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Analyzing the most frequent disease loci in targeted patient categories optimizes disease gene identification and test accuracy worldwide.

ABSTRACT: BACKGROUND:Our genomewide studies support targeted testing the most frequent genetic diseases by patient category: (1) pregnant patients, (2) at-risk conceptuses, (3) affected children, and (4) abnormal adults. This approach not only identifies most reported disease causing sequences accurately, but also minimizes incorrectly identified additional disease causing loci. METHODS:Diseases were grouped in descending order of occurrence from four data sets: (1) GeneTests 534 listed population prevalences, (2) 4129 high risk prenatal karyotypes, (3) 1265 affected patient microarrays, and (4) reanalysis of 25,452 asymptomatic patient results screened prenatally for 108 genetic diseases. These most frequent diseases are categorized by transmission: (A) autosomal recessive, (B) X-linked, (C) autosomal dominant, (D) microscopic chromosome rearrangements, (E) submicroscopic copy number changes, and (F) frequent ethnic diseases. RESULTS:Among affected and carrier patients worldwide, most reported mutant genes would be identified correctly according to one of four patient categories from at-risk couples with <64 tested genes to affected adults with 314 tested loci. Three clinically reported patient series confirmed this approach. First, only 54 targeted chromosomal sites would have detected all 938 microscopically visible unbalanced karyotypes among 4129 karyotyped POC, CVS, and amniocentesis samples. Second, 37 of 48 reported aneuploid regions were found among our 1265 clinical microarrays confirming the locations of 8 schizophrenia loci and 20 aneuploidies altering intellectual ability, while also identifying 9 of the most frequent deletion syndromes. Third, testing 15 frequent genes would have identified 124 couples with a 1 in 4 risk of a fetus with a recessive disease compared to the 127 couples identified by testing all 108 genes, while testing all mutations in 15 genes could have identified more couples. CONCLUSION:Testing the most frequent disease causing abnormalities in 1 of 8 reported disease loci [~1 of 84 total genes] will identify ~ 7 of 8 reported abnormal Caucasian newborn genotypes. This would eliminate ~8 to 10 of ~10 Caucasian newborn gene sequences selected as abnormal that are actually normal variants identified when testing all ~2500 diseases looking for the remaining 1 of 8 disease causing genes. This approach enables more accurate testing within available laboratory and reimbursement resources.

SUBMITTER: Lebo RV

PROVIDER: S-EPMC4312458 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature

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Analyzing the most frequent disease loci in targeted patient categories optimizes disease gene identification and test accuracy worldwide.

Lebo Roger V RV Tonk Vijay S VS

Journal of translational medicine 20150121

<h4>Background</h4>Our genomewide studies support targeted testing the most frequent genetic diseases by patient category: (1) pregnant patients, (2) at-risk conceptuses, (3) affected children, and (4) abnormal adults. This approach not only identifies most reported disease causing sequences accurately, but also minimizes incorrectly identified additional disease causing loci.<h4>Methods</h4>Diseases were grouped in descending order of occurrence from four data sets: (1) GeneTests 534 listed pop ...[more]

PMID: 25604770

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Project description:BackgroundIndividual patient data meta-analyses (IPD-MA) are regarded as the gold standard for systematic reviews, which also applies to systematic reviews of diagnostic test accuracy (DTA) studies. An increasing number of DTA systematic reviews with IPD-MA have been published in recent years, but there is much variation in how these IPD-MA were performed. A number of existing methods were found, but there is no consensus as to which methods are preferred as the standard methods for statistical analysis in DTA IPD-MA.ObjectivesTo create a web-based tool which integrates recommended statistical analyses for DTA IPD-MA, and allows researchers to analyse the data and visualize the results with interactive plots.MethodsA systematic methodological review was performed to identify statistical analyses and data visualization methods used in DTA IPD-MA. Methods were evaluated by the authors and recommended analyses were integrated into the IPDmada tool which is freely available online with the user interface developed with R Shiny package.ResultsIPDmada allows users to upload their own data, perform the meta-analysis with both continuous and dichotomized tests, and incorporate individual level covariate-adjusted analysis. All tables and figures can be exported as .csv or .pdf files. A hypothetical dataset was used to illustrate the application of IPDmada.ConclusionsIPDmada will be very helpful to researchers doing DTA IPD-MA, since it not only facilitates the statistical analysis but also provides a standard framework. The introduction of IPDmada will harmonize the methods used in DTA IPD-MA and ensure the quality of such analyses.HighlightsIPDmada is a newly developed web-based tool for performing statistical analysis of individual patient data meta-analysis of diagnostic accuracy and visualizing the results. The tool is freely available to all the researchers, and requiring no installation of statistical software/packages. The tool has an user-friendly interface, and allows meta-analysis on both dichotomized and continuous test results. Researchers can easily use this tool to investigate the threshold effect and covariate effect on the summary accuracy. The introduction and implementation of IPDmada will serve as a useful tool for DTA IPD-MA and increase the quality of such studies.

Project description:Despite the growing consensus on the importance of testing gene-gene interactions in genetic studies of complex diseases, the effect of gene-gene interactions has often been defined as a deviance from genetic additive effects, which is essentially treated as a residual term in genetic analysis and leads to low power in detecting the presence of interacting effects. To what extent the definition of gene-gene interaction at population level reflects the genes' biochemical or physiological interaction remains a mystery. In this article, we introduce a novel definition and a new measure of gene-gene interaction between two unlinked loci (or genes). We developed a general theory for studying linkage disequilibrium (LD) patterns in disease population under two-locus disease models. The properties of using the LD measure in a disease population as a function of the measure of gene-gene interaction between two unlinked loci were also investigated. We examined how interaction between two loci creates LD in a disease population and showed that the mathematical formulation of the new definition for gene-gene interaction between two loci was similar to that of the LD between two loci. This finding motived us to develop an LD-based statistic to detect gene-gene interaction between two unlinked loci. The null distribution and type I error rates of the LD-based statistic for testing gene-gene interaction were validated using extensive simulation studies. We found that the new test statistic was more powerful than the traditional logistic regression under three two-locus disease models and demonstrated that the power of the test statistic depends on the measure of gene-gene interaction. We also investigated the impact of using tagging SNPs for testing interaction on the power to detect interaction between two unlinked loci. Finally, to evaluate the performance of our new method, we applied the LD-based statistic to two published data sets. Our results showed that the P values of the LD-based statistic were smaller than those obtained by other approaches, including logistic regression models.

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Analyzing the most frequent disease loci in targeted patient categories optimizes disease gene identification and test accuracy worldwide.

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Analyzing the most frequent disease loci in targeted patient categories optimizes disease gene identification and test accuracy worldwide.

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