A multi-scenario genome-wide medical population genetics simulation framework

Mugo, Jacquiline W; Geza, Ephifania; Defo, Joel; Elsheikh, Samar S. M.; Mazandu, Gaston K.; Mulder, Nicola; Chimusa, Emile R.

doi:10.1093/bioinformatics/btx369

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…To facilitate the assessment of common GWAS tools, we simulated homogeneous and heterogeneous datasets based on haplotypes from the 1000 Genomes project spanning the genome and realistic enough to mimic African, European, and admixed populations to challenge the statistical methods for association testing in real-world conditions. We used a resampling model with recombination breakpoints while mimicking mutation rates as implemented in FractalSIM (Mugo et al, 2017).…”

Section: Methodsmentioning

confidence: 99%

Data Simulation to Optimize the GWAS Framework in Diverse Populations

Mugo,

Chimusa,

Mulder

2023

Preprint

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Whole-genome or genome-wide association studies have become a fundamental part of modern genetic studies and methods for dissecting the genetic architecture of common traits based on common polymorphisms in random populations. It is hoped that there will be many potential uses of these identified variants, including a better understanding of the pathogenesis of traits, the discovery of biomarkers and protein targets, and the clinical prediction of drug treatments for global health. Questions have been raised on whether associations that are largely discovered in populations of European descent are replicable in diverse populations, can inform medical decision-making globally, and how efficiently current GWAS tools perform in populations of high genetic diversity, multi-wave genetic admixture, and low linkage disequilibrium (LD), such as African populations. In this study, we employ genomic data simulation to mimic structured African, European, and multi-way admixed populations to evaluate the replicability of association signals from current state-of-the-art GWAS tools in these populations. We then leverage the results to discuss an optimized framework for the analysis of GWAS data in diverse populations and outline the implications, challenges, and opportunities these studies present for populations of non-European descent.

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Section: Methodsmentioning

confidence: 99%

Data Simulation to Optimize the GWAS Framework in Diverse Populations

Mugo,

Chimusa,

Mulder

2023

Preprint

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“…The genetic model with 30 disease-associated SNPs out of total 10,000 preserves the characteristic sparsity of “true signal” in GWAS datasets, while keeping the dataset size manageable for rigorous simulations. Number of samples, number of SNPs, number of disease-associated SNPs, and effect sizes were set in accordance with precedence in literature[106-108], where in particular, effect sizes were selected to ensure genotype-specific disease odds ratio remained realistic (in the range: 1-3)[83-85] (S3 Fig). For each simulated true disease phenotype Y’ , differential misclassification was introduced at varying degrees by switching a fraction of randomly selected controls to cases.…”

Section: Methodsmentioning

confidence: 99%

Identifying novel associations in GWAS by hierarchical Bayesian latent variable detection of differentially misclassified phenotypes

Shafquat

Crystal

Mezey

2019

Preprint

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AbstractHeterogeneity in definition and measurement of complex diseases in Genome-Wide Association Studies (GWAS) may lead to misdiagnoses and misclassification errors that can significantly impact discovery of disease loci. While well appreciated, almost all analyses of GWAS data consider reported disease phenotype values as is without accounting for potential misclassification. Here, we introduce Phenotype Latent variable Extraction of disease misdiagnosis (PheLEx), a GWAS analysis framework that learns and corrects misclassified phenotypes using structured genotype associations within a dataset. PheLEx consists of a hierarchical Bayesian latent variable model, where inference of differential misclassification is accomplished using filtered genotypes while implementing a full mixed model to account for population structure and genetic relatedness in study populations. Through simulations, we show that the PheLEx framework dramatically improves recovery of the correct disease state when considering realistic allele effect sizes compared to existing methodologies designed for Bayesian recovery of disease phenotypes. We also demonstrate the potential of PheLEx for extracting new candidate loci from existing GWAS data by analyzing epilepsy and bipolar disorder phenotypes available from the UK Biobank dataset, where we identify new candidate disease loci not previously reported for these datasets that have biological connections to the disease phenotypes and/or were identified in independent GWAS. In the discussion, we consider both the broader consequences and importance of careful interpretation of misclassification correction in GWAS phenotypes, as well as potential of PheLEx for re-analyzing existing GWAS data to make novel discoveries.Author SummaryPrevalent misdiagnosis of diseases due to lack of understanding and/or gold-standard diagnostic measures can impact any analytics that follow. These misdiagnosis errors are especially significant in the domain of psychiatric or psychological disorders where the definition of disease and/or their diagnostic tools are always in flux or under further improvement. Here, we propose a method to extract misdiagnosis from disease and infer the correct disease phenotype. We examined the performance of this method on rigorous simulations and real disease phenotypes obtained from the UK Biobank database. We found that this method successfully recovered misdiagnosed individuals in simulations using a carefully designed hierarchical Bayesian latent variable model framework. For real disease phenotypes, epilepsy and bipolar disorder, this method not only suggested an alternate phenotype but results from this method were also used to discover new genomic loci that have been previously showed to be associated with the respective phenotypes, suggesting that this method can be further used to reanalyze large-scale genetic datasets to discover novel loci that might be ignored using traditional methodologies.

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“…The genetic model with 300 disease-associated SNPs with realistic effect sizes out of total 100,000 SNPs preserves the characteristic sparsity of "true signal" in GWAS datasets, with phenotypic variance explained by each SNP in the empirically observed range of 1e − 9 %-3%. Overall, the number of SNPs, number of diseaseassociated SNPs, phenotype heritability values, and simulated effect sizes were set in accordance with precedence in literature [86,87,[138][139][140].…”

Section: Simulation Study Simulation Datasetsmentioning

confidence: 99%

Identifying novel associations in GWAS by hierarchical Bayesian latent variable detection of differentially misclassified phenotypes

2020

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Background: Heterogeneity in the definition and measurement of complex diseases in Genome-Wide Association Studies (GWAS) may lead to misdiagnoses and misclassification errors that can significantly impact discovery of disease loci. While well appreciated, almost all analyses of GWAS data consider reported disease phenotype values as is without accounting for potential misclassification. Results: Here, we introduce Phenotype Latent variable Extraction of disease misdiagnosis (PheLEx), a GWAS analysis framework that learns and corrects misclassified phenotypes using structured genotype associations within a dataset. PheLEx consists of a hierarchical Bayesian latent variable model, where inference of differential misclassification is accomplished using filtered genotypes while implementing a full mixed model to account for population structure and genetic relatedness in study populations. Through simulations, we show that the PheLEx framework dramatically improves recovery of the correct disease state when considering realistic allele effect sizes compared to existing methodologies designed for Bayesian recovery of disease phenotypes. We also demonstrate the potential of PheLEx for extracting new potential loci from existing GWAS data by analyzing bipolar disorder and epilepsy phenotypes available from the UK Biobank. From the PheLEx analysis of these data, we identified new candidate disease loci not previously reported for these datasets that have value for supplemental hypothesis generation. Conclusion: PheLEx shows promise in reanalyzing GWAS datasets to provide supplemental candidate loci that are ignored by traditional GWAS analysis methodologies.

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A multi-scenario genome-wide medical population genetics simulation framework

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 5 publications

References 17 publications

Data Simulation to Optimize the GWAS Framework in Diverse Populations

Data Simulation to Optimize the GWAS Framework in Diverse Populations

Identifying novel associations in GWAS by hierarchical Bayesian latent variable detection of differentially misclassified phenotypes

Identifying novel associations in GWAS by hierarchical Bayesian latent variable detection of differentially misclassified phenotypes

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