Gene-based Analysis Research Articles

303 The role of rare variants in male-pattern hair loss: Analysis of whole exome sequencing data in the UK Biobank

Male-pattern hair loss (MPHL) is a highly heritable and prevalent form of hair loss. While genome-wide association studies (GWAS) have provided insights into the contribution of common genetic variants to MPHL etiology, the relevance of rare variants is unclear. In this study, we tested for a contribution of rare coding variants to MPHL etiology based on exome data of 72,469 men from the UK Biobank. We performed SKAT-O and GenRisk gene-based association analyses as well as single-variant tests, followed by interpretation, risk prediction modelling and enrichment testing.

The effect of pre-pregnancy obesity on gut and meconium microbiome and relationship with fetal growth

Objective To investigate the effect of pre-pregnancy obesity on maternal and newborn microbiomes and fetal growth. Methods Individuals who gained body weight in accordance with the recommendations during pregnancy and normal gestastional age are included in the study and were separated into two groups, normal (n = 20) and obese (n = 20), based on their body mass index (BMI) value of pre-pregnancy. Maternal stool samples collected during the first trimester of pregnancy and meconium samples collected at birth were evaluated using 16S rRNA gene-based microbiome analysis. Results The stool samples of mothers who were obese before pregnancy harbored a higher (59.9 versus 52.3%) relative abundance of Firmicutes and a lower (7.1 versus 4.1%) relative abundance of Proteobacteria than the stool samples of mothers with normal body weight pre-pregnancy. In contrast, in the meconium samples of mothers who were obese pre-pregnancy, compared to those of mothers who had a normal body weight pre-pregnancy, the phylum Firmicutes was less (56.0 versus 69.0%) abundant and Proteobacteria (9.0 versus 8.5%) was more abundant. There was a negative correlation between pre-pregnancy BMI, birth weight, weight/height ratio and alpha diversity indices (Shannon and Chao1). Conclusions Pre-pregnancy obesity can affect pregnant and newborn gut microbiota, which might related to fetal growth of the newborn.

Dynamics and prevalence of specific hydrocarbonoclastic bacterial population with respect to nutrient treatment levels in crude oil sludge.

The bacterial community composition in soil sediments changes with respect to nutrient concentrations and environmental conditions. Reports on the correlation between bacterial populations and inorganic nutrient concentrations in oily sediments are limited. The present time series study reports the prevalence of specific hydrocarbon-degrading bacterial communities in nutrient-treated oily sludge microcosms. The hydrocarbon degradation was maximum at 625µg nitrogen (N) and 62.5µg phosphorus (P)/g sludge sediment. The 16S rRNA gene-based DGGE analyses revealed noticeable changes in bacterial community composition with time and levels of nutrient treatment. BLASTn analysis of the 16S rRNA gene clone sequence showed the abundance of γ-Proteobacteria (44%), α-Proteobacteria (16%), β-Proteobacteria (10%), CFB (4%), and unidentified bacterial clones (26%). The catechol 1,2-dioxygenase (C12O) and catechol 2,3-dioxygenase (C23O) gene clones were affiliated to the genus Sphingomonas, highlighting the vital role of Sphingomonas in aromatic hydrocarbon degradation. The quantity of the 16S rRNA gene and the alkane hydroxylase (alkB) gene reached maximum levels in extended duration microcosms treated with 625µgN and 62.5µg P/g sludge sediment. In contrast, the C12O gene reached its highest abundance at a low N concentration.

The Effects of Iron Deficiency on the Gut Microbiota in Young Women

Background: Iron deficiency anemia (IDA) is highly prevalent worldwide, especially in premenopausal women during their reproductive years because of iron loss associated with menstruation and pregnancy. In 2019, the global prevalence of anemia was 36.5% among pregnant women, 29.6% in non-pregnant women Although iron is one of the most abundant elements on earth, it is not readily available for colonizing bacteria due to its low solubility in the human body. Hosts and microbiota compete fiercely for iron. Iron deficiency not only causes anemia, but also causes a variety symptoms including dyspnea, general weakness. Also, iron deficiency impact gut microbial health. There are some previous studies on the gut microbiome of patients with IDA. However, the study of the human microbiome is difficult to control for various variables that can influence the microbiome. To the best of our knowledge, there are limited studies regarding know the effects of iron supplementation on the gut microbiota in human. Aims: The aim of this study is to compare the microbiome of patients with IDA and normal individuals from young women. We also wanted to know the effects of iron supplementation on the gut microbiota. Methods: We enrolled 31 women in the study, including 15 IDA patients and 16 healthy volunteers. Participants, between 20 and 50 years of age, premenopausal women. All patients in the IDA group were diagnosed with IDA according to WHO diagnostic standards. Control participants were self-reported as healthy and did not suffer from an illness at the time of recruitment. Paticipants didn't use antibiotics and any other medical treatment that influences intestinal microbiota during 3 months before the study start. Stool sampling was conducted according to the Manual of Procedures for Human Microbiome Project Core Microbiome Sampling Protocol by the NIH at the time of study start. Following iron replacement for 3~6 months, the IDA patients performed a blood test to confirm that the anemia had improved, and stool sampling was conducted again at that time. Samples were obtained from 10 IDA patients before and after treatment with iron. 16S rRNA gene-based metagenomic analysis of fecal microbiome was performed. Results: Among the taxa with an average taxonomic composition greater than 1%, five classes (c-Coriobacteria, c-Negativicutes, c-Betaproteobacteria, c-Erysipelotrichi and c-Closteridia) and three orders (o-Clostridiales, o-Coriobacterials and o-Erysipelotrichales) significantly differ in three groups (Figure). Alpha diversity indices showed no differences between the groups and beta set-significance analysis indicated distinctive differences between normal and pre-treatment groups. Moreover, taxonomic biomarkers discovery by linear discriminant analysis Effect Size (LEfSe) analysis between normal and pre-treatment group showed higher effect size of f-Ruminococcaceae, o-Clostridiales, c-Clostridia, g-Faecalibacterium in normal and c-Negativicutes in pre-treatment group. Similarly, LEfSe analysis among treatment groups presented higher effect size of c-Erysipelotrichi, o-Erysipelotrichales, f-Erysipelotrichaceae in pre-treatment group. Whereas, LEfSe analysis between normal and post-treatment groups indicated that c-Coriobacteria, f-Coriobacteriaceae and o-Coriobacteriales were prominent in normal and g-Veillonella and g-Collinsella were significant in Post-treatment group. Finally, functional biomarker discovery by LEfSe analysis showed 15 Kos, 4 modules and two pathways distinctively different among the groups. Conclusions: In conclusion, there was a significant difference between the gut microbiome of the patients of IDA and that of healthy volunteers. We found a tendency of recovery of gut microbiome after iron supplementation treatment. Figure. Averaged taxonomic composition in stool of Normal, Pre- and Post-treatment IDA (iron deficiency anemia) groups. Taxonomic relative abundance was classified at the (A) phylum, (B) class, (C) order, (D) family, and (E) genus level, and relative abundances less than 1 % were expressed as ETC. Wilcoxon rank-sum test was used to analyze the significance between the two groups. a: Comparison between Normal and Pre; b: Comparison between Normal and Post; c: Comparison between Pre and Post;*, p<0.05. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

Meta-analysis of genome-wide association studies of hoarding symptoms in 27,651 individuals

Hoarding Disorder (HD) is a mental disorder characterized by persistent difficulties discarding or parting with possessions, often resulting in cluttered living spaces, distress, and impairment. Its etiology is largely unknown, but twin studies suggest that it is moderately heritable. In this study, we pooled phenotypic and genomic data from seven international cohorts (N = 27,651 individuals) and conducted a genome wide association study (GWAS) meta-analysis of parent- or self-reported hoarding symptoms (HS). We followed up the results with gene-based and gene-set analyses, as well as leave-one-out HS polygenic risk score (PRS) analyses. To examine a possible genetic association between hoarding symptoms and other phenotypes we conducted cross-trait PRS analyses. Though we did not report any genome-wide significant SNPs, we report heritability estimates for the twin-cohorts between 26–48%, and a SNP-heritability of 11% for an unrelated sub-cohort. Cross-trait PRS analyses showed that the genetic risk for schizophrenia and autism spectrum disorder were significantly associated with hoarding symptoms. We also found suggestive evidence for an association with educational attainment. There were no significant associations with other phenotypes previously linked to HD, such as obsessive-compulsive disorder, depression, anxiety, or attention-deficit hyperactivity disorder. To conclude, we found that HS are heritable, confirming and extending previous twin studies but we had limited power to detect any genome-wide significant loci. Much larger samples will be needed to further extend these findings and reach a “gene discovery zone”. To move the field forward, future research should not only include genetic analyses of quantitative hoarding traits in larger samples, but also in samples of individuals meeting strict diagnostic criteria for HD, and more ethnically diverse samples.

Prognostic Impact and Landscape of Mitochondrial DNA Variants in Myelodysplastic Syndromes Undergoing Allogenic Hematopoietic Cell Transplantation

Background: Myelodysplastic syndromes (MDS) are a heterogenous group of clonal hematopoietic cell disorders characterized by blood cytopenias and a tendency to progress to acute myeloid leukemia. Despite treatment advances, allogeneic hematopoietic stem-cell transplantation (allo-HCT) remains the only potentially curative therapy for MDS. However, mortality after allo-HCT is high due to disease relapse and transplant-related complications. Current prognostic tools defining MDS risk groups, such as the International Prognostic Scoring System (IPSS) and the revised IPSS (IPSS-R), are used to predict posttransplant outcomes for clinical decision-making. A major limitation of these scoring systems is a failure to account for variables other than clinical and hematologic parameters. Comprehensive genomic analyses have shown that mutations in specific genes (e.g., TP53, TET2, and DNMT3A) could inform prognostic stratification of MDS undergoing allo-HCT. However, most studies of MDS genome have focused on mutations occurring in the nuclear genome and ignored the mitochondrial genome. The current study aims to characterize mitochondrial genomic landscape of MDS and identify mitochondrial DNA (mtDNA) genetic variants prognostic for post allo-HCT outcomes in MDS. Methods: We performed whole genome sequencing (WGS) on whole-blood samples obtained before allo-HCT from 494 patients with MDS who were enrolled in the Center for International Blood and Marrow Transplant Research (CIBMTR) between 2014 and 2018. Mitochondrial genome sequences were extracted from the WGS data and aligned to the mtDNA reference genome (Revised Cambridge Reference Sequence, rCRS) by GSNAP. Putative mtDNA variants, including single nucleotide variants (SNV) and indels, were identified by parsing the SAM CIGAR string. Pathogenicity prediction was conducted for mtDNA variants in the protein-coding genes, MT-tRNAs and MT-rRNAs using different databases. We evaluated the impact of mtDNA variants on the overall survival (OS) and relapse with adjustment for MDS- and HCT-related factors. A random survival forest algorithm was applied to evaluate the prognostic performance of models that include mtDNA variants alone and combined with MDS- and HCT-related clinical factors. Results: The patients in the cohort have a median age of 67 years, 36.4% had poor/very poor Revised International Prognostic Scores (IPSS-R), and 26% received myeloablative conditioning regimens. On average, the sequencing depth of the mitochondrial genome was 14,596× (213× - 17,070×), and the average coverage was 99.90% (99.89% - 100%). A total of 2,666 mtDNA variants are identified, including 411 potential pathogenic variants. Mitochondrial control region (also known as D-Loop) was the most frequently mutated region in our cohort and the most frequently mutated protein-coding gene was MT-ND5 (16%). MDS patients with higher number of mitochondrial mutations (continuous variable) had significantly worse OS (hazard ratio, HR, 1.11; 95% CI, 1.01-1.22; P = 0.029) and higher risk of relapse (HR, 1.13; 95% CI, 1.00-1.27; P = 0.049) than patients with lower number of mutations. Gene-based analysis yielded significant associations with OS for MT-CYB (P = 1.04×10-3), MT-ND2 (P = 3.06×10-3) and MT-ND4 (P = 1.41×10-3) variants after Bonferroni correction (P < 0.05/16=3.13×10-3) (Table 1). Four mtDNA genes were significantly associated with relapse, including MT-CYB (P = 7.03×10-4), MT-ND2 (P = 5.92×10-4), MT-ND5 (P = 1.91×10-3) and MT-tRNA (P = 1.99×10-4). (Table 1). To investigate whether mtDNA variants could improve the prognostic stratification of MDS patients receiving allo-HCT, we fitted random survival forest models with and without inclusion of mtDNA variants in the models. The model based only on mtDNA genes had a c-index of 0.58 to predict OS, which was slightly higher than the IPSS-R (c-index = 0.48) and clinical model (IPSS-R plus MDS type and pre-transplantation treatments, c-index = 0.57). Adding mtDNA genes improved the predictive performance of the model, with c-index increased from 0.48 to 0.63 for the IPSS-R model and from 0.57 to 0.66 for the clinical model (Figure 1). Similar results were observed for the models in predicting relapse (Figure 1). Conclusions: There may be clinical utility of mtDNA variants to predict allo-HCT outcomes in combination with more standard clinical parameters. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource.

The NHGRI-EBI GWAS Catalog (www.ebi.ac.uk/gwas) is a FAIR knowledgebase providing detailed, structured, standardised and interoperable genome-wide association study (GWAS) data to>200 000 users per year from academic research, healthcare and industry. The Catalog contains variant-trait associations and supporting metadata for>45 000 published GWAS across>5000 human traits, and>40 000 full P-value summary statistics datasets. Content is curated from publications or acquired via author submission of prepublication summary statistics through a new submission portal and validation tool. GWAS data volume has vastly increased in recent years. We have updated our software to meet this scaling challenge and to enable rapid release of submitted summary statistics. The scope of the repository has expanded to include additional data types of high interest to the community, including sequencing-based GWAS, gene-based analyses and copy number variation analyses. Community outreach has increased the number of shared datasets from under-represented traits, e.g. cancer, and we continue to contribute to awareness of the lack of population diversity in GWAS. Interoperability of the Catalog has been enhanced through links to other resources including the Polygenic Score Catalog and the International Mouse Phenotyping Consortium, refinements to GWAS trait annotation, and the development of a standard format for GWAS data.

Integrative Analysis of Transcriptome-Wide Association Study and Gene-Based Association Analysis Identifies In Silico Candidate Genes Associated with Juvenile Idiopathic Arthritis.

Genome-wide association study (GWAS) of Juvenile idiopathic arthritis (JIA) suffers from low power due to limited sample size and the interpretation challenge due to most signals located in non-coding regions. Gene-level analysis could alleviate these issues. Using GWAS summary statistics, we performed two typical gene-level analysis of JIA, transcriptome-wide association studies (TWAS) using FUnctional Summary-based ImputatiON (FUSION) and gene-based analysis using eQTL Multi-marker Analysis of GenoMic Annotation (eMAGMA), followed by comprehensive enrichment analysis. Among 33 overlapped significant genes from these two methods, 11 were previously reported, including TYK2 (PFUSION = 5.12 × 10-6, PeMAGMA = 1.94 × 10-7 for whole blood), IL-6R (PFUSION = 8.63 × 10-7, PeMAGMA = 2.74 × 10-6 for cells EBV-transformed lymphocytes), and Fas (PFUSION = 5.21 × 10-5, PeMAGMA = 1.08 × 10-6 for muscle skeletal). Some newly plausible JIA-associated genes are also reported, including IL-27 (PFUSION = 2.10 × 10-7, PeMAGMA = 3.93 × 10-8 for Liver), LAT (PFUSION = 1.53 × 10-4, PeMAGMA = 4.62 × 10-7 for Artery Aorta), and MAGI3 (PFUSION = 1.30 × 10-5, PeMAGMA = 1.73 × 10-7 for Muscle Skeletal). Enrichment analysis further highlighted 4 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways and 10 Gene Ontology (GO) terms. Our findings can benefit the understanding of genetic determinants and potential therapeutic targets for JIA.

Gene-Based Burden Analysis of De Novo Sphingolipid Pathway Genes Identifies CERS3 as a Potential Risk Gene in Advanced Diabetic Kidney Disease

Gene-Based Burden Analysis of De Novo Sphingolipid Pathway Genes Identifies CERS3 as a Potential Risk Gene in Advanced Diabetic Kidney Disease

Description of a moderately acidotolerant and aerotolerant anaerobic bacterium Acidilutibacter cellobiosedens gen. nov., sp. nov. within the family Acidilutibacteraceae fam. nov., and proposal of Sporanaerobacteraceae fam. nov. and Tepidimicrobiaceae fam. nov.

A Gram-stain positive, moderately thermophilic, acidotolerant and aerotolerant anaerobic bacterium, designated JN-28 T, was isolated from the pit mud of Chinese strong-flavor liquor. Growth was observed at 25–50 °C and pH 5.5–8.0 in the presence of 0–25 g l−1 NaCl (optimally at 45 °C, pH 6.0, without NaCl). Strain JN-28 T was heterotrophic, requiring yeast extract for growth. The major cellular fatty acids were iso-C15:0 and C14:0. The DNA G + C content of genomic DNA was 33.54 mol%. The strain was resistant to vancomycin (10 mg l−1). Genome analysis revealed the presence of genes involved in the response to mild acid stress and oxidative stress, and resistance to vancomycin. 16S rRNA gene-based phylogenetic analysis showed that strain JN-28 T shares ≤ 89.3 % sequence similarity with its closest relatives Sporanaerobacter acetigenes DSM 13106 T and other members in the order Tissierellales. Based on phenotypic and phylogenetic characteristics, Acidilutibacter cellobiosedens gen. nov., sp. nov. is proposed for the new genus and novel species with the type strain JN-28 T (=CCAM 418 T = JCM 39087 T). Further phylogenetic and phylogenomic analyses suggested strain JN-28 T represents a novel family within the order Tissierellales, for which Acidilutibacteraceae fam. nov. is proposed. In addition, the family Tissierellaceae was reclassified, Sporanaerobacteraceae fam. nov. and Tepidimicrobiaceae fam. nov. were formally proposed. Emended description of the family Tissierellaceae is also provided.

Common variability in oestrogen-related genes and pancreatic ductal adenocarcinoma risk in women

The incidence of pancreatic ductal adenocarcinoma (PDAC) is different among males and females. This disparity cannot be fully explained by the difference in terms of exposure to known risk factors; therefore, the lower incidence in women could be attributed to sex-specific hormones. A two-phase association study was conducted in 12,387 female subjects (5436 PDAC cases and 6951 controls) to assess the effect on risk of developing PDAC of single nucleotide polymorphisms (SNPs) in 208 genes involved in oestrogen and pregnenolone biosynthesis and oestrogen-mediated signalling. In the discovery phase 14 polymorphisms showed a statistically significant association (P < 0.05). In the replication none of the findings were validated. In addition, a gene-based analysis was performed on the 208 selected genes. Four genes (NR5A2, MED1, NCOA2 and RUNX1) were associated with PDAC risk, but only NR5A2 showed an association (P = 4.08 × 10−5) below the Bonferroni-corrected threshold of statistical significance. In conclusion, despite differences in incidence between males and females, our study did not identify an effect of common polymorphisms in the oestrogen and pregnenolone pathways in relation to PDAC susceptibility. However, we validated the previously reported association between NR5A2 gene variants and PDAC risk.

Berberine Depresses Inflammation and Adjusts Smooth Muscle to Ameliorate Ulcerative Colitis of Cats by Regulating Gut Microbiota.

Intestinal microbiota dysbiosis is a well established characteristic of ulcerative colitis (UC). Regulating the gut microbiota is an effective UC treatment strategy. Berberine (BBR), an alkaloid extracted from several Chinese herbs, is a common traditional Chinese medicine. To establish the efficacy and mechanism of action of BBR, we constructed a UC model using healthy adult shorthair cats to conduct a systematic study of colonic tissue pathology, inflammatory factor expression, and gut microbiota structure. We investigated the therapeutic capacity of BBR for regulating the gut microbiota and thus work against UC in cats using 16S rRNA genes amplicon sequencing technology. Our results revealed that dextran sulfate sodium (DSS)-induced cat models of UC showed weight loss, diarrhea accompanied by mucous and blood, histological abnormalities, and shortening of the colon, all of which were significantly alleviated by supplementation with BBR. A 16S rRNA gene-based microbiota analysis demonstrated that BBR could significantly benefit gut microbiota. Western blot, quantitative PCR, and enzyme-linked immunosorbent assays (ELISAs) showed that in DSS-induced cat models, the expression of the inflammatory factors was increased, activating the JAK2/STAT3 signaling pathway, and treatment with BBR reversed this effect. The myosin light chain (MLC) phosphorylation in the smooth muscle of the intestines is associated with motility of inflammation-related diarrhea in cats. This study used gut flora analyses to demonstrate the anti-UC effects of BBR and its potential therapeutic mechanisms and offers novel insights into the prevention of inflammatory diseases using natural products. IMPORTANCE Ulcerative colitis (UC) is common in clinics. Intestinal microbiota disorder is correlated with ulcerative colitis. Although there are many studies on ulcerative colitis in rats, there are few studies on colitis in cats. Therefore, this study explored the possibility of the use of BBR as a safe and efficient treatment for colitis in cats. The results demonstrated the therapeutic effects of BBR on UC based on the state of the intestinal flora. The study found BBR supplementation to be effective against dextran sulfate sodium (DSS)-induced colitis, smooth muscle damage, and gut microbiota dysbiosis.

Annotating genetic variants to target genes using H-MAGMA.

An outstanding goal in modern genomics is to systematically predict the functional outcome of noncoding variation associated with complex traits. To address this, we developed Hi-C-coupled multi-marker analysis of genomic annotation (H-MAGMA), which builds on traditional MAGMA-a gene-based analysis tool that assigns variants to their target genes-by incorporating 3D chromatin configuration in assigning variants to their putative target genes. Applying this approach, we identified key biological pathways implicated in a wide range of brain disorders and showed its utility in complementing other functional genomic resources such as expression quantitative trait loci-based variant annotation. Here, we provide a detailed protocol for generating the H-MAGMA variant-gene annotation file by using chromatin interaction data from the adult human brain. In addition, we provide an example of how H-MAGMA is run by using genome-wide association study summary statistics of Parkinson's disease. Lastly, we generated variant-gene annotation files for 28 tissues and cell types, with the hope of contributing a resource for researchers studying a broad set of complex genetic disorders. H-MAGMA can be performed in <2 h for any cell type in which Hi-C data are available.

CLIN_SKAT: an R package to conduct association analysis using functionally relevant variants

BackgroundAvailability of next generation sequencing data, allows low-frequency and rare variants to be studied through strategies other than the commonly used genome-wide association studies (GWAS). Rare variants are important keys towards explaining the heritability for complex diseases that remains to be explained by common variants due to their low effect sizes. However, analysis strategies struggle to keep up with the huge amount of data at disposal therefore creating a bottleneck. This study describes CLIN_SKAT, an R package, that provides users with an easily implemented analysis pipeline with the goal of (i) extracting clinically relevant variants (both rare and common), followed by (ii) gene-based association analysis by grouping the selected variants.ResultsCLIN_SKAT offers four simple functions that can be used to obtain clinically relevant variants, map them to genes or gene sets, calculate weights from global healthy populations and conduct weighted case–control analysis. CLIN_SKAT introduces improvements by adding certain pre-analysis steps and customizable features to make the SKAT results clinically more meaningful. Moreover, it offers several plot functions that can be availed towards obtaining visualizations for interpretation of the analyses results. CLIN_SKAT is available on Windows/Linux/MacOS and is operative for R version 4.0.4 or later. It can be freely downloaded from https://github.com/ShihChingYu/CLIN_SKAT, installed through devtools::install_github("ShihChingYu/CLIN_SKAT", force=T) and executed by loading the package into R using library(CLIN_SKAT). All outputs (tabular and graphical) can be downloaded in simple, publishable formats.ConclusionsStatistical association analysis is often underpowered due to low sample sizes and high numbers of variants to be tested, limiting detection of causal ones. Therefore, retaining a subset of variants that are biologically meaningful seems to be a more effective strategy for identifying explainable associations while reducing the degrees of freedom. CLIN_SKAT offers users a one-stop R package that identifies disease risk variants with improved power via a series of tailor-made procedures that allows dimension reduction, by retaining functionally relevant variants, and incorporating ethnicity based priors. Furthermore, it also eliminates the requirement for high computational resources and bioinformatics expertise.

Interaction-based transcriptome analysis via differential network inference.

Gene-based transcriptome analysis, such as differential expression analysis, can identify the key factors causing disease production, cell differentiation and other biological processes. However, this is not enough because basic life activities are mainly driven by the interactions between genes. Although there have been already many differential network inference methods for identifying the differential gene interactions, currently, most studies still only use the information of nodes in the network for downstream analyses. To investigate the insight into differential gene interactions, we should perform interaction-based transcriptome analysis (IBTA) instead of gene-based analysis after obtaining the differential networks. In this paper, we illustrated a workflow of IBTA by developing a Co-hub Differential Network inference (CDN) algorithm, and a novel interaction-based metric, pivot APC2. We confirmed the superior performance of CDN through simulation experiments compared with other popular differential network inference algorithms. Furthermore, three case studies are given using colorectal cancer, COVID-19 and triple-negative breast cancer datasets to demonstrate the ability of our interaction-based analytical process to uncover causative mechanisms.

Ideonella alba sp. nov. and Ideonella aquatica sp. nov. isolated from an aquaculture farm.

The three novel bacterial strains designated as 3Y2T, 4Y16 and 4Y11T were isolated from an aquaculture farm and characterized using a polyphasic taxonomic approach. These strains were determined to be catalase- and oxidase-positive and to hydrolyze gelatin and aesculin. The results of 16S rRNA gene-based phylogenetic analysis indicated that the three strains were related to members of the genus Ideonella. The phylogenomic results further indicated that the three strains formed two independent branches distinct from reference type strains within this genus. The digital DNA-DNA hybridization (dDDH), average nucleotide identity (ANI) and average amino acid identity (AAI) values between the three strains and their relatives were far below the thresholds of 70 % dDDH, 95-96 % ANI and 95 % AAI for species definition, respectively, indicating that the three strains represent two novel genospecies. The results of chemotaxonomic characterization indicated that the major cellular fatty acids of the three strains were summed feature 3 (C16 : 1ω6c and/or C16 : 1 ω7c) and C16 : 0; the common main polar lipids were diphosphatidylglycerol, phosphatidylethanolamine and phosphatidylglycerol; the respiratory quinone was ubiquinone-8. The genomic DNA G+C contents of the three strains were 70.2, 70.1 and 69.7%, respectively. On the basis of the different genotypes and distinctive phenotypes such as the phosphatidylcholine and glycolipid only in 3Y2T and the utilization of malic acid and trisodium citrate only in 4Y11T, strains 3Y2T and 4Y11T are concluded to represent two novel species of the genus Ideonella, for which the names Ideonella alba sp. nov. (type strain 3Y2T = GDMCC 1.2584T = KCTC 82813T) and Ideonella aquatica sp. nov. (type strain 4Y11T = GDMCC 1.1935T = JCM 34285T) are proposed.

Genome-wide association study meta-analysis of suicide death and suicidal behavior

Suicide is a worldwide health crisis. We aimed to identify genetic risk variants associated with suicide death and suicidal behavior. Meta-analysis for suicide death was performed using 3765 cases from Utah and matching 6572 controls of European ancestry. Meta-analysis for suicidal behavior using data across five cohorts (n = 8315 cases and 256,478 psychiatric or populational controls of European ancestry) was also performed. One locus in neuroligin 1 (NLGN1) passing the genome-wide significance threshold for suicide death was identified (top SNP rs73182688, with p = 5.48 × 10−8 before and p = 4.55 × 10−8 after mtCOJO analysis conditioning on MDD to remove genetic effects on suicide mediated by MDD). Conditioning on suicidal attempts did not significantly change the association strength (p = 6.02 × 10−8), suggesting suicide death specificity. NLGN1 encodes a member of a family of neuronal cell surface proteins. Members of this family act as splice site-specific ligands for beta-neurexins and may be involved in synaptogenesis. The NRXN-NLGN pathway was previously implicated in suicide, autism, and schizophrenia. We additionally identified ROBO2 and ZNF28 associations with suicidal behavior in the meta-analysis across five cohorts in gene-based association analysis using MAGMA. Lastly, we replicated two loci including variants near SOX5 and LOC101928519 associated with suicidal attempts identified in the ISGC and MVP meta-analysis using the independent FinnGen samples. Suicide death and suicidal behavior showed positive genetic correlations with depression, schizophrenia, pain, and suicidal attempt, and negative genetic correlation with educational attainment. These correlations remained significant after conditioning on depression, suggesting pleiotropic effects among these traits. Bidirectional generalized summary-data-based Mendelian randomization analysis suggests that genetic risk for the suicidal attempt and suicide death are both bi-directionally causal for MDD.

Genetic Overlap Analysis Identifies a Shared Etiology between Migraine and Headache with Type 2 Diabetes.

Migraine and headache frequently co-occur with type 2 diabetes (T2D), suggesting a shared aetiology between the two conditions. We used genome-wide association study (GWAS) data to investigate the genetic overlap and causal relationship between migraine and headache with T2D. Using linkage disequilibrium score regression (LDSC), we found a significant genetic correlation between migraine and T2D (rg = 0.06, p = 1.37 × 10−5) and between headache and T2D (rg = 0.07, p = 3.0 × 10−4). Using pairwise GWAS (GWAS-PW) analysis, we identified 11 pleiotropic regions between migraine and T2D and 5 pleiotropic regions between headache and T2D. Cross-trait SNP meta-analysis identified 23 novel SNP loci (Pmeta < 5 × 10−8) associated with migraine and T2D, and three novel SNP loci associated with headache and T2D. Cross-trait gene-based overlap analysis identified 33 genes significantly associated (Pgene-based < 3.85 × 10−6) with migraine and T2D, and 11 genes associated with headache and T2D, with 7 genes (EHMT2, SLC44A4, PLEKHA1, CFDP1, TMEM170A, CHST6, and BCAR1) common between them. There was also a significant overlap of genes nominally associated (Pgene-based < 0.05) with both migraine and T2D (Pbinomial-test = 2.83 × 10−46) and headache and T2D (Pbinomial-test = 4.08 × 10−29). Mendelian randomisation (MR) analyses did not provide consistent evidence for a causal relationship between migraine and T2D. However, we found headache was causally associated (inverse-variance weighted, ORIVW = 0.90, Pivw = 7 × 10−3) with T2D. Our findings robustly confirm the comorbidity of migraine and headache with T2D, with shared genetically controlled biological mechanisms contributing to their co-occurrence, and evidence for a causal relationship between headache and T2D.

The associations of APP, PSEN1, and PSEN2 genes with Alzheimer's disease: A large case-control study in Chinese population.

The associations of non-pathogenic variants of APP, PSEN1, and PSEN2 with Alzheimer's disease (AD) remain unclear. This study is aimed at determining the role of these variants in AD. Our study recruited 1154 AD patients and 2403 controls. APP, PSEN1, PSEN2, and APOE were sequenced using a targeted panel. Variants were classified into common or rare variants with the minor allele frequencies (MAF) cutoff of 0.01. Common variant (MAF≥0.01)-based association test was performed by PLINK 1.9, and gene-based (MAF <0.01) association analysis was conducted using Sequence Kernel Association Test-Optimal (SKAT-O test). Additionally, using PLINK 1.9, we performed AD endophenotypes association studies. A common variant, PSEN2 rs11405, was suggestively associated with AD risk (p= 1.08 × 10-2 ). The gene-based association analysis revealed that the APP gene exhibited a significant association with AD (p= 1.43 × 10-2 ). In the AD endophenotypes association studies, APP rs459543 was nominally correlated with CSF Aβ42 level (p= 7.91 × 10-3 ). Our study indicated that non-pathogenic variants in PSEN2 and APP may be involved in AD pathogenesis in the Chinese population.

Multi-trait genome-wide association study of opioid addiction: OPRM1 and beyond

Opioid addiction (OA) is moderately heritable, yet only rs1799971, the A118G variant in OPRM1, has been identified as a genome-wide significant association with OA and independently replicated. We applied genomic structural equation modeling to conduct a GWAS of the new Genetics of Opioid Addiction Consortium (GENOA) data together with published studies (Psychiatric Genomics Consortium, Million Veteran Program, and Partners Health), comprising 23,367 cases and effective sample size of 88,114 individuals of European ancestry. Genetic correlations among the various OA phenotypes were uniformly high (rg > 0.9). We observed the strongest evidence to date for OPRM1: lead SNP rs9478500 (p = 2.56 × 10–9). Gene-based analyses identified novel genome-wide significant associations with PPP6C and FURIN. Variants within these loci appear to be pleiotropic for addiction and related traits.