Ultra-rare disruptive and damaging mutations influence educational attainment in the general population

Ganna, Andrea; Genovese, Giulio; Howrigan, Daniel P.; Byrnes, Andrea; Kurki, Mitja; Zekavat, Seyedeh M.; Whelan, Christopher W.; Kals, Mart; Nivard, Michel G.; Bloemendal, Alex; Bloom, Jonathan M.; Goldstein, Jacqueline I.; Poterba, Timothy; Seed, Cotton; Handsaker, Robert E.; Natarajan, Pradeep; Mägi, Reedik; Gage, Diane; Robinson, Elise; Metspalu, Andres; Salomaa, Veikko; Suvisaari, Jaana; Purcell, Shaun; Sklar, Pamela; Kathiresan, Sekar; Daly, Mark J.; McCarroll, Steven A.; Sullivan, Patrick F.; Palotie, Aarno; Esko, Tõnu; Hultman, Christina M.; Neale, Benjamin M.

doi:10.1038/nn.4404

Cited by 92 publications

(89 citation statements)

References 36 publications

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“…Therefore, we proceeded to test a genome-wide excess of rare non-synonymous variants among ALS patients. In contrast to similar analyses in schizophrenia and educational attainment (Ganna et al, 2016), we found no evidence for such excess in any variant set combining all allele frequency cut-offs and functional classification.…”

Section: Quality Control and Association Analysiscontrasting

confidence: 99%

The Project MinE databrowser: bringing large-scale whole-genome sequencing in ALS to researchers and the public

Spek

Rheenen

Pulit

et al. 2018

Preprint

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Amyotrophic lateral sclerosis (ALS) is a rapidly progressive fatal neurodegenerative disease affecting 1 in 350 people. The aim of Project MinE is to elucidate the pathophysiology of ALS through whole-genome sequencing at least 15,000 ALS patients and 7,500 controls at 30Xcoverage. Here, we present the Project MinE data browser (databrowser.projectmine.com), a unique and intuitive one-stop, open-access server that provides detailed information on genetic variation analyzed in a new and still growing set of 4,366 ALS cases and 1,832 matched controls.Through its visual components and interactive design, the browser specifically aims to be a resource to those without a biostatistics background and allow clinicians and preclinical researchers to integrate Project MinE data into their own research. The browser allows users to query a transcript and immediately access a unique combination of detailed (meta)data, annotations and association statistics that would otherwise require analytic expertise and visits to scattered resources.

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Section: Quality Control and Association Analysiscontrasting

confidence: 99%

The Project MinE databrowser: bringing large-scale whole-genome sequencing in ALS to researchers and the public

Spek

Rheenen

Pulit

et al. 2018

Preprint

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“…Many biologically plausible gene sets have been previously implicated to refine the polygenic basis of SCZ risk towards more biologically tractable components. Among 85 candidate gene sets analyzed (supplementary section 14), BrainSpan high brain expression (8928 genes tested) and GTEx brain enriched (6214 genes tested; see (Ganna, et al 2016)) represented two of the four gene sets that surpassed multiple-testing correction in both the DNM model and against controls (the other two gene sets being missense constraint and RVIS intolerant). Both gene sets represent gene expression across the entirety of human brain tissues and cell types measured from post-mortem brain tissue (http://www.brainspan.org/ and https://www.gtexportal.org/home/), and reinforce the notion that the genetic risk for SCZ, while brain-specific, is polygenic across the allele frequency spectrum.…”

Section: Enrichment In Highly Brain Expressed and Constrained Genesmentioning

confidence: 99%

Schizophrenia risk conferred by protein-coding de novo mutations

Howrigan

Rose

Samocha

et al. 2018

Preprint

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Protein-coding de novo mutations (DNMs) in the form of single nucleotide changes and short insertions/deletions are significant genetic risk factors for autism, intellectual disability, developmental delay, and epileptic encephalopathy. In contrast, the burden of DNMs has thus far only had a modest documented impact on schizophrenia (SCZ) risk. Here, we analyze whole-exome sequence from 1,695 SCZ affected parent-offspring trios from Taiwan along with DNMs from 1,077 published SCZ trios to better understand the contribution of coding DNMs to SCZ risk. Among 2,772 SCZ affected probands, the increased burden of DNMs is modest. Gene set analyses show that the modest increase in risk from DNMs in SCZ probands is concentrated in genes that are either highly brain expressed, under strong evolutionary constraint, and/or overlap with genes identified as DNM risk factors in other neurodevelopmental disorders. No single gene meets the criteria for genome-wide significance, but we identify 16 genes that are recurrently hit by a protein-truncating DNM, which is a 3.15-fold higher rate than mutation model expectation of 5.1 genes (permuted 95% CI=1-10 genes, permuted p=3e-5). Overall, DNMs explain only a small fraction of SCZ risk, and this risk is polygenic in nature suggesting that coding variation across many different genes will be a risk factor for SCZ in the population.

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“…Where such tests have found an association these have been in small samples and subsequently failed to replicate [30]. However, in large samples, rare variants found within regions of the genome under purifying selection have been found to be associated with educational success [31], an effect that was greater for genes expressed in the brain. Hence, rare variants found in some genes appear to have an effect on intelligence.…”

Section: Introductionmentioning

confidence: 99%

Genomic analysis of family data reveals additional genetic effects on intelligence and personality

et al. 2018

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Pedigree-based analyses of intelligence have reported that genetic differences account for 50-80% of the phenotypic variation. For personality traits these effects are smaller, with 34-48% of the variance being explained by genetic differences. However, molecular genetic studies using unrelated individuals typically report a heritability estimate of around 30% for intelligence and between 0 and 15% for personality variables. Pedigree-based estimates and molecular genetic estimates may differ because current genotyping platforms are poor at tagging causal variants, variants with low minor allele frequency, copy number variants, and structural variants. Using~20,000 individuals in the Generation Scotland family cohort genotyped for~700,000 single-nucleotide polymorphisms (SNPs), we exploit the high levels of linkage disequilibrium (LD) found in members of the same family to quantify the total effect of genetic variants that are not tagged in GWAS of unrelated individuals. In our models, genetic variants in low LD with genotyped SNPs explain over half of the genetic variance in intelligence, education, and neuroticism. By capturing these additional genetic effects our models closely approximate the heritability estimates from twin studies for intelligence and education, but not for neuroticism and extraversion. We then replicated our finding using imputed molecular genetic data from unrelated individuals to show that 50% of differences in intelligence, and~40% of the differences in education, can be explained by genetic effects when a larger number of rare SNPs are included. From an evolutionary genetic perspective, a substantial contribution of rare genetic variants to individual differences in intelligence, and education is consistent with mutation-selection balance.

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Ultra-rare disruptive and damaging mutations influence educational attainment in the general population

Cited by 92 publications

References 36 publications

The Project MinE databrowser: bringing large-scale whole-genome sequencing in ALS to researchers and the public

The Project MinE databrowser: bringing large-scale whole-genome sequencing in ALS to researchers and the public

Schizophrenia risk conferred by protein-coding de novo mutations

Genomic analysis of family data reveals additional genetic effects on intelligence and personality

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