Conditional and interaction gene-set analysis reveals novel functional pathways for blood pressure

Leeuw, Christiaan de; Stringer, Sven; Dekkers, Ilona A.; Heskes, Tom; Posthuma, Daniëlle

doi:10.1038/s41467-018-06022-6

Cited by 59 publications

(73 citation statements)

References 41 publications

(48 reference statements)

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“…Pairwise conditional gene set analysis allows one to disentangle overlapping associations of gene sets (see Methods). Conditional gene-set analyses can be used to examine whether a gene set is independently associated with a trait, rather than being associated because it is nested within a larger associated gene set 38 . These analyses indicated that nine gene sets constitute largely independent associations, defined as remaining significant in the majority of the conditional analyses ( Supplementary Figs.…”

Section: Resultsmentioning

confidence: 99%

Genome-wide meta-analysis of brain volume identifies genomic loci and genes shared with intelligence

et al. 2020

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The phenotypic correlation between human intelligence and brain volume (BV) is considerable (r ≈ 0.40), and has been shown to be due to shared genetic factors. To further examine specific genetic factors driving this correlation, we present genomic analyses of the genetic overlap between intelligence and BV using genome-wide association study (GWAS) results. First, we conduct a large BV GWAS meta-analysis (N = 47,316 individuals), followed by functional annotation and gene-mapping. We identify 18 genomic loci (14 not previously associated), implicating 343 genes (270 not previously associated) and 18 biological pathways for BV. Second, we use an existing GWAS for intelligence (N = 269,867 individuals), and estimate the genetic correlation (rg) between BV and intelligence to be 0.24. We show that the rg is partly attributable to physical overlap of GWAS hits in 5 genomic loci. We identify 92 shared genes between BV and intelligence, which are mainly involved in signaling pathways regulating cell growth. Out of these 92, we prioritize 32 that are most likely to have functional impact. These results provide information on the genetics of BV and provide biological insight into BV’s shared genetic etiology with intelligence.

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Section: Resultsmentioning

confidence: 99%

Genome-wide meta-analysis of brain volume identifies genomic loci and genes shared with intelligence

et al. 2020

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“…Suggestive associations of additional pathways (corrected p<0.1), also implicated synaptic, potassium channel, and ionotropic pathways. To account for some genes appearing in multiple associated pathways, we conducted a conditional gene-set analysis accounting for overlap in genes amongst the top pathways 30 (excluding the Gene Ontology 31,32 [GO] terms "synapse," "GABA A gene," and "voltage-gated potassium channel" pathways due to multicollinearity). Results indicated that the GO "GABA receptor complex" and "regulation of synapse structure or activity" pathways were associated with cEF over and above other discovered pathways.…”

Section: Genetic Associations With Cef Implicate Gabaergic and Synaptmentioning

confidence: 99%

Genome-Wide Association Study of Over 427,000 Individuals Establishes Executive Functioning as a Neurocognitive Basis of Psychiatric Disorders Influenced by GABAergic Processes

et al. 2019

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Executive functions (EFs) are top-down cognitive control mechanisms that direct goalorientated behaviors. EF deficits are associated with psychopathology and neurological disorders, but little is known about the molecular bases of EF individual differences. Existing genome-wide association studies (GWAS) of EFs used small sample sizes and/or individual tasks, which are mixtures of higher and lower order cognitive mechanisms. To remedy these limitations, we conducted a GWAS of a "Common EF" (cEF) factor based on multiple tasks in the UK Biobank (N=93,027-427,037), finding 299 independent loci. Gene-based analysis found synaptic, potassium channel and GABA pathways associated with cEF. cEF genetically correlated with almost all psychiatric traits and with behavioral and health outcomes. These patterns of genetic correlations were different than those previously found for intelligence. Our results suggest that cEF is neurologically complex and that fast-neuronal processes form a basis for genetically influenced cognitive outcomes in health and psychiatric dysfunction.

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“…If this set of genes is enriched for strong statistical associations with a trait of interest, traditional GSA testing will result in significant associations for the "vesicle docking" and the "expressed in brain tissue" gene sets, unable to tell which of these is the most likely causal factor. Conditional GSA testing (94) addresses this issue by evaluating the evidence of association of one set (e.g., "vesicle docking") versus or in combination with another set (e.g., "expressed in brain tissue"). Such post hoc GSA testing has been shown to refine hypotheses on where convergence of polygenic signal resides (95), which is of crucial importance for designing meaningful functional follow-up experiments.…”

Section: Tools For Systematic Evaluation Of Convergence Of Trait-assomentioning

confidence: 99%

Emerging Methods and Resources for Biological Interrogation of Neuropsychiatric Polygenic Signal

Uffelmann

Posthuma

2021

Biological Psychiatry

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Most neuropsychiatric disorders are highly polygenic, implicating hundreds to thousands of causal genetic variants that span much of the genome. This widespread polygenicity complicates biological understanding because no single variant can explain disease etiology. A strategy to advance biological insight is to seek convergent functions among the large set of variants and map them to a smaller set of disease-relevant genes and pathways. Accordingly, functional genomic resources that provide data on intermediate molecular phenotypes, such as gene-expression and methylation status, can be leveraged to functionally annotate variants and map them to genes. Such molecular quantitative trait locus mappings can be integrated with genome-wide association studies to make sense of the polygenic signal that underlies complex disease. Other resources that provide data on the 3-dimensional structure of chromatin and functional importance of specific genomic regions can be integrated similarly. In addition, mapped genes can then be tested for convergence in biological function, tissue, cell type, or developmental stage. In this review, we provide an overview of functional genomic resources and methods that can be used to interpret results from genome-wide association studies, and we discuss current challenges for biological understanding and future requirements to overcome them.

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Conditional and interaction gene-set analysis reveals novel functional pathways for blood pressure

Cited by 59 publications

References 41 publications

Genome-wide meta-analysis of brain volume identifies genomic loci and genes shared with intelligence

Genome-wide meta-analysis of brain volume identifies genomic loci and genes shared with intelligence

Genome-Wide Association Study of Over 427,000 Individuals Establishes Executive Functioning as a Neurocognitive Basis of Psychiatric Disorders Influenced by GABAergic Processes

Emerging Methods and Resources for Biological Interrogation of Neuropsychiatric Polygenic Signal

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