Expressive

rs115600411 - CYP20A1

Magnitude 2.2 · 6 studies on file

Reported associations

  • Genome-wide meta-analysis of insomnia prioritizes genes associated with metabolic and psychiatric pathways. - Nature genetics (2022) · Watanabe K, Jansen PR, Savage JE, Nandakumar P, Wang X, Hinds DA, Gelernter J, Levey DF, Polimanti R, Stein MB, Van Someren EJW, Smit AB, Posthuma D · PubMed 35835914

    Insomnia is a heritable, highly prevalent sleep disorder for which no sufficient treatment currently exists. Previous genome-wide association studies with up to 1.3 million subjects identified over 200 associated loci. This extreme polygenicity suggested that many more loci remain to be discovered. The current study almost doubled the sample size to 593,724 cases and 1,771,286 controls, thereby increasing statistical power, and identified 554 risk loci (including 364 novel loci). To capitalize on this large number of loci, we propose a novel strategy to prioritize genes using external biological resources and functional interactions between genes across risk loci. Of all 3,898 genes naively implicated from the risk loci, we prioritize 289 and find brain-tissue expression spec

  • Genome-wide association study of cerebral small vessel disease reveals established and novel loci. - Brain : a journal of neurology (2020) · Chung J, Marini S, Pera J, Norrving B, Jimenez-Conde J, Roquer J, Fernandez-Cadenas I, Tirschwell DL, Selim M, Brown DL, Silliman SL, Worrall BB, Meschia JF, Demel S, Greenberg SM, Slowik A, Lindgren A, Schmidt R, Traylor M, Sargurupremraj M, Tiedt S, Malik R, Debette S, Dichgans M, Langefeld CD, Woo D, Rosand J, Anderson CD · PubMed 31430377

    Intracerebral haemorrhage and small vessel ischaemic stroke (SVS) are the most acute manifestations of cerebral small vessel disease, with no established preventive approaches beyond hypertension management. Combined genome-wide association study (GWAS) of these two correlated diseases may improve statistical power to detect novel genetic factors for cerebral small vessel disease, elucidating underlying disease mechanisms that may form the basis for future treatments. Because intracerebral haemorrhage location is an adequate surrogate for distinct histopathological variants of cerebral small vessel disease (lobar for cerebral amyloid angiopathy and non-lobar for arteriolosclerosis), we performed GWAS of intracerebral haemorrhage by location in 1813 subjects (755 lobar and 1005 non-lobar) a

  • Trans-ethnic and ancestry-specific blood-cell genetics in 746,667 individuals from 5 global populations - Unknown journal (n.d.) · Unknown authors · PubMed 32888493

    ABSTRACT: SUMMARY Most loci identified by GWAS have been found in populations of European ancestry (EUR). In trans-ethnic meta-analyses for 15 hematological traits in 746,667 participants, including 184,535 non-EUR individuals, we identified 5,552 trait-variant associations at P<5×10−9, including 71 novel loci not found in EUR populations. We also identified 28 additional novel variants in ancestry-specific, non-EUR meta-analyses, including an IL7 missense variant in South Asians associated with lymphocyte count in vivo and IL7 secretion levels in vitro. Fine-mapping prioritized variants annotated as functional, and generated 95% credible sets that were 30% smaller when using the trans-ethnic as opposed to the EUR-only results. We explored the clinical significance and predictive value

  • Common genetic variation indicates separate etiologies for periventricular and deep white matter hyperintensities - Unknown journal (n.d.) · Unknown authors · PubMed 32517579

    ABSTRACT: Background and Purpose Periventricular (PVWMH) and deep white matter hyperintensities (DWMH) are regional classifications of white matter hyperintensities (WMH) and reflect proposed differences in etiology. In the first study to date, we undertook genome-wide association analyses (GWAS) of DWMH and PVWMH to show that these phenotypes have different genetic underpinnings. Methods Participants were aged 45 years and older; free of stroke and dementia. We conducted GWAS of PVWMH and DWMH in 26,654 participants from CHARGE, ENIGMA, and the UK Biobank (UKB). Regional correlations were investigated using the GWAS-pairwise method. Cross-trait genetic correlations between PVWMH, DWMH, stroke, and dementia were estimated using LDSC. Results In the discovery and replication analysis, for P

  • Mapping the proteo-genomic convergence of human diseases - Unknown journal (n.d.) · Unknown authors · PubMed 34648354

    ABSTRACT: Characterization of the genetic regulation of proteins is essential for understanding disease etiology and developing therapies. We identified 10,674 genetic associations for 3,892 plasma proteins to create a cis-anchored gene-protein-disease map of 1,859 connections that highlights strong cross-disease biological convergence. This proteo-genomic map provides a framework to 1) connect etiologically related diseases, 2) provide biological context for new or emerging disorders, and 3) integrate different biological domains to establish mechanisms for known gene-disease links. Our results identify proteo-genomic connections within and between diseases and establish the value of cis-protein variants for annotation of likely causal disease genes at GWAS loci, addressing a major barrie

  • Unsupervised deep representation learning enables phenotype discovery for genetic association studies of brain imaging - Unknown journal (n.d.) · Unknown authors · PubMed 38580839

    ABSTRACT: Understanding the genetic architecture of brain structure is challenging, partly due to difficulties in designing robust, non-biased descriptors of brain morphology. Until recently, brain measures for genome-wide association studies (GWAS) consisted of traditionally expert-defined or software-derived image-derived phenotypes (IDPs) that are often based on theoretical preconceptions or computed from limited amounts of data. Here, we present an approach to derive brain imaging phenotypes using unsupervised deep representation learning. We train a 3-D convolutional autoencoder model with reconstruction loss on 6130 UK Biobank (UKBB) participants' T1 or T2-FLAIR (T2) brain MRIs to create a 128-dimensional representation known as Unsupervised Deep learning derived Imaging Phenotypes

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