Placental gene networks at the interface between maternal PM2.5 exposure early in gestation and reduced infant birthweight.

BACKGROUND: A growing body of evidence links maternal exposure to particulate matter <2.5 µM in diameter (PM2.5) and deviations in fetal growth. Several studies suggest that the placenta plays a critical role in conveying the effects of maternal PM2.5 exposure to the developing fetus. These include observed associations between air pollutants and candidate placental features, such as mitochondrial DNA content, DNA methylation and telomere length. However, gaps remain in delineating the pathways linking the placenta to air pollution-related health effects, including a comprehensive profiling of placental processes impacted by maternal PM2.5 exposure. In this study, we examined alterations in a placental transcriptome-wide network in relation to maternal PM2.5 exposure prior to and during pregnancy and infant birthweight. METHODS: We evaluated PM2.5 exposure and placental RNA-sequencing data among study participants enrolled in the Rhode Island Child Health Study (RICHS). Daily residential PM2.5 levels were estimated using a hybrid model incorporating land-use regression and satellite remote sensing data. Distributed lag models were implemented to assess the impact on infant birthweight due to PM2.5 weekly averages ranging from 12 weeks prior to gestation until birth. Correlations were assessed between PM2.5 levels averaged across the identified window of susceptibility and a placental transcriptome-wide gene coexpression network previously generated using the WGCNA R package. RESULTS: We identified a sensitive window spanning 12 weeks prior to and 13 weeks into gestation during which maternal PM2.5 exposure is significantly associated with reduced infant birthweight. Two placental coexpression modules enriched for genes involved in amino acid transport and cellular respiration were correlated with infant birthweight as well as maternal PM2.5 exposure levels averaged across the identified growth restriction window. CONCLUSION: Our findings suggest that maternal PM2.5 exposure may alter placental programming of fetal growth, with potential implications for downstream health effects, including susceptibility to cardiometabolic health outcomes and viral infections.

EnsembleCNV: an ensemble machine learning algorithm to identify and genotype copy number variation using SNP array data.

The associations between diseases/traits and copy number variants (CNVs) have not been systematically investigated in genome-wide association studies (GWASs), primarily due to a lack of robust and accurate tools for CNV genotyping. Herein, we propose a novel ensemble learning framework, ensembleCNV, to detect and genotype CNVs using single nucleotide polymorphism (SNP) array data. EnsembleCNV (a) identifies and eliminates batch effects at raw data level; (b) assembles individual CNV calls into CNV regions (CNVRs) from multiple existing callers with complementary strengths by a heuristic algorithm; (c) re-genotypes each CNVR with local likelihood model adjusted by global information across multiple CNVRs; (d) refines CNVR boundaries by local correlation structure in copy number intensities; (e) provides direct CNV genotyping accompanied with confidence score, directly accessible for downstream quality control and association analysis. Benchmarked on two large datasets, ensembleCNV outperformed competing methods and achieved a high call rate (93.3%) and reproducibility (98.6%), while concurrently achieving high sensitivity by capturing 85% of common CNVs documented in the 1000 Genomes Project. Given this CNV call rate and accuracy, which are comparable to SNP genotyping, we suggest ensembleCNV holds significant promise for performing genome-wide CNV association studies and investigating how CNVs predispose to human diseases.

Genetic regulation of the placental transcriptome underlies birth weight and risk of childhood obesity.

GWAS identified variants associated with birth weight (BW), childhood obesity (CO) and childhood BMI (CBMI), and placenta is a critical organ for fetal development and postnatal health. We examined the role of placental transcriptome and eQTLs in mediating the genetic causes for BW, CO and CBMI, and applied integrative analysis (Colocalization and MetaXcan). GWAS loci associated with BW, CO, and CBMI were substantially enriched for placenta eQTLs (6.76, 4.83 and 2.26 folds, respectively). Importantly, compared to eQTLs of adult tissues, only placental eQTLs contribute significantly to both anthropometry outcomes at birth (BW) and childhood phenotypes (CO/CBMI). Eight, six and one transcripts colocalized with BW, CO and CBMI risk loci, respectively. Our study reveals that placental transcription in utero likely plays a key role in determining postnatal body size, and as such may hold new possibilities for therapeutic interventions to prevent childhood obesity.

Expression quantitative trait loci (eQTLs) in human placentas suggest developmental origins of complex diseases.

Epidemiologic studies support that at least part of the risk of chronic diseases in childhood and even adulthood may have an in utero origin, and the placenta is a key organ that plays a pivotal role in fetal growth and development. The transcriptomes of 159 human placenta tissues were profiled by genome-wide RNA sequencing (Illumina High-Seq 2500), and linked to fetal genotypes assessed by a high density single nucleotide polymorphism (SNP) genotyping array (Illumina MegaEx). Expression quantitative trait loci (eQTLs) across all annotated transcripts were mapped and examined for enrichment for disease susceptibility loci annotated in the genome-wide association studies (GWAS) catalog. We discovered 3218 cis- and 35 trans-eQTLs at ≤10% false discovery rate in human placentas. Among the 16 439 known disease loci of genome-wide significance, 835 were placental eSNPs (enrichment fold = 1.68, P = 7.41e-42). Stronger effect sizes were observed between GWAS SNPs and gene expression in placentas than what has been reported in other tissues, such as the correlation between asthma risk allele, rs7216389-T and Gasdermin-B (GSDMB) in placenta (r2=27%) versus lung (r2=6%). Finally, our results suggest the placental eQTLs may mediate the function of GWAS loci on postnatal disease susceptibility. Results suggest that transcripts in placenta are under tight genetic control, and that placental gene networks may influence postnatal risk of multiple human diseases lending support for the Developmental Origins of Health and Disease.

Whole-transcriptome analysis delineates the human placenta gene network and its associations with fetal growth.

BACKGROUND: The placenta is the principal organ regulating intrauterine growth and development, performing critical functions on behalf of the developing fetus. The delineation of functional networks and pathways driving placental processes has the potential to provide key insight into intrauterine perturbations that result in adverse birth as well as later life health outcomes. RESULTS: We generated the transcriptome-wide profile of 200 term human placenta using the Illumina HiSeq 2500 platform and characterized the functional placental gene network using weighted gene coexpression network analysis (WGCNA). We identified 17 placental coexpression network modules that were dominated by functional processes including growth, organ development, gas exchange and immune response. Five network modules, enriched for processes including cellular respiration, amino acid transport, hormone signaling, histone modifications and gene expression, were associated with birth weight; hub genes of all five modules (CREB3, DDX3X, DNAJC14, GRHL1 and C21orf91) were significantly associated with fetal growth restriction, and one hub gene (CREB3) was additionally associated with fetal overgrowth. CONCLUSIONS: In this largest RNA-Seq based transcriptome-wide profiling study of human term placenta conducted to date, we delineated a placental gene network with functional relevance to fetal growth using a network-based approach with superior scale reduction capacity. Our study findings not only implicate potential molecular mechanisms underlying fetal growth but also provide a reference placenta gene network to inform future studies investigating placental dysfunction as a route to future disease endpoints.

Detecting genetic association of common human facial morphological variation using high density 3D image registration.

Human facial morphology is a combination of many complex traits. Little is known about the genetic basis of common facial morphological variation. Existing association studies have largely used simple landmark-distances as surrogates for the complex morphological phenotypes of the face. However, this can result in decreased statistical power and unclear inference of shape changes. In this study, we applied a new image registration approach that automatically identified the salient landmarks and aligned the sample faces using high density pixel points. Based on this high density registration, three different phenotype data schemes were used to test the association between the common facial morphological variation and 10 candidate SNPs, and their performances were compared. The first scheme used traditional landmark-distances; the second relied on the geometric analysis of 15 landmarks and the third used geometric analysis of a dense registration of ∼30,000 3D points. We found that the two geometric approaches were highly consistent in their detection of morphological changes. The geometric method using dense registration further demonstrated superiority in the fine inference of shape changes and 3D face modeling. Several candidate SNPs showed potential associations with different facial features. In particular, one SNP, a known risk factor of non-syndromic cleft lips/palates, rs642961 in the IRF6 gene, was validated to strongly predict normal lip shape variation in female Han Chinese. This study further demonstrated that dense face registration may substantially improve the detection and characterization of genetic association in common facial variation.

Integrative gene regulatory network analysis discloses key driver genes of fibromuscular dysplasia.

Fibromuscular dysplasia (FMD) is a poorly understood disease affecting 3-5% of adult females. The pathobiology of FMD involves arterial lesions of stenosis, dissection, tortuosity, dilation and aneurysm, which can lead to hypertension, stroke, myocardial infarction and even death. Currently, there are no animal models for FMD and few insights as to its pathobiology. In this study, by integrating DNA genotype and RNA sequence data from primary fibroblasts of 83 patients with FMD and 71 matched healthy controls, we inferred 18 gene regulatory co-expression networks, four of which were found to act together as an FMD-associated supernetwork in the arterial wall. After in vivo perturbation of this co-expression supernetwork by selective knockout of a top network key driver, mice developed arterial dilation, a hallmark of FMD. Molecular studies indicated that this supernetwork governs multiple aspects of vascular cell physiology and functionality, including collagen/matrix production. These studies illuminate the complex causal mechanisms of FMD and suggest a potential therapeutic avenue for this challenging disease.

Placental Gene Transcript Proportions are Altered in the Presence of In Utero Arsenic and Cadmium Exposures, Genetic Variants, and Birth Weight Differences.

Background: In utero arsenic and cadmium exposures are linked with reduced birth weight as well as alterations in placental molecular features. However, studies thus far have focused on summarizing transcriptional activity at the gene level and do not capture transcript specification, an important resource during fetal development to enable adaptive responses to the rapidly changing in utero physiological conditions. In this study, we conducted a genome-wide analysis of the placental transcriptome to evaluate the role of differential transcript usage (DTU) as a potential marker of in utero arsenic and cadmium exposure and fetal growth restriction. Methods: Transcriptome-wide RNA sequencing was performed in placenta samples from the Rhode Island Child Health Study (RICHS, n = 199). Arsenic and cadmium levels were measured in maternal toenails using ICP-MS. Differential transcript usage (DTU) contrasting small (SGA) and appropriate (AGA) for gestational age infants as well as above vs. below median exposure to arsenic and cadmium were assessed using the DRIMSeq R package. Genetic variants that influence transcript usage were determined using the sQTLseeker R package. Results: We identified 82 genes demonstrating DTU in association with SGA status at an FDR <0.05. Among these, one gene, ORMDL1, also demonstrated DTU in association with arsenic exposure, and fifteen genes (CSNK1E, GBA, LAMTOR4, MORF4L1, PIGO, PSG1, PSG3, PTMA, RBMS1, SLC38A2, SMAD4, SPCS2, TUBA1B, UBE2A, YIPF5) demonstrated DTU in association with cadmium exposure. In addition to cadmium exposure and SGA status, proportions of the LAMTOR4 transcript ENST00000474141.5 also differed by genetic variants (rs10231604, rs12878, and rs3736591), suggesting a pathway by which an in utero exposure and genetic variants converge to impact fetal growth through perturbations of placental processes. Discussion: We report the first genome-wide characterization of placental transcript usage and associations with intrauterine metal exposure and fetal growth restriction. These results highlight the utility of interrogating the transcriptome at finer-scale transcript-level resolution to identify novel placental biomarkers of exposure-induced outcomes.

Transcriptomic Changes Highly Similar to Alzheimer's Disease Are Observed in a Subpopulation of Individuals During Normal Brain Aging.

Aging is a major risk factor for late-onset Alzheimer's disease (LOAD). How aging contributes to the development of LOAD remains elusive. In this study, we examined multiple large-scale transcriptomic datasets from both normal aging and LOAD brains to understand the molecular interconnection between aging and LOAD. We found that shared gene expression changes between aging and LOAD are mostly seen in the hippocampal and several cortical regions. In the hippocampus, the expression of phosphoprotein, alternative splicing and cytoskeleton genes are commonly changed in both aging and AD, while synapse, ion transport, and synaptic vesicle genes are commonly down-regulated. Aging-specific changes are associated with acetylation and methylation, while LOAD-specific changes are more related to glycoprotein (both up- and down-regulations), inflammatory response (up-regulation), myelin sheath and lipoprotein (down-regulation). We also found that normal aging brain transcriptomes from relatively young donors (45-70 years old) clustered into several subgroups and some subgroups showed gene expression changes highly similar to those seen in LOAD brains. Using brain transcriptomic datasets from another cohort of older individuals (>70 years), we found that samples from cognitively normal older individuals clustered with the "healthy aging" subgroup while AD samples mainly clustered with the "AD similar" subgroups. This may imply that individuals in the healthy aging subgroup will likely remain cognitively normal when they become older and vice versa. In summary, our results suggest that on the transcriptome level, aging and LOAD have strong interconnections in some brain regions in a subpopulation of cognitively normal aging individuals. This supports the theory that the initiation of LOAD occurs decades earlier than the manifestation of clinical phenotype and it may be essential to closely study the "normal brain aging" to identify the very early molecular events that may lead to LOAD development.

Transcriptome analysis reveals the difference between "healthy" and "common" aging and their connection with age-related diseases.

A key goal of aging research was to understand mechanisms underlying healthy aging and develop methods to promote the human healthspan. One approach is to identify gene regulations unique to healthy aging compared with aging in the general population (i.e., "common" aging). Here, we leveraged Genotype-Tissue Expression (GTEx) project data to investigate "healthy" and "common" aging gene expression regulations at a tissue level in humans and their interconnection with diseases. Using GTEx donors' disease annotations, we defined a "healthy" aging cohort for each tissue. We then compared the age-associated genes derived from this cohort with age-associated genes from the "common" aging cohort which included all GTEx donors; we also compared the "healthy" and "common" aging gene expressions with various disease-associated gene expressions to elucidate the relationships among "healthy," "common" aging and disease. Our analyses showed that 1. GTEx "healthy" and "common" aging shared a large number of gene regulations; 2. Despite the substantial commonality, "healthy" and "common" aging genes also showed distinct function enrichment, and "common" aging genes had a higher enrichment for disease genes; 3. Disease-associated gene regulations were overall different from aging gene regulations. However, for genes regulated by both, their regulation directions were largely consistent, implying some aging processes could increase the susceptibility to disease development; and 4. Possible protective mechanisms were associated with some "healthy" aging gene regulations. In summary, our work highlights several unique features of GTEx "healthy" aging program. This new knowledge could potentially be used to develop interventions to promote the human healthspan.

Human geroprotector discovery by targeting the converging subnetworks of aging and age-related diseases.

A key goal of geroscience research is to identify effective interventions to extend human healthspan, the years of healthy life. Currently, majority of the geroprotectors are found by screening compounds in model organisms; whether they will be effective in humans is largely unknown. Here we present a new strategy called ANDRU (aging network based drug discovery) to help the discovery of human geroprotectors. It first identifies human aging subnetworks that putatively function at the interface between aging and age-related diseases; it then screens for pharmacological interventions that may "reverse" the age-associated transcriptional changes occurred in these subnetworks. We applied ANDRU to human adipose gene expression data from the Genotype Tissue Expression (GTEx) project. For the top 31 identified compounds, 19 of them showed at least some evidence supporting their function in improving metabolic traits or lifespan, which include type 2 diabetes drugs such as pioglitazone. As the query aging genes were refined to the ones with more intimate links to diseases, ANDRU identified more meaningful drug hits than the general approach without considering the underlying network structures. In summary, ANDRU represents a promising human data-driven strategy that may speed up the discovery of interventions to extend human healthspan.

Placental lncRNA expression associated with placental cadmium concentrations and birth weight.

Heavy metal exposures, such as cadmium, can have negative effects on infant birth weight (BW)-among other developmental outcomes-with placental dysfunction potentially playing a role in these effects. In this study, we examined how differential placental expression of long non-coding RNAs (lncRNAs) may be associated with cadmium levels in placenta and whether differences in the expression of those lncRNAs were associated with fetal growth. In the Rhode Island Child Health Study, we used data from Illumina HiSeq whole transcriptome RNA sequencing (n = 199) to examine association between lncRNA expression and measures of infant BW as well as placental cadmium concentrations controlled for appropriate covariates. Of the 1191 lncRNAs sequenced, 46 demonstrated associations (q < 0.05) with BW in models controlling for infant sex, maternal age, BMI, maternal education, and smoking during pregnancy. Furthermore, four of these transcripts were associated with placental cadmium concentrations, with MIR22HG and ERVH48-1 demonstrating increases in expression associated with increasing cadmium exposure and elevated odds of small for gestational age birth, while AC114763.2 and LINC02595 demonstrated reduced expression associated with cadmium, but elevated odds of large for gestational age birth with increasing expression. We identified relationships between lncRNA expression with both placental cadmium concentrations and BW. This study provides evidence that disrupted placental expression of lncRNAs may be a part of cadmium's mechanisms of reproductive toxicity.

Maternal and fetal genetic effects on birth weight and their relevance to cardio-metabolic risk factors.

Birth weight variation is influenced by fetal and maternal genetic and non-genetic factors, and has been reproducibly associated with future cardio-metabolic health outcomes. In expanded genome-wide association analyses of own birth weight (n = 321,223) and offspring birth weight (n = 230,069 mothers), we identified 190 independent association signals (129 of which are novel). We used structural equation modeling to decompose the contributions of direct fetal and indirect maternal genetic effects, then applied Mendelian randomization to illuminate causal pathways. For example, both indirect maternal and direct fetal genetic effects drive the observational relationship between lower birth weight and higher later blood pressure: maternal blood pressure-raising alleles reduce offspring birth weight, but only direct fetal effects of these alleles, once inherited, increase later offspring blood pressure. Using maternal birth weight-lowering genotypes to proxy for an adverse intrauterine environment provided no evidence that it causally raises offspring blood pressure, indicating that the inverse birth weight-blood pressure association is attributable to genetic effects, and not to intrauterine programming.

Variants in the fetal genome near pro-inflammatory cytokine genes on 2q13 associate with gestational duration.

The duration of pregnancy is influenced by fetal and maternal genetic and non-genetic factors. Here we report a fetal genome-wide association meta-analysis of gestational duration, and early preterm, preterm, and postterm birth in 84,689 infants. One locus on chromosome 2q13 is associated with gestational duration; the association is replicated in 9,291 additional infants (combined P = 3.96 × 10-14). Analysis of 15,588 mother-child pairs shows that the association is driven by fetal rather than maternal genotype. Functional experiments show that the lead SNP, rs7594852, alters the binding of the HIC1 transcriptional repressor. Genes at the locus include several interleukin 1 family members with roles in pro-inflammatory pathways that are central to the process of parturition. Further understanding of the underlying mechanisms will be of great public health importance, since giving birth either before or after the window of term gestation is associated with increased morbidity and mortality.

Intrauterine multi-metal exposure is associated with reduced fetal growth through modulation of the placental gene network.

BACKGROUND: Intrauterine metal exposures and aberrations in placental processes are known contributors to being born small for gestational age (SGA). However, studies to date have largely focused on independent effects, failing to account for potential interdependence among these markers. OBJECTIVES: We evaluated the inter-relationship between multi-metal indices and placental gene network modules related to SGA status to highlight potential molecular pathways through which in utero multi-metal exposure impacts fetal growth. METHODS: Weighted quantile sum (WQS) regression was performed using a panel of 16 trace metals measured in post-partum maternal toe nails collected from the Rhode Island Child Health Study (RICHS, n = 195), and confirmation of the derived SGA-related multi-metal index was conducted using Bayesian kernel machine regression (BKMR). We leveraged existing placental weighted gene coexpression network data to examine associations between the SGA multi-metal index and placental gene expression. Expression of select genes were assessed using RT-PCR in an independent birth cohort, the New Hampshire Birth Cohort Study (NHBCS, n = 237). RESULTS: We identified a multi-metal index, predominated by arsenic (As) and cadmium (Cd), that was positively associated with SGA status (Odds ratio = 2.73 [1.04, 7.18]). This index was also associated with the expression of placental gene modules involved in "gene expression" (ß = -0.02 [-0.04, -0.01]) and "metabolic hormone secretion" (ß = 0.02 [0.00, 0.05]). We validated the association between cadmium exposure and the expression of GRHL1 and INHBA, genes in the "metabolic hormone secretion" module, in NHBCS. CONCLUSION: We present a novel approach that integrates the application of advanced bioinformatics and biostatistics methods to delineate potential placental pathways through which trace metal exposures impact fetal growth.

Genome-wide variants of Eurasian facial shape differentiation and a prospective model of DNA based face prediction.

It is a long-standing question as to which genes define the characteristic facial features among different ethnic groups. In this study, we use Uyghurs, an ancient admixed population to query the genetic bases why Europeans and Han Chinese look different. Facial traits were analyzed based on high-dense 3D facial images; numerous biometric spaces were examined for divergent facial features between European and Han Chinese, ranging from inter-landmark distances to dense shape geometrics. Genome-wide association studies (GWAS) were conducted on a discovery panel of Uyghurs. Six significant loci were identified, four of which, rs1868752, rs118078182, rs60159418 at or near UBASH3B, COL23A1, PCDH7 and rs17868256 were replicated in independent cohorts of Uyghurs or Southern Han Chinese. A prospective model was also developed to predict 3D faces based on top GWAS signals and tested in hypothetic forensic scenarios.

Genome-wide DNA methylation associations with spontaneous preterm birth in US blacks: findings in maternal and cord blood samples.

Preterm birth (PTB) affects one in six Black babies in the United States. Epigenetics is believed to play a role in PTB; however, only a limited number of epigenetic studies of PTB have been reported, most of which have focused on cord blood DNA methylation (DNAm) and/or were conducted in white populations. Here we conducted, by far, the largest epigenome-wide DNAm analysis in 300 Black women who delivered early spontaneous preterm (sPTB, n = 150) or full-term babies (n = 150) and replicated the findings in an independent set of Black mother-newborn pairs from the Boston Birth Cohort. DNAm in maternal blood and/or cord blood was measured using the Illumina HumanMethylation450 BeadChip. We identified 45 DNAm loci in maternal blood associated with early sPTB, with a false discovery rate (FDR) <5%. Replication analyses confirmed sPTB associations for cg03915055 and cg06804705, located in the promoter regions of the CYTIP and LINC00114 genes, respectively. Both loci had comparable associations with early sPTB and early medically-indicated PTB, but attenuated associations with late sPTB. These associations could not be explained by cell composition, gestational complications, and/or nearby maternal genetic variants. Analyses in the newborns of the 110 Black women showed that cord blood methylation levels at both loci had no associations with PTB. The findings from this study underscore the role of maternal DNAm in PTB risk, and provide a set of maternal loci that may serve as biomarkers for PTB. Longitudinal studies are needed to clarify temporal relationships between maternal DNAm and PTB risk.

Genetic Pleiotropy between Nicotine Dependence and Respiratory Outcomes.

Smoking is a major cause of respiratory conditions. To date, the genetic pleiotropy between smoking behavior and lung function/chronic obstructive pulmonary disease (COPD) have not been systematically explored. We leverage large data sets of smoking behavior, lung function and COPD, and addressed two questions, (1) whether the genetic predisposition of nicotine dependence influence COPD risk and lung function; and (2) the genetic pleiotropy follow causal or independent model. We found the genetic predisposition of nicotine dependence was associated with COPD risk, even after adjusting for smoking behavior, indicating genetic pleiotropy and independent model. Two known nicotine dependent loci (15q25.1 and 19q13.2) were associated with smoking adjusted lung function, and 15q25.1 reached genome-wide significance. At various suggestive p-value thresholds, the smoking adjusted lung function traits share association signals with cigarettes per day and former smoking, substantially greater than random chance. Empirical data showed the genetic pleiotropy between nicotine dependence and COPD or lung function. The basis of pleiotropic effect is rather complex, attributable to a large number of genetic variants, and many variants functions through independent model, where the pleiotropic variants directly affect lung function, not mediated by influencing subjects' smoking behavior.

Using molecular functional networks to manifest connections between obesity and obesity-related diseases.

Obesity is a primary risk factor for many diseases such as certain cancers. In this study, we have developed three algorithms including a random-walk based method OBNet, a shortest-path based method OBsp and a direct-overlap method OBoverlap, to reveal obesity-disease connections at protein-interaction subnetworks corresponding to thousands of biological functions and pathways. Through literature mining, we also curated an obesity-associated disease list, by which we compared the methods. As a result, OBNet outperforms other two methods. OBNet can predict whether a disease is obesity-related based on its associated genes. Meanwhile, OBNet identifies extensive connections between obesity genes and genes associated with a few diseases at various functional modules and pathways. Using breast cancer and Type 2 diabetes as two examples, OBNet identifies meaningful genes that may play key roles in connecting obesity and the two diseases. For example, TGFB1 and VEGFA are inferred to be the top two key genes mediating obesity-breast cancer connection in modules associated with brain development. Finally, the top modules identified by OBNet in breast cancer significantly overlap with modules identified from TCGA breast cancer gene expression study, revealing the power of OBNet in identifying biological processes involved in the disease.