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      Genome-wide mega-analysis identifies 16 loci and highlights diverse biological mechanisms in the common epilepsies

      The International League Against Epilepsy Consortium on Complex Epilepsies
      Nature Communications
      Springer Nature

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          Abstract

          The epilepsies affect around 65 million people worldwide and have a substantial missing heritability component. We report a genome-wide mega-analysis involving 15,212 individuals with epilepsy and 29,677 controls, which reveals 16 genome-wide significant loci, of which 11 are novel. Using various prioritization criteria, we pinpoint the 21 most likely epilepsy genes at these loci, with the majority in genetic generalized epilepsies. These genes have diverse biological functions, including coding for ion-channel subunits, transcription factors and a vitamin-B6 metabolism enzyme. Converging evidence shows that the common variants associated with epilepsy play a role in epigenetic regulation of gene expression in the brain. The results show an enrichment for monogenic epilepsy genes as well as known targets of antiepileptic drugs. Using SNP-based heritability analyses we disentangle both the unique and overlapping genetic basis to seven different epilepsy subtypes. Together, these findings provide leads for epilepsy therapies based on underlying pathophysiology.

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          Most cited references37

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          A human phenome-interactome network of protein complexes implicated in genetic disorders.

          We performed a systematic, large-scale analysis of human protein complexes comprising gene products implicated in many different categories of human disease to create a phenome-interactome network. This was done by integrating quality-controlled interactions of human proteins with a validated, computationally derived phenotype similarity score, permitting identification of previously unknown complexes likely to be associated with disease. Using a phenomic ranking of protein complexes linked to human disease, we developed a Bayesian predictor that in 298 of 669 linkage intervals correctly ranks the known disease-causing protein as the top candidate, and in 870 intervals with no identified disease-causing gene, provides novel candidates implicated in disorders such as retinitis pigmentosa, epithelial ovarian cancer, inflammatory bowel disease, amyotrophic lateral sclerosis, Alzheimer disease, type 2 diabetes and coronary heart disease. Our publicly available draft of protein complexes associated with pathology comprises 506 complexes, which reveal functional relationships between disease-promoting genes that will inform future experimentation.
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            An xQTL map integrates the genetic architecture of the human brain’s transcriptome and epigenome

            We report a novel multi-omic resource generated by applying quantitative trait locus (xQTL) analyses to RNA sequence, DNA methylation, and histone acetylation data from the dorsolateral prefrontal cortex of 411 older adult individuals that have all three data types. We identify SNPs significantly associated with gene expression, DNA methylation, and histone modification levels. Many of these SNPs influence multiple molecular features, and we demonstrate that SNP effects on RNA expression are fully mediated by epigenetic features in 9% of these loci. Further, we illustrate the utility of our new resource, xQTL Serve, by using it to prioritize the cell type(s) most affected by an xQTL. We also re-analyze published genome wide association studies (GWAS) using a xQTL-weighted analysis approach and identify 18 new schizophrenia and 2 new bipolar susceptibility variants, which is more than double the number of loci that can be discovered with a larger blood-based eQTL resource.
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              Re-evaluation of SNP heritability in complex human traits

              SNP heritability, the proportion of phenotypic variance explained by SNPs, has been reported for many hundreds of traits. Its estimation requires strong prior assumptions about the distribution of heritability across the genome, but the assumptions in current use have not been thoroughly tested. By analyzing imputed data for a large number of human traits, we empirically derive a model that more accurately describes how heritability varies with minor allele frequency, linkage disequilibrium and genotype certainty. Across 19 traits, our improved model leads to estimates of common SNP heritability on average 43% (standard deviation 3) higher than those obtained from the widely-used software GCTA, and 25% (standard deviation 2) higher than those from the recently-proposed extension GCTA-LDMS. Previously, DNaseI hypersensitivity sites were reported to explain 79% of SNP heritability; using our improved heritability model their estimated contribution is only 24%.
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                Author and article information

                Journal
                Nature Communications
                Nat Commun
                Springer Nature
                2041-1723
                December 2018
                December 10 2018
                December 2018
                : 9
                : 1
                Article
                10.1038/s41467-018-07524-z
                bb7e1580-48bd-4086-a051-a49e146d6705
                © 2018

                http://creativecommons.org/licenses/by/4.0

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