The more the merrier? Multivariate approaches to genome-wide association analysis

Vroom, César-Reyer; Leeuw, Christiaan de; Posthuma, Daniëlle; Dolan, Conor V.; Sluis, Sophie van der

doi:10.1101/610287

Cited by 5 publications

(4 citation statements)

References 71 publications

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“…In a recent comparison of statistical power of 19 multivariate genomewide association methods, it was shown that S Het seems to benefit from the presence of opposite effect estimates, which is relevant for dietary composition. In addition, Shet performed slightly better when applied to highly correlated traits (Vroom et al, 2019).…”

Section: Single-trait and Multi-trait Genome-wide Association Meta-analysismentioning

confidence: 98%

Genetic Analysis of Dietary Intake Identifies New Loci and Functional Links with Metabolic Traits

et al. 2019

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Dietary intake, a major contributor to the global obesity epidemic, is a complex phenotype partially affected by innate physiological processes. However, previous genome-wide association studies have only implicated a few loci in variability of dietary intake. Here, we present a multi-trait genome-wide association meta-analysis of inter-individual variation in dietary intake in 283,119 European-ancestry participants from UK Biobank and CHARGE consortium identifying 96 genome-wide significant loci. Dietary intake signals map to different brain tissues and are enriched for genes expressed in 1-tanycytes and serotonergic and GABAergic neurons. We also find enrichment of biological pathways related to neurogenesis. Integration of cell-line and brain-specific epigenomic annotations identify 15 additional loci. Clustering of genome-wide significant variants based on clinical and physiological similarities yields three main genetic clusters with distinct associations with obesity and type 2 diabetes. Overall, these results enhance biological understanding of dietary composition, highlight neural mechanisms, and support functional follow-up experiments.

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Section: Single-trait and Multi-trait Genome-wide Association Meta-analysismentioning

confidence: 98%

Genetic Analysis of Dietary Intake Identifies New Loci and Functional Links with Metabolic Traits

et al. 2019

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“…The ABCD Data Acquisition and Integration Core provided similar, but not identical, restingstate fMRI-derived phenotypes for replication purposes. Instead of the 17 Yeo & Krienen networks based on 1,000 parcels, temporal variance in 333 Gordon-defined parcels and 66 average correlations between 12 Gordon-defined networks were available 45 . We averaged the parcel variances belonging to the same network to achieve comparability to our discovery phenotypes.…”

Section: Functional Mri-derived Phenotypesmentioning

confidence: 99%

“…Multivariate GWAS approaches gain their statistical power due to the distributed nature of genetic influences across phenotypes 12 . Such approaches are well-suited for the identification of pleiotropic variants and genes with effects across neuroimaging modalities.…”

Section: Introductionmentioning

confidence: 99%

Abundant pleiotropy across neuroimaging modalities identified through a multivariate genome-wide association study

Tissink

Shadrin

Meer

et al. 2022

Preprint

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Genetic pleiotropy is abundant across spatially distributed brain characteristics derived from one neuroimaging modality (e.g. structural, functional or diffusion MRI). A better understanding of pleiotropy across modalities could inform us on the integration of brain function, micro- and macrostructure. Here we show extensive genetic overlap across neuroimaging modalities at a locus and gene level in the UK Biobank (N=34,029) and ABCD study (N=8,607). When jointly analysing phenotypes derived from structural, functional and diffusion MRI with the Multivariate Omnibus Statistical Test (MOSTest) method, we boost the discovery of loci and genes associated with brain features beyond previously identified effects for each modality individually. Cross-modality genes are involved in fundamental biological processes and predominantly expressed during prenatal brain development. We additionally boost genetic discovery for psychiatric disorders by conditioning independent GWAS on our multimodal multivariate GWAS. These findings shed light on the shared genetic mechanisms underlying variation in brain morphology, functional connectivity, and tissue composition - features often concurrently altered within psychiatric disorders.

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“…These challenges include trans-ethnic bias across populations (i.e. GWAS sample size for non-European populations is relatively lower and a PRS derived from one population is expected to perform less well in other populations due to differences in allele frequencies, linkage disequilibrium (LD) patterns and effect sizes across different populations) [ 20 , 21 , 22 ] and architectural diversity across multiple correlated traits [ 23 ]. Some other challenges include the lack of clear guidelines on clinical interpretation of PGx polygenic models, PGx studies usually requiring more well-defined drug response clinical endpoints, and the lack of PRS reporting guidelines.…”

Section: Introductionmentioning

confidence: 99%

Applying polygenic risk score methods to pharmacogenomics GWAS: challenges and opportunities

Zhai,

Mehrotra,

Shen

2023

Briefings in Bioinformatics

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Polygenic risk scores (PRSs) have emerged as promising tools for the prediction of human diseases and complex traits in disease genome-wide association studies (GWAS). Applying PRSs to pharmacogenomics (PGx) studies has begun to show great potential for improving patient stratification and drug response prediction. However, there are unique challenges that arise when applying PRSs to PGx GWAS beyond those typically encountered in disease GWAS (e.g. Eurocentric or trans-ethnic bias). These challenges include: (i) the lack of knowledge about whether PGx or disease GWAS/variants should be used in the base cohort (BC); (ii) the small sample sizes in PGx GWAS with corresponding low power and (iii) the more complex PRS statistical modeling required for handling both prognostic and predictive effects simultaneously. To gain insights in this landscape about the general trends, challenges and possible solutions, we first conduct a systematic review of both PRS applications and PRS method development in PGx GWAS. To further address the challenges, we propose (i) a novel PRS application strategy by leveraging both PGx and disease GWAS summary statistics in the BC for PRS construction and (ii) a new Bayesian method (PRS-PGx-Bayesx) to reduce Eurocentric or cross-population PRS prediction bias. Extensive simulations are conducted to demonstrate their advantages over existing PRS methods applied in PGx GWAS. Our systematic review and methodology research work not only highlights current gaps and key considerations while applying PRS methods to PGx GWAS, but also provides possible solutions for better PGx PRS applications and future research.

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The more the merrier? Multivariate approaches to genome-wide association analysis

Cited by 5 publications

References 71 publications

Genetic Analysis of Dietary Intake Identifies New Loci and Functional Links with Metabolic Traits

Genetic Analysis of Dietary Intake Identifies New Loci and Functional Links with Metabolic Traits

Abundant pleiotropy across neuroimaging modalities identified through a multivariate genome-wide association study

Applying polygenic risk score methods to pharmacogenomics GWAS: challenges and opportunities

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