Potential Risk Genes Research Articles

A microRNA Transcriptome-wide Association Study of Prostate Cancer Risk.

Large genome-wide association studies have identified hundreds of single-nucleotide polymorphisms associated with increased risk of prostate cancer (PrCa), and many of these risk loci is presumed to confer regulatory effects on gene expression. While eQTL studies of long RNAs has yielded many potential risk genes, the relationship between PrCa risk genetics and microRNA expression dysregulation is understudied. We performed an microRNA transcriptome-wide association study of PrCa risk using small RNA sequencing and genome-wide genotyping data from N = 441 normal prostate epithelium tissue samples along with N = 411 prostate adenocarcinoma tumor samples from the Cancer Genome Atlas (TCGA). Genetically regulated expression prediction models were trained for all expressed microRNAs using the FUSION TWAS software. TWAS for PrCa risk was performed with both sets of models using single-SNP summary statistics from the recent PRACTICAL consortium PrCa case-control OncoArray GWAS meta-analysis. A total of 613 and 571 distinct expressed microRNAs were identified in the normal and tumor tissue datasets, respectively (overlap: 480). Among these, 79 (13%) normal tissue microRNAs demonstrated significant cis-heritability (median cis-h2 = 0.15, range: 0.03–0.79) for model training. Similar results were obtained from TCGA tumor samples, with 48 (9%) microRNA expression models successfully trained (median cis-h2 = 0.14, range: 0.06–0.60). Using normal tissue models, we identified two significant TWAS microRNA associations with PrCa risk: over-expression of mir-941 family microRNAs (PTWAS = 2.9E-04) and reduced expression of miR-3617-5p (PTWAS = 1.0E-03). The TCGA tumor TWAS also identified a significant association with miR-941 overexpression (PTWAS = 9.7E-04). Subsequent finemapping of the TWAS results using a multi-tissue database indicated limited evidence of causal status for each microRNA with PrCa risk (posterior inclusion probabilities <0.05). Future work will examine downstream regulatory effects of microRNA dysregulation as well as microRNA-mediated risk mechanisms via competing endogenous RNA relationships.

Predicting embryonic aneuploidy rate in IVF patients using whole-exome sequencing.

Infertility is a major reproductive health issue that affects about 12% of women of reproductive age in the United States. Aneuploidy in eggs accounts for a significant proportion of early miscarriage and in vitro fertilization failure. Recent studies have shown that genetic variants in several genes affect chromosome segregation fidelity and predispose women to a higher incidence of egg aneuploidy. However, the exact genetic causes of aneuploid egg production remain unclear, making it difficult to diagnose infertility based on individual genetic variants in mother's genome. In this study, we evaluated machine learning-based classifiers for predicting the embryonic aneuploidy risk in female IVF patients using whole-exome sequencing data. Using two exome datasets, we obtained an area under the receiver operating curve of 0.77 and 0.68, respectively. High precision could be traded off for high specificity in classifying patients by selecting different prediction score cutoffs. For example, a strict prediction score cutoff of 0.7 identified 29% of patients as high-risk with 94% precision. In addition, we identified MCM5, FGGY, and DDX60L as potential aneuploidy risk genes that contribute the most to the predictive power of the model. These candidate genes and their molecular interaction partners are enriched for meiotic-related gene ontology categories and pathways, such as microtubule organizing center and DNA recombination. In summary, we demonstrate that sequencing data can be mined to predict patients' aneuploidy risk thus improving clinical diagnosis. The candidate genes and pathways we identified are promising targets for future aneuploidy studies.

Integrated Analysis of Coexpression and Exome Sequencing to Prioritize Susceptibility Genes for Familial Cutaneous Melanoma

The application of whole-exome sequencing has led to the identification of high- and moderate-risk variants that contribute to cutaneous melanoma susceptibility. However, confirming disease-causing variants remains challenging. We applied a gene coexpression network analysis to prioritize the candidate genes identified from whole-exome sequencing of 34 melanoma-prone families, with at least three affected members sequenced per family (N= 119 cases). A coexpression network was constructed from genotype-tissue expression project, skin melanoma from the cancer genome atlas, and primary melanocyte cultures. We performed module-specific enrichment and focused on modules associated with pigmentation processes because they are the best-studied and most well-known risk factors for melanoma susceptibility. We found that pigmentation-associated modules across the four expression datasets examined were enriched for well-known melanoma susceptibility genes plus genes associated with pigmentation. We also used network properties to prioritize genes within pigmentation modules as candidate susceptibility genes. Integrating information from coexpression network analysis and variant prioritization, we identified 36 genes (such as DCT, TPCN2, TRPM1, ATP10A, and EPHA5) as potential melanoma risk genes in the families. Our approach also allowed us to link families with private gene mutations on the basis of gene coexpression patterns and thereby may provide an innovative perspective in gene identification in high-risk families.

Machine learning prediction and tau-based screening identifies potential Alzheimer\u2019s disease genes relevant to immunity

With increased research funding for Alzheimer’s disease (AD) and related disorders across the globe, large amounts of data are being generated. Several studies employed machine learning methods to understand the ever-growing omics data to enhance early diagnosis, map complex disease networks, or uncover potential drug targets. We describe results based on a Target Central Resource Database protein knowledge graph and evidence paths transformed into vectors by metapath matching. We extracted features between specific genes and diseases, then trained and optimized our model using XGBoost, termed MPxgb(AD). To determine our MPxgb(AD) prediction performance, we examined the top twenty predicted genes through an experimental screening pipeline. Our analysis identified potential AD risk genes: FRRS1, CTRAM, SCGB3A1, FAM92B/CIBAR2, and TMEFF2. FRRS1 and FAM92B are considered dark genes, while CTRAM, SCGB3A1, and TMEFF2 are connected to TREM2-TYROBP, IL-1β-TNFα, and MTOR-APP AD-risk nodes, suggesting relevance to the pathogenesis of AD.

Analysis of cancer-promoting genes related to chemotherapy resistance in esophageal squamous cell carcinoma

BackgroundAccording to histopathology, esophageal cancer can be divided into squamous cell carcinoma (SCC) and esophageal adenocarcinoma (adeno arcinoma). In China, 90% of esophageal cancer patients are squamous cell carcinoma. Cisplatin and fluaziridine are the main chemotherapy before and after surgery. Long-term drug treatment is often accompanied by the emergence of drug resistance of tumor cells. There are many mechanisms for the emergence of drug resistance of tumor cells, including the increase of drug efflux, the decrease of drug intake, the inhibition of cell apoptosis, and so on. This study aimed to investigate the key cancer-promoting genes related to chemotherapy resistance in esophageal squamous cell carcinoma (ESCC).MethodsTwo datasets from the Gene Expression Omnibus (GEO) database (GSE86099 and GSE50224) were retrieved. We performed microRNA (miRNA) and messenger RNA (mRNA) expression analysis to identify differentially expressed genes (DEGs). The intersection of the downregulated miRNA targets and the upregulated mRNAs were used for Gene Ontology (GO) enrichment analysis, and survival risk was assessed using data from The Cancer Genome Atlas (TCGA).ResultsThere were 35 common genes, of which, based on GO enrichment, most were related to the cardiac muscle cell action. Four genes showed significant association with the estimated half-maximal inhibitory concentration (IC50) of paclitaxel: bone morphogenetic protein 1 (BMP1), dumbbell former 4 protein (DBF4), angiogenin (ANG), and MAP7 domain containing 2 (MAP7D2). Four risk factors (MP1, HIP1, ANG, and MAP7D2) were selected to generate a signature using least absolute shrinkage and selection operator (LASSO) regression. Protein-protein interaction (PPI) analysis showed guanine nucleotide-binding protein subunit beta 4 (GNB4), calcium voltage-gated channel auxiliary subunit beta 2 (CACNB2), and sodium voltage-gated channel alpha subunit 1 (SCN1A) were located at key positions of the network. Among potential risk genes, only the high expression of dedicator of cytokinesis 8 (DOCK8) was associated with poorer survival.ConclusionsThe 35 oncogenes may be involved in mechanisms of chemotherapy resistance in ESCC, as well as the corresponding enrichment and regulatory network. The signature containing 4 key risk genes merits further investigation and may provide a deeper understanding of the molecular mechanisms in ESCC treatment failure.

Deep post-GWAS analysis identifies potential risk genes and risk variants for Alzheimer\u2019s disease, providing new insights into its disease mechanisms

Alzheimer’s disease (AD) is a genetically complex, multifactorial neurodegenerative disease. It affects more than 45 million people worldwide and currently remains untreatable. Although genome-wide association studies (GWAS) have identified many AD-associated common variants, only about 25 genes are currently known to affect the risk of developing AD, despite its highly polygenic nature. Moreover, the risk variants underlying GWAS AD-association signals remain unknown. Here, we describe a deep post-GWAS analysis of AD-associated variants, using an integrated computational framework for predicting both disease genes and their risk variants. We identified 342 putative AD risk genes in 203 risk regions spanning 502 AD-associated common variants. 246 AD risk genes have not been identified as AD risk genes by previous GWAS collected in GWAS catalogs, and 115 of 342 AD risk genes are outside the risk regions, likely under the regulation of transcriptional regulatory elements contained therein. Even more significantly, for 109 AD risk genes, we predicted 150 risk variants, of both coding and regulatory (in promoters or enhancers) types, and 85 (57%) of them are supported by functional annotation. In-depth functional analyses showed that AD risk genes were overrepresented in AD-related pathways or GO terms—e.g., the complement and coagulation cascade and phosphorylation and activation of immune response—and their expression was relatively enriched in microglia, endothelia, and pericytes of the human brain. We found nine AD risk genes—e.g., IL1RAP, PMAIP1, LAMTOR4—as predictors for the prognosis of AD survival and genes such as ARL6IP5 with altered network connectivity between AD patients and normal individuals involved in AD progression. Our findings open new strategies for developing therapeutics targeting AD risk genes or risk variants to influence AD pathogenesis.

Sequencing of neurofilament genes identified NEFH Ser787Arg as a novel risk variant of sporadic amyotrophic lateral sclerosis in Chinese subjects

BackgroundAmyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease with neuronal cell inclusions composed of neurofilaments and other abnormal aggregative proteins as pathological hallmarks. Approximately 90% of patients have sporadic cases (sALS), and at least 4 genes, i.e. C9orf72, SOD1, FUS and TARDBP, have been identified as the main causative genes, while many others have been proposed as potential risk genes. However, these mutations could explain only ~ 10% of sALS cases. The neurofilament polypeptides encoded by NEFH, NEFM, and NEFL are promising protein biomarkers for ALS and other degenerative diseases. However, whether the genetic variants of these genes were associated with ALS remain ambiguous.MethodsHere, we used PCR-Sanger to sequence the exons of these three genes in a cohort of 371 sALS patients and 711 healthy controls (Phase I) and validated the risk variant in another 300 sALS patients and 1076 controls (Phase II).ResultsA total of 92 variants were identified, including 36 rare heterozygous variants in NEFH, 27 in NEFM, and 16 in NEFL, and only rs568759161 (p.Ser787Arg) in NEFH reached nominal statistical power (P = 0.02 at Phase I, P = 0.009 at Phase II) in the case–control comparison. Together, the Phase I and II studies showed the significantly higher frequency of the variant in cases (9/1342, 0.67%) than in controls (2/3574, 0.07%) (OR 12.06; 95% CI 2.60–55.88; P = 0.0003). No variants passed multiple testing in the discovery cohort, but rs568759161 was associated with ALS in a replication cohort.ConclusionsOur results confirmed that NEFH Ser787Arg is a novel sALS risk variant in Chinese subjects, but NEFM and NEFL were not associated with sALS. These data may have implications for genetic counselling and for understanding the pathogenesis of sALS.

Insight into susceptibility genes associated with bipolar disorder: a systematic review.

Bipolar disorder (BD) is a severe disorder, and it is associated with an increased risk of mortality. About 25% of patients with BD have attempted and 11% have died by suicide. All these characteristics suggest that the disorders within the bipolar spectrum are a crucial public health problem. With the development of molecular genetics in recent decades, it was possible to more easily detect risk genes associated with this disorder. This study aimed at summarizing the findings of systematic reviews and meta-analyses on the topic and assessing the quality of the available evidence. PubMed/Medline and Web of Science were searched to identify systematic reviews and meta-analyses published during 2013-2019. Standard methodology was applied to synthesize and assess the retrieved literature. This systematic review identifies a number of potential risk genes associated with bipolar disorder whose mechanism of action has yet to be confirmed. They are divided into several groups: 1) a list of the most significant susceptibility genetic factors associated with BD; 2) the implication of the ZNF804A gene in BD; 3) the role of genes involved in calcium signaling in BD; 4) DNA methylation in BD; 5) BD and risk suicide genes; 6) susceptibility genes for early-onset BD; 7) candidate genes common to both BD and schizophrenia; 8) genes involved in cognitive status in BD cases; 9) genes involved in structural alteration in BD brain tissue; 10) genes involved in lithium response in BD. Future research should concentrate on molecular mechanisms by which genetic variants play a major role in BD. Supplemental research is needed to replicate the applicable results.

Genetic analysis of TRIM family genes for early-onset Parkinson's disease in Chinese population

IntroductionAmounting evidence has suggested the Tripartite Motif (TRIM) family proteins as related to Parkinson's disease (PD). However, many of the risk genes were still awaiting further explorations, and their genetic role in PD has not been investigated yet. MethodsHere, we aimed to systematically evaluate the genetic associations of TRIMs with PD in a large Chinese early-onset PD (EOPD, age at onset < 50 years) cohort. We identified rare variants (minor allele frequency < 0.01) in 743 unrelated EOPD patients using whole exome sequencing, and evaluated the association between rare variants and EOPD at allele and gene levels. ResultsTotally 123 rare variants were identified in 13 TRIM protein family members, including TRIM3, TRIM6, TRIM8, TRIM9, TRIM10, TRIM11, TRIM17, TRIM24, TRIM27, TRIM28, TRIM34, TRIM40 and TRIM41. At the allele level, three variants were nominally associated with PD, namely p.R65H in TRIM10, p.P467S in TRIM11, and p.I425V in TRIM24. Gene-based burden analysis showed a clear enrichment of rare variants of TRIM24 in EOPD. ConclusionThese results demonstrate TRIM24 as a potential risk gene for PD, provide a better understanding for the genetic involvement of TRIM protein family members in EOPD and broaden the current mutation spectrum of PD.

The Association Between ANKRD55 rs6859219 SNP and the Risk of Multiple Sclerosis in Iranian Patients

Background: Multiple Sclerosis (MS) is a neuroinflammatory autoimmune disease of the central nervous system. ANKRD55 rs6859219 Single Nucleotide Polymorphisms (SNPs) are reported as a potential novel MS risk gene, but the biological reasons underlying this correlation are still mysterious. Objectives: This study aimed to investigate the potential association of ANKRD55 rs6859219 SNP with the risk of MS in Iranian patients. Materials &amp; Methods: Blood samples were taken from 80 patients and healthy individuals. Afterward, the effects of some related-risk factors, such as age, gender, smoking status, drug allergy, and exposure to chemicals (such as detergents, cleansing chemical, acids, etc., in their common work area or their daily home exposure) were measured on the incidence of Relapsing-Remitting MS (RRMS) patients. Polymerase Chain Reaction (PCR) was performed, and the PCR products were genotyped using the Sanger sequencing system. Results: The GT genotype of ANKRD55 rs6859219 SNP more increased in MS patients than healthy subjects, and a significant association was found between MS and the rs6859219 SNP in the single-variable association analysis (P=0.04). In the additive model, our findings indicated that the rs6859219 SNP increased the risk of MS. Additionally, the results of all crude models, additive, recessive, dominant, and overdominant, were significantly different in participants with MS compared to the controls. Conclusion: Eventually, the GT genotype of ANKRD55 rs6859219 SNP significantly increased the risk of MS compared to the TT genotype.

Network analysis of potential risk genes for psoriasis

BackgroundPsoriasis is a complex chronic inflammatory skin disease. The aim of this study was to analyze potential risk genes and molecular mechanisms associated with psoriasis.MethodsGSE54456, GSE114286, and GSE121212 were collected from gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) between psoriasis and controls were screened respectively in three datasets and common DEGs were obtained. The biological role of common DEGs were identified by enrichment analysis. Hub genes were identified using protein–protein interaction (PPI) networks and their risk for psoriasis was evaluated through logistic regression analysis. Moreover, differentially methylated positions (DMPs) between psoriasis and controls were obtained in the GSE115797 dataset. Methylation markers were identified after comparison with the common genes.ResultsA total of 118 common DEGs were identified, which were mainly involved in keratinocyte differentiation and IL-17 signaling pathway. Through PPI network, we identified top 10 degrees as hub genes. Among them, high expression of CXCL9 and SPRR1B may be risk factors for psoriasis. In addition, we selected 10 methylation-modified genes with the higher area under receiver operating characteristic curve (AUC) value as methylation markers. Nomogram showed that TGM6 and S100A9 may be associated with an increased risk of psoriasis.ConclusionThis suggests that immune and inflammatory responses are active in keratinocytes of psoriatic skin. CXCL9, SPRR1B, TGM6 and S100A9 may be potential targets for the diagnosis and treatment of psoriasis.

Deep learning model reveals potential risk genes for ADHD, especially Ephrin receptor gene EPHA5.

Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder. Although genome-wide association studies (GWAS) identify the risk ADHD-associated variants and genes with significant P-values, they may neglect the combined effect of multiple variants with insignificant P-values. Here, we proposed a convolutional neural network (CNN) to classify 1033 individuals diagnosed with ADHD from 950 healthy controls according to their genomic data. The model takes the single nucleotide polymorphism (SNP) loci of P-values n}{}le{1times 10^{-3}}, i.e. 764 loci, as inputs, and achieved an accuracy of 0.9018, AUC of 0.9570, sensitivity of 0.8980 and specificity of 0.9055. By incorporating the saliency analysis for the deep learning network, a total of 96 candidate genes were found, of which 14 genes have been reported in previous ADHD-related studies. Furthermore, joint Gene Ontology enrichment and expression Quantitative Trait Loci analysis identified a potential risk gene for ADHD, EPHA5 with a variant of rs4860671. Overall, our CNN deep learning model exhibited a high accuracy for ADHD classification and demonstrated that the deep learning model could capture variants’ combining effect with insignificant P-value, while GWAS fails. To our best knowledge, our model is the first deep learning method for the classification of ADHD with SNPs data.

Transcriptome-wide association study identifies new susceptibility genes and pathways for depression

Depression is the most prevalent mental disorder with substantial morbidity and mortality. Although genome-wide association studies (GWASs) have identified multiple risk variants for depression, due to the complicated gene regulatory mechanisms and complexity of linkage disequilibrium (LD), the biological mechanisms by which the risk variants exert their effects on depression remain largely unknown. Here, we perform a transcriptome-wide association study (TWAS) of depression by integrating GWAS summary statistics from 807,553 individuals (246,363 depression cases and 561,190 controls) and summary-level gene-expression data (from the dorsolateral prefrontal cortex (DLPFC) of 1003 individuals). We identified 53 transcriptome-wide significant (TWS) risk genes for depression, of which 23 genes were not implicated in risk loci of the original GWAS. Seven out of 53 risk genes (B3GALTL, FADS1, TCTEX1D1, XPNPEP3, ZMAT2, ZNF501 and ZNF502) showed TWS associations with depression in two independent brain expression quantitative loci (eQTL) datasets, suggesting that these genes may represent promising candidates. We further conducted conditional analyses and identified the potential risk genes that driven the TWAS association signal in each locus. Finally, pathway enrichment analysis revealed biologically pathways relevant to depression. Our study identified new depression risk genes whose expression dysregulation may play a role in depression. More importantly, we translated the GWAS associations into risk genes and relevant pathways. Further mechanistic study and functional characterization of the TWS depression risk genes will facilitate the diagnostics and therapeutics for depression.

Methylation factor MRPL15 identified as a potential biological target in Alzheimer's disease.

Alzheimer’s disease (AD) is the most common form of dementia. However, the molecular basis of the development and progression of AD is still unclear. To elucidate the molecular processes related to AD, we obtained the expression profiles and analyzed the differentially expressed genes (DEGs). The genes potentially involved in the AD process were identified by PPI network and STEM analysis. The molecular mechanisms related to the recognition of AD were determined by GSEA and enrichment analysis. The differences from immune cells in AD were calculated. The methylation factors were identified by methylation difference analysis. Among them, MRPL15 was identified as suitable for diagnosing AD. Its expression trend had been verified in GSE5281. Importantly, MRPL15 was also a methylation factor. In addition, macrophages and neutrophils were up-regulated in AD patients. This was consistent with previous immune inflammation responses identified as being involved in the development of AD. The results of the present study revealed the genetic changes and molecular mechanisms involved in the process of the development and deterioration of AD patients. The potential AD risk genes and potential biological targets were identified. It provided evidence that immune inflammation and immune cells play an important role in AD.

Evidence for CAT gene being functionally involved in the susceptibility of COVID-19.

Novel coronary pneumonia (COVID‐19) is a respiratory distress syndrome caused by a new type of coronavirus. Understanding the genetic basis of susceptibility and prognosis to COVID‐19 is of great significance to disease prevention, molecular typing, prognosis, and treatment. However, so far, there have been only two genome‐wide association studies (GWASs) on the susceptibility of COVID‐19. Starting with these reported DNA variants, we found the genes regulated by these variants through cis‐eQTL and cis‐meQTL acting. We further did a series of bioinformatics analysis on these potential risk genes. The analysis shows that the genetic variants on EHF regulate the expression of its neighbor CAT gene via cis‐eQTL. There was significant evidence that CAT and the SARS‐CoV‐2‐related S protein binding protein ACE2 interact with each other. Intracellular localization results showed that CAT and ACE2 proteins both exists in the cell membrane and extracellular area and their interaction could have an impact on the cell invasion ability of S protein. In addition, the expression of these three genes showed a significant positive correlation in the lungs. Based on these results, we propose that CAT plays a crucial intermediary role in binding effectiveness of ACE2, thereby affecting the susceptibility to COVID‐19.

Peripheral Anomalies in USH2A Cause Central Auditory Anomalies in a Mouse Model of Usher Syndrome and CAPD.

Central auditory processing disorder (CAPD) is associated with difficulties hearing and processing acoustic information, as well as subsequent impacts on the development of higher-order cognitive processes (i.e., attention and language). Yet CAPD also lacks clear and consistent diagnostic criteria, with widespread clinical disagreement on this matter. As such, identification of biological markers for CAPD would be useful. A recent genome association study identified a potential CAPD risk gene, USH2A. In a homozygous state, this gene is associated with Usher syndrome type 2 (USH2), a recessive disorder resulting in bilateral, high-frequency hearing loss due to atypical cochlear hair cell development. However, children with heterozygous USH2A mutations have also been found to show unexpected low-frequency hearing loss and reduced early vocabulary, contradicting assumptions that the heterozygous (carrier) state is “phenotype free”. Parallel evidence has confirmed that heterozygous Ush2a mutations in a transgenic mouse model also cause low-frequency hearing loss (Perrino et al., 2020). Importantly, these auditory processing anomalies were still evident after covariance for hearing loss, suggesting a CAPD profile. Since usherin anomalies occur in the peripheral cochlea and not central auditory structures, these findings point to upstream developmental feedback effects of peripheral sensory loss on high-level processing characteristic of CAPD. In this study, we aimed to expand upon the mouse behavioral battery used in Perrino et al. (2020) by evaluating central auditory brain structures, including the superior olivary complex (SOC) and medial geniculate nucleus (MGN), in heterozygous and homozygous Ush2a mice. We found that heterozygous Ush2a mice had significantly larger SOC volumes while homozygous Ush2a had significantly smaller SOC volumes. Heterozygous mutations did not affect the MGN; however, homozygous Ush2a mutations resulted in a significant shift towards more smaller neurons. These findings suggest that alterations in cochlear development due to USH2A variation can secondarily impact the development of brain regions important for auditory processing ability.

BARD1 Pathogenic Variants are Associated with Triple-Negative Breast Cancer in a Spanish Hereditary Breast and Ovarian Cancer Cohort.

Only a small fraction of hereditary breast and/or ovarian cancer (HBOC) cases are caused by germline variants in the high-penetrance breast cancer 1 and 2 genes (BRCA1 and BRCA2). BRCA1-associated ring domain 1 (BARD1), nuclear partner of BRCA1, has been suggested as a potential HBOC risk gene, although its prevalence and penetrance are variable according to populations and type of tumor. We aimed to investigate the prevalence of BARD1 truncating variants in a cohort of patients with clinical suspicion of HBOC. A comprehensive BARD1 screening by multigene panel analysis was performed in 4015 unrelated patients according to our regional guidelines for genetic testing in hereditary cancer. In addition, 51,202 Genome Aggregation Database (gnomAD) non-Finnish, non-cancer European individuals were used as a control population. In our patient cohort, we identified 19 patients with heterozygous BARD1 truncating variants (0.47%), whereas the frequency observed in the gnomAD controls was 0.12%. We found a statistically significant association of truncating BARD1 variants with overall risk (odds ratio (OR) = 3.78; CI = 2.10–6.48; p = 1.16 × 10−5). This association remained significant in the hereditary breast cancer (HBC) group (OR = 4.18; CI = 2.10–7.70; p = 5.45 × 10−5). Furthermore, deleterious BARD1 variants were enriched among triple-negative BC patients (OR = 5.40; CI = 1.77–18.15; p = 0.001) compared to other BC subtypes. Our results support the role of BARD1 as a moderate penetrance BC predisposing gene and highlight a stronger association with triple-negative tumors.

Identification of genetic loci associated with nocturnal enuresis: a genome-wide association study

Nocturnal enuresis (bedwetting) is a common disorder affecting 10-16% of 7-year-old children globally. Nocturnal enuresis is highly heritable, but its genetic determinants remain unknown. We aimed to identify genetic variants associated with nocturnal enuresis and explore its genetic architecture and underlying biology. We did a genome-wide association study (GWAS) of nocturnal enuresis. Nocturnal enuresis cases were identified in iPSYCH2012, a large Danish population-based case cohort established to investigate mental disorders, on the basis of 10th revision of the International Statistical Classification of Diseases (ICD-10) diagnoses and redeemed desmopressin prescriptions in Danish registers. The GWAS was done in a genetically homogeneous sample of unrelated individuals using logistic regression with relevant covariates. All genome-wide significant variants were analysed for their association with nocturnal enuresis in an independent Icelandic sample from deCODE genetics. Standardised polygenic risk scores for attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder were constructed from summary statistics of large GWASs and analysed for association with nocturnal enuresis. The GWAS included 3882 nocturnal enuresis cases and 31 073 controls. We found two loci at chromosome 6 and chromosome 13 significantly associated with nocturnal enuresis. Six genetic variants at the two loci (five variants at chromosome 6q16.2 and one variant at chromosome 13q22.3) surpassed the threshold for genome-wide significance (p<5 × 10-8). There were two lead variants: rs9376454 (chromosome 6q16.2), with an odds ratio (OR) of 1·199 (95% CI 1·135-1·267; p=9·91 × 10-11), and rs60721117 (chromosome 13q22.3), with an OR of 1·149 (1·095-1·205; p=1·21 × 10-8). All associated variants in the chromosome 6 locus were replicated (p<8 × 10-3) in the independent Icelandic cohort of 5475 nocturnal enuresis cases and 303 996 controls, whereas the associated variant in the chromosome 13 locus showed nominal significant association (p=0·031). The percentage of nocturnal enuresis phenotypic variance explained by the common genetic variants was 23·9-30·4%. Polygenic risk for ADHD was associated with nocturnal enuresis (OR 1·06, 95% CI, 1·01-1·10; p=0·011). Among the potential nocturnal enuresis risk genes mapped, PRDM13 and EDNRB have biological functions associated with known pathophysiological mechanisms in nocturnal enuresis, and SIM1 regulates the formation of the hypothalamic neuroendocrine lineage that produces arginine vasopressin, a well known nocturnal enuresis drug target. This study shows that common genetic variants contribute considerably to nocturnal enuresis, and it identifies potential nocturnal enuresis risk genes with roles in sleep, urine production, and bladder function. Given that available treatments target these mechanisms, any of the identified genes and their functional gene networks are potential drug targets. The Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), Stanley Foundation.

Independent replications and integrative analyses confirm TRANK1 as a susceptibility gene for bipolar disorder.

Genetic analyses for bipolar disorder (BD) have achieved prominent success in Europeans in recent years, whereas its genetic basis in other populations remains relatively less understood. We herein report that the leading risk locus for BD in European genome-wide association studies (GWAS), the single-nucleotide polymorphism (SNP) rs9834970 near TRANK1 at 3p22 region, is also genome-wide significantly associated with BD in a meta-analysis of four independent East Asian samples including 5748 cases and 65,361 controls (p = 2.27 × 10-8, odds ratio = 1.136). Expression quantitative trait loci (eQTL) analyses and summary data-based Mendelian randomization (SMR) analyses in multiple human brain samples suggest that lower TRANK1 mRNA expression is a principal BD risk factor explaining its genetic risk signals at 3p22. We also identified another SNP rs4789 in the 3' untranslated region (3'UTR) of TRANK1 showing stronger eQTL associations as well as genome-wide significant association with BD. Despite the relatively unclear neuronal function of TRANK1, our mRNA expression analyses in the human brains and in rat primary cortical neurons reveal that genes highly correlated with TRANK1 are significantly enriched in the biological processes related to dendritic spine, synaptic plasticity, axon guidance and circadian entrainment, and are also more likely to exhibit strong associations in psychiatric GWAS (e.g., the CACNA1C gene). Overall, our results support that TRANK1 is a potential BD risk gene. Further studies elucidating its roles in this illness are needed.

Network-based Transcriptome-wide Expression Study for Postmenopausal Osteoporosis.

Menopause is a crucial physiological transition during a woman's life, and it occurs with growing risks of health issues like osteoporosis. To identify postmenopausal osteoporosis-related genes, we performed transcriptome-wide expression analyses for human peripheral blood monocytes (PBMs) using Affymetrix 1.0 ST arrays in 40 Caucasian postmenopausal women with discordant bone mineral density (BMD) levels. We performed multiscale embedded gene coexpression network analysis (MEGENA) to study functionally orchestrating clusters of differentially expressed genes in the form of functional networks. Gene sets net correlations analysis (GSNCA) was applied to assess how the coexpression structure of a predefined gene set differs in high and low BMD groups. Bayesian network (BN) analysis was used to identify important regulation patterns between potential risk genes for osteoporosis. A small interfering ribonucleic acid (siRNA)-based gene silencing in vitro experiment was performed to validate the findings from BN analysis. MEGENA showed that the "T cell receptor signaling pathway" and the "osteoclast differentiation pathway" were significantly enriched in the identified compact network, which is significantly correlated with BMD variation. GSNCA revealed that the coexpression structure of the "Signaling by TGF-beta receptor complex pathway" is significantly different between the 2 BMD discordant groups; the hub genes in the postmenopausal low and high BMD group are FURIN and SMAD3 respectively. With siRNA in vitro experiments, we confirmed the regulation relationship of TGFBR2-SMAD7 and TGFBR1-SMURF2. The present study suggests that biological signals involved in monocyte recruitment, monocyte/macrophage lineage development, osteoclast formation, and osteoclast differentiation might function together in PBMs that contribute to the pathogenesis of postmenopausal osteoporosis.