ARTEMIS: An independently validated prognostic prediction model of breast cancer incorporating epigenetic biomarkers with main effects and gene-gene interactions.

INTRODUCTION: Breast cancer, a heterogeneous disease, is influenced by multiple genetic and epigenetic factors. The majority of prognostic models for breast cancer focus merely on the main effects of predictors, disregarding the crucial impacts of gene-gene interactions on prognosis. OBJECTIVES: Using DNA methylation data derived from nine independent breast cancer cohorts, we developed an independently validated prognostic prediction model of breast cancer incorporating epigenetic biomarkers with main effects and gene-gene interactions (ARTEMIS) with an innovative 3-D modeling strategy. ARTEMIS was evaluated for discrimination ability using area under the receiver operating characteristics curve (AUC), and calibration using expected and observed (E/O) ratio. Additionally, we conducted decision curve analysis to evaluate its clinical efficacy by net benefit (NB) and net reduction (NR). Furthermore, we conducted a systematic review to compare its performance with existing models. RESULTS: ARTEMIS exhibited excellent risk stratification ability in identifying patients at high risk of mortality. Compared to those below the 25th percentile of ARTEMIS scores, patients with above the 90th percentile had significantly lower overall survival time (HR = 15.43, 95% CI: 9.57-24.88, P = 3.06 × 10-29). ARTEMIS demonstrated satisfactory discrimination ability across four independent populations, with pooled AUC3-year = 0.844 (95% CI: 0.805-0.883), AUC5-year = 0.816 (95% CI: 0.775-0.857), and C-index = 0.803 (95% CI: 0.776-0.830). Meanwhile, ARTEMIS had well calibration performance with pooled E/O ratio 1.060 (95% CI: 1.038-1.083) and 1.090 (95% CI: 1.057-1.122) for 3- and 5-year survival prediction, respectively. Additionally, ARTEMIS is a clinical instrument with acceptable cost-effectiveness for detecting breast cancer patients at high risk of mortality (Pt = 0.4: NB3-year = 19‰, NB5-year = 62‰; NR3-year = 69.21%, NR5-year = 56.01%). ARTEMIS has superior performance compared to existing models in terms of accuracy, extrapolation, and sample size, as indicated by the systematic review. ARTEMIS is implemented as an interactive online tool available at http://bigdata.njmu.edu.cn/ARTEMIS/. CONCLUSION: ARTEMIS is an efficient and practical tool for breast cancer prognostic prediction.

Biomedical literature mining: graph kernel-based learning for gene-gene interaction extraction.

The supervised machine learning method is often used for biomedical relationship extraction. The disadvantage is that it requires much time and money to manually establish an annotated dataset. Based on distant supervision, the knowledge base is combined with the corpus, thus, the training corpus can be automatically annotated. As many biomedical databases provide knowledge bases for study with a limited number of annotated corpora, this method is practical in biomedicine. The clinical significance of each patient's genetic makeup can be understood based on the healthcare provider's genetic database. Unfortunately, the lack of previous biomedical relationship extraction studies focuses on gene-gene interaction. The main purpose of this study is to develop extraction methods for gene-gene interactions that can help explain the heritability of human complex diseases. This study referred to the information on gene-gene interactions in the KEGG PATHWAY database, the abstracts in PubMed were adopted to generate the training sample set, and the graph kernel method was adopted to extract gene-gene interactions. The best assessment result was an F1-score of 0.79. Our developed distant supervision method automatically finds sentences through the corpus without manual labeling for extracting gene-gene interactions, which can effectively reduce the time cost for manual annotation data; moreover, the relationship extraction method based on a graph kernel can be successfully applied to extract gene-gene interactions. In this way, the results of this study are expected to help achieve precision medicine.

Unraveling schizophrenia's genetic complexity through advanced causal inference and chromatin 3D conformation.

Schizophrenia is a polygenic complex disease with a heritability as high as 80 %, yet the mechanism of polygenic interaction in its pathogenesis remains unclear. Studying the interaction and regulation of schizophrenia susceptibility genes is crucial for unraveling the pathogenesis of schizophrenia and developing antipsychotic drugs. Therefore, we developed a bioinformatics method named GRACI (Gene Regulation Analysis based on Causal Inference) based on the principles of information theory, a causal inference model, and high order chromatin 3D conformation. GRACI captures the interaction and regulatory relationships between schizophrenia susceptibility genes by analyzing genotyping data. Two datasets, comprising 1459 and 2065 samples respectively, were analyzed, and the gene networks from both datasets were constructed. GRACI showcased superior accuracy when compared to widely adopted methods for detecting gene-gene interactions and intergenic regulation. This alignment was further substantiated by its correlation with chromatin high-order conformation patterns. Using GRACI, we identified three potential genes-KCNN3, KCNH1, and KCND3-that are directly associated with schizophrenia pathogenesis. Furthermore, the results of GRACI on the standalone dataset illustrated the method's applicability to other complex diseases. GRACI download: https://github.com/liuliangjie19/GRACI.

Skin pigmentation related variants in Mexican population and interaction effects on serum 25(OH)D concentration and vitamin D deficiency.

Skin pigmentation is negatively associated with circulating vitamin D (VD) concentration. Therefore, genetic factors involved in skin pigmentation could influence the risk of vitamin D deficiency (VDD). We evaluated the impact genetic variants related to skin pigmentation on VD in Mexican population. This cross-sectional analysis included 848 individuals from the Health Worker Cohort Study (ratio males to females ~ 1:3). Eight genetic variants: rs16891982 (SLC45A2), rs12203592 (IRF4), rs1042602 and rs1126809 (TYR), rs1800404 (OCA2), rs12913832 (HERC2), rs1426654 (SLC24A5), and rs2240751 (MFSD12); involved in skin pigmentation were genotyped. Skin pigmentation was assessed by self-report. Linear and logistic regression were used to assess the association between the variants of interest and VD and VDD, as appropriate. In our study, eight genetic variants were associated with skin pigmentation. A genetic risk score built with the variants rs1426654 and rs224075 was associated with lower VD levels (ß = - 1.38, 95% CI - 2.59, - 0.17, p = 0.025). Nevertheless, when examining gene-gene interactions, we observed that rs2240751 × rs12203592 were associated with VD levels (P interaction = 0.021). Whereas rs2240751 × rs12913832 (P interaction = 0.0001) were associated with VDD. Our results suggest that skin pigmentation-related gene variants are associated with lower VD levels in Mexican population. These results underscore the importance of considering genetic interactions when assessing the impact of genetic polymorphisms on VD levels.

Gene-gene interaction analysis for age at onset of bipolar disorder in a Korean population.

BACKGROUND: Multiple genes might interact to determine the age at onset of bipolar disorder. We investigated gene-gene interactions related to age at onset of bipolar disorder in the Korean population, using genome-wide association study (GWAS) data. METHODS: The study population consisted of 303 patients with bipolar disorder. First, the top 1000 significant single-nucleotide polymorphisms (SNPs) associated with age at onset of bipolar disorder were selected through single SNP analysis by simple linear regression. Subsequently, the QMDR method was used to find gene-gene interactions. RESULTS: The best 10 SNPs from simple regression were located in chromosome 1, 2, 3, 10, 11, 14, 19, and 21. Only five SNPs were found in several genes, such as FOXN3, KIAA1217, OPCML, CAMSAP2, and PTPRS. On QMDR analyses, five pairs of SNPs showed significant interactions with a CVC exceeding 1/5 in a two-locus model. The best interaction was found for the pair of rs60830549 and rs12952733 (CVC = 1/5, P < 1E-07). In three-locus models, four combinations of SNPs showed significant associations with age at onset, with a CVC of >1/5. The best three-locus combination was rs60830549, rs12952733, and rs12952733 (CVC = 2/5, P < 1E-6). The SNPs showing significant interactions were located in the KIAA1217, RBFOX3, SDK2, CYP19A1, NTM, SMYD3, and RBFOX1 genes. CONCLUSIONS: Our analysis confirmed genetic interactions influencing the age of onset for bipolar disorder and identified several potential candidate genes. Further exploration of the functions of these promising genes, which may have multiple roles within the neuronal network, is necessary.

MYLK*FLNB and DOCK1*LAMA2 gene-gene interactions associated with rheumatoid arthritis in the focal adhesion pathway.

Rheumatoid arthritis (RA) is a chronic, systemic autoimmune disease caused by a combination of genetic and environmental factors. Rare variants with low predicted effects in genes participating in the same biological function might be involved in developing complex diseases such as RA. From whole-exome sequencing (WES) data, we identified genes containing rare non-neutral variants with complete penetrance and no phenocopy in at least one of nine French multiplex families. Further enrichment analysis highlighted focal adhesion as the most significant pathway. We then tested if interactions between the genes participating in this function would increase or decrease the risk of developing RA disease. The model-based multifactor dimensionality reduction (MB-MDR) approach was used to detect epistasis in a discovery sample (19 RA cases and 11 healthy individuals from 9 families and 98 unrelated CEU controls from the International Genome Sample Resource). We identified 9 significant interactions involving 11 genes (MYLK, FLNB, DOCK1, LAMA2, RELN, PIP5K1C, TNC, PRKCA, VEGFB, ITGB5, and FLT1). One interaction (MYLK*FLNB) increasing RA risk and one interaction decreasing RA risk (DOCK1*LAMA2) were confirmed in a replication sample (200 unrelated RA cases and 91 GBR unrelated controls). Functional and genomic data in RA samples or relevant cell types argue the key role of these genes in RA.

Hypoxia-inducible factor-1α in myocardial infarction.

Hypoxia-inducible factor 1 (HIF1) has a crucial function in the regulation of oxygen levels in mammalian cells, especially under hypoxic conditions. Its importance in cardiovascular diseases, particularly in cardiac ischemia, is because of its ability to alleviate cardiac dysfunction. The oxygen-responsive subunit, HIF1α, plays a crucial role in this process, as it has been shown to have cardioprotective effects in myocardial infarction through regulating the expression of genes affecting cellular survival, angiogenesis, and metabolism. Furthermore, HIF1α expression induced reperfusion in the ischemic skeletal muscle, and hypoxic skin wounds in diabetic animal models showed reduced HIF1α expression. Increased expression of HIF1α has been shown to reduce apoptosis and oxidative stress in cardiomyocytes during acute myocardial infarction. Genetic variations in HIF1α have also been found to correlate with altered responses to ischemic cardiovascular disease. In addition, a link has been established between the circadian rhythm and hypoxic molecular signaling pathways, with HIF1α functioning as an oxygen sensor and circadian genes such as period circadian regulator 2 responding to changes in light. This editorial analyzes the relationship between HIF1α and the circadian rhythm and highlights its significance in myocardial adaptation to hypoxia. Understanding the changes in molecular signaling pathways associated with diseases, specifically cardiovascular diseases, provides the opportunity for innovative therapeutic interventions, especially in low-oxygen environments such as myocardial infarction.

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.

Gene-gene interaction network analysis indicates CNTN2 is a candidate gene for idiopathic generalized epilepsy.

Twin and family studies have established the genetic contribution to idiopathic generalized epilepsy (IGE). The genetic architecture of IGE is generally complex and heterogeneous, and the majority of the genetic burden in IGE remains unsolved. We hypothesize that gene-gene interactions contribute to the complex inheritance of IGE. CNTN2 (OMIM* 615,400) variants have been identified in cases with familial adult myoclonic epilepsy and other epilepsies. To explore the gene-gene interaction network in IGE, we took the CNTN2 gene as an example and investigated its co-occurrent genetic variants in IGE cases. We performed whole-exome sequencing in 114 unrelated IGE cases and 296 healthy controls. Variants were qualified with sequencing quality, minor allele frequency, in silico prediction, genetic phenotype, and recurrent case numbers. The STRING_TOP25 gene interaction network analysis was introduced with the bait gene CNTN2 (denoted as A). The gene-gene interaction pair mode was presumed to be A + c, A + d, A + e, with a leading gene A, or A + B + f, A + B + g, A + B + h, with a double-gene A + B, or other combinations. We compared the number of gene interaction pairs between the case and control groups. We identified three pairs in the case group, CNTN2 + PTPN18, CNTN2 + CNTN1 + ANK2 + ANK3 + SNTG2, and CNTN2 + PTPRZ1, while we did not discover any pairs in the control group. The number of gene interaction pairs in the case group was much more than in the control group (p = 0.021). Taking together the genetic bioinformatics, reported epilepsy cases, and statistical evidence in the study, we supposed CNTN2 as a candidate pathogenic gene for IGE. The gene interaction network analysis might help screen candidate genes for IGE or other complex genetic disorders.

Detecting Gene-Gene Interaction among DNA Repair Genes in Chinese non-Syndromic Cleft lip with or Without Palate Trios.

OBJECTIVE: The objective of this study is to investigate the gene-gene interactions associated with NSCL/P among DNA repair genes. DESIGN: This study included 806 NSCL/P case-parent trios from China. Quality control process was conducted for genotyped single nucleotide polymorphisms (SNPs) located in six DNA repair genes (ATR, ERCC4, RFC1, TYMS, XRCC1 and XRCC3). We tested gene-gene interactions with Cordell's method using statistical package TRIO in R software. Bonferroni corrected significance level was set as P = 4.24 × 10-4. We also test the robustness of the interactions by permutation tests. SETTING: Not applicable. PATIENTS/PARTICIPANTS: A total of 806 NSCL/P case-parent trios (complete trios: 682, incomplete trios: 124) with Chinese ancestry. INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURE(S): Not applicable. RESULTS: A total of 118 SNPs were extracted for the interaction tests. Fourteen pairs of significant interactions were identified after Bonferroni correction, which were confirmed in permutation tests. Twelve pairs were between ATR and ERCC4 or XRCC3. The most significant interaction occurred between rs2244500 in TYMS and rs3213403 in XRCC1(P = 8.16 × 10-15). CONCLUSIONS: The current study identified gene-gene interactions among DNA repair genes in 806 Chinese NSCL/P trios, providing additional evidence for the complicated genetic structure underlying NSCL/P. ATR, ERCC4, XRCC3, TYMS and RFC1 were suggested to be possible candidate genes for NSCL/P.

A Comprehensive Evaluation of Large Language Models in Mining Gene Interactions and Pathway Knowledge.

Background: Understanding complex biological pathways, including gene-gene interactions and gene regulatory networks, is critical for exploring disease mechanisms and drug development. Manual literature curation of biological pathways is useful but cannot keep up with the exponential growth of the literature. Large-scale language models (LLMs), notable for their vast parameter sizes and comprehensive training on extensive text corpora, have great potential in automated text mining of biological pathways. Method: This study assesses the effectiveness of 21 LLMs, including both API-based models and open-source models. The evaluation focused on two key aspects: gene regulatory relations (specifically, 'activation', 'inhibition', and 'phosphorylation') and KEGG pathway component recognition. The performance of these models was analyzed using statistical metrics such as precision, recall, F1 scores, and the Jaccard similarity index. Results: Our results indicated a significant disparity in model performance. Among the API-based models, ChatGPT-4 and Claude-Pro showed superior performance, with an F1 score of 0.4448 and 0.4386 for the gene regulatory relation prediction, and a Jaccard similarity index of 0.2778 and 0.2657 for the KEGG pathway prediction, respectively. Open-source models lagged their API-based counterparts, where Falcon-180b-chat and llama1-7b led with the highest performance in gene regulatory relations (F1 of 0.2787 and 0.1923, respectively) and KEGG pathway recognition (Jaccard similarity index of 0.2237 and 0. 2207, respectively). Conclusion: LLMs are valuable in biomedical research, especially in gene network analysis and pathway mapping. However, their effectiveness varies, necessitating careful model selection. This work also provided a case study and insight into using LLMs as knowledge graphs.

Toll-Like Receptor (TLR) 1, 2, and 6 Gene Polymorphisms Support Evidence of Innate Immune Factors in Schizophrenia.

Introduction: Schizophrenia is a complex psychiatric disorder with an important genetic contribution. Immunological abnormalities have been reported in schizophrenia. Toll-like receptor (TLR) genes play an important role in the activation of the innate immune response, which may help to explain the presence of inflammation in people with this disorder. The aim of this study was to analyze the association of TLR1, TLR2, and TLR6 gene polymorphisms in the etiology of schizophrenia. Methods: We included 582 patients with schizophrenia and 525 healthy controls. Genetic analysis was performed using allelic discrimination with TaqMan probes. Results: We observed significant differences between patients and controls in the genotype and allele frequencies of TLR1/rs4833093 (χ2 = 17.3, p = 0.0002; χ2 = 15.9, p = 0.0001, respectively) and TLR2/rs5743709 (χ2 = 29.5, p = 0.00001; χ2 = 7.785, p = 0.0053, respectively), and in the allele frequencies of TLR6/rs3775073 (χ2 = 31.1, p = 0.00001). Finally, we found an interaction between the TLR1/rs4833093 and TLR2/rs5743709 genes, which increased the risk of developing schizophrenia (OR = 2.29, 95% CI [1.75, 3.01]). Discussion: Our findings add to the evidence suggesting that the activation of innate immune response might play an important role in the development of schizophrenia.

Complex effects of sequence variants on lipid levels and coronary artery disease.

Many sequence variants have additive effects on blood lipid levels and, through that, on the risk of coronary artery disease (CAD). We show that variants also have non-additive effects and interact to affect lipid levels as well as affecting variance and correlations. Variance and correlation effects are often signatures of epistasis or gene-environmental interactions. These complex effects can translate into CAD risk. For example, Trp154Ter in FUT2 protects against CAD among subjects with the A1 blood group, whereas it associates with greater risk of CAD in others. His48Arg in ADH1B interacts with alcohol consumption to affect lipid levels and CAD. The effect of variants in TM6SF2 on blood lipids is greatest among those who never eat oily fish but absent from those who often do. This work demonstrates that variants that affect variance of quantitative traits can allow for the discovery of epistasis and interactions of variants with the environment.

A computational model of current control mechanism for long-term potentiation (LTP) in human episodic memory based on gene-gene interaction.

The establishment of long-term potentiation (LTP) is a prime process for the formation of episodic memory. During the establishment of LTP, activations of various components are required in the signaling cascade of the LTP pathway. Past efforts to determine the activation of components relied extensively on the cellular or molecular level. In this paper, we have proposed a computational model based on gene-level cascading and interaction in LTP signaling for the establishment and control of current signals for achieving the desired level of activation in the formation of episodic memory. This paper also introduces a model for a generalized signaling pathway in episodic memory. A back-propagation feedback mechanism is used for updating the interaction levels in the signaling cascade starting from the last stage and ending at the start stage of the signaling cascade. Simulation of the proposed model has been performed for the LTP signaling pathway in the context of human episodic memory. We found through simulation that the qualifying genes correction factors of all stages are updated to their maximum limit. The article explains the signaling pathway for episodic memory and proves its effectiveness through simulation results.

Five Critical Gene-Based Biomarkers With Optimal Performance for Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the most fatal cancers in the world. There is an urgent need to understand the molecular background of HCC to facilitate the identification of biomarkers and discover effective therapeutic targets. Published transcriptomic studies have reported a large number of genes that are individually significant for HCC. However, reliable biomarkers remain to be determined. In this study, built on max-linear competing risk factor models, we developed a machine learning analytical framework to analyze transcriptomic data to identify the most miniature set of differentially expressed genes (DEGs). By analyzing 9 public whole-transcriptome datasets (containing 1184 HCC samples and 672 nontumor controls), we identified 5 critical differentially expressed genes (DEGs) (ie, CCDC107, CXCL12, GIGYF1, GMNN, and IFFO1) between HCC and control samples. The classifiers built on these 5 DEGs reached nearly perfect performance in identification of HCC. The performance of the 5 DEGs was further validated in a US Caucasian cohort that we collected (containing 17 HCC with paired nontumor tissue). The conceptual advance of our work lies in modeling gene-gene interactions and correcting batch effect in the analytic framework. The classifiers built on the 5 DEGs demonstrated clear signature patterns for HCC. The results are interpretable, robust, and reproducible across diverse cohorts/populations with various disease etiologies, indicating the 5 DEGs are intrinsic variables that can describe the overall features of HCC at the genomic level. The analytical framework applied in this study may pave a new way for improving transcriptome profiling analysis of human cancers.

Sex- and APOE-specific genetic risk factors for late-onset Alzheimer's disease: Evidence from gene-gene interaction of longevity-related loci.

Advanced age is the largest risk factor for late-onset Alzheimer's disease (LOAD), a disease in which susceptibility correlates to almost all hallmarks of aging. Shared genetic signatures between LOAD and longevity were frequently hypothesized, likely characterized by distinctive epistatic and pleiotropic interactions. Here, we applied a multidimensional reduction approach to detect gene-gene interactions affecting LOAD in a large dataset of genomic variants harbored by genes in the insulin/IGF1 signaling, DNA repair, and oxidative stress pathways, previously investigated in human longevity. The dataset was generated from a collection of publicly available Genome Wide Association Studies, comprising a total of 2,469 gene variants genotyped in 20,766 subjects of Northwestern European ancestry (11,038 LOAD cases and 9,728 controls). The stratified analysis according to APOE*4 status and sex corroborated evidence that pathways leading to longevity also contribute to LOAD. Among the significantly interacting genes, PTPN1, TXNRD1, and IGF1R were already found enriched in gene-gene interactions affecting survival to old age. Furthermore, interacting variants associated with LOAD in a sex- and APOE-specific way. Indeed, while in APOE*4 female carriers we found several inter-pathway interactions, no significant epistasis was found in APOE*4 negative females; conversely, in males, significant intra- and inter-pathways epistasis emerged according to APOE*4 status. These findings suggest that interactions of risk factors may drive different trajectories of cognitive aging. Beyond helping to disentangle the genetic architecture of LOAD, such knowledge may improve precision in predicting the risk of dementia and enable effective sex- and APOE-stratified preventive and therapeutic interventions for LOAD.

Transforming Growth Factor ß1 and Gap Junction Protein Alpha 4 Gene Heterogeneity in Relation to the Severity of Clinical Disease in Cystic Fibrosis.

BACKGROUND: High TGFß1-producing variants cause severe clinical disease in F508del homozygous patients. Lately, we showed that a single nucleotide polymorphism (SNP), rs41266431, in the GJA4 gene modifies the disease severity of cystic fibrosis (CF). Our aim was to investigate whether the clinical phenotype associated with GJA4 variants was independent of TGFß1 variants. METHODS: Homozygous F508del patients (n = 115, mean age 27.2 years, m/f (65/50)) were included in this study. A deep sequence analysis was performed for GJA4 and TGBß1, and disease severity was assessed over 3 years using lung function tests (LFTs), body mass index, diabetes mellitus, colonization with Pseudomonas aeruginosa, survival to end-stage lung disease (ESLD), as well as distinct inflammatory biomarkers. RESULTS: The analyses revealed that one SNP (rs41266431) in GJA4 may be clinically relevant. Carriers homozygous for the G variant (n = 84; 73%) presented with worse LFTs (forced vital capacity (FVC) % predicted: mean 80/86.6, p < 0.035) and a lower survival to ESLD (p < 0.029). For the TGBß1 variant: 509 carriers of the C variant (CT + CC genotype, n = 105, 91.3%) had better LFTs (Forced expiratory flow at 75% of the FVC (FEF75% predicted: median 40/29.5, p < 0.015), although a similar outcome to ESLD. A gene-gene interaction was not observed between TGBß1 and GJA4 variants for any clinical measure. CONCLUSIONS: GJA4 variants are independent of TGBß1 variants. Both variants had an impact on the LFTs, although only GJA4 variants were associated with an improved outcome for ESLD. CLINICAL TRIAL REGISTRATION: The study was registered with ClinicalTrials.gov, number NCT04242420, retrospectively on January 24th, 2020.

Biomedical association analysis between G2/M checkpoint genes and susceptibility to HIV-1 infection and AIDS progression from a northern chinese MSM population.

BACKGROUND: MSM are at high risk of HIV infection. Previous studies have shown that the cell cycle regulation plays an important role in HIV-1 infection, especially at the G2/M checkpoint. ATR, Chk1, Cdc25C and CDK1 are key genes of G2/M checkpoint. However, the association between SNPs of these genes and susceptibility to HIV-1 infection and AIDS progression remains unknown. METHODS: In this study, 42 tSNPs from the above four G2/M checkpoint genes were genotyped in 529 MSM and 529 control subjects from northern China to analyze this association. RESULTS: The results showed that rs34660854 A and rs75368165 A in ATR gene and rs3756766 A in Cdc25C gene could increase the risk of HIV-1 infection (P = 0.049, OR = 1.234, 95% CI 1.001-1.521; P = 0.020, OR = 1.296, 95% CI 1.042-1.611; P = 0.011, OR = 1.392, 95% CI 1.080-1.794, respectively), while Chk1 rs10893405 (P = 0.029, OR = 1.629, 95% CI 1.051-2.523) were significantly associated with AIDS progression. Besides, rs34660854 (P = 0.019, OR = 1.364, 95% CI 1.052-1.769; P = 0.022, OR = 1.337, 95% CI 1.042-1.716, under Codominant model and Dominant model, respectively) and rs75368165 (P = 0.006, OR = 1.445, 95% CI = 1.114-1.899; P = 0.007, OR = 1.418, 95% CI 1.099-1.831, under Codominant model and Dominant model, respectively) in ATR gene, rs12576279 (P = 0.013, OR = 0.343, 95% CI 0.147-0.800; P = 0.048, OR = 0.437, 95% CI 0.192-0.991, under Codominant model and Dominant model, respectively) and rs540436 (P = 0.012, OR = 1.407, 95% CI 1.077-1.836; P = 0.021, OR = 1.359, 95% CI 1.048-1.762, under Codominant model and Dominant model, respectively) in Chk1 gene, rs3756766 (P = 0.013, OR = 1.455, 95% CI 1.083-1.954; P = 0.009, OR = 1.460, 95% CI 1.098-1.940, under Codominant model and Dominant model, respectively) in Cdc25C gene and rs139245206 (P = 0.022, OR = 5.011, 95% CI 1.267-19.816; P = 0.020, OR = 5.067, 95% CI 1.286-19.970, under Codominant model and Recessive model, respectively) in CDK1 gene were significantly associated with HIV-1 infection under different models. CONCLUSIONS: We found that genetic variants of G2/M checkpoint genes had a molecular influence on the occurrence of HIV-1 infection and AIDS progression in a northern Chinese MSM population.

Overlapping group screening for binary cancer classification with TCGA high-dimensional genomic data.

Precision medicine has been a global trend of medical development, wherein cancer diagnosis plays an important role. With accurate diagnosis of cancer, we can provide patients with appropriate medical treatments for improving patients' survival. Since disease developments involve complex interplay among multiple factors such as gene-gene interactions, cancer classifications based on microarray gene expression profiling data are expected to be effective, and hence, have attracted extensive attention in computational biology and medicine. However, when using genomic data to build a diagnostic model, there exist several problems to be overcome, including the high-dimensional feature space and feature contamination. In this paper, we propose using the overlapping group screening (OGS) approach to build an accurate cancer diagnosis model and predict the probability of a patient falling into some disease classification category in the logistic regression framework. This new proposal integrates gene pathway information into the procedure for identifying genes and gene-gene interactions associated with the classification of cancer outcome groups. We conduct a series of simulation studies to compare the predictive accuracy of our proposed method for cancer diagnosis with some existing machine learning methods, and find the better performances of the former method. We apply the proposed method to the genomic data of The Cancer Genome Atlas related to lung adenocarcinoma (LUAD), liver hepatocellular carcinoma (LHC), and thyroid carcinoma (THCA), to establish accurate cancer diagnosis models.

Genome-wide epistasis study highlights genetic interactions influencing severity of COVID-19.

Coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) may lead to life-threatening respiratory symptoms. Understanding the genetic basis of the prognosis of COVID-19 is important for risk profiling of potentially severe symptoms. Here, we conducted a genome-wide epistasis study of COVID-19 severity in 2243 patients with severe symptoms and 12,612 patients with no or mild symptoms from the UK Biobank, followed by a replication study in an independent Spanish cohort (1416 cases, 4382 controls). Our study highlighted 3 interactions with genome-wide significance in the discovery phase, nominally significant in the replication phase, and enhanced significance in the meta-analysis. For example, the lead interaction was found between rs9792388 upstream of PDGFRL and rs3025892 downstream of SNAP25, where the composite genotype of rs3025892 CT and rs9792388 CA/AA showed higher risk of severe disease than any other genotypes (P = 2.77 × 10-12, proportion of severe cases = 0.24 ~ 0.29 vs. 0.09 ~ 0.18, genotypic OR = 1.96 ~ 2.70). This interaction was replicated in the Spanish cohort (P = 0.002, proportion of severe cases = 0.30 ~ 0.36 vs. 0.14 ~ 0.25, genotypic OR = 1.45 ~ 2.37) and showed enhanced significance in the meta-analysis (P = 4.97 × 10-14). Notably, these interactions indicated a possible molecular mechanism by which SARS-CoV-2 affects the nervous system. The first exhaustive genome-wide screening for epistasis improved our understanding of genetic basis underlying the severity of COVID-19.