The metaCCA method identified novel variants associated with psychiatric disorders by effectively incorporating information from different GWAS datasets. Our analyses may provide insights for some common therapeutic approaches of these five major psychiatric disorders based on the pleiotropic genes and common mechanisms identified.
The effects of age, period, and cohort on mortality rates of bladder cancer in China remained vague. This study aimed to analyze the secular trends of bladder cancer mortality in China and estimate the independent effects of age, period, and cohort. Methods: Data for bladder cancer mortality from 1991 to 2015 was obtained from the WHO Mortality Database and China Health Statistical Yearbook. The age-period-cohort model was used to estimate the effect of age, period, and cohort. The intrinsic estimator method was used to solve the nonidentification problem of collinearity among age, period, and cohort. Results: The age-standardized mortality rates of total residents (2.33-1.87/100,000), male (3.45-2.89/100,000), and female (1.24-0.82/100,000) showed decreasing trends, which was more obvious in males than in females. Age effects increased consistently with age in all age groups (coefficients:-2.02 to 1.91 in the total population,-2.06 to 2.02 in males and-2.04 to 1.81 in females). Cohort effects decreased overall (coefficients: 0.96 to-1.62 in the total population, 1.11 to-1.66 in males and 0.78 to-1.46 in females). Period effects were not found in China. Conclusion: Although a decreasing mortality was observed, the bladder cancer burden in China will likely increase in the next few years due to population aging, environmental pollution, and food safety. The findings suggested that preventive measures should be taken corresponding to the changes in age-and cohort-related factors in the population.
19Although genome-wide association studies (GWAS) have a dramatic impact on susceptibility 20 locus discovery, this univariate approach has limitation in detecting complex genotype-21 phenotype correlations. It is essential to identify shared genetic risk factors acting through 22 common biological mechanisms of autoimmune diseases with a multivariate analysis. In this 23 study, the GWAS summary statistics including 41,274 single nucleotide polymorphisms 24 (SNPs) located in 11,516 gene regions was analyzed to identify shared variants of seven 25 autoimmune diseases using metaCCA method. Gene-based association analysis was used to 26 refine the pleiotropic genes. In addition, GO term enrichment analysis and protein-protein 27 interaction network analysis were applied to explore the potential biological function of the 28 identified genes. After metaCCA analysis, 4,962 SNPs (P<1.21×10 −6 ) and 1,044 pleotropic 29 genes (P<4.34×10 −6 ) were identified. By screening the results of gene-based p-values, we 30 identified the existence of 27 confirmed pleiotropic genes and highlighted 40 novel pleiotropic 31 genes which achieved significance threshold in metaCCA analysis and were also associated 32 with at least one autoimmune disease in the VEGAS2 analysis. The metaCCA method could 33 identify novel variants associated with complex diseases incorporating different GWAS 34 datasets. Our analysis may provide insights for some common therapeutic approaches of 35 autoimmune diseases based on the pleiotropic genes and common mechanisms identified. 36Author summary 37 Although previous researches have clearly indicated varying degrees of overlapping genetic 38 sensitivities in autoimmune diseases, it has proven GWAS only explain small percent of 39 heritability. Here, we take advantage of recent technical and methodological advances to 40 identify pleiotropic genes that act on common biological mechanisms and the overlapping 41 pathophysiological pathways of autoimmune diseases. After selection using multivariate 3 42 analysis and verification using gene-based analyses, we successfully identified a total of 67 43 pleiotropic genes and performed the functional term enrichment analysis. In particularly, 27 44 genes were identified to be pleiotropic in previous different types of studies, which were 45 validated by our present study. Forty significant genes (16 genes were associated with one 46 disease earlier, and 24 were novel) might be the novel pleiotropic candidate genes for seven 47 autoimmune diseases. The improved detection not only yielded the shared genetic components 48 but also provided better understanding for exploring the potential common biological 49 pathogenesis of these major autoimmune diseases. 4 50 51 Autoimmune diseases are chronic conditions initiated by loss of immunological tolerance to 52 self-antigens[1]. An estimated incidence of autoimmune diseases is about 90 cases per 100,000 53 person-year and the prevalence is about 7.6-9.4% in Europe and North America[2]. The chronic 54 nature of such di...
Acute myeloid leukemia with complex karyotype (CK-AML) is associated with poor prognosis, which is only in part explained by underlying TP53 mutations. Especially in the presence of complex chromosomal rearrangements, such as chromothripsis, the outcome of CK-AML is dismal. However, this degree of complexity of genomic rearrangements contributes to the leukemogenic phenotype and treatment resistance of CK-AML remains largely unknown. Applying an integrative workflow for the detection of structural variants (SVs) based on Oxford Nanopore (ONT) genomic DNA long-read sequencing (gDNA-LRS) and high-throughput chromosome confirmation capture (Hi-C) in a well-defined cohort of CK-AML identified regions with an extreme density of SVs. These rearrangements consisted to a large degree of focal amplifications enriched in the proximity of mammalian-wide interspersed repeat (MIR) elements, which often result in oncogenic fusion transcripts, such as USP7::MVD, or the deregulation of oncogenic driver genes as confirmed by RNA-seq and ONT direct cDNA sequencing. We termed this novel phenomenon chromocataclysm. Thus, our integrative SV detection workflow combing gDNA-LRS and Hi-C enables to unravel complex genomic rearrangements at a very high resolution in regions hard to analyze by conventional sequencing technology, thereby providing an important tool to identify novel important drivers underlying cancer with complex karyotypic changes.
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