Geographical variations in mood and psychotic disorders have been found in upper-income countries. We looked for geographic variation in these disorders in Colombia, a middle-income country. We ...analyzed electronic health records from the Clínica San Juan de Dios Manizales (CSJDM), which provides comprehensive mental healthcare for the one million inhabitants of Caldas.
We constructed a friction surface map of Caldas and used it to calculate the travel-time to the CSJDM for 16,295 patients who had received an initial diagnosis of mood or psychotic disorder. Using a zero-inflated negative binomial regression model, we determined the relationship between travel-time and incidence, stratified by disease severity. We employed spatial scan statistics to look for patient clusters.
We show that travel-times (for driving) to the CSJDM are less than 1 h for ~50% of the population and more than 4 h for ~10%. We find a distance-decay relationship for outpatients, but not for inpatients: for every hour increase in travel-time, the number of expected outpatient cases decreases by 20% (RR = 0.80, 95% confidence interval 0.71, 0.89, p = 5.67E-05). We find nine clusters/hotspots of inpatients.
Our results reveal inequities in access to healthcare: many individuals requiring only outpatient treatment may live too far from the CSJDM to access healthcare. Targeting of resources to comprehensively identify severely ill individuals living in the observed hotspots could further address treatment inequities and enable investigations to determine factors generating these hotspots.
Cells adjust gene expression profiles in response to environmental and physiological changes through a series of signal transduction pathways. Upon activation or deactivation, the terminal regulators ...bind to or dissociate from DNA, respectively, and modulate transcriptional activities on particular promoters. Traditionally, individual reporter genes have been used to detect the activity of the transcription factors. This approach works well for simple, non-overlapping transcription pathways. For complex transcriptional networks, more sophisticated tools are required to deconvolute the contribution of each regulator. Here, we demonstrate the utility of network component analysis in determining multiple transcription factor activities based on transcriptome profiles and available connectivity information regarding network connectivity. We used Escherichia coli carbon source transition from glucose to acetate as a model system. Key results from this analysis were either consistent with physiology or verified by using independent measurements.
The observation that variants regulating gene expression (expression quantitative trait loci, eQTL) are at a high frequency among SNPs associated with complex traits has made the genome-wide ...characterization of gene expression an important tool in genetic mapping studies of such traits. As part of a study to identify genetic loci contributing to bipolar disorder and other quantitative traits in members of 26 pedigrees from Costa Rica and Colombia, we measured gene expression in lymphoblastoid cell lines derived from 786 pedigree members. The study design enabled us to comprehensively reconstruct the genetic regulatory network in these families, provide estimates of heritability, identify eQTL, evaluate missing heritability for the eQTL, and quantify the number of different alleles contributing to any given locus. In the eQTL analysis, we utilize a recently proposed hierarchical multiple testing strategy which controls error rates regarding the discovery of functional variants. Our results elucidate the heritability and regulation of gene expression in this unique Latin American study population and identify a set of regulatory SNPs which may be relevant in future investigations of complex disease in this population. Since our subjects belong to extended families, we are able to compare traditional kinship-based estimates with those from more recent methods that depend only on genotype information.
We consider resequencing studies of associated loci and the problem of prioritizing sequence variants for functional follow-up. Working within the multivariate linear regression framework helps us to ...account for the joint effects of multiple genes; and adopting a Bayesian approach leads to posterior probabilities that coherently incorporate all information about the variants' function. We describe two novel prior distributions that facilitate learning the role of each variable site by borrowing evidence across phenotypes and across mutations in the same gene. We illustrate their potential advantages with simulations and reanalyzing a data set of sequencing variants.
The genetic programs underlying neural stem cell (NSC) proliferation and pluripotentiality have only been partially elucidated. We compared the gene expression profile of proliferating neural stem ...cell cultures (NS) with cultures differentiated for 24 h (DC) to identify functionally coordinated alterations in gene expression associated with neural progenitor proliferation. The majority of differentially expressed genes (65%) were upregulated in NS relative to DC. Microarray analysis of this in vitro system was followed by high throughput screening in situ hybridization to identify genes enriched in the germinal neuroepithelium, so as to distinguish those expressed in neural progenitors from those expressed in more differentiated cells in vivo. NS cultures were characterized by the coordinate upregulation of genes involved in cell cycle progression, DNA synthesis, and metabolism, not simply related to general features of cell proliferation, since many of the genes identified were highly enriched in the CNS ventricular zones and not widely expressed in other proliferating tissues. Components of specific metabolic and signal transduction pathways, and several transcription factors, including Sox3, FoxM1, and PTTG1, were also enriched in neural progenitor cultures. We propose a putative network of gene expression linking cell cycle control to cell fate pathways, providing a framework for further investigations of neural stem cell proliferation and differentiation.
Genomic copy number variations (CNVs) and increased parental age are both associated with the risk to develop a variety of clinical neuropsychiatric disorders such as autism, schizophrenia and ...bipolar disorder. At the same time, it has been shown that the rate of transmitted de novo single nucleotide mutations is increased with paternal age. To address whether paternal age also affects the burden of structural genomic deletions and duplications, we examined various types of CNV burden in a large population sample from the Netherlands. Healthy participants with parental age information (
n
= 6,773) were collected at different University Medical Centers. CNVs were called with the PennCNV algorithm using Illumina genome-wide SNP array data. We observed no evidence in support of a paternal age effect on CNV load in the offspring. Our results were negative for global measures as well as several proxies for de novo CNV events in this unique sample. While recent studies suggest de novo single nucleotide mutation rate to be dominated by the age of the father at conception, our results strongly suggest that at the level of global CNV burden there is no influence of increased paternal age. While it remains possible that local genomic effects may exist for specific phenotypes, this study indicates that global CNV burden and increased father’s age may be independent disease risk factors.
The goal of expression quantitative trait loci (eQTL) studies is to identify the genetic variants that influence the expression levels of the genes in an organism. High throughput technology has made ...such studies possible: in a given tissue sample, it enables us to quantify the expression levels of approximately 20 000 genes and to record the alleles present at millions of genetic polymorphisms. While obtaining this data is relatively cheap once a specimen is at hand, obtaining human tissue remains a costly endeavor: eQTL studies continue to be based on relatively small sample sizes, with this limitation particularly serious for tissues as brain, liver, etc.-often the organs of most immediate medical relevance. Given the high-dimensional nature of these datasets and the large number of hypotheses tested, the scientific community has adopted early on multiplicity adjustment procedures. These testing procedures primarily control the false discoveries rate for the identification of genetic variants with influence on the expression levels. In contrast, a problem that has not received much attention to date is that of providing estimates of the effect sizes associated with these variants, in a way that accounts for the considerable amount of selection. Yet, given the difficulty of procuring additional samples, this challenge is of practical importance. We illustrate in this work how the recently developed conditional inference approach can be deployed to obtain confidence intervals for the eQTL effect sizes with reliable coverage. The procedure we propose is based on a randomized hierarchical strategy with a 2-fold contribution: (1) it reflects the selection steps typically adopted in state of the art investigations and (2) it introduces the use of randomness instead of data-splitting to maximize the use of available data. Analysis of the GTEx Liver dataset (v6) suggests that naively obtained confidence intervals would likely not cover the true values of effect sizes and that the number of local genetic polymorphisms influencing the expression level of genes might be underestimated.
We describe domain pair exclusion analysis (DPEA), a method for inferring domain interactions from databases of interacting proteins. DPEA features a log odds score, Eij, reflecting confidence that ...domains i and j interact. We analyzed 177,233 potential domain interactions underlying 26,032 protein interactions. In total, 3,005 high-confidence domain interactions were inferred, and were evaluated using known domain interactions in the Protein Data Bank. DPEA may prove useful in guiding experiment-based discovery of previously unrecognized domain interactions.
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