FUMA: Functional mapping and annotation of genetic associations

Watanabe, Kyoko; Taskesen, Erdogan; Bochoven, Arjen van; Posthuma, Daniëlle

doi:10.1101/110023

Cited by 47 publications

(32 citation statements)

References 28 publications

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“…1a). These SNPs were found in 187 independent loci, identified using FUMA (Supplementary Table 2); [49,36] this represents an increase of 169 loci compared to those reported in the Sniekers et al GWAS alone [16]. In order to determine if differences in loci construction were influencing the difference across the intelligence (Sniekers), education (Okbay), and present study, we performed FUMA (using the same parameters as in the current study) on the Sniekers and Okbay datasets and compared the loci found across phenotypes.…”

Section: Resultsmentioning

confidence: 99%

“…Using the meta-analytic dataset produced by MTAG, genetic loci related to intelligence were identified using FUMA [36]. First, independent significant SNPs were identified.…”

Section: Identification Of Independent Genomic Loci and Functional Anmentioning

confidence: 99%

“…Functional annotation was carried out in FUMA [36] using all SNPs found within the independent genomic loci which were in LD of r 2 ≥ 0.6, were nominally significant, and had a MAF of 0.01. To gauge the functional consequences of genetic variation at these SNPs they were first matched based on chromosome, base pair position, reference, and non-reference alleles to a database containing functional annotations including the ANNOVAR categories [37], combined annotation dependent depletion (CADD) scores [38], Regulome DB (RDB) scores [39], and chromatin states [40][41][42].…”

Section: Identification Of Independent Genomic Loci and Functional Anmentioning

confidence: 99%

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A combined analysis of genetically correlated traits identifies 187 loci and a role for neurogenesis and myelination in intelligence

et al. 2018

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Intelligence, or general cognitive function, is phenotypically and genetically correlated with many traits, including a wide range of physical, and mental health variables. Education is strongly genetically correlated with intelligence (r g = 0.70). We used these findings as foundations for our use of a novel approach-multi-trait analysis of genome-wide association studies (MTAG;Turley et al. 2017)-to combine two large genome-wide association studies (GWASs) of education and intelligence, increasing statistical power and resulting in the largest GWAS of intelligence yet reported. Our study had four goals: first, to facilitate the discovery of new genetic loci associated with intelligence; second, to add to our understanding of the biology of intelligence differences; third, to examine whether combining genetically correlated traits in this way produces results consistent with the primary phenotype of intelligence; and, finally, to test how well this new meta-analytic data sample on intelligence predicts phenotypic intelligence in an independent sample. By combining datasets using MTAG, our functional sample size increased from 199,242 participants to 248,482. We found 187 independent loci associated with intelligence, implicating 538 genes, using both SNP-based and gene-based GWAS. We found evidence that neurogenesis and myelination-as well as genes expressed in the synapse, and those involved in the regulation of the nervous systemmay explain some of the biological differences in intelligence. The results of our combined analysis demonstrated the same pattern of genetic correlations as those from previous GWASs of intelligence, providing support for the meta-analysis of these genetically-related phenotypes.Intelligence, also known as general cognitive function or simply g, describes the shared variance that exists between diverse measures of cognitive ability [1]. In a population with a range of cognitive ability, intelligence accounts for around 40% of the variation between individuals in scores on diverse cognitive tests [2]. Intelligence is predictive of health states, including mortality; [3,4] a lower level of cognitive function in youth is associated with earlier death over the next several decades [5]. Intelligence is a heritable trait, with twin-and family-based estimates of heritability indicating that between 50-80% of differences in intelligence can be explained by genetic factors [6]. These genetic factors make a greater contribution to phenotypic differences as age increases from childhood to adulthood [7].C.R. Gale, G. Davies and I. J. Deary contributed equally to this work. Relatively few genetic variants have reliably been associated with intelligence differences [16]. The sparsity of genome-wide significant SNPs discovered so far, combined with the substantial heritability estimate, suggests a phenotype with a highly polygenic architecture, where the total effect of all associated variants is substantial, but in which each individual variant exerts only a small influence. This is compel...

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Section: Resultsmentioning

confidence: 99%

“…Using the meta-analytic dataset produced by MTAG, genetic loci related to intelligence were identified using FUMA [36]. First, independent significant SNPs were identified.…”

Section: Identification Of Independent Genomic Loci and Functional Anmentioning

confidence: 99%

Section: Identification Of Independent Genomic Loci and Functional Anmentioning

confidence: 99%

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A combined analysis of genetically correlated traits identifies 187 loci and a role for neurogenesis and myelination in intelligence

et al. 2018

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“…To assess whether the 12 prioritized genes from family P2 converge on biological shared functions, we tested for enrichment in Gene Ontology (GO) terms for biological processes using FUMA [42]. Overrepresentation of biological functions of prioritized genes was tested for by comparison with gene-sets obtained from the Molecular Signature Database (MsigDB) v5.2 (i.e., GO gene sets), using hypergeometric tests.…”

Section: Gene Ontology Enrichment Analysismentioning

confidence: 99%

Identification of ADHD risk genes in extended pedigrees by combining linkage analysis and whole-exome sequencing

et al. 2018

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Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder with a complex genetic background, hampering identification of underlying genetic risk factors. We hypothesized that combining linkage analysis and whole-exome sequencing (WES) in multi-generation pedigrees with multiple affected individuals can point toward novel ADHD genes. Three families with multiple ADHD-affected members (N total = 70) and apparent dominant inheritance pattern were included in this study. Genotyping was performed in 37 family members, and WES was additionally carried out in 10 of those. Linkage analysis was performed using multi-point analysis in Superlink Online SNP 1.1. From prioritized linkage regions with a LOD score ≥ 2, a total of 24 genes harboring rare variants were selected. Those genes were taken forward and were jointly analyzed in gene-set analyses of exome-chip data using the MAGMA software in an independent sample of patients with persistent ADHD and healthy controls (N = 9365). The gene-set including all 24 genes together, and particularly the gene-set from one of the three families (12 genes), were significantly associated with persistent ADHD in this sample. Among the latter, gene-wide analysis for the AAED1 gene reached significance. A rare variant (rs151326868) within AAED1 segregated with ADHD in one of the families. The analytic strategy followed here is an effective approach for identifying novel ADHD risk genes. Additionally, this study suggests that both rare and more frequent variants in multiple genes act together in contributing to ADHD risk, even in individual multi-case families.

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“…Tissue enrichment analysis was conducted using FUMA 51 . Enrichment for pathways and ontologies was performed in EnrichR 27,28 using the human genome as the reference set and a minimum number of 2 genes per category.…”

Section: Gene Pathway and Tissue-enrichment Analysesmentioning

confidence: 99%

Biological and clinical insights from genetics of insomnia symptoms

Lane

Jones

Dashti

et al. 2018

Preprint

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Insomnia is a common disorder linked with adverse long-term medical and psychiatric outcomes, but underlying pathophysiological processes and causal relationships with disease are poorly understood. Here we identify 57 loci for self-reported insomnia symptoms in the UK Biobank (n=453,379) and confirm their impact on self-reported insomnia symptoms in the HUNT study (n=14,923 cases, 47,610 controls), physician diagnosed insomnia in Partners Biobank (n=2,217 cases, 14,240 controls), and accelerometer-derived measures of sleep efficiency and sleep duration in the UK Biobank (n=83,726). Our results suggest enrichment of genes involved in ubiquitin-mediated proteolysis, phototransduction and muscle development pathways and of genes expressed in multiple brain regions, skeletal muscle and adrenal gland. Evidence of shared genetic factors is found between frequent insomnia symptoms and restless legs syndrome, aging, cardio-metabolic, behavioral, psychiatric and reproductive traits. Evidence is found for a possible causal link between insomnia symptoms and coronary heart disease, depressive symptoms and subjective well-being.

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FUMA: Functional mapping and annotation of genetic associations

Cited by 47 publications

References 28 publications

A combined analysis of genetically correlated traits identifies 187 loci and a role for neurogenesis and myelination in intelligence

A combined analysis of genetically correlated traits identifies 187 loci and a role for neurogenesis and myelination in intelligence

Identification of ADHD risk genes in extended pedigrees by combining linkage analysis and whole-exome sequencing

Biological and clinical insights from genetics of insomnia symptoms

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