GWAS of longitudinal trajectories at biobank scale

Ko, Seyoon; German, Christopher A.; Jensen, Aubrey; Shen, Judong; Wang, Anran; Mehrotra, Devan V.; Sun, Yan V.; Sinsheimer, Janet S.; Zhou, Hua; Zhou, Jin

doi:10.1016/j.ajhg.2022.01.018

Cited by 25 publications

(25 citation statements)

References 68 publications

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“…Indeed, each of these traits can be decomposed into vectors of interrelated components, but treating these components as independent phenotypes within existing univariate epistatic mapping tools would be inefficient because of their statistical dependence. As an alternative, the mvMAPIT framework can be used to make joint inferences about epistasis across any number of correlated phenotypic components-which, in the case of longitudinal studies for example [121][122][123][124] , could be used to interrogate how non-additive variation of trait architecture changes or evolves over time. In the first two columns, we list SNPs and their genetic location according to the mouse assembly NCBI build 34 (accessed from Shifman et al 135 ) in the format Chromosome:Basepair.…”

Section: Discussionmentioning

confidence: 99%

Leveraging the Genetic Correlation between Traits Improves the Detection of Epistasis in Genome-wide Association Studies

Stamp

DenAdel

Weinreich

et al. 2022

Preprint

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Epistasis, commonly defined as the interaction between genetic loci, is known to play an important role in the phenotypic variation of complex traits. As a result, many statistical methods have been developed to identify genetic variants that are involved in epistasis, and nearly all of these approaches carry out this task by focusing on analyzing one trait at a time. Previous studies have shown that jointly modeling multiple phenotypes can often dramatically increase statistical power for association mapping. In this study, we present the "multivariate MArginal ePIstasis Test" (mvMAPIT) - a multi-outcome generalization of a recently proposed epistatic detection method which seeks to detect marginal epistasis or the combined pairwise interaction effects between a given variant and all other variants. By searching for marginal epistatic effects, one can identify genetic variants that are involved in epistasis without the need to identify the exact partners with which the variants interact - thus, potentially alleviating much of the statistical and computational burden associated with conventional explicit search-based methods. Our proposed mvMAPIT builds upon this strategy by taking advantage of correlation structure between traits to improve the identification of variants involved in epistasis. We formulate mvMAPIT as a multivariate linear mixed model and develop a multi-trait variance component estimation algorithm for efficient parameter inference and P-value computation. Together with reasonable model approximations, our proposed approach is scalable to moderately sized GWA studies. With simulations, we illustrate the benefits of mvMAPIT over univariate (or single-trait) epistatic mapping strategies. We also apply mvMAPIT framework to protein sequence data from two broadly neutralizing anti-influenza antibodies and approximately 2,000 heterogenous stock of mice from the Wellcome Trust Centre for Human Genetics. The mvMAPIT R package can be downloaded at https://github.com/lcrawlab/mvMAPIT.

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Section: Discussionmentioning

confidence: 99%

Leveraging the Genetic Correlation between Traits Improves the Detection of Epistasis in Genome-wide Association Studies

Stamp

DenAdel

Weinreich

et al. 2022

Preprint

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“…Then each trait was standardized to mean 0 and variance 1, so that the traits were treated similarly in mean-squared error (MSE) cross-validation. Following [8, 14, 19], we first filtered samples exhibiting sex discordance, high heterozygosity, or high SNP missingness. We then excluded samples of non-European ancestry and first and second-degree relatives based on empirical kinship coefficients.…”

Section: Methodsmentioning

confidence: 99%

“…Following [8,14,19], we first filtered samples exhibiting sex discordance, high heterozygosity, or high SNP missingness. We then excluded samples of non-European ancestry and first and second-degree relatives based on empirical kinship coefficients.…”

Section: Quality Control For Uk Biobankmentioning

confidence: 99%

Multivariate Genomewide Association Analysis by Iterative Hard Thresholding

Chu¹,

Zhou

et al. 2021

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1AbstractIn genome-wide association studies (GWAS), analyzing multiple correlated traits is potentially superior to conducting multiple univariate analyses. Standard methods for multivariate GWAS operate marker-by-marker and are computationally intensive. We present a penalized regression algorithm for multivariate GWAS based on iterative hard thresholding (IHT) and implement it in a convenient Julia package MendelIHT.jl (https://github.com/OpenMendel/MendelIHT.jl). In simulation studies with up to 100 traits, IHT exhibits similar true positive rates, smaller false positive rates, and faster execution times than GEMMA’s linear mixed models and mv-PLINK’s canonical correlation analysis. As evidence of its scalability, our IHT code completed a trivariate trait analysis on the UK Biobank with 200,000 samples and 500,000 SNPs in 20 hours on a single machine.

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“…GWAS of the clozapine and norclozapine plasma concentrations, and the metabolic ratio, were performed using TrajGWAS v0.13 24 , which uses a GLMM framework to model all individuals and longitudinal measurements in each analysis. This is an advance over previous approaches that required representative single phenotypes for each individual to be derived from the repeated measures 14,15,25 ; as such constructs may not capture all the relevant variability of the data 26 .…”

Section: Genomic Data Analysesmentioning

confidence: 99%

Pharmacokinetics and pharmacogenomics of clozapine in an ancestrally diverse sample: A longitudinal analysis and GWAS using clinical monitoring data from the UK

Pardiñas

Kappel

Roberts

et al. 2022

Preprint

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BackgroundThe antipsychotic clozapine is the only drug with proven effectiveness against the treatment-resistant symptoms that affect 20-30% of those with schizophrenia. Despite this, clozapine is markedly under-prescribed, partly due to concerns about its narrow therapeutic range and adverse drug reaction profile. Both concerns are linked to drug metabolism, which varies across worldwide populations and is partially genetically determined. There is, however, a lack of clozapine pharmacogenomic data based on study participants of multiple ancestries.MethodsWe analysed data from 4,495 individuals linked to 16,068 assays from a clozapine monitoring service in the UK. Genomic information was used to identify five biogeographical ancestries (European, Sub-Saharan African, North African, Southwest Asian and East Asian) as well as admixed individuals. Pharmacokinetic modelling, GWAS, and a polygenic score association analysis were conducted on this longitudinal dataset using three outcome variables: two metabolite plasma concentrations (clozapine and norclozapine) and their ratio.FindingsA faster average clozapine metabolism was seen in those of Sub-Saharan African ancestry compared to Europeans. In contrast, East and Southwest Asians were more likely to be slow clozapine metabolisers. Eight pharmacogenomic loci were identified in the GWAS, with consistent cross-ancestral effects. Polygenic scores generated from these loci led to significant associations with clozapine outcome variables in the whole sample and within individual ancestries, with variances explained between 0.61%-7.26%.InterpretationLongitudinal cross-ancestry GWAS can discover pharmacogenomic markers of clozapine metabolism that, individually or as polygenic scores, have consistent effects across ancestries. While the potential clinical role of these predictors is evaluated, we provide strong evidence that ancestral differences in clozapine metabolism should be incorporated into clozapine dosing and managing protocols to optimise their utility for diverse populations.FundingMedical Research Council (MRC).

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GWAS of longitudinal trajectories at biobank scale

Cited by 25 publications

References 68 publications

Leveraging the Genetic Correlation between Traits Improves the Detection of Epistasis in Genome-wide Association Studies

Leveraging the Genetic Correlation between Traits Improves the Detection of Epistasis in Genome-wide Association Studies

Multivariate Genomewide Association Analysis by Iterative Hard Thresholding

Pharmacokinetics and pharmacogenomics of clozapine in an ancestrally diverse sample: A longitudinal analysis and GWAS using clinical monitoring data from the UK

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