 patients having between 2 and 19 visits with recorded MPV values. The intra-individual MPV variation was low, with a median variation of 0.7 fL. This was further supported by the finding that 84% of patients stayed within their reference interval category (i.e., small, normal or large) over time. In our cohort, no correlation between disease activity and MPV neither cross-sectionally nor longitudinally was found. Mean platelet volume values were significantly smaller in SLE patients (mean 10.5 fL) compared to controls (mean 10.8 fL), p < 0.0001. Based on the reference interval, 2.4% (n = 5) of patients had large-sized platelets, 84.4% (n = 179) had normal-sized and 13.2% (n = 28) had small-sized. A larger proportion (85.7%) of patients with small-sized platelets met the anti-dsDNA criterion (ACR10b; p = 0.003) compared to patients with normal and large (57.6%) sized platelets. In conclusion, the intra-individual MPV variation was of low magnitude and fluctuations in disease activity did not have any significant impact on MPV longitudinally. This lack of variability in MPV over time indicates that measuring MPV at any time-point is sufficient. Further studies are warranted to evaluate MPV as a possible biomarker in SLE, as well as to determine the underlying mechanisms influencing platelet size in SLE.

An Asian-specific MPL genetic variant alters JAK-STAT signaling and influences platelet count in the population

Genomic discovery efforts for hematological traits have been successfully conducted through genome-wide association study on samples of predominantly European ancestry. We sought to conduct unbiased genetic discovery for coding variants that influence hematological traits in a Han Chinese population. A total of 5257 Han Chinese subjects from Beijing, China were included in the discovery cohort and analyzed by an Illumina ExomeChip array. Replication analyses were conducted in 3827 independent Chinese subjects. We analyzed 12 hematological traits and identified 22 exome-wide significant single-nucleotide polymorphisms (SNP)-trait associations with 15 independent SNPs. Our study provides replication for two associations previously reported but not replicated. Further, one association was identified and replicated in the current study, of a coding variant in the myeloproliferative leukemia (MPL) gene, c.793C > T, p.Leu265Phe (L265F) with increased platelet count (β = 20.6 109 cells/l, Pmeta-analysis = 2.6 × 10-13). This variant is observed at ~2% population frequency in East Asians, whereas it has not been reported in gnomAD European or African populations. Functional analysis demonstrated that expression of MPL L265F in Ba/F3 cells resulted in enhanced phosphorylation of Stat3 and ERK1/2 as compared with the reference MPL allele, supporting altered activation of the JAK-STAT signal transduction pathway as the mechanism underlying the novel association between MPL L265F and platelet count.

Circulating chemerin levels are determined through circulating platelet counts in nondiabetic Taiwanese people: A bidirectional Mendelian randomization study

BACKGROUND AND AIMS

Platelet count (PLT) is a predictor of metabolic and inflammation-related disorders. Platelets can release prochemerin, which acts as a link between coagulation and inflammation and between innate and adaptive immunity. The causal effect between PLT and circulating chemerin level has not been elucidated.

METHODS

Nondiabetic participants with samples in the Taiwan Biobank were recruited for a genome-wide association study (GWAS) based on PLT (17,037 participants) and chemerin levels (3887 participants). A bidirectional Mendelian randomization (MR) study was conducted to determine the association between circulating PLT and chemerin levels.

RESULTS

For a GWAS of PLT, 11 gene loci were found to have genome-wide significance. For a GWAS of chemerin levels, two gene loci, RARRES2 and HLADQA2-HLADQB1, were found to have genome-wide significance. Age, sex, body mass index, leukocyte count, hemoglobin, mean blood pressure, hemoglobin A1C, serum total bilirubin, aspartate aminotransferase, triglyceride, and low-density-lipoprotein cholesterol levels, estimated glomerular filtration rate, and circulating chemerin level were found to be independently associated with PLT through a stepwise regression analysis. A bidirectional MR study revealed weighted genetic risk scores (WGRSs) for PLT were significantly associated with chemerin levels by using a two-stage least-square method in a multivariate analysis (p = 0.0031), and no significant association between chemerin level WGRSs and PLT was noted. Sensitivity analysis further revealed no violation of the exclusion-restriction assumption with PLT-determining genotypes on chemerin levels.

CONCLUSIONS

Through a bidirectional MR analysis, our data revealed that chemerin levels were determined based on circulating PLT. Circulating chemerin levels can be intermediates between PLT and future metabolic and inflammation-related disorders.

Clinical laboratory test-wide association scan of polygenic scores identifies biomarkers of complex disease

BACKGROUND

Clinical laboratory (lab) tests are used in clinical practice to diagnose, treat, and monitor disease conditions. Test results are stored in electronic health records (EHRs), and a growing number of EHRs are linked to patient DNA, offering unprecedented opportunities to query relationships between genetic risk for complex disease and quantitative physiological measurements collected on large populations.

METHODS

A total of 3075 quantitative lab tests were extracted from Vanderbilt University Medical Center's (VUMC) EHR system and cleaned for population-level analysis according to our QualityLab protocol. Lab values extracted from BioVU were compared with previous population studies using heritability and genetic correlation analyses. We then tested the hypothesis that polygenic risk scores for biomarkers and complex disease are associated with biomarkers of disease extracted from the EHR. In a proof of concept analyses, we focused on lipids and coronary artery disease (CAD). We cleaned lab traits extracted from the EHR performed lab-wide association scans (LabWAS) of the lipids and CAD polygenic risk scores across 315 heritable lab tests then replicated the pipeline and analyses in the Massachusetts General Brigham Biobank.

RESULTS

Heritability estimates of lipid values (after cleaning with QualityLab) were comparable to previous reports and polygenic scores for lipids were strongly associated with their referent lipid in a LabWAS. LabWAS of the polygenic score for CAD recapitulated canonical heart disease biomarker profiles including decreased HDL, increased pre-medication LDL, triglycerides, blood glucose, and glycated hemoglobin (HgbA1C) in European and African descent populations. Notably, many of these associations remained even after adjusting for the presence of cardiovascular disease and were replicated in the MGBB.

CONCLUSIONS

Polygenic risk scores can be used to identify biomarkers of complex disease in large-scale EHR-based genomic analyses, providing new avenues for discovery of novel biomarkers and deeper understanding of disease trajectories in pre-symptomatic individuals. We present two methods and associated software, QualityLab and LabWAS, to clean and analyze EHR labs at scale and perform a Lab-Wide Association Scan.

RCL1 copy number variants are associated with a range of neuropsychiatric phenotypes

Mendelian and early-onset severe psychiatric phenotypes often involve genetic variants having a large effect, offering opportunities for genetic discoveries and early therapeutic interventions. Here, the index case is an 18-year-old boy, who at 14 years of age had a decline in cognitive functioning over the course of a year and subsequently presented with catatonia, auditory and visual hallucinations, paranoia, aggression, mood dysregulation, and disorganized thoughts. Exome sequencing revealed a stop-gain mutation in RCL1 (NM_005772.4:c.370 C > T, p.Gln124Ter), encoding an RNA 3'-terminal phosphate cyclase-like protein that is highly conserved across eukaryotic species. Subsequent investigations across two academic medical centers identified eleven additional cases of RCL1 copy number variations (CNVs) with varying neurodevelopmental or psychiatric phenotypes. These findings suggest that dosage variation of RCL1 contributes to a range of neurological and clinical phenotypes.

Novel Approaches to Fine-Tune Therapeutic Targeting of Platelets in Atherosclerosis: A Critical Appraisal

The pathogenesis of atherosclerotic vascular disease is driven by a multitude of risk factors intertwining metabolic and inflammatory pathways. Increasing knowledge about platelet biology sheds light on how platelets take part in these processes from early to later stages of plaque development. Recent insights from experimental studies and mouse models substantiate platelets as initiators and amplifiers in atherogenic leukocyte recruitment. These studies are complemented by results from genetics studies shedding light on novel molecular mechanisms which provide an interesting prospect as novel targets. For instance, experimental studies provide further details how platelet-decorated von Willebrand factor tethered to activated endothelial cells plays a role in atherogenic monocyte recruitment. Novel aspects of platelets as atherogenic inductors of neutrophil extracellular traps and particularities in signaling pathways such as cyclic guanosine monophosphate and the inhibitory adaptor molecule SHB23/LNK associating platelets with atherogenesis are shared. In summary, it was our intention to balance insights from recent experimental data that support a plausible role for platelets in atherogenesis against a paucity of clinical evidence needed to validate this concept in humans.

Potential positive and negative consequences of ZnT8 inhibition

SLC30A8 encodes the zinc transporter ZnT8. SLC30A8 haploinsufficiency protects against type 2 diabetes (T2D), suggesting that ZnT8 inhibitors may prevent T2D. We show here that, while adult chow fed Slc30a8 haploinsufficient and knockout (KO) mice have normal glucose tolerance, they are protected against diet-induced obesity (DIO), resulting in improved glucose tolerance. We hypothesize that this protection against DIO may represent one mechanism whereby SLC30A8 haploinsufficiency protects against T2D in humans and that, while SLC30A8 is predominantly expressed in pancreatic islet beta cells, this may involve a role for ZnT8 in extra-pancreatic tissues. Consistent with this latter concept we show in humans, using electronic health record-derived phenotype analyses, that the 'C' allele of the non-synonymous rs13266634 SNP, which confers a gain of ZnT8 function, is associated not only with increased T2D risk and blood glucose, but also with increased risk for hemolytic anemia and decreased mean corpuscular hemoglobin (MCH). In Slc30a8 KO mice, MCH was unchanged but reticulocytes, platelets and lymphocytes were elevated. Both young and adult Slc30a8 KO mice exhibit a delayed rise in insulin after glucose injection, but only the former exhibit increased basal insulin clearance and impaired glucose tolerance. Young Slc30a8 KO mice also exhibit elevated pancreatic G6pc2 gene expression, potentially mediated by decreased islet zinc levels. These data indicate that the absence of ZnT8 results in a transient impairment in some aspects of metabolism during development. These observations in humans and mice suggest the potential for negative effects associated with T2D prevention using ZnT8 inhibitors.

Sepsis in the era of data-driven medicine: personalizing risks, diagnoses, treatments and prognoses

Sepsis is a series of clinical syndromes caused by the immunological response to infection. The clinical evidence for sepsis could typically attribute to bacterial infection or bacterial endotoxins, but infections due to viruses, fungi or parasites could also lead to sepsis. Regardless of the etiology, rapid clinical deterioration, prolonged stay in intensive care units and high risk for mortality correlate with the incidence of sepsis. Despite its prevalence and morbidity, improvement in sepsis outcomes has remained limited. In this comprehensive review, we summarize the current landscape of risk estimation, diagnosis, treatment and prognosis strategies in the setting of sepsis and discuss future challenges. We argue that the advent of modern technologies such as in-depth molecular profiling, biomedical big data and machine intelligence methods will augment the treatment and prevention of sepsis. The volume, variety, veracity and velocity of heterogeneous data generated as part of healthcare delivery and recent advances in biotechnology-driven therapeutics and companion diagnostics may provide a new wave of approaches to identify the most at-risk sepsis patients and reduce the symptom burden in patients within shorter turnaround times. Developing novel therapies by leveraging modern drug discovery strategies including computational drug repositioning, cell and gene-therapy, clustered regularly interspaced short palindromic repeats -based genetic editing systems, immunotherapy, microbiome restoration, nanomaterial-based therapy and phage therapy may help to develop treatments to target sepsis. We also provide empirical evidence for potential new sepsis targets including FER and STARD3NL. Implementing data-driven methods that use real-time collection and analysis of clinical variables to trace, track and treat sepsis-related adverse outcomes will be key. Understanding the root and route of sepsis and its comorbid conditions that complicate treatment outcomes and lead to organ dysfunction may help to facilitate identification of most at-risk patients and prevent further deterioration. To conclude, leveraging the advances in precision medicine, biomedical data science and translational bioinformatics approaches may help to develop better strategies to diagnose and treat sepsis in the next decade.

Mean platelet volume and its genetic variants relate to stroke severity and 1-year mortality

OBJECTIVE

To determine whether mean platelet volume (MPV) and selected single nucleotide polymorphisms (SNPs) that have been associated with MPV in genome-wide association studies relate to stroke severity, functional outcome on discharge, and 1-year mortality in patients with ischemic stroke, we retrospectively analyzed 577 patients with first-ever ischemic stroke.

METHODS

Genotyping of 3 SNPs (rs342293, rs1354034, rs7961894) was performed using a real-time PCR allelic discrimination assay. Multivariable regression was used to determine the association of MPV and MPV-associated SNPs with the NIH Stroke Scale (NIHSS) score on admission, modified Rankin Scale score on discharge, and data on 1-year mortality.

RESULTS

Rs7961894, but not rs342293 or rs1354034 SNP, was independently associated with an MPV in the highest quartile (MPV Q4). MPV Q4 was associated with significantly greater admission NIHSS (p = 0.006), poor discharge outcome (p = 0.034), and worse 1-year mortality (p = 0.033). After adjustment for pertinent covariates, MPV Q4 remained independently associated with a greater admission NIHSS score (p = 0.025). The T>C variant of rs7961894 SNP was an independent marker of a lower 1-year mortality (hazard ratio, 0.30; 95% confidence interval, 0.13-0.70; p = 0.006) in the studied population.

CONCLUSION

MPV is a marker of stroke severity and T>C variant of rs7961894 is independently associated with greater MPV in acute phase of ischemic stroke and relates to decreased 1-year mortality after stroke.

Large and small platelets-(When) do they differ?

Platelets are most important in providing cellular hemostasis but also take part in inflammation and immune processes. Increased platelet size has been regarded as a feature describing a young and more reactive subpopulation until studies were published which questioned this concept. Moreover, changes of platelet size given by the mean platelet volume (MPV) were described for immune thrombocytopenia, cardiovascular disease, atherosclerosis, venous thromboembolism, chronic lung disease, sepsis, cancer-associated thrombosis, autoimmune disorders, and others. This review summarizes the literature on what is known about platelets with different size and describes controversies of studies with large and small platelets putting a focus on their thrombogenicity, age, and on the association of MPV with the mentioned diseases.

Pancreatic islet beta cell-specific deletion of G6pc2 reduces fasting blood glucose

The G6PC1, G6PC2 and G6PC3 genes encode distinct glucose-6-phosphatase catalytic subunit (G6PC) isoforms. In mice, germline deletion of G6pc2 lowers fasting blood glucose (FBG) without affecting fasting plasma insulin (FPI) while, in isolated islets, glucose-6-phosphatase activity and glucose cycling are abolished and glucose-stimulated insulin secretion (GSIS) is enhanced at submaximal but not high glucose. These observations are all consistent with a model in which G6PC2 regulates the sensitivity of GSIS to glucose by opposing the action of glucokinase. G6PC2 is highly expressed in human and mouse islet beta cells however, various studies have shown trace G6PC2 expression in multiple tissues raising the possibility that G6PC2 also affects FBG through non-islet cell actions. Using real-time PCR we show here that expression of G6pc1 and/or G6pc3 are much greater than G6pc2 in peripheral tissues, whereas G6pc2 expression is much higher than G6pc3 in both pancreas and islets with G6pc1 expression not detected. In adult mice, beta cell-specific deletion of G6pc2 was sufficient to reduce FBG without changing FPI. In addition, electronic health record-derived phenotype analyses showed no association between G6PC2 expression and phenotypes clearly unrelated to islet function in humans. Finally, we show that germline G6pc2 deletion enhances glycolysis in mouse islets and that glucose cycling can also be detected in human islets. These observations are all consistent with a mechanism by which G6PC2 action in islets is sufficient to regulate the sensitivity of GSIS to glucose and hence influence FBG without affecting FPI.

Targeted Sequencing Study to Uncover Shared Genetic Susceptibility Between Peripheral Artery Disease and Coronary Heart Disease-Brief Report

Objective- It is unclear to what extent genetic susceptibility variants are shared between peripheral artery disease (PAD) and coronary heart disease (CHD), both manifestations of atherosclerotic vascular disease. We investigated whether common and low-frequency/rare variants in loci associated with CHD are also associated with PAD. Approach and Results- Targeted sequencing of 41 genomic regions associated with CHD in genome-wide association studies was performed in 1749 PAD cases (65±11 years, 61% men) and 1855 controls (60±11 years, 56% men) of European ancestry. PAD cases had a resting/postexercise ankle-brachial index ≤0.9, or history of lower extremity revascularization; controls had no history of PAD. We tested the association of common (defined as minor allele frequency ≥5%) variants with PAD assuming an additive genetic model with adjustment for age and sex. To identify low-frequency/rare variants (minor allele frequency <5%) associated with PAD, we conducted gene-level analyses using sequence kernel association test and permutation test. After Bonferroni correction, we found common variants in SH2B3, ABO, and ZEB2 to be associated with PAD ( P<4.5×10^-5). At the gene level, the strongest associations were for LPL and SH2B3. Conclusions- Targeted sequencing of 41 genomic regions associated with CHD revealed several common variants/genes to be associated with PAD, highlighting the basis of shared genetic susceptibility between CHD and PAD.

PheProb: probabilistic phenotyping using diagnosis codes to improve power for genetic association studies

Objective

Standard approaches for large scale phenotypic screens using electronic health record (EHR) data apply thresholds, such as ≥2 diagnosis codes, to define subjects as having a phenotype. However, the variation in the accuracy of diagnosis codes can impair the power of such screens. Our objective was to develop and evaluate an approach which converts diagnosis codes into a probability of a phenotype (PheProb). We hypothesized that this alternate approach for defining phenotypes would improve power for genetic association studies.

Methods

The PheProb approach employs unsupervised clustering to separate patients into 2 groups based on diagnosis codes. Subjects are assigned a probability of having the phenotype based on the number of diagnosis codes. This approach was developed using simulated EHR data and tested in a real world EHR cohort. In the latter, we tested the association between low density lipoprotein cholesterol (LDL-C) genetic risk alleles known for association with hyperlipidemia and hyperlipidemia codes (ICD-9 272.x). PheProb and thresholding approaches were compared.

Results

Among n = 1462 subjects in the real world EHR cohort, the threshold-based p-values for association between the genetic risk score (GRS) and hyperlipidemia were 0.126 (≥1 code), 0.123 (≥2 codes), and 0.142 (≥3 codes). The PheProb approach produced the expected significant association between the GRS and hyperlipidemia: p = .001.

Conclusions

PheProb improves statistical power for association studies relative to standard thresholding approaches by leveraging information about the phenotype in the billing code counts. The PheProb approach has direct applications where efficient approaches are required, such as in Phenome-Wide Association Studies.

Disease associations depend on visit type: results from a visit-wide association study

Introduction

Widespread adoption of Electronic Health Records (EHR) increased the number of reported disease association studies, or Phenome-Wide Association Studies (PheWAS). Traditional PheWAS studies ignore visit type (i.e., department/service conducting the visit). In this study, we investigate the role of visit type on disease association results in the first Visit-Wide Association Study or ‘VisitWAS’.

Results

We studied this visit type effect on association results using EHR data from the University of Pennsylvania. Penn EHR data comes from 1,048 different departments and clinics. We analyzed differences between cancer and obstetrics/gynecologist (Ob/Gyn) visits. Some findings were expected (i.e., increase of neoplasm diagnoses among cancer visits), but others were surprising, including an increase in infectious disease conditions among those visiting the Ob/Gyn.

Conclusion

We conclude that assessing visit type is important for EHR studies because different medical centers have different visit type distributions. To increase reproducibility among EHR data mining algorithms, we recommend that researchers report visit type in studies.

Electronic supplementary material

The online version of this article (10.1186/s13040-019-0203-2) contains supplementary material, which is available to authorized users.

GWAS and PheWAS of red blood cell components in a Northern Nevadan cohort

In this study, we perform a full genome-wide association study (GWAS) to identify statistically significantly associated single nucleotide polymorphisms (SNPs) with three red blood cell (RBC) components and follow it with two independent PheWASs to examine associations between phenotypic data (case-control status of diagnoses or disease), significant SNPs, and RBC component levels. We first identified associations between the three RBC components: mean platelet volume (MPV), mean corpuscular volume (MCV), and platelet counts (PC), and the genotypes of approximately 500,000 SNPs on the Illumina Infimum DNA Human OmniExpress-24 BeadChip using a single cohort of 4,673 Northern Nevadans. Twenty-one SNPs in five major genomic regions were found to be statistically significantly associated with MPV, two regions with MCV, and one region with PC, with p<5x10^-8. Twenty-nine SNPs and nine chromosomal regions were identified in 30 previous GWASs, with effect sizes of similar magnitude and direction as found in our cohort. The two strongest associations were SNP rs1354034 with MPV (p = 2.4x10^-13) and rs855791 with MCV (p = 5.2x10^-12). We then examined possible associations between these significant SNPs and incidence of 1,488 phenotype groups mapped from International Classification of Disease version 9 and 10 (ICD9 and ICD10) codes collected in the extensive electronic health record (EHR) database associated with Healthy Nevada Project consented participants. Further leveraging data collected in the EHR, we performed an additional PheWAS to identify associations between continuous red blood cell (RBC) component measures and incidence of specific diagnoses. The first PheWAS illuminated whether SNPs associated with RBC components in our cohort were linked with other hematologic phenotypic diagnoses or diagnoses of other nature. Although no SNPs from our GWAS were identified as strongly associated to other phenotypic components, a number of associations were identified with p-values ranging between 1x10^-3 and 1x10^-4 with traits such as respiratory failure, sleep disorders, hypoglycemia, hyperglyceridemia, GERD and IBS. The second PheWAS examined possible phenotypic predictors of abnormal RBC component measures: a number of hematologic phenotypes such as thrombocytopenia, anemias, hemoglobinopathies and pancytopenia were found to be strongly associated to RBC component measures; additional phenotypes such as (morbid) obesity, malaise and fatigue, alcoholism, and cirrhosis were also identified to be possible predictors of RBC component measures.

Mean Platelet Volume (MPV): New Perspectives for an Old Marker in the Course and Prognosis of Inflammatory Conditions

Platelet size has been demonstrated to reflect platelet activity and seems to be a useful predictive and prognostic biomarker of cardiovascular events. It is associated with a variety of prothrombotic and proinflammatory diseases. The aim is a review of literature reports concerning changes in the mean platelet volume (MPV) and its possible role as a biomarker in inflammatory processes and neoplastic diseases. PubMed database was searched for sources using the following keywords: platelet activation, platelet count, mean platelet volume and: inflammation, cancer/tumor, cardiovascular diseases, myocardial infarction, diabetes, lupus disease, rheumatoid arthritis, tuberculosis, ulcerative colitis, renal disease, pulmonary disease, influencing factors, age, gender, genetic factors, oral contraceptives, smoking, lifestyle, methods, standardization, and hematological analyzer. Preference was given to the sources which were published within the past 20 years. Increased MPV was observed in cardiovascular diseases, cerebral stroke, respiratory diseases, chronic renal failure, intestine diseases, rheumatoid diseases, diabetes, and various cancers. Decreased MPV was noted in tuberculosis during disease exacerbation, ulcerative colitis, SLE in adult, and different neoplastic diseases. The study of MPV can provide important information on the course and prognosis in many inflammatory conditions. Therefore, from the clinical point of view, it would be interesting to establish an MPV cut-off value indicating the intensity of inflammatory process, presence of the disease, increased risk of disease development, increased risk of thrombotic complications, increased risk of death, and patient's response on applied treatment. Nevertheless, this aspect of MPV evaluation allowing its use in clinical practice is limited and requires further studies.

Effect of preanalytical and analytical variables on the clinical utility of mean platelet volume

BACKGROUND

The study endpoint was to analyze the effect of preanalytical (time, temperature, anticoagulant) and analytical (imprecision, correlation between volume and platelet concentration) variables on mean platelet volume (MPV). A further aim was to calculate in an adult population the reference intervals using the Sysmex XE-5000 analyzer. A critical evaluation was also made of the clinical utility of these parameters.

METHODS

Analyses of the above values were performed in duplicate in 170 healthy adults of both sexes: (1) within 30 min from collection, and (2) after 4 h. To evaluate stability over time, the value of the platelet parameters of 20 subjects were determined, a re-analysis being performed for a period of up to 24 h on samples maintained at room temperature and 4°C using either K2-EDTA or Na-citrate as anticoagulants.

RESULTS

The stability over time of MPV closely depends on the anticoagulant used, storage temperature and time interval between venipuncture and analysis. An inverse, non-linear correlation between MPV and platelet count was also found.

CONCLUSIONS

In view of their effect on MPV and other related indices, the preanalytical and analytical variables make them, little more than experimental.

Linking endotypes to omics profiles in difficult-to-control asthma using the diagnostic Chinese medicine syndrome differentiation algorithm

Objective: Patients with difficult-to-control asthma have difficulty breathing almost all of the time, even leading to life-threatening asthma attacks. However, only few diagnostic markers for this disease have been identified. We aimed to take advantage of unique Chinese medicine theories for phenotypic classification and to explore molecular signatures in difficult-to-control asthma. Methods: The Chinese medicine syndrome differentiation algorithm (CMSDA) is a syndrome-scoring classification method based on the Chinese medicine overall observation theory. Patients with difficult-to-control asthma were classified into Cold- and Hot-pattern groups according to the CMSDA. DNA methylation and metabolomic profiles were obtained using Infinium Human Methylation 450 BeadChip and gas chromatography-mass spectrometer. Subsequently, an integrated bioinformatics analysis was performed to compare those two patterns and identify Cold/Hot-associated candidates, followed by functional validation studies. Results: A total of 20 patients with difficult-to-control asthma were enrolled in the study. Ten were grouped as Cold and 10 as Hot according to the CMSDA. We identified distinct whole-genome DNA methylation and metabolomic profiles between Cold- and Hot-pattern groups. ALDH3A1 gene exhibited variations in the DNA methylation probe cg10791966, while two metabolic pathways were associated with those two patterns. Conclusions: Our study introduced a novel diagnostic classification approach, the CMSDA, for difficult-to-control asthma. This is an alternative way to categorize diverse syndromes and link endotypes with omics profiles of this disease. ALDH3A1 might be a potential biomarker for precision diagnosis of difficult-to-control asthma.

Genetics of platelet traits in ischaemic stroke: focus on mean platelet volume and platelet count

Purpose/Aim of the study: The aim of this review is to summarize the role of genetic variants affecting mean platelet volume (MPV) and platelet count (PLT) leading to higher platelet reactivity and in turn to thrombotic events like stroke and cardiovascular diseases.

MATERIALS AND METHODS

A search was conducted in PUBMED, MEDLINE, EMBASE, PROQUEST, Science Direct, Cochrane Library, and Google Scholar related to the studies focussing on genome-wide association studies (GWAS), whole exome sequencing (WES), whole genome sequencing (WGS), phenome-wide association studies (PheWAS) and multi-omic analysis that have been employed to identify the genetic variants influencing MPV and PLT.

RESULTS

Antiplatelet agents underscore the crucial role of platelets in the pathogenesis of stroke. Higher platelet reactivity in terms of mean platelet volume (MPV) and platelet count (PLT) contributes significantly to the interindividual variation in platelet reaction at the site of vessel wall injury. Some individuals encounter thrombotic events as platelets get occluded at the site of vessel wall injury whereas others heal the injury without occluding the circulation. Evidence suggests that MPV and PLT have a strong genetic component. High throughput techniques including genome-wide association studies (GWAS), whole exome sequencing (WES), whole genome sequencing (WGS), phenome-wide association studies (PheWAS) and multi-omic analysis have identified different genetic variants influencing MPV and PLT.

CONCLUSIONS

Identification of complex genetic cross talks affecting PLT and MPV might help to develop novel treatment strategies in treating neurovascular diseases like stroke.

Genomic and Phenomic Research in the 21st Century

The field of human genomics has changed dramatically over time. Initial genomic studies were predominantly restricted to rare disorders in small families. Over the past decade, researchers changed course from family-based studies and instead focused on common diseases and traits in populations of unrelated individuals. With further advancements in biobanking, computer science, electronic health record (EHR) data, and more affordable high-throughput genomics, we are experiencing a new paradigm in human genomic research. Rapidly changing technologies and resources now make it possible to study thousands of diseases simultaneously at the genomic level. This review will focus on these advancements as scientists begin to incorporate phenome-wide strategies in human genomic research to understand the etiology of human diseases and develop new drugs to treat them.

Learning by counting blood platelets in population studies: survey and perspective a long way after Bizzozero

Platelet count represents a useful tool in clinical practice to discriminate individuals at higher risk of bleeding. Less obvious is the role of platelet count variability within the normal range of distribution in shaping the individual's disease risk profile. Epidemiological studies have shown that platelet count in the adult general population is associated with a number of health outcomes related to hemostasis and thrombosis. However, recent studies are suggesting a possible role of this platelet index also as an independent risk factor. In this review of adult population studies, we will first focus on known genetic and non-genetic determinants of platelet number variability. Next, we will evaluate platelet count as a marker and/or a predictor of disease risk and its interaction with other risk factors. We will then discuss the role of platelet count variability within the normal distribution range as a contribution to disease and mortality risk. The possibility of considering platelet count as a simple, inexpensive indicator of increased risk of disease and death in general populations could open new opportunities to investigate novel platelet pathophysiological roles as well as therapeutic opportunities. Future studies should also consider platelet count, not only platelet function, as a modulator of disease and mortality risk.

Genome-Wide Association Study Links Receptor Tyrosine Kinase Inhibitor Sprouty 2 to Thrombocytopenia after Coronary Artery Bypass Surgery

INTRODUCTION

 Thrombocytopenia after cardiac surgery independently predicts stroke, acute kidney injury and death. To understand the underlying risks and mechanisms, we analysed genetic variations associated with thrombocytopenia in patients undergoing coronary artery bypass grafting (CABG) surgery.

MATERIALS AND METHODS

 Study subjects underwent isolated on-pump CABG surgery at Duke University Medical Center. Post-operative thrombocytopenia was defined as platelet count < 100 × 10⁹/L. Using a logistic regression model adjusted for clinical risk factors, we performed a genome-wide association study in a discovery cohort (n = 860) and validated significant findings in a replication cohort (n = 296). Protein expression was assessed in isolated platelets by immunoblot.

RESULTS

 A total of 63 single-nucleotide polymorphisms met a priori discovery thresholds for replication, but only 1 (rs9574547) in the intergenic region upstream of sprouty 2 (SPRY2) met nominal significance in the replication cohort. The minor allele of rs9574547 was associated with a lower risk for thrombocytopenia (discovery cohort, odds ratio, 0.45, 95% confidence interval, 0.30-0.67, p = 9.76 × 10^-5) with the overall association confirmed by meta-analysis (meta-p = 7.88 × 10^-6). Immunoblotting demonstrated expression of SPRY2 and its dynamic regulation during platelet activation. Treatment with a functional SPRY2 peptide blunted platelet extracellular signal-regulated kinase (ERK) phosphorylation after agonist stimulation.

CONCLUSION

 We identified the association of a genetic polymorphism in the intergenic region of SPRY2 with a decreased incidence of thrombocytopenia after CABG surgery. Because SPRY2-an endogenous receptor tyrosine kinase inhibitor-is present in platelets and modulates essential signalling pathways, these findings support a role for SPRY2 as a novel modulator of platelet responses after cardiac surgery.

Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies

In genome-wide association studies (GWAS) for thousands of phenotypes in large biobanks, most binary traits have substantially fewer cases than controls. Both of the widely used approaches, linear mixed model and the recently proposed logistic mixed model, perform poorly - producing large type I error rates - in the analysis of unbalanced case-control phenotypes. Here we propose a scalable and accurate generalized mixed model association test that uses the saddlepoint approximation to calibrate the distribution of score test statistics. This method, SAIGE, provides accurate p-values even when case-control ratios are extremely unbalanced. It utilizes state-of-art optimization strategies to reduce computational cost, and hence is applicable to GWAS for thousands of phenotypes by large biobanks. Through the analysis of UK Biobank data of 408,961 white British European-ancestry samples for >1400 binary phenotypes, we show that SAIGE can efficiently analyze large sample data, controlling for unbalanced case-control ratios and sample relatedness.

A genome-wide association study of red-blood cell fatty acids and ratios incorporating dietary covariates: Framingham Heart Study Offspring Cohort

Recent analyses have suggested a strong heritable component to circulating fatty acid (FA) levels; however, only a limited number of genes have been identified which associate with FA levels. In order to expand upon a previous genome wide association study done on participants in the Framingham Heart Study Offspring Cohort and FA levels, we used data from 2,400 of these individuals for whom red blood cell FA profiles, dietary information and genotypes are available, and then conducted a genome-wide evaluation of potential genetic variants associated with 22 FAs and 15 FA ratios, after adjusting for relevant dietary covariates. Our analysis found nine previously identified loci associated with FA levels (FADS, ELOVL2, PCOLCE2, LPCAT3, AGPAT4, NTAN1/PDXDC1, PKD2L1, HBS1L/MYB and RAB3GAP1/MCM6), while identifying four novel loci. The latter include an association between variants in CALN1 (Chromosome 7) and eicosapentaenoic acid (EPA), DHRS4L2 (Chromosome 14) and a FA ratio measuring delta-9-desaturase activity, as well as two loci associated with less well understood proteins. Thus, the inclusion of dietary covariates had a modest impact, helping to uncover four additional loci. While genome-wide association studies continue to uncover additional genes associated with circulating FA levels, much of the heritable risk is yet to be explained, suggesting the potential role of rare genetic variation, epistasis and gene-environment interactions on FA levels as well. Further studies are needed to continue to understand the complex genetic picture of FA metabolism and synthesis.

A simulation study investigating power estimates in phenome-wide association studies

Background

Phenome-wide association studies (PheWAS) are a high-throughput approach to evaluate comprehensive associations between genetic variants and a wide range of phenotypic measures. PheWAS has varying sample sizes for quantitative traits, and variable numbers of cases and controls for binary traits across the many phenotypes of interest, which can affect the statistical power to detect associations. The motivation of this study is to investigate the various parameters which affect the estimation of statistical power in PheWAS, including sample size, case-control ratio, minor allele frequency, and disease penetrance.

Results

We performed a PheWAS simulation study, where we investigated variations in statistical power based on different parameters, such as overall sample size, number of cases, case-control ratio, minor allele frequency, and disease penetrance. The simulation was performed on both binary and quantitative phenotypic measures. Our simulation on binary traits suggests that the number of cases has more impact on statistical power than the case to control ratio; also, we found that a sample size of 200 cases or more maintains the statistical power to identify associations for common variants. For quantitative traits, a sample size of 1000 or more individuals performed best in the power calculations. We focused on common genetic variants (MAF > 0.01) in this study; however, in future studies, we will be extending this effort to perform similar simulations on rare variants.

Conclusions

This study provides a series of PheWAS simulation analyses that can be used to estimate statistical power for some potential scenarios. These results can be used to provide guidelines for appropriate study design for future PheWAS analyses.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2135-0) contains supplementary material, which is available to authorized users.

Association of human platelet antigen polymorphisms with platelet count and mean platelet volume

OBJECTIVES

Although recent genome-wide association studies have identified a number of single nucleotide polymorphisms associated with platelet count and mean platelet volume (MPV), it is unclear whether polymorphisms in the human platelet antigens (HPA) genes are associated with platelet count and MPV. The aim of this study was to determine the association of the HPA-2, -3, -5 and -15 polymorphisms with platelet count and MPV.

METHODS

The HPA were genotyped by 5'-nuclease assay in 139 healthy Chinese Han individuals, while platelet count and MPV from the same samples were measured using an hematology cell analyzer.

RESULTS

The platelet count was significantly lower in the individuals with the HPA-2aa genotype compared to those with HPA-2ab (P = 0.020), and significantly higher in individuals with HPA-5aa and HPA-15aa genotypes compared to those with HPA-5ab (P = 0.045) and HPA-15ab/bb (P = 0.032), respectively. On the other hand, platelet count of individuals with the HPA-3aa and HPA-3ab/bb genotypes did not differ significantly (P = 0.084). The MPV was significantly lower in individuals with HPA-5aa genotype compared to those with HPA-5ab (P = 0.001) but did not differ among the HPA-2, -3 and -15 genotypes. Furthermore, HPA-2, -5 and -15 polymorphisms were identified as independent factors for the platelet count and HPA-5 polymorphism was shown as an independent factor for MPV.

CONCLUSIONS

This study demonstrates that HPA-2, -5 and -15 polymorphisms are associated with the platelet count while HPA-5 polymorphism is associated with MPV. This finding will further our understanding of the association of HPA polymorphisms with platelet-related diseases.

Toward the Relevance of Platelet Subpopulations for Transfusion Medicine

Circulating platelets consist of subpopulations with different age, maturation state and size. In this review, we address the association between platelet size and platelet function and summarize the current knowledge on platelet subpopulations including reticulated platelets, procoagulant platelets and platelets exposing signals to mediate their clearance. Thereby, we emphasize the impact of platelet turnover as an important condition for platelet production in vivo. Understanding of the features that characterize platelet subpopulations is very relevant for the methods of platelet concentrate production, which may enrich or deplete particular platelet subpopulations. Moreover, the concept of platelet size being associated with platelet function may be attractive for transfusion medicine as it holds the perspective to separate platelet subpopulations with specific functional capabilities.

Machine learning in cardiovascular medicine: are we there yet?

Artificial intelligence (AI) broadly refers to analytical algorithms that iteratively learn from data, allowing computers to find hidden insights without being explicitly programmed where to look. These include a family of operations encompassing several terms like machine learning, cognitive learning, deep learning and reinforcement learning-based methods that can be used to integrate and interpret complex biomedical and healthcare data in scenarios where traditional statistical methods may not be able to perform. In this review article, we discuss the basics of machine learning algorithms and what potential data sources exist; evaluate the need for machine learning; and examine the potential limitations and challenges of implementing machine in the context of cardiovascular medicine. The most promising avenues for AI in medicine are the development of automated risk prediction algorithms which can be used to guide clinical care; use of unsupervised learning techniques to more precisely phenotype complex disease; and the implementation of reinforcement learning algorithms to intelligently augment healthcare providers. The utility of a machine learning-based predictive model will depend on factors including data heterogeneity, data depth, data breadth, nature of modelling task, choice of machine learning and feature selection algorithms, and orthogonal evidence. A critical understanding of the strength and limitations of various methods and tasks amenable to machine learning is vital. By leveraging the growing corpus of big data in medicine, we detail pathways by which machine learning may facilitate optimal development of patient-specific models for improving diagnoses, intervention and outcome in cardiovascular medicine.

When Enough Is Enough: Decision Criteria for Moving a Known Drug into Clinical Testing for a New Indication in the Absence of Preclinical Efficacy Data

Many animal models of disease are suboptimal in their representation of human diseases and lack of predictive power in the success of pivotal human trials. In the context of repurposing drugs with known human safety, it is sometimes appropriate to conduct the "last experiment first," that is, progressing directly to human investigations. However, there are not accepted criteria for when to proceed straight to humans to test a new indication. We propose a specific set of criteria to guide the decision-making around when to initiate human proof of principle without preclinical efficacy studies in animal models. This approach could accelerate the transition of novel therapeutic approaches to human applications.

Current Scope and Challenges in Phenome-Wide Association Studies

PURPOSE OF REVIEW

Over many decades, researchers have been designing studies to investigate the relationship between genotypes and phenotypes to gain an understanding about the effect of genetics on disease. Recently, a high-throughput approach called phenome-wide associations studies (PheWAS) have been extensively used to identify associations between genetic variants and many diseases and traits simultaneously. In this review, we describe the value of PheWAS along with methodological issues and challenges in interpretation for current applications of PheWAS.

RECENT FINDINGS

PheWAS have uncovered a paradigm to identify new associations for genetic loci across many diseases. The application of PheWAS have been effective with phenotype data from electronic health records, epidemiological studies, and clinical trials data.

SUMMARY

The key strength of a PheWAS is to identify the association of one or more genetic variants with multiple phenotypes, which can showcase interconnections among the phenotypes due to shared genetic associations. While the PheWAS approach appears promising, there are a number of challenges that need to be addressed to provide additional robustness to PheWAS findings.