Autoimmunity-related LINC01934 and AP002954.4 lncRNA polymorphisms may be effective in pediatric celiac disease: a case-control study.

OBJECTIVE: Various studies have reported that certain long non-coding RNA levels are unusually low in the intestines of celiac disease patients, suggesting that this may be associated with the inflammation observed in celiac disease. Despite these studies, the research aimed at uncovering the potential role of long non-coding RNAs in the pathogenesis of autoimmune diseases like celiac disease remains insufficient. Therefore, in this study, we plan to assess long non-coding RNA polymorphisms associated with autoimmunity in children diagnosed with celiac disease according to the European Society for Paediatric Gastroenterology Hepatology and Nutrition criteria. METHODS: DNA was isolated from paraffin tissue samples of 88 pediatric celiac disease patients and 74 healthy pediatric individuals. Single-nucleotide polymorphism genotyping of five long non-coding RNA polymorphisms associated with autoimmunity (LINC01934-rs1018326, IL18RAP-rs917997, AP002954.4-rs10892258, UQCRC2P1-rs6441961, and HCG14 rs3135316) was conducted using the TaqMan single-nucleotide polymorphism genotyping assays with the LightCycler 480. RESULTS: In our study, the genotypic and allelic frequency distribution of LINC01934-rs1018326 and AP002954.4-rs10892258 polymorphisms was found to be statistically significant in the comparison between the two groups (p<0.05). According to the multiple genetic model analyses, the LINC01934-rs1018326 polymorphism was observed to confer a 1.14-fold risk in the recessive model and a 1.2-fold risk in the additive model for pediatric celiac disease. Similarly, the AP002954.4-rs10892258 polymorphism was found to pose a 1.40-fold risk in the dominant model and a 1.7-fold risk in the additive model. CONCLUSION: Our study results draw attention to the LINC01934-rs1018326 and AP002954.4-rs10892258 polymorphisms in celiac disease and suggest that these polymorphisms may be associated with inflammation in autoimmune diseases like celiac disease.

Association between Interleukin-6 Gene Polymorphism (rs1800795 and rs1800796) and Type 2 Diabetes Mellitus in a Ghanaian Population: A Case-Control Study in the Ho Municipality.

Background: There is no conclusive evidence on the association between interleukin- (IL-) 6 gene polymorphism and type 2 diabetes mellitus (type 2 DM). Thus, this study is aimed at evaluating the role of rs1800795 and rs1800796 polymorphisms in the pathogenesis of type 2 DM among Ghanaians in the Ho Municipality. Materials and Methods: We recruited into this hospital-based case-control study 174 patients with type 2 DM (75 DM alone and 99 with DM+HTN) and 149 healthy individuals between 2018 and 2020. Demographic, lifestyle, clinical, anthropometric, and haemodynamic variables were obtained. Fasting blood samples were collected for haematological, biochemical, and molecular analyses. Genomic DNA was extracted, amplified using Tetra-primer amplification refractory mutation system-polymerase chain reaction (T-ARMS-PCR) technique, and genotyped for IL-6 gene polymorphism. Logistic regression analyses were performed to assess the association between IL-6 gene polymorphism and type 2 DM. Results: The minor allele frequency (MAF) of the rs1800795 and rs1800796 polymorphisms was higher in DM alone (57.5%, 62.0%) and DM with HTN groups (58.3%, 65.3%) than controls (33.1%, 20.0%). Carriers of the rs1800795GC genotype (aOR = 2.35, 95% CI: 1.13-4.90, p = 0.022) and mutant C allele (aOR = 2.41, 95% CI: 1.16-5.00, p = 0.019) as well as those who carried the rs1800796GC (aOR = 8.67, 95% CI: 4.00-18.90, p < 0.001) and mutant C allele (aOR = 8.84, 95% CI: 4.06-19.26, p = 0.001) had increased odds of type 2 DM. For both polymorphisms, carriers of the GC genotype had comparable levels of insulin, HOMA-IR, and fasting blood glucose (FBG) with those who carried the GG genotype. IL-6 levels were higher among carriers of the rs1800796GC variant compared to carriers of the rs1800796GG variant (p = 0.023). The rs1800796 polymorphism, dietary sugar intake, and exercise status, respectively, explained approximately 3% (p = 0.046), 3.2% (p = 0.038, coefficient = 1.456), and 6.2% (p = 0.004, coefficient = -2.754) of the variability in IL-6 levels, suggesting weak effect sizes. Conclusion: The GC genotype and mutant C allele are risk genetic variants associated with type 2 DM in the Ghanaian population. The rs1800796 GC variant, dietary sugar intake, and exercise status appear to contribute significantly to the variations in circulating IL-6 levels but with weak effect sizes.

Atypical functional connectivity between the amygdala and visual, salience regions in infants with genetic liability for autism.

The amygdala undergoes a period of overgrowth in the first year of life, resulting in enlarged volume by 12 months in infants later diagnosed with ASD. The overgrowth of the amygdala may have functional consequences during infancy. We investigated whether amygdala connectivity differs in 12-month-olds at high likelihood (HL) for ASD (defined by having an older sibling with autism), compared to those at low likelihood (LL). We examined seed-based connectivity of left and right amygdalae, hypothesizing that the HL and LL groups would differ in amygdala connectivity, especially with the visual cortex, based on our prior reports demonstrating that components of visual circuitry develop atypically and are linked to genetic liability for autism. We found that HL infants exhibited weaker connectivity between the right amygdala and the left visual cortex, as well as between the left amygdala and the right anterior cingulate, with evidence that these patterns occur in distinct subgroups of the HL sample. Amygdala connectivity strength with the visual cortex was related to motor and communication abilities among HL infants. Findings indicate that aberrant functional connectivity between the amygdala and visual regions is apparent in infants with genetic liability for ASD and may have implications for early differences in adaptive behaviors.

ANK3 rs10994336 and ZNF804A rs7597593 polymorphisms: genetic interaction for emotional and behavioral symptoms of alcohol withdrawal syndrome.

OBJECTIVE: Alcohol withdrawal syndrome (AWS) is a complex condition associated with alcohol use disorder (AUD), characterized by significant variations in symptom severity among patients. The psychological and emotional symptoms accompanying AWS significantly contribute to withdrawal distress and relapse risk. Despite the importance of neural adaptation processes in AWS, limited genetic investigations have been conducted. This study primarily focuses on exploring the single and interaction effects of single-nucleotide polymorphisms in the ANK3 and ZNF804A genes on anxiety and aggression severity manifested in AWS. By examining genetic associations with withdrawal-related psychopathology, we ultimately aim to advance understanding the genetic underpinnings that modulate AWS severity. METHODS: The study involved 449 male patients diagnosed with alcohol use disorder. The Self-Rating Anxiety Scale (SAS) and Buss-Perry Aggression Questionnaire (BPAQ) were used to assess emotional and behavioral symptoms related to AWS. Genomic DNA was extracted from peripheral blood, and genotyping was performed using PCR. RESULTS: Single-gene analysis revealed that naturally occurring allelic variants in ANK3 rs10994336 (CC homozygous vs. T allele carriers) were associated with mood and behavioral symptoms related to AWS. Furthermore, the interaction between ANK3 and ZNF804A was significantly associated with the severity of psychiatric symptoms related to AWS, as indicated by MANOVA. Two-way ANOVA further demonstrated a significant interaction effect between ANK3 rs10994336 and ZNF804A rs7597593 on anxiety, physical aggression, verbal aggression, anger, and hostility. Hierarchical regression analyses confirmed these findings. Additionally, simple effects analysis and multiple comparisons revealed that carriers of the ANK3 rs10994336 T allele experienced more severe AWS, while the ZNF804A rs7597593 T allele appeared to provide protection against the risk associated with the ANK3 rs10994336 mutation. CONCLUSION: This study highlights the gene-gene interaction between ANK3 and ZNF804A, which plays a crucial role in modulating emotional and behavioral symptoms related to AWS. The ANK3 rs10994336 T allele is identified as a risk allele, while the ZNF804A rs7597593 T allele offers protection against the risk associated with the ANK3 rs10994336 mutation. These findings provide initial support for gene-gene interactions as an explanation for psychiatric risk, offering valuable insights into the pathophysiological mechanisms involved in AWS.

Role of PON1 L55M Gene Polymorphism in Parkinson's Disease among North Indian Population.

BACKGROUND AND OBJECTIVES: The role of various genetic markers including alpha synuclein, Parkin, etc., is known in the pathogenesis of Parkinson's disease (PD). Novel genetic markers including paraoxonase 1 (PON1) have also been linked to PD pathogenesis in recent studies. The PON1 L55M allele carriers may have defective clearance of environmental toxins and may result in increased susceptibility to PD. Hence, we studied the role of PON1 L55M polymorphism in PD among a North Indian population. MATERIALS AND METHOD: Seventy-four PD patients and 74 age- and sex-matched controls were recruited in this hospital-based case-control study. Baseline characteristics were recorded using structured questionnaire. DNA was extracted from 3-4 ml of venous blood, followed by PCR and restriction digestion. PON1 L55M genotypes were visualized as bands: LL (177 bp), LM (177, 140 bp) and MM (140,44 bp) on 3% agarose gel. Mann-Whitney U test and Chi-squared test were used for comparing two groups of skewed and categorical variables, respectively. Measures of strength of association were calculated by binary regression analysis. P value < 0.05 was considered as significant. RESULTS: Parkinson's disease patients had significantly higher exposure to pesticides (12.2%; P (organophosphate exposure) < 0.001) and well water drinking (28.4%; P = 0.006) compared to controls. Frequency distribution of LL, LM, MM genotypes was 67.5% (50/74), 28.4% (21/74), and 4.1% (3/74), respectively, for cases and 72.6% (54/74), 26% (19/74) and 1.4% (1/74), respectively, for controls. PON1 L55M genotype distribution between Parkinson's disease cases and controls was not significant (P = 0.53). PON1 L55M polymorphism was not associated with PD after adjusting for confounders by binary regression analysis. CONCLUSION: There was no significant association between PON1 L55M polymorphism and PD. Larger population-based studies would be required from India before drawing any definite conclusions.

Association Analysis of Essential Tremor-Associated Genetic Variants in Sporadic Late-Onset Parkinson's Disease.

Background: Parkinson's disease (PD) and Essential tremor (ET) are the two most common tremor diseases with recognized genetic pathogenesis. The overlapping clinical features suggest they may share genetic predispositions. Our previous study systematically investigated the association between rare coding variants in ET-associated genes and early-onset PD (EOPD), and found the suggestive association between teneurin transmembrane protein 4 (TENM4) and EOPD. In the current research, we explored the potential genetic interplay between ET-associated genetic loci/genes and sporadic late-onset PD (LOPD). Methods: We performed whole-genome sequencing in the 1962 sporadic LOPD cases and 1279 controls from mainland China. We first used logistic regression analysis to test the top 16 SNPs identified by the ET genome-wide association study for the association between ET and LOPD. Then we applied the optimized sequence kernel association testing to explore the rare variant burden of 33 ET-associated genes in this cohort. Results: We did not observe a significant association between the included SNPs with LOPD. We also did not discover a significant burden of rare deleterious variants of ET-associated genes in association with LOPD risk. Conclusion: Our results do not support the role of ET-associated genetic loci and variants in LOPD. Highlights: 1962 cases and 1279 controls were recruited to study the potential genetic interplay between ET-associated genetic loci/variants and sporadic LOPD.No significant association between the ET-associated SNPs and LOPD were observed.No significant burden of rare deleterious variants of ET-associated gene in LOPD risk were found.

Neighborhood based computational approaches for the prediction of lncRNA-disease associations.

MOTIVATION: Long non-coding RNAs (lncRNAs) are a class of molecules involved in important biological processes. Extensive efforts have been provided to get deeper understanding of disease mechanisms at the lncRNA level, guiding towards the detection of biomarkers for disease diagnosis, treatment, prognosis and prevention. Unfortunately, due to costs and time complexity, the number of possible disease-related lncRNAs verified by traditional biological experiments is very limited. Computational approaches for the prediction of disease-lncRNA associations allow to identify the most promising candidates to be verified in laboratory, reducing costs and time consuming. RESULTS: We propose novel approaches for the prediction of lncRNA-disease associations, all sharing the idea of exploring associations among lncRNAs, other intermediate molecules (e.g., miRNAs) and diseases, suitably represented by tripartite graphs. Indeed, while only a few lncRNA-disease associations are still known, plenty of interactions between lncRNAs and other molecules, as well as associations of the latters with diseases, are available. A first approach presented here, NGH, relies on neighborhood analysis performed on a tripartite graph, built upon lncRNAs, miRNAs and diseases. A second approach (CF) relies on collaborative filtering; a third approach (NGH-CF) is obtained boosting NGH by collaborative filtering. The proposed approaches have been validated on both synthetic and real data, and compared against other methods from the literature. It results that neighborhood analysis allows to outperform competitors, and when it is combined with collaborative filtering the prediction accuracy further improves, scoring a value of AUC equal to 0966. AVAILABILITY: Source code and sample datasets are available at: https://github.com/marybonomo/LDAsPredictionApproaches.git.

Assessing the causal relationship between immune cell traits and depression by Mendelian randomization analysis.

BACKGROUND: Observational studies suggested that immune system disorder is associated with depression. However, the causal association has not been fully elucidated. Thus, we aim to assess the causality of the associations of immune cell profiles with risk of depression through Mendelian randomization analysis. METHODS: We extracted genetic variances of immune cell traits from a large publicly available genome-wide association study (GWAS) involving 3757 participants and depression from a GWAS containing 246,363 cases and 561,190 controls of European ancestry. Inverse variance weighting (IVW) was performed as the MR primary analysis. Simultaneously apply MR-Egger and weighted median as supplementary enhancements to the final result. We further performed heterogeneity and horizontal pleiotropy test to validate the main MR results. RESULTS: Five immunophenotypes were identified to be significantly associated with depression risk: CD27 on IgD-CD38dimB cell (OR = 1.019, 95 % CI = 1.010-1.028, P = 1.24 × 10-5), CD45RA-CD4+T cell Absolute Count (OR = 0.974, 95 % CI = 0.962-0.986, P = 3.88 × 10-5), CD40 on CD14-CD16+monocyte (OR = 0.987, 95 % CI = 0.981-0.993, P = 2.1 × 10-4), CD27 on switched memory B cell (OR = 1.015, 95 % CI = 1.006-1.023, P = 2.6 × 10-4), CD27 on IgD-CD38-B cell (OR = 1.017, 95 % CI = 1.008-1.027, P = 3.1 × 10-4). CONCLUSION: Our findings shed light on the intricate interaction pattern between the immune system and depression, offering a novel direction for researchers to investigate the underlying biological mechanisms of depression.

Frailty and psychiatric disorders: A bidirectional Mendelian randomization study.

BACKGROUND: The association between frailty and psychiatric disorders has been reported in observational studies. However, it is unclear whether frailty facilitates the appearance of psychiatric disorders or vice versa. Therefore, we conducted a bidirectional Mendelian randomization (MR) study to evaluate the causality. METHODS: Independent genetic variants associated with frailty index (FI) and psychiatric disorders were obtained from large genome-wide association studies (GWAS). The inverse variance weighted method was utilized as the primary method to estimate causal effects, followed by various sensitivity analyses. Multivariable analyses were performed to further adjust for potential confounders. RESULTS: The present MR study revealed that genetically predicted FI was significantly and positively associated with the risk of major depressive disorder (MDD) (odds ratio [OR] 1.79, 95 % confidence interval [CI] 1.48-2.15, P = 1.06 × 10-9), anxiety disorder (OR 1.61, 95 % CI 1.19-2.18, P = 0.002) and neuroticism (OR 1.38, 95 % CI 1.18-1.61, P = 3.73 × 10-5). In the reverse MR test, genetic liability to MDD (beta 0.232, 95 % CI 0.189-0.274, P = 1.00 × 10-26) and neuroticism (beta 0.128, 95 % CI 0.081-0.175, P = 8.61 × 10-8) were significantly associated with higher FI. Multivariable analyses results supported the causal association between FI and MDD and neuroticism. LIMITATIONS: Restriction to European populations, and sample selection bias. CONCLUSIONS: Our study suggested a bidirectional causal association between frailty and MDD neuroticism, and a positive correlation of genetically predicted frailty on the risk of anxiety disorder. Developing a deeper understanding of these associations is essential to effectively manage frailty and optimize mental health in older adults.

Schizophrenia polygenic risk scores, clinical variables and genetic pathways as predictors of phenotypic traits of bipolar I disorder.

AIM: We investigated the predictive value of polygenic risk scores (PRS) derived from the schizophrenia GWAS (Trubetskoy et al., 2022) (SCZ3) for phenotypic traits of bipolar disorder type-I (BP-I) in 1878 BP-I cases and 2751 controls from Romania and UK. METHODS: We used PRSice-v2.3.3 and PRS-CS for computing SCZ3-PRS for testing the predictive power of SCZ3-PRS alone and in combination with clinical variables for several BP-I subphenotypes and for pathway analysis. Non-linear predictive models were also used. RESULTS: SCZ3-PRS significantly predicted psychosis, incongruent and congruent psychosis, general age-of-onset (AO) of BP-I, AO-depression, AO-Mania, rapid cycling in univariate regressions. A negative correlation between the number of depressive episodes and psychosis, mainly incongruent and an inverse relationship between increased SCZ3-SNP loading and BP-I-rapid cycling were observed. In random forest models comparing the predictive power of SCZ3-PRS alone and in combination with nine clinical variables, the best predictions were provided by combinations of SCZ3-PRS-CS and clinical variables closely followed by models containing only clinical variables. SCZ3-PRS performed worst. Twenty-two significant pathways underlying psychosis were identified. LIMITATIONS: The combined RO-UK sample had a certain degree of heterogeneity of the BP-I severity: only the RO sample and partially the UK sample included hospitalized BP-I cases. The hospitalization is an indicator of illness severity. Not all UK subjects had complete subphenotype information. CONCLUSION: Our study shows that the SCZ3-PRS have a modest clinical value for predicting phenotypic traits of BP-I. For clinical use their best performance is in combination with clinical variables.

On the effect heterogeneity of established disease susceptibility loci for Alzheimer's disease across different genetic ancestries.

INTRODUCTION: Genome-wide association studies have identified numerous disease susceptibility loci (DSLs) for Alzheimer's disease (AD). However, only a limited number of studies have investigated the dependence of the genetic effect size of established DSLs on genetic ancestry. METHODS: We utilized the whole genome sequencing data from the Alzheimer's Disease Sequencing Project (ADSP) including 35,569 participants. A total of 25,459 subjects in four distinct populations (African ancestry, non-Hispanic White, admixed Hispanic, and Asian) were analyzed. RESULTS: We found that nine DSLs showed significant heterogeneity across populations. Single nucleotide polymorphism (SNP) rs2075650 in translocase of outer mitochondrial membrane 40 (TOMM40) showed the largest heterogeneity (Cochran's Q = 0.00, I2 = 90.08), followed by other SNPs in apolipoprotein C1 (APOC1) and apolipoprotein E (APOE). Two additional loci, signal-induced proliferation-associated 1 like 2 (SIPA1L2) and solute carrier 24 member 4 (SLC24A4), showed significant heterogeneity across populations. DISCUSSION: We observed substantial heterogeneity for the APOE-harboring 19q13.32 region with TOMM40/APOE/APOC1 genes. The largest risk effect was seen among African Americans, while Asians showed a surprisingly small risk effect.

EGR3 Polymorphism Is a Potential Susceptibility Factor of Schizophrenia Risk in a Chinese Population.

Objective: The purpose of this study was to evaluate the association between the single nucleotide polymorphisms (SNPs) (EGR3 rs1996147; EGR4 rs3813226, rs6747506; ERBB3 rs2292238; and ERBB4 rs707284, rs7560730) and the risk of schizophrenia (SZ) in a Chinese population. Materials and Methods: We conducted a case-control study, including 248 patients with SZ and 236 healthy controls matched for age and sex. The Mass-array platform was used to detect all the genotypes of the SNPs. Results: The results revealed that the EGR3 rs1996147 AA genotype was associated with borderline decreased SZ risk (AA vs. GG: adjusted OR = 0.43, 95% CI: 0.18-1.02, p = 0.06). However, no significant correlation was found between the other SNPs and overall SZ risk. Subgroup analysis also failed to show any significant association between all SNPs and the risk of SZ. Conclusion: In summary, this study revealed that the EGR3 rs1996147 AA genotype was associated with a borderline risk for SZ.

HGCLAMIR: Hypergraph contrastive learning with attention mechanism and integrated multi-view representation for predicting miRNA-disease associations.

Existing studies have shown that the abnormal expression of microRNAs (miRNAs) usually leads to the occurrence and development of human diseases. Identifying disease-related miRNAs contributes to studying the pathogenesis of diseases at the molecular level. As traditional biological experiments are time-consuming and expensive, computational methods have been used as an effective complement to infer the potential associations between miRNAs and diseases. However, most of the existing computational methods still face three main challenges: (i) learning of high-order relations; (ii) insufficient representation learning ability; (iii) importance learning and integration of multi-view embedding representation. To this end, we developed a HyperGraph Contrastive Learning with view-aware Attention Mechanism and Integrated multi-view Representation (HGCLAMIR) model to discover potential miRNA-disease associations. First, hypergraph convolutional network (HGCN) was utilized to capture high-order complex relations from hypergraphs related to miRNAs and diseases. Then, we combined HGCN with contrastive learning to improve and enhance the embedded representation learning ability of HGCN. Moreover, we introduced view-aware attention mechanism to adaptively weight the embedded representations of different views, thereby obtaining the importance of multi-view latent representations. Next, we innovatively proposed integrated representation learning to integrate the embedded representation information of multiple views for obtaining more reasonable embedding information. Finally, the integrated representation information was fed into a neural network-based matrix completion method to perform miRNA-disease association prediction. Experimental results on the cross-validation set and independent test set indicated that HGCLAMIR can achieve better prediction performance than other baseline models. Furthermore, the results of case studies and enrichment analysis further demonstrated the accuracy of HGCLAMIR and unconfirmed potential associations had biological significance.

Investigating the shared genetic architecture between attention-deficit/hyperactivity disorder and risk taking behavior: A large-scale genomewide cross-trait analysis.

BACKGROUND: This study aims to explore the genetic architecture shared between Attention-Deficit/Hyperactivity Disorder (ADHD) and risk behavior. METHODS: Based on the latest large-scale Genome-wide association studies (GWAS), we firstly employed Linkage disequilibrium score regression (LDSC) and Local Analysis of Variant Association (LAVA) to investigate the genetic correlation between risk behavior and ADHD. Then, we conducted cross-trait analysis to identified the Pleiotropic loci. Finally, bidirectional Mendelian randomization analysis (MR) was applied to examine the causal relationship. RESULTS: We found a significant positive genetic correlation between ADHD and risk-taking behavior (rg = 0.351, p = 6.50E-37). The cross-trait meta-analysis identified 27 significant SNPs shared between ADHD and risk behavior. The most significant locus, located near the CADM2 gene on chromosome 3, had been identified associated with this two trait (pADHD = 3.07E-05 and prisk-taking behavior = 2.47E-30). The same situation can also be observed near the FOXP2 gene on chromosome 7 (rs8180817, pmeta = 5.72E-21). We found CCDC171 gene and other genes played a significant role in ADHD and risk behavior in mRNA level. Bidirectional MR analysis found a causal relationship between them. LIMITATION: The majority of our data sources were of European origin, which may limit the generalizability of our findings to other ethnic populations. CONCLUSION: This article reveals in depth the shared genetic structure between ADHD and risk-taking behavior, finding a significant positive genetic correlation between ADHD and risk-taking behavior. Providing insights for the future treatment and management of these two traits.

The relationship between preeclampsia risk and SENCR rs555172 gene polymorphism and expression.

Preeclampsia, the more severe manifestation of gestational hypertensive disorders, is a major cause of maternal and perinatal morbidity and mortality worldwide. Genetic polymorphisms in long non-coding RNAs (lncRNAs) are considered as potential genetic preeclampsia. This study aimed to explore the association between SENCR rs555172 SNP and PE risk in healthy pregnant women compared to women with preeclampsia. A total of 140 healthy pregnant women and 130 preeclampsia cases were included in the study. The rs555172 genotype was determined using polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP), and the expression of the SENCR gene was analyzed in 40 placenta tissue samples from both groups. Various statistical approaches were employed to assess the genotypic and allelic frequencies. The results showed no significant difference in the frequency of the rs555172 polymorphism between healthy pregnant women and those with preeclampsia in terms of the dominant (p=0.82), recessive (p=0.39), and over-dominant (p=0.42) models. Additionally, the analysis of SENCR relative expression revealed no significant difference between the two groups (p=0.48). In conclusion, the LncRNA SENCR rs555172(G/A) seems not associated with an increased risk of Preeclampsia in pregnant women.

Genetic correlation and causal associations between psychiatric disorders and lung cancer risk.

BACKGROUND: Patients with certain psychiatric disorders have increased lung cancer incidence. However, establishing a causal relationship through traditional epidemiological methods poses challenges. METHODS: Available summary statistics of genome-wide association studies of cigarette smoking, lung cancer, and eight psychiatric disorders, including attention deficit/hyperactivity disorder (ADHD), autism, depression, major depressive disorder, bipolar disorder, insomnia, neuroticism, and schizophrenia (range N: 46,350-1,331,010) were leveraged to estimate genetic correlations using Linkage Disequilibrium Score Regression and assess causal effect of each psychiatric disorder on lung cancer using two-sample Mendelian randomization (MR) models, comprising inverse-variance weighted (IVW), weighted median, MR-Egger, pleiotropy residual sum and outlier testing (MR-PRESSO), and a constrained maximum likelihood approach (cML-MR). RESULTS: Significant positive correlations were observed between each psychiatric disorder and both smoking and lung cancer (all FDR < 0.05), except for the correlation between autism and lung cancer. Both univariable and the cML-MA MR analyses demonstrated that liability to schizophrenia, depression, ADHD, or insomnia was associated with an increased risk of overall lung cancer. Genetic liability to insomnia was linked specifically to squamous cell carcinoma (SCC), while genetic liability to ADHD was associated with an elevated risk of both SCC and small cell lung cancer (all P < 0.05). The later was further supported by multivariable MR analyses, which accounted for smoking. LIMITATIONS: Participants were constrained to European ancestry populations. Causal estimates from binary psychiatric disorders may be biased. CONCLUSION: Our findings suggest appropriate management of several psychiatric disorders, particularly ADHD, may potentially reduce the risk of developing lung cancer.

Obstructive sleep apnea and mental disorders: a bidirectional mendelian randomization study.

BACKGROUND: Previous studies have reported associations between obstructive sleep apnea (OSA) and several mental disorders. However, further research is required to determine whether these associations are causal. Therefore, we evaluated the bidirectional causality between the genetic liability for OSA and nine mental disorders by using Mendelian randomization (MR). METHOD: We performed two-sample bidirectional MR of genetic variants for OSA and nine mental disorders. Summary statistics on OSA and the nine mental disorders were extracted from the FinnGen study and the Psychiatric Genomics Consortium. The primary analytical approach for estimating causal effects was the inverse-variance weighted (IVW), with the weighted median and MR Egger as complementary methods. The MR Egger intercept test, Cochran's Q test, Rucker's Q test, and the MR pleiotropy residual sum and outlier (MR-PRESSO) test were used for sensitivity analyses. RESULT: MR analyses showed that genetic liability for major depressive disorder (MDD) was associated with an increased risk of OSA (odds ratio [OR] per unit increase in the risk of MDD, 1.29; 95% CI, 1.11-1.49; P < 0.001). In addition, genetic liability for OSA may be associated with an increased risk of attention-deficit/hyperactivity disorder (ADHD) (OR = 1.26; 95% CI, 1.02-1.56; p = 0.032). There was no evidence that OSA is associated with other mental disorders. CONCLUSION: Our study indicated that genetic liability for MDD is associated with an increased risk of OSA without a bidirectional relationship. Additionally, there was suggestive evidence that genetic liability for OSA may have a causal effect on ADHD. These findings have implications for prevention and intervention strategies targeting OSA and ADHD. Further research is needed to investigate the biological mechanisms underlying our findings and the relationship between OSA and other mental disorders.

The causal relationship between atopic dermatitis and brain cancer: A bidirectional Mendelian randomization study.

BACKGROUND: Atopic dermatitis ranks among the prevalent skin disorders. Research has indicated a potential association with brain cancer. Yet, establishing a direct causal relationship between atopic dermatitis and brain cancer continues to be challenging. MATERIALS AND METHODS: We extracted single nucleotide polymorphisms (SNPs) significantly associated with atopic dermatitis (sample size = 382 254) at a genome-wide level from a large Finnish Genome-Wide Association Study (GWAS) dataset (n cases = 15 208, n controls = 367 046). Summary data for 372 622 cases of brain cancer (n cases = 606, n controls = 372 016) were obtained via the IEU Open GWAS database. We employed the Inverse Variance Weighted (IVW) method as our primary analytical approach for Mendelian Randomization (MR) analysis. Additionally, heterogeneity was measured using Cochran's Q value, and horizontal pleiotropy was evaluated using MR-Egger 、Mendelian Randomization Pleiotropy RESidual Sum and Outlier and leave-one-out analyses. RESULTS: The risk of brain cancer increases with the presence of atopic dermatitis, as evidenced by the odds ratios (ORs) and 95% confidence intervals (CIs),(OR = 1.0005; 95% CI = 1.0001, 1.0009; p = 0.0096). However, when conducting the analysis in reverse, no significant link was observed. CONCLUSION: The findings from our study indicate a causative link between atopic dermatitis and brain cancer, highlighting the importance of conducting broader clinical investigations into their potential association going forward.

Benchmarking multi-ancestry prostate cancer polygenic risk scores in a real-world cohort.

Prostate cancer is a heritable disease with ancestry-biased incidence and mortality. Polygenic risk scores (PRSs) offer promising advancements in predicting disease risk, including prostate cancer. While their accuracy continues to improve, research aimed at enhancing their effectiveness within African and Asian populations remains key for equitable use. Recent algorithmic developments for PRS derivation have resulted in improved pan-ancestral risk prediction for several diseases. In this study, we benchmark the predictive power of six widely used PRS derivation algorithms, including four of which adjust for ancestry, against prostate cancer cases and controls from the UK Biobank and All of Us cohorts. We find modest improvement in discriminatory ability when compared with a simple method that prioritizes variants, clumping, and published polygenic risk scores. Our findings underscore the importance of improving upon risk prediction algorithms and the sampling of diverse cohorts.