Dataset Information

Meta-analysis of repository data: impact of data regularization on NIMH schizophrenia linkage results.

ABSTRACT: Human geneticists are increasingly turning to study designs based on very large sample sizes to overcome difficulties in studying complex disorders. This in turn almost always requires multi-site data collection and processing of data through centralized repositories. While such repositories offer many advantages, including the ability to return to previously collected data to apply new analytic techniques, they also have some limitations. To illustrate, we reviewed data from seven older schizophrenia studies available from the NIMH-funded Center for Collaborative Genomic Studies on Mental Disorders, also known as the Human Genetics Initiative (HGI), and assessed the impact of data cleaning and regularization on linkage analyses. Extensive data regularization protocols were developed and applied to both genotypic and phenotypic data. Genome-wide nonparametric linkage (NPL) statistics were computed for each study, over various stages of data processing. To assess the impact of data processing on aggregate results, Genome-Scan Meta-Analysis (GSMA) was performed. Examples of increased, reduced and shifted linkage peaks were found when comparing linkage results based on original HGI data to results using post-processed data within the same set of pedigrees. Interestingly, reducing the number of affected individuals tended to increase rather than decrease linkage peaks. But most importantly, while the effects of data regularization within individual data sets were small, GSMA applied to the data in aggregate yielded a substantially different picture after data regularization. These results have implications for analyses based on other types of data (e.g., case-control GWAS or sequencing data) as well as data obtained from other repositories.

SUBMITTER: Walters KA

PROVIDER: S-EPMC3891773 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Meta-analysis of repository data: impact of data regularization on NIMH schizophrenia linkage results.

Walters Kimberly A KA Huang Yungui Y Azaro Marco M Tobin Kathleen K Lehner Thomas T Brzustowicz Linda M LM Vieland Veronica J VJ

PloS one 20140114 1

Human geneticists are increasingly turning to study designs based on very large sample sizes to overcome difficulties in studying complex disorders. This in turn almost always requires multi-site data collection and processing of data through centralized repositories. While such repositories offer many advantages, including the ability to return to previously collected data to apply new analytic techniques, they also have some limitations. To illustrate, we reviewed data from seven older schizop ...[more]

PMID: 24454738

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Project description:BackgroundIndividuals with schizophrenia are at higher risk of physical illnesses, which are a major contributor to their 20-year reduced life expectancy. It is currently unknown what causes the increased risk of physical illness in schizophrenia.AimsTo link genetic data from a clinically ascertained sample of individuals with schizophrenia to anonymised National Health Service (NHS) records. To assess (a) rates of physical illness in those with schizophrenia, and (b) whether physical illness in schizophrenia is associated with genetic liability.MethodWe linked genetic data from a clinically ascertained sample of individuals with schizophrenia (Cardiff Cognition in Schizophrenia participants, n = 896) to anonymised NHS records held in the Secure Anonymised Information Linkage (SAIL) databank. Physical illnesses were defined from the General Practice Database and Patient Episode Database for Wales. Genetic liability for schizophrenia was indexed by (a) rare copy number variants (CNVs), and (b) polygenic risk scores.ResultsIndividuals with schizophrenia in SAIL had increased rates of epilepsy (standardised rate ratio (SRR) = 5.34), intellectual disability (SRR = 3.11), type 2 diabetes (SRR = 2.45), congenital disorders (SRR = 1.77), ischaemic heart disease (SRR = 1.57) and smoking (SRR = 1.44) in comparison with the general SAIL population. In those with schizophrenia, carrier status for schizophrenia-associated CNVs and neurodevelopmental disorder-associated CNVs was associated with height (P = 0.015-0.017), with carriers being 7.5-7.7 cm shorter than non-carriers. We did not find evidence that the increased rates of poor physical health outcomes in schizophrenia were associated with genetic liability for the disorder.ConclusionsThis study demonstrates the value of and potential for linking genetic data from clinically ascertained research studies to anonymised health records. The increased risk for physical illness in schizophrenia is not caused by genetic liability for the disorder.

Project description:Significance statementAlterations in gut microbiota contribute to the pathophysiology of a diverse range of diseases, leading to suggestions that chronic uremia may cause intestinal dysbiosis that contributes to the pathophysiology of CKD. Various small, single-cohort rodent studies have supported this hypothesis. In this meta-analysis of publicly available repository data from studies of models of kidney disease in rodents, cohort variation far outweighed any effect of experimental kidney disease on the gut microbiota. No reproducible changes in animals with kidney disease were seen across all cohorts, although a few trends observed in most experiments may be attributable to kidney disease. The findings suggest that rodent studies do not provide evidence for the existence of "uremic dysbiosis" and that single-cohort studies are unsuitable for producing generalizable results in microbiome research.BackgroundRodent studies have popularized the notion that uremia may induce pathological changes in the gut microbiota that contribute to kidney disease progression. Although single-cohort rodent studies have yielded insights into host-microbiota relationships in various disease processes, their relevance is limited by cohort and other effects. We previously reported finding metabolomic evidence that batch-to-batch variations in the microbiome of experimental animals are significant confounders in an experimental study.MethodsTo attempt to identify common microbial signatures that transcend batch variability and that may be attributed to the effect of kidney disease, we downloaded all data describing the molecular characterization of the gut microbiota in rodents with and without experimental kidney disease from two online repositories comprising 127 rodents across ten experimental cohorts. We reanalyzed these data using the DADA2 and Phyloseq packages in R, a statistical computing and graphics system, and analyzed data both in a combined dataset of all samples and at the level of individual experimental cohorts.ResultsCohort effects accounted for 69% of total sample variance ( P <0.001), substantially outweighing the effect of kidney disease (1.9% of variance, P =0.026). We found no universal trends in microbial population dynamics in animals with kidney disease, but observed some differences (increased alpha diversity, a measure of within-sample bacterial diversity; relative decreases in Lachnospiraceae and Lactobacillus ; and increases in some Clostridia and opportunistic taxa) in many cohorts that might represent effects of kidney disease on the gut microbiota .ConclusionsThese findings suggest that current evidence that kidney disease causes reproducible patterns of dysbiosis is inadequate. We advocate meta-analysis of repository data as a way of identifying broad themes that transcend experimental variation.

Dataset Information

Meta-analysis of repository data: impact of data regularization on NIMH schizophrenia linkage results.

Publications

Meta-analysis of repository data: impact of data regularization on NIMH schizophrenia linkage results.

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