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OBJECTIVE: This study aims to identify novel candidate genes associated with osteonecrosis of the femoral head (ONFH). METHODS: A transcriptome-wide association study (TWAS) was performed by integrating the genome-wide association study dataset of osteonecrosis (ON) in the UK Biobank with pre-computed mRNA expression reference weights of muscle skeleton (MS) and blood. The ON-associated genes identified by TWAS were further subjected to gene ontology (GO) analysis by the DAVID tool. Finally, a trans-omics comparative analysis of TWAS and genome-wide mRNA expression profiling was conducted to identify the common genes and the GO terms shared by both DNA-level TWAS and mRNA-level expression profile for ONFH. RESULTS: TWAS totally identified 564 genes that were with P TWAS value <0.05 for MS and blood, such as CBX1 (P TWAS = 0.0001 for MS), SRPK2 (P TWAS = 0.0002 for blood), and MYO5A (P TWAS = 0.0005 for blood). After comparing the genes detected by TWAS with the differentially expressed genes identified by mRNA expression profiling, we detected 59 overlapped genes, such as STEAP4 [P TWAS = 0.0270, FC (fold change)mRNA = 7.03], RABEP1 (P TWAS = 0.010, FCmRNA = 2.22), and MORC3 (P TWAS = 0.0053, FCmRNA = 2.92). The GO analysis of TWAS-identified genes discovered 53 GO terms for ON. Further comparing the GO results of TWAS and mRNA expression profiling identified four overlapped GO terms, including cysteine-type endopeptidase activity (P TWAS = 0.0006, P mRNA = 0.0227), extracellular space (P TWAS = 0.0342, P mRNA = 0.0012), protein binding (P TWAS = 0.0112, P mRNA = 0.0106), and ATP binding (P TWAS = 0.0464, P mRNA = 0.0033). CONCLUSION: Several ONFH-associated genes and GO terms were identified by integrating TWAS and mRNA expression profiling. It provides novel clues to reveal the pathogenesis of ONFH.

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Objective: Development and progression of immune-mediated inflammatory diseases (IMIDs) involve intricate dysregulation of the disease-associated genes (DAGs) and their expressing immune cells. Identifying the crucial disease-associated cells (DACs) in IMIDs has been challenging due to the underlying complex molecular mechanism. Methods: Using transcriptome profiles of 40 different immune cells, unsupervised machine learning, and disease-gene networks, we constructed the Disease-gene IMmune cell Expression (DIME) network and identified top DACs and DAGs of 12 phenotypically different IMIDs. We compared the DIME networks of IMIDs to identify common pathways between them. We used the common pathways and publicly available drug-gene network to identify promising drug repurposing targets. Results: We found CD4+Treg, CD4+Th1, and NK cells as top DACs in inflammatory arthritis such as ankylosing spondylitis (AS), psoriatic arthritis, and rheumatoid arthritis (RA); neutrophils, granulocytes, and BDCA1+CD14+ cells in systemic lupus erythematosus and systemic scleroderma; ILC2, CD4+Th1, CD4+Treg, and NK cells in the inflammatory bowel diseases (IBDs). We identified lymphoid cells (CD4+Th1, CD4+Treg, and NK) and their associated pathways to be important in HLA-B27 type diseases (psoriasis, AS, and IBDs) and in primary-joint-inflammation-based inflammatory arthritis (AS and RA). Based on the common cellular mechanisms, we identified lifitegrast as a potential drug repurposing candidate for Crohn's disease and other IMIDs. Conclusions: Existing methods are inadequate in capturing the intricate involvement of the crucial genes and cell types essential to IMIDs. Our approach identified the key DACs, DAGs, common mechanisms between IMIDs, and proposed potential drug repurposing targets using the DIME network. To extend our method to other diseases, we built the DIME tool (https://bitbucket.org/systemsimmunology/dime/) to help scientists uncover the etiology of complex and rare diseases to further drug development by better-determining drug targets, thereby mitigating the risk of failure in late clinical development.

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Folate deficiency is an environmental risk factor for several developmental disorders. De novo mutations (DNMs) also play important etiological roles in various developmental disorders. However, it remains unclear whether DNMs in folate-related genes (FRGs) contribute to developmental disorders. We obtained a list of 1,821 FRGs from folate metabolism pathways and the Comparative Toxicogenomics Database, along with data concerning DNMs in 15,404 cases and 3,391 controls from the Gene4Denovo database. We used a TADA-Denovo model to prioritize candidate disease-associated FRGs, and characterized these genes in terms of genic intolerance, functional networks, and expression patterns. Compared with the controls, FRGs were significantly enriched in likely damaging DNMs (ldDNMs) in patients with developmental disorders (1.54 ≤ odds ratio ≤ 3.39, P adj ≤ 0.0075). Furthermore, FRGs with ldDNMs rather than with likely non-damaging DNMs (lndDNMs) overlapped significantly among the five developmental disorders included in the datasets. The TADA-Denovo model prioritized 96 candidate disease-associated FRGs, which were intolerant to genetic variants. Their functional networks mainly involved pathways associated with chromatin modification, organ development, and signal transduction pathways. DNMT3A, KMT2B, KMT2C, and YY1 emerged as hub FRGs from the protein-protein interaction network. These candidate disease-associated FRGs are preferentially expressed in the excitatory neurones during embryonic development, and in the cortex, cerebellum, striatum, and amygdala during foetal development. Overall, these findings show that DNMs in FRGs are associated with the risk of developmental disorders. Further research on these DNMs may facilitate the discovery of developmental disorder biomarkers and therapeutic targets, enabling detailed, personalized, and precise folate treatment plan.

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BACKGROUND Understanding the associations among different disorders remarkably improves their diagnosis and treatments. Celiac disease is the most complicated and prevalent form of immune-mediated diseases. On the other hand, inflammatory bowel diseases lead to inflammation of the intestine with an unknown cause. Although inflammatory bowel diseases have been often thought of as an autoimmune disorder, they can be triggered by whatever that can lead to the inflammation in the whole bowel. Henceforth, both aforementioned diseases are related to autoimmune attacks and cause a sort of inflammatory event, which exploring trade-off among them supposedly will lead to discovering important genes and, in turn, to the possible common therapeutic protocols. In the current study, we aimed to determine the correlation between the common genes in celiac disease and inflammatory bowel diseases. METHODS 314 and 851 genes correlated with celiac disease and inflammatory bowel diseases respectively extracted from DisGeNET were subjected to an in-silico data analysis framework to mine prognosticates genes and the associated pathways. RESULTS 149 shared genes between these diseases regulated by highlighted transcription factors NFKB1, IRF1, STAT1, HSF1, GATA3 were characterized as discriminating molecules, which by further screening were enriched in pathways mostly involved in apoptosis, T cell activation, and cytokine, chemokine, and interleukin signaling. CONCLUSION We observed that the identified common genes were associated with a wide range of pathogenic mechanisms underlying these diseases.

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Although the heritability of cognitive function in old age is substantial, genome-wide association studies have had limited success in elucidating its genetic basis, leaving a considerable amount of "missing heritability." Aside from single nucleotide polymorphisms, genome-wide association studies are unable to assess other large sources of genetic variation, such as tandem repeat polymorphisms. Therefore, here, we studied the association of cytosine-adenine-guanine (CAG) repeat variations in polyglutamine disease-associated genes (PDAGs) with cognitive function in older adults. In a large cohort consisting of 5786 participants, we found that the CAG repeat number in 3 PDAGs (TBP, HTT, and AR) were significantly associated with the decline in cognitive function, which together accounted for 0.49% of the variation. Furthermore, in an magnetic resonance imaging substudy, we found that CAG repeat polymorphisms in 4 PDAGs (ATXN2, CACNA1A, ATXN7, and AR) were associated with different imaging characteristics, including brain stem, putamen, globus pallidus, thalamus, and amygdala volumes. Our findings indicate that tandem repeat polymorphisms are associated with cognitive function in older adults and highlight the importance of PDAGs in elucidating its missing heritability.

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Genetic disease genes are considered a promising source of drug targets. Most diseases are caused by more than one pathogenic factor; thus, it is reasonable to consider that chemical agents targeting multiple disease genes are more likely to have desired activities. This is supported by a comprehensive analysis on the relationships between agent activity/druggability and target genetic characteristics. The therapeutic potential of agents increases steadily with increasing number of targeted disease genes, and can be further enhanced by strengthened genetic links between targets and diseases. By using the multi-label classification models for genetics-based drug activity prediction, we provide universal tools for prioritizing drug candidates. All of the documented data and the machine-learning prediction service are available at SCG-Drug (http://zhanglab.hzau.edu.cn/scgdrug).

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Due to the complexity of the pathological mechanisms of neurodegenerative diseases, traditional differentially-expressed gene selection methods cannot detect disease-associated genes accurately. Recent studies have shown that consensus-guided unsupervised feature selection (CGUFS) performs well in feature selection for identifying disease-associated genes. Since the random initialization of the feature selection matrix in CGUFS results in instability of the final disease-associated gene set, for the purposes of this study we proposed an ensemble method based on CGUFS-namely, ensemble consensus-guided unsupervised feature selection (ECGUFS) in order to further improve the accuracy of disease-associated genes and the stability of feature gene sets. We also proposed a bagging integration strategy to integrate the results of CGUFS. Lastly, we conducted experiments with Huntington's disease RNA sequencing (RNA-Seq) data and obtained the final feature gene set, where we detected 287 disease-associated genes. Enrichment analysis on these genes has shown that postsynaptic density and the postsynaptic membrane, synapse, and cell junction are all affected during the disease's progression. However, ECGUFS greatly improved the accuracy of disease-associated gene prediction and the stability of the disease-associated gene set. We conducted a classification of samples with labels based on the linear support vector machine with 10-fold cross-validation. The average accuracy is 0.9, which suggests the effectiveness of the feature gene set.

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Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are neurodegenerative disorders that share genetic risk factors and pathological hallmarks. Intriguingly, these shared factors result in a high rate of comorbidity of these diseases in patients. Intracellular protein aggregates are a common pathological hallmark of both diseases. Emerging evidence suggests that impaired RNA processing and disrupted protein homeostasis are two major pathogenic pathways for these diseases. Indeed, recent evidence from genetic and cellular studies of the etiology and pathogenesis of ALS-FTD has suggested that defects in autophagy may underlie various aspects of these diseases. In this review, we discuss the link between genetic mutations, autophagy dysfunction, and the pathogenesis of ALS-FTD. Although dysfunction in a variety of cellular pathways can lead to these diseases, we provide evidence that ALS-FTD is, in many cases, an autophagy disease.

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An important application of modern genomics is diagnosing genetic disorders. We use the largest publicly available exome sequence database to show that this key clinical service can currently be performed much more effectively in individuals of European genetic ancestry.

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Ohnologs -paralogous gene pairs generated by whole genome duplication- are enriched for dosage sensitive genes, that is, genes that have a phenotype due to copy number changes. Dosage sensitive genes frequently occur in the same metabolic pathway and in physically interacting proteins. Accumulating evidence reveals that functionally related genes tend to co-localize in the three-dimensional (3D) arrangement of chromosomes. We query whether the spatial distribution of ohnologs has implications for their dosage balance. We analyzed the colocalization frequency of ohnologs based on chromatin interaction datasets of seven human cell lines and found that ohnolog pairs exhibit higher spatial proximity in 3D nuclear organization than other paralog pairs and than randomly chosen ohnologs in the genome. We also found that colocalized ohnologs are more resistant to copy number variations and more likely to be disease-associated genes, which indicates a stronger dosage balance in ohnologs with high spatial proximity. This phenomenon is further supported by the stronger similarity of gene co-expression and of gene ontology terms of colocalized ohnologs. In addition, for a large fraction of ohnologs, the spatial colocalization is conserved in mouse cells, suggestive of functional constraint on their 3D positioning in the nucleus.

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IntegraGen SA is at the stage of commercializing a series of innovative IntegraTests™ to position itself as a leader within the rapidly growing market of predictive medicine. By applying its proprietary gene-mapping technology GenomeHIP™ (Genome Hybrid Identity Profiling) in premier patient collections, IntegraGen has rapidly discovered novel genes and genetic markers associated with a variety of complex, multifactorial diseases to use in its IntegraTests - a new class of personalized medicine diagnostics. IntegraTests provide prediction, prevention, detailed diagnosis and tailored treatment of complex diseases. Depending on the disease and the applied set of genetic markers, these tests can calculate the personal risk for acquiring the disease, define the various subforms of complex diseases, predict susceptibility to severe comorbidities, and help to define a personalized health management program. Focusing on metabolic syndrome and neuropsychiatric disorders, IntegraGen will launch its first tests in Europe in 2005.