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Seraphim Kudryashov
Seraphim Kudryashov

My Shared Pathway Pdf _VERIFIED_ Download

Hypospadias is one of the most common birth defects among males. Although the birth prevalence of hypospadias has been reported as increasing in some geographic regions, inconsistencies in ascertainment of mild cases and variability in reporting standards are likely contributing to these apparent trends. While hypospadias is highly heritable, there is limited knowledge about the specific genetic and epigenetic factors that play a role in its etiology. Risk factors for hypospadias include family history, older maternal age, nulliparity, high maternal prepregnancy body mass index, hypertension or preeclampsia, multiple gestations, reduced birth weight, and small for gestational age. Of the various prenatal exposures to medications that have been studied, the strongest evidence supports valproic acid as a contributor to hypospadias. Studies evaluating the impact of assisted reproductive technologies (ART) on hypospadias are inconclusive because of potential confounding by subfertility. Many causes of hypospadias may act through a few shared pathways, such as placental dysfunction.

my shared pathway pdf download

Many causes of hypospadias may act through a few shared pathways. For example, placental dysfunction may contribute to the development of hypospadias and also cause growth restriction, and upstream causes of placental dysfunction would be implicated as causes of hypospadias.

Genome-wide association studies have confirmed the polygenic nature of schizophrenia and suggest that there are hundreds or thousands of alleles associated with increased liability for the disorder. However, the generalizability of any one allelic marker of liability is remarkably low and has bred the notion that schizophrenia may be better conceptualized as a pathway(s) disorder. Here, we empirically tested this notion by conducting a pathway-wide association study (PWAS) encompassing 255 experimentally validated Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways among 5033 individuals diagnosed with schizophrenia and 5332 unrelated healthy controls across three distinct ethnic populations; European-American (EA), African-American (AA) and Han Chinese (CH). We identified 103, 74 and 87 pathways associated with schizophrenia liability in the EA, CH and AA populations, respectively. About half of these pathways were uniquely associated with schizophrenia liability in each of the three populations. Five pathways (serotonergic synapse, ubiquitin mediated proteolysis, hedgehog signaling, adipocytokine signaling and renin secretion) were shared across all three populations and the single-nucleotide polymorphism sets representing these five pathways were enriched for single-nucleotide polymorphisms with regulatory function. Our findings provide empirical support for schizophrenia as a pathway disorder and suggest schizophrenia is not only a polygenic but likely also a poly-pathway disorder characterized by both genetic and pathway heterogeneity.

One such approach is to examine SNPs within the biological pathway(s) in which they reside. Underlying this approach is the notion that schizophrenia is a pathway(s) disorder,3 whereby one or a number of SNPs within a pathway could result in an increased liability to schizophrenia by altering sensitivity to environmental insults and/or disruption of brain development. In the context of schizophrenia, this pathway approach has been applied in a variety of forms ranging from pathway clustering analysis,4 where SNPs in key genes within a single pathway are examined, to post-hoc pathway enrichment analyses of candidate SNP-sets using the SNP ratio test5 or bioinformatics resources (for example, Ingenuity Pathway Analysis).6, 7, 8 These approaches undoubtedly have a pathway focus but provide an incomplete examination of the compendium of known human biological pathways. Our primary aim was to conduct a comprehensive pathway-wide association study (PWAS) of schizophrenia. Here, we report results of that analysis in which we tested 255 biological pathway-based SNP-sets for their association and potential function in schizophrenia in three ancestral distinct populations.

SNPs surviving quality control were mapped to gene loci using the annotation provided by the National Center for Biotechnology Information (see Supplementary Methods for details). Genes were then mapped to pathways curated by the Kyoto Encyclopedia of Genes and Genomes (KEGG, Release 76.0, 1 October, 2015),13 which includes 301 human pathways from six main categories (metabolism, genetic Information processing, environmental information processing, cellular processes, organismal systems and human diseases). A mega KEGG pathway (metabolic pathways, hsa01100) that encompasses several other pathways was excluded, leaving 300 pathways available for further analysis.

The analysis pipeline used to assess each of the 300 KEGG pathways for their association with schizophrenia is depicted in Figure 1, evolving from our previously published pathway analysis pipeline.14 For each pathway the discovery dataset for the EA and CH cohorts as well as the single dataset available for the AA cohort were randomly split (maintaining the case:control ratio of the full dataset) 100 times into two subsets, a SNP (that is, feature) selection set (80% of the participants) and a test set (20% of participants). Within each SNP selection set, 80% of participants were randomly selected 10 times (maintaining the case:control ratio of the full dataset) and the resulting subsets were subjected to the maximum relevance minimum redundancy (mRmR) feature selection procedure (blue box, Figure 1).15 The mRmR procedure was chosen as an alternative to P-value-based feature selection procedures that are dependent on sample size and do not necessarily result in feature sets that maximize relevance and minimize redundancy (that is, increase mutual information; see Supplementary Materials and Supplementary Figure S2 for details and a comparison of the two feature selection methods in our datasets). This procedure resulted in 300 SNP sets, one for each of the KEGG pathways (Supplementary Table S2). Among these 300 SNP sets, 45 sets containing less than two features (SNPs) at one or more of the 100 iterations were excluded from further analysis, as our algorithm requires two or more features to fit a model.

To further evaluate our pathway analysis pipeline, we selected 129 previously identified gene ontology (GO) pathways associated with schizophrenia and applied our pipeline to each of the 129 pathways in all three populations.

To assess whether the mRmR feature selection approach was capable of selecting informative features and to evaluate the potential functional relevance of selected features, we utilized the brain expression quantitative trait loci (eQTLs) dataset obtained from the genotype-tissue expression (GTEx) portal v6.0,19 as well as the functional annotation information obtained from the RegulomeDB, a database that annotates SNPs with known and predicted regulatory elements.20 We hypothesized that the selected features with greater appearance rates within significant schizophrenia liability pathways would be enriched for functional SNPs compared with SNP sets derived from non-significant pathways.

Analysis of SNPs selected to represent the five pathways that overlapped in the three populations showed SNPs with greater appearance rates had a greater probability of being an eQTL or having some other regulatory function (Figure 3; Supplementary Figures S5 and S6). SNPs that appeared >50% of the time during our feature selection procedure were more likely to be functional compared with 100 random SNP sets of equal size, with the exception of the serotonergic synapse SNPs in EAs. Likewise, SNPs with appearance rates >75% also had a higher probability to be functional, although for six of the pathway-population pairs (Figure 3) our feature selection procedure did not identify SNP sets enriched for functional SNPs.

Summary of the functional analysis performed on single-nucleotide polymorphisms (SNPs) with an appearance rate of >50% and >75% during feature selection in the five shared pathways across the three populations. Red boxes indicate the proportion of functional SNPs (based on GTEx or RegulomeDB data) was significantly greater compared with SNP sets derived from non-significant pathways within that population. Blue boxes indicate the proportion of functional SNPs was significantly lower compared with SNP sets derived from non-significant pathways within that population.

The notion that schizophrenia is a pathway disease has only recently been proposed3 and as such empirical testing of this notion is limited. We conducted a PWAS of schizophrenia in three ancestrally distinct cohorts. We found evidence of pathway heterogeneity in schizophrenia liability, identified five pathways conferring liability across populations and showed that the SNP sets representing these five pathways were enriched for SNPs with regulatory functions.

Furthermore, our results suggest disruption of certain pathways may be necessary (but perhaps not sufficient) for the development of schizophrenia across populations. About one-fourth (27%) of the pathways we tested were associated with liability to schizophrenia in two or more of the populations, among which five pathways were associated with schizophrenia liability in all three cohorts. These pathways included the serotonergic synapse, ubiquitin mediated proteolysis, hedgehog signaling, renin secretion and adipocytokine signaling, all of which have been implicated in schizophrenia and/or related phenotypes.

A number of post-mortem, functional neuroimaging and peripheral biomarker studies have implicated the serotonergic system in the pathophysiology of schizophrenia (for review see: ref. 23) and many atypical antipsychotic agents (for example, clozapine, olanzapine) are potent serotonin receptor 2A antagonists.24 Thus, identification of the serotonergic synapse pathway in the current study is perhaps not surprising. In fact, the largest schizophrenia GWAS to date found SNPs in three genes (CACNA1C, ITPR3 and CYP2D6) within the serotonergic synapse pathway reached GWAS significance (P 350c69d7ab


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