The estimation of environmental and genetic parental influences.

Parents share half of their genes with their children, but they also share background social factors and actively help shape their child's environment - making it difficult to disentangle genetic and environmental causes of parent-offspring similarity. While adoption and extended twin family designs have been extremely useful for distinguishing genetic and nongenetic parental influences, these designs entail stringent assumptions about phenotypic similarity between relatives and require samples that are difficult to collect and therefore are typically small and not publicly shared. Here, we describe these traditional designs, as well as modern approaches that use large, publicly available genome-wide data sets to estimate parental effects. We focus in particular on an approach we recently developed, structural equation modeling (SEM)-polygenic score (PGS), that instantiates the logic of modern PGS-based methods within the flexible SEM framework used in traditional designs. Genetically informative designs such as SEM-PGS rely on different and, in some cases, less rigid assumptions than traditional approaches; thus, they allow researchers to capitalize on new data sources and answer questions that could not previously be investigated. We believe that SEM-PGS and similar approaches can lead to improved insight into how nature and nurture combine to create the incredible diversity underlying human behavior.

Estimation of Parental Effects Using Polygenic Scores.

Offspring resemble their parents for both genetic and environmental reasons. Understanding the relative magnitude of these alternatives has long been a core interest in behavioral genetics research, but traditional designs, which compare phenotypic covariances to make inferences about unmeasured genetic and environmental factors, have struggled to disentangle them. Recently, Kong et al. (2018) showed that by correlating offspring phenotypic values with the measured polygenic score of parents' nontransmitted alleles, one can estimate the effect of "genetic nurture"-a type of passive gene-environment covariation that arises when heritable parental traits directly influence offspring traits. Here, we instantiate this basic idea in a set of causal models that provide novel insights into the estimation of parental influences on offspring. Most importantly, we show how jointly modeling the parental polygenic scores and the offspring phenotypes can provide an unbiased estimate of the variation attributable to the environmental influence of parents on offspring, even when the polygenic score accounts for a small fraction of trait heritability. This model can be further extended to (a) account for the influence of different types of assortative mating, (b) estimate the total variation due to additive genetic effects and their covariance with the familial environment (i.e., the full genetic nurture effect), and (c) model situations where a parental trait influences a different offspring trait. By utilizing structural equation modeling techniques developed for extended twin family designs, our approach provides a general framework for modeling polygenic scores in family studies and allows for various model extensions that can be used to answer old questions about familial influences in new ways.

Bias and Precision of Parameter Estimates from Models Using Polygenic Scores to Estimate Environmental and Genetic Parental Influences.

In a companion paper Balbona et al. (Behav Genet, in press), we introduced a series of causal models that use polygenic scores from transmitted and nontransmitted alleles, the offspring trait, and parental traits to estimate the variation due to the environmental influences the parental trait has on the offspring trait (vertical transmission) as well as additive genetic effects. These models also estimate and account for the gene-gene and gene-environment covariation that arises from assortative mating and vertical transmission respectively. In the current study, we simulated polygenic scores and phenotypes of parents and offspring under genetic and vertical transmission scenarios, assuming two types of assortative mating. We instantiated the models from our companion paper in the OpenMx software, and compared the true values of parameters to maximum likelihood estimates from models fitted on the simulated data to quantify the bias and precision of estimates. We show that parameter estimates from these models are unbiased when assumptions are met, but as expected, they are biased to the degree that assumptions are unmet. Standard errors of the estimated variances due to vertical transmission and to genetic effects decrease with increasing sample sizes and with increasing [Formula: see text] values of the polygenic score. Even when the polygenic score explains a modest amount of trait variation ([Formula: see text]), standard errors of these standardized estimates are reasonable ([Formula: see text]) for [Formula: see text] trios, and can even be reasonable for smaller sample sizes (e.g., down to 4K) when the polygenic score is more predictive. These causal models offer a novel approach for understanding how parents influence their offspring, but their use requires polygenic scores on relevant traits that are modestly predictive (e.g., [Formula: see text] as well as datasets with genomic and phenotypic information on parents and offspring. The utility of polygenic scores for elucidating parental influences should thus serve as additional motivation for large genomic biobanks to perform GWAS's on traits that may be relevant to parenting and to oversample close relatives, particularly parents and offspring.

Psychopathologic structure of bipolar disorders: exploring dimensional phenotypes, their relationships, and their associations with bipolar I and II disorders.

BACKGROUND: Given its diverse disease courses and symptom presentations, multiple phenotype dimensions with different biological underpinnings are expected with bipolar disorders (BPs). In this study, we aimed to identify lifetime BP psychopathology dimensions. We also explored the differing associations with bipolar I (BP-I) and bipolar II (BP-II) disorders. METHODS: We included a total of 307 subjects with BPs in the analysis. For the factor analysis, we chose six variables related to clinical courses, 29 indicators covering lifetime symptoms of mood episodes, and 6 specific comorbid conditions. To determine the relationships among the identified phenotypic dimensions and their effects on differentiating BP subtypes, we applied structural equation modeling. RESULTS: We selected a six-factor solution through scree plot, Velicer's minimum average partial test, and face validity evaluations; the six factors were cyclicity, depression, atypical vegetative symptoms, elation, psychotic/irritable mania, and comorbidity. In the path analysis, five factors excluding atypical vegetative symptoms were associated with one another. Cyclicity, depression, and comorbidity had positive associations, and they correlated negatively with psychotic/irritable mania; elation showed positive correlations with cyclicity and psychotic/irritable mania. Depression, cyclicity, and comorbidity were stronger in BP-II than in BP-I, and they contributed significantly to the distinction between the two disorders. CONCLUSIONS: We identified six phenotype dimensions; in addition to symptom features of manic and depressive episodes, various comorbidities and high cyclicity constructed separate dimensions. Except for atypical vegetative symptoms, all factors showed a complex interdependency and played roles in discriminating BP-II from BP-I.

Hierarchical structural component modeling of microRNA-mRNA integration analysis.

BACKGROUND: Identification of multi-markers is one of the most challenging issues in personalized medicine era. Nowadays, many different types of omics data are generated from the same subject. Although many methods endeavor to identify candidate markers, for each type of omics data, few or none can facilitate such identification. RESULTS: It is well known that microRNAs affect phenotypes only indirectly, through regulating mRNA expression and/or protein translation. Toward addressing this issue, we suggest a hierarchical structured component analysis of microRNA-mRNA integration ("HisCoM-mimi") model that accounts for this biological relationship, to efficiently study and identify such integrated markers. In simulation studies, HisCoM-mimi showed the better performance than the other three methods. Also, in real data analysis, HisCoM-mimi successfully identified more gives more informative miRNA-mRNA integration sets relationships for pancreatic ductal adenocarcinoma (PDAC) diagnosis, compared to the other methods. CONCLUSION: As exemplified by an application to pancreatic cancer data, our proposed model effectively identified integrated miRNA/target mRNA pairs as markers for early diagnosis, providing a much broader biological interpretation.

Pathway-based approach using hierarchical components of rare variants to analyze multiple phenotypes.

BACKGROUND: As one possible solution to the "missing heritability" problem, many methods have been proposed that apply pathway-based analyses, using rare variants that are detected by next generation sequencing technology. However, while a number of methods for pathway-based rare-variant analysis of multiple phenotypes have been proposed, no method considers a unified model that incorporate multiple pathways. RESULTS: Simulation studies successfully demonstrated advantages of multivariate analysis, compared to univariate analysis, and comparison studies showed the proposed approach to outperform existing methods. Moreover, real data analysis of six type 2 diabetes-related traits, using large-scale whole exome sequencing data, identified significant pathways that were not found by univariate analysis. Furthermore, strong relationships between the identified pathways, and their associated metabolic disorder risk factors, were found via literature search, and one of the identified pathway, was successfully replicated by an analysis with an independent dataset. CONCLUSIONS: Herein, we present a powerful, pathway-based approach to investigate associations between multiple pathways and multiple phenotypes. By reflecting the natural hierarchy of biological behavior, and considering correlation between pathways and phenotypes, the proposed method is capable of analyzing multiple phenotypes and multiple pathways simultaneously.

Rare variant association test with multiple phenotypes.

Although genome-wide association studies (GWAS) have now discovered thousands of genetic variants associated with common traits, such variants cannot explain the large degree of "missing heritability," likely due to rare variants. The advent of next generation sequencing technology has allowed rare variant detection and association with common traits, often by investigating specific genomic regions for rare variant effects on a trait. Although multiple correlated phenotypes are often concurrently observed in GWAS, most studies analyze only single phenotypes, which may lessen statistical power. To increase power, multivariate analyses, which consider correlations between multiple phenotypes, can be used. However, few existing multivariant analyses can identify rare variants for assessing multiple phenotypes. Here, we propose Multivariate Association Analysis using Score Statistics (MAAUSS), to identify rare variants associated with multiple phenotypes, based on the widely used sequence kernel association test (SKAT) for a single phenotype. We applied MAAUSS to whole exome sequencing (WES) data from a Korean population of 1,058 subjects to discover genes associated with multiple traits of liver function. We then assessed validation of those genes by a replication study, using an independent dataset of 3,445 individuals. Notably, we detected the gene ZNF620 among five significant genes. We then performed a simulation study to compare MAAUSS's performance with existing methods. Overall, MAAUSS successfully conserved type 1 error rates and in many cases had a higher power than the existing methods. This study illustrates a feasible and straightforward approach for identifying rare variants correlated with multiple phenotypes, with likely relevance to missing heritability.

Proposed Nomogram Predicting the Individual Risk of Malignancy in the Patients With Branch Duct Type Intraductal Papillary Mucinous Neoplasms of the Pancreas.

OBJECTIVES: This study evaluated individual risks of malignancy and proposed a nomogram for predicting malignancy of branch duct type intraductal papillary mucinous neoplasms (BD-IPMNs) using the large database for IPMN. BACKGROUND: Although consensus guidelines list several malignancy predicting factors in patients with BD-IPMN, those variables have different predictability and individual quantitative prediction of malignancy risk is limited. METHODS: Clinicopathological factors predictive of malignancy were retrospectively analyzed in 2525 patients with biopsy proven BD-IPMN at 22 tertiary hospitals in Korea and Japan. The patients with main duct dilatation >10 mm and inaccurate information were excluded. RESULTS: The study cohort consisted of 2258 patients. Malignant IPMNs were defined as those with high grade dysplasia and associated invasive carcinoma. Of 2258 patients, 986 (43.7%) had low, 443 (19.6%) had intermediate, 398 (17.6%) had high grade dysplasia, and 431 (19.1%) had invasive carcinoma. To construct and validate the nomogram, patients were randomly allocated into training and validation sets, with fixed ratios of benign and malignant lesions. Multiple logistic regression analysis resulted in five variables (cyst size, duct dilatation, mural nodule, serum CA19-9, and CEA) being selected to construct the nomogram. In the validation set, this nomogram showed excellent discrimination power through a 1000 times bootstrapped calibration test. CONCLUSION: A nomogram predicting malignancy in patients with BD-IPMN was constructed using a logistic regression model. This nomogram may be useful in identifying patients at risk of malignancy and for selecting optimal treatment methods. The nomogram is freely available at http://statgen.snu.ac.kr/software/nomogramIPMN.

Association between the zinc finger protein 804A (ZNF804A) gene and the risk of schizophrenia and bipolar I disorder across diagnostic boundaries.

OBJECTIVES: In this study, we aimed to determine the role of genetic variations within the zinc finger protein 804A (ZNF804A) gene, a candidate for a psychosis risk-conferring gene, in the development of schizophrenia (SZ) and bipolar disorder (BP) in the Korean population. METHODS: A total of 921 patients with SZ, bipolar I (BP-I) and II (BP-II) disorder, and 502 control subjects participated in the study. Twenty-one tag single nucleotide polymorphisms (SNPs) across the genomic region of ZNF804A and seven reference SNPs based on previous reports were genotyped. We applied logistic regression analyses under additive, dominant and recessive models. RESULTS: Fifteen of the 28 SNPs showed a nominally significant association with at least one diagnostic group. However, none of these associations remained significant after false discovery rate (FDR) correction. As the trend of association was observed mostly in SZ and BP-I with similar patterns, we performed a post hoc analysis for the combined SZ and BP-I group. Five SNPs (rs2369595, rs6755404, rs10931156, rs12476147 and rs1366842) showed a significant association with an FDR-corrected P of <.05. CONCLUSIONS: This study supports a possible role of ZNF804A in the common susceptibility of major psychoses, and identified additional candidate variants of the gene in the Korean population.

Biomarker Development for Intraductal Papillary Mucinous Neoplasms Using Multiple Reaction Monitoring Mass Spectrometry.

Intraductal papillary mucinous neoplasm (IPMN) is a common precursor of pancreatic cancer (PC). Much clinical attention has been directed toward IPMNs due to the increase in the prevalence of PC. The diagnosis of IPMN depends primarily on a radiological examination, but the diagnostic accuracy of this tool is not satisfactory, necessitating the development of accurate diagnostic biomarkers for IPMN to prevent PC. Recently, high-throughput targeted proteomic quantification methods have accelerated the discovery of biomarkers, rendering them powerful platforms for the evolution of IPMN diagnostic biomarkers. In this study, a robust multiple reaction monitoring (MRM) pipeline was applied to discovery and verify IPMN biomarker candidates in a large cohort of plasma samples. Through highly reproducible MRM assays and a stringent statistical analysis, 11 proteins were selected as IPMN marker candidates with high confidence in 184 plasma samples, comprising a training (n = 84) and test set (n = 100). To improve the discriminatory power, we constructed a six-protein panel by combining marker candidates. The multimarker panel had high discriminatory power in distinguishing between IPMN and controls, including other benign diseases. Consequently, the diagnostic accuracy of IPMN can be improved dramatically with this novel plasma-based panel in combination with a radiological examination.

ß-Caryophyllene potently inhibits solid tumor growth and lymph node metastasis of B16F10 melanoma cells in high-fat diet-induced obese C57BL/6N mice.

We reported previously that high-fat diet (HFD) feeding stimulated solid tumor growth and lymph node (LN) metastasis in C57BL/6N mice injected with B16F10 melanoma cells. ß-caryophyllene (BCP) is a natural bicyclic sesquiterpene found in many essential oils and has been shown to exert anti-inflammatory activities. To examine whether BCP inhibits HFD-induced melanoma progression, 4-weeks old, male C57BL/6N mice were fed a control diet (CD, 10 kcal% fat) or HFD (60 kcal% fat + 0, 0.15 or 0.3% BCP) for the entire experimental period. After 16 weeks of feeding, B16F10s were subcutaneously injected into mice. Three weeks later, tumors were resected, and mice were killed 2 weeks post-resection. Although HFD feeding increased body weight gain, fasting blood glucose levels, solid tumor growth, LN metastasis, tumor cell proliferation, angiogenesis and lymphangiogenesis, it decreased apoptotic cells, all of which were suppressed by dietary BCP. HFD feeding increased the number of lipid vacuoles and F4/80+ macrophage (MΦ) and macrophage mannose receptor (MMR)+ M2-MΦs in tumor tissues and adipose tissues surrounding the LN, which was suppressed by BCP. HFD feeding increased the levels of CCL19 and CCL21 in the LN and the expression of CCR7 in the tumor; these changes were blocked by dietary BCP. In vitro culture results revealed that BCP inhibited lipid accumulation in 3T3-L1 preadipocytes; monocyte migration and monocyte chemoattractant protein-1 secretion by B16F10s, adipocytes and M2-MΦs; angiogenesis and lymphangiogenesis. The suppression of adipocyte and M2-cell accumulation and the inhibition of CCL19/21-CCR7 axis may be a part of mechanisms for the BCP suppression of HFD-stimulated melanoma progression.

Integrative analysis of multi-omics data for identifying multi-markers for diagnosing pancreatic cancer.

BACKGROUND: microRNA (miRNA) expression plays an influential role in cancer classification and malignancy, and miRNAs are feasible as alternative diagnostic markers for pancreatic cancer, a highly aggressive neoplasm with silent early symptoms, high metastatic potential, and resistance to conventional therapies. METHODS: In this study, we evaluated the benefits of multi-omics data analysis by integrating miRNA and mRNA expression data in pancreatic cancer. Using support vector machine (SVM) modelling and leave-one-out cross validation (LOOCV), we evaluated the diagnostic performance of single- or multi-markers based on miRNA and mRNA expression profiles from 104 PDAC tissues and 17 benign pancreatic tissues. For selecting even more reliable and robust markers, we performed validation by independent datasets from the Gene Expression Omnibus (GEO) data depository. For validation, miRNA activity was estimated by miRNA-target gene interaction and mRNA expression datasets in pancreatic cancer. RESULTS: Using a comprehensive identification approach, we successfully identified 705 multi-markers having powerful diagnostic performance for PDAC. In addition, these marker candidates annotated with cancer pathways using gene ontology analysis. CONCLUSIONS: Our prediction models have strong potential for the diagnosis of pancreatic cancer.

Benzyl isothiocyanate suppresses high-fat diet-stimulated mammary tumor progression via the alteration of tumor microenvironments in obesity-resistant BALB/c mice.

We previously reported that a high-fat diet (HFD) and M2-macrophages induce changes in tumor microenvironments and stimulate tumor growth and metastasis of 4T1 mammary cancer cells in BALB/c mice. In this study, we attempted to determine whether benzyl isothiocyanate (BITC) inhibits HFD-induced changes in tumor progression and in tumor microenvironments. Four groups of female BALB/c mice (4-week-old) were fed on a control diet (CD, 10 kcal% fat) and HFD (60 kcal% fat) containing BITC (0, 25, or 100 mg/kg diet) for 20 weeks. Following 16 weeks of feeding, 4T1 cells (5×10(4) cells) were injected into the mammary fat pads, and animals were killed 30 d after the injection. HFD feeding increased solid tumor growth and the number of tumor nodules in the lung and liver, as compared to the CD group, and these increases were inhibited by BITC supplementation. The number of lipid vacuoles, CD45+ leukocytes and CD206+ M2-macrophages, expression of Ki67, levels of cytokines/chemokines, including macrophage-colony stimulating factor (M-CSF) and monocyte chemoattractant protein-1, and mRNA levels of F4/80, CD86, Ym1, CD163, CCR2, and M-CSF receptor were increased in the tumor tissues of HFD-fed mice, and these increases were inhibited by BITC supplementation. In vitro culture results demonstrated that BITC inhibited macrophage migration as well as lipid droplet accumulation in 3T3-L1 cells. These results suggest that suppression of lipid accumulation and macrophage infiltration in tumor tissues may be one of the mechanisms by which BITC suppresses tumor progression in HFD-fed mice.

Triglyceride-Glucose Index Predicts Future Atherosclerotic Cardiovascular Diseases: A 16-Year Follow-up in a Prospective, Community-Dwelling Cohort Study.

BACKGRUOUND: While the triglyceride-glucose (TyG) index is a measure of insulin resistance, its association with cardiovascular disease (CVD) has not been well elucidated. We evaluated the TyG index for prediction of CVDs in a prospective large communitybased cohort. METHODS: Individuals 40 to 70 years old were prospectively followed for a median 15.6 years. The TyG index was calculated as the Ln [fasting triglycerides (mg/dL)×fasting glucose (mg/dL)/2]. CVDs included any acute myocardial infarction, coronary artery disease or cerebrovascular disease. We used a Cox proportional hazards model to estimate CVD risks according to quartiles of the TyG index and plotted the receiver operating characteristics curve for the incident CVD. RESULTS: Among 8,511 subjects (age 51.9±8.8 years; 47.5% males), 931 (10.9%) had incident CVDs during the follow-up. After adjustment for age, sex, body mass index, diabetes mellitus, hypertension, total cholesterol, smoking, alcohol, exercise, and C-reactive protein, subjects in the highest TyG quartile had 36% increased risk of incident CVD compared with the lowest TyG quartile (hazard ratio, 1.36; 95% confidence interval, 1.10 to 1.68). Carotid plaque, assessed by ultrasonography was more frequent in subjects in the higher quartile of TyG index (P for trend=0.049 in men and P for trend <0.001 in women). The TyG index had a higher predictive power for CVDs than the homeostasis model assessment of insulin resistance (HOMA-IR) (area under the curve, 0.578 for TyG and 0.543 for HOMA-IR). Adding TyG index on diabetes or hypertension alone gave sounder predictability for CVDs. CONCLUSION: The TyG index is independently associated with future CVDs in 16 years of follow-up in large, prospective Korean cohort.

Within-sibship genome-wide association analyses decrease bias in estimates of direct genetic effects.

Estimates from genome-wide association studies (GWAS) of unrelated individuals capture effects of inherited variation (direct effects), demography (population stratification, assortative mating) and relatives (indirect genetic effects). Family-based GWAS designs can control for demographic and indirect genetic effects, but large-scale family datasets have been lacking. We combined data from 178,086 siblings from 19 cohorts to generate population (between-family) and within-sibship (within-family) GWAS estimates for 25 phenotypes. Within-sibship GWAS estimates were smaller than population estimates for height, educational attainment, age at first birth, number of children, cognitive ability, depressive symptoms and smoking. Some differences were observed in downstream SNP heritability, genetic correlations and Mendelian randomization analyses. For example, the within-sibship genetic correlation between educational attainment and body mass index attenuated towards zero. In contrast, analyses of most molecular phenotypes (for example, low-density lipoprotein-cholesterol) were generally consistent. We also found within-sibship evidence of polygenic adaptation on taller height. Here, we illustrate the importance of family-based GWAS data for phenotypes influenced by demographic and indirect genetic effects.

Identifying miRNA-mRNA Integration Set Associated With Survival Time.

In the "personalized medicine" era, one of the most difficult problems is identification of combined markers from different omics platforms. Many methods have been developed to identify candidate markers for each type of omics data, but few methods facilitate the identification of multiple markers on multi-omics platforms. microRNAs (miRNAs) is well known to affect only indirectly phenotypes by regulating mRNA expression and/or protein translation. To take into account this knowledge into practice, we suggest a miRNA-mRNA integration model for survival time analysis, called mimi-surv, which accounts for the biological relationship, to identify such integrated markers more efficiently. Through simulation studies, we found that the statistical power of mimi-surv be better than other models. Application to real datasets from Seoul National University Hospital and The Cancer Genome Atlas demonstrated that mimi-surv successfully identified miRNA-mRNA integrations sets associated with progression-free survival of pancreatic ductal adenocarcinoma (PDAC) patients. Only mimi-surv found miR-96, a previously unidentified PDAC-related miRNA in these two real datasets. Furthermore, mimi-surv was shown to identify more PDAC related miRNAs than other methods because it used the known structure for miRNA-mRNA regularization. An implementation of mimi-surv is available at http://statgen.snu.ac.kr/software/mimi-surv.

Association between the Arylalkylamine N-Acetyltransferase (AANAT) Gene and Seasonality in Patients with Bipolar Disorder.

OBJECTIVE: Bipolar disorder (BD) is complex genetic disorder. Therefore, approaches using clinical phenotypes such as biological rhythm disruption could be an alternative. In this study, we explored the relationship between melatonin pathway genes with circadian and seasonal rhythms of BD. METHODS: We recruited clinically stable patients with BD (n=324). We measured the seasonal variation of mood and behavior (seasonality), and circadian preference, on a lifetime basis. We analyzed 34 variants in four genes (MTNR1a, MTNR1b, AANAT, ASMT) involved in the melatonin pathway. RESULTS: Four variants were nominally associated with seasonality and circadian preference. After multiple test corrections, the rs116879618 in AANAT remained significantly associated with seasonality (corrected p=0.0151). When analyzing additional variants of AANAT through imputation, the rs117849139, rs77121614 and rs28936679 (corrected p=0.0086, 0.0154, and 0.0092) also showed a significant association with seasonality. CONCLUSION: This is the first study reporting the relationship between variants of AANAT and seasonality in patients with BD. Since AANAT controls the level of melatonin production in accordance with light and darkness, this study suggests that melatonin may be involved in the pathogenesis of BD, which frequently shows a seasonality of behaviors and symptom manifestations.

The relationships of present vegetation, bacteria, and soil properties with soil organic matter characteristics in moist acidic tundra in Alaska.

Soil organic matter (SOM) is related to vegetation, soil bacteria, and soil properties; however, not many studies link all these parameters simultaneously, particularly in tundra ecosystems vulnerable to climate change. Our aim was to describe the relationships between vegetation, bacteria, soil properties, and SOM composition in moist acidic tundra by integrating physical, chemical, and molecular methods. A total of 70 soil samples were collected at two different depths from 36 spots systematically arranged over an area of about 300 m × 50 m. Pyrolysis-gas chromatography/mass spectrometry and pyrosequencing of the 16S rRNA gene were used to identify the molecular compositions of the SOM and bacterial community, respectively. Vegetation and soil physicochemical properties were also measured. The sampling sites were grouped into three, based on their SOM compositions: Sphagnum moss-derived SOM, lipid-rich materials, and aromatic-rich materials. Our results show that SOM composition is spatially structured and linked to microtopography; however, the vegetation, soil properties, and bacterial community composition did not show overall spatial structuring. Simultaneously, soil properties and bacterial community composition were the main factors explaining SOM compositional variation, while vegetation had a residual effect. Verrucomicrobia and Acidobacteria were related to polysaccharides, and Chloroflexi was linked to aromatic compounds. These relationships were consistent across different hierarchical levels. Our results suggest that SOM composition at a local scale is closely linked with soil factors and the bacterial community. Comprehensive observation of ecosystem components is recommended to understand the in-situ function of bacteria and the fate of SOM in the moist acidic tundra.

Diagnostic model for pancreatic cancer using a multi-biomarker panel.

PURPOSE: Diagnostic biomarkers of pancreatic ductal adenocarcinoma (PDAC) have been used for early detection to reduce its dismal survival rate. However, clinically feasible biomarkers are still rare. Therefore, in this study, we developed an automated multi-marker enzyme-linked immunosorbent assay (ELISA) kit using 3 biomarkers (leucine-rich alpha-2-glycoprotein [LRG1], transthyretin [TTR], and CA 19-9) that were previously discovered and proposed a diagnostic model for PDAC based on this kit for clinical usage. METHODS: Individual LRG1, TTR, and CA 19-9 panels were combined into a single automated ELISA panel and tested on 728 plasma samples, including PDAC (n = 381) and normal samples (n = 347). The consistency between individual panels of 3 biomarkers and the automated multi-panel ELISA kit were accessed by correlation. The diagnostic model was developed using logistic regression according to the automated ELISA kit to predict the risk of pancreatic cancer (high-, intermediate-, and low-risk groups). RESULTS: The Pearson correlation coefficient of predicted values between the triple-marker automated ELISA panel and the former individual ELISA was 0.865. The proposed model provided reliable prediction results with a positive predictive value of 92.05%, negative predictive value of 90.69%, specificity of 90.69%, and sensitivity of 92.05%, which all simultaneously exceed 90% cutoff value. CONCLUSION: This diagnostic model based on the triple ELISA kit showed better diagnostic performance than previous markers for PDAC. In the future, it needs external validation to be used in the clinic.