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      An Atlas of Genetic Correlations across Human Diseases and Traits

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          Abstract

          Identifying genetic correlations between complex traits and diseases can provide useful etiological insights and help prioritize likely causal relationships. The major challenges preventing estimation of genetic correlation from genome-wide association study (GWAS) data with current methods are the lack of availability of individual genotype data and widespread sample overlap among meta-analyses. We circumvent these difficulties by introducing a technique – cross-trait LD Score regression – for estimating genetic correlation that requires only GWAS summary statistics and is not biased by sample overlap. We use this method to estimate 276 genetic correlations among 24 traits. The results include genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity and associations between educational attainment and several diseases. These results highlight the power of genome-wide analyses, since there currently are no significantly associated SNPs for anorexia nervosa and only three for educational attainment.

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          'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease?

          George Davey Smith, Shah Ebrahim (2003)
          Associations between modifiable exposures and disease seen in observational epidemiology are sometimes confounded and thus misleading, despite our best efforts to improve the design and analysis of studies. Mendelian randomization-the random assortment of genes from parents to offspring that occurs during gamete formation and conception-provides one method for assessing the causal nature of some environmental exposures. The association between a disease and a polymorphism that mimics the biological link between a proposed exposure and disease is not generally susceptible to the reverse causation or confounding that may distort interpretations of conventional observational studies. Several examples where the phenotypic effects of polymorphisms are well documented provide encouraging evidence of the explanatory power of Mendelian randomization and are described. The limitations of the approach include confounding by polymorphisms in linkage disequilibrium with the polymorphism under study, that polymorphisms may have several phenotypic effects associated with disease, the lack of suitable polymorphisms for studying modifiable exposures of interest, and canalization-the buffering of the effects of genetic variation during development. Nevertheless, Mendelian randomization provides new opportunities to test causality and demonstrates how investment in the human genome project may contribute to understanding and preventing the adverse effects on human health of modifiable exposures.
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            GWAS of 126,559 individuals identifies genetic variants associated with educational attainment.

            Cornelius Rietveld, Sarah E. Medland, Jaime Derringer … (2013)
            A genome-wide association study (GWAS) of educational attainment was conducted in a discovery sample of 101,069 individuals and a replication sample of 25,490. Three independent single-nucleotide polymorphisms (SNPs) are genome-wide significant (rs9320913, rs11584700, rs4851266), and all three replicate. Estimated effects sizes are small (coefficient of determination R(2) ≈ 0.02%), approximately 1 month of schooling per allele. A linear polygenic score from all measured SNPs accounts for ≈2% of the variance in both educational attainment and cognitive function. Genes in the region of the loci have previously been associated with health, cognitive, and central nervous system phenotypes, and bioinformatics analyses suggest the involvement of the anterior caudate nucleus. These findings provide promising candidate SNPs for follow-up work, and our effect size estimates can anchor power analyses in social-science genetics.
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              Parent-of-origin specific allelic associations among 106 genomic loci for age at menarche

              John RB Perry, Felix Day, Cathy E Elks … (2014)
              Age at menarche is a marker of timing of puberty in females. It varies widely between individuals, is a heritable trait and is associated with risks for obesity, type 2 diabetes, cardiovascular disease, breast cancer and all-cause mortality 1 . Studies of rare human disorders of puberty and animal models point to a complex hypothalamic-pituitary-hormonal regulation 2,3 , but the mechanisms that determine pubertal timing and underlie its links to disease risk remain unclear. Here, using genome-wide and custom-genotyping arrays in up to 182,416 women of European descent from 57 studies, we found robust evidence (P<5×10−8) for 123 signals at 106 genomic loci associated with age at menarche. Many loci were associated with other pubertal traits in both sexes, and there was substantial overlap with genes implicated in body mass index and various diseases, including rare disorders of puberty. Menarche signals were enriched in imprinted regions, with three loci (DLK1/WDR25, MKRN3/MAGEL2 and KCNK9) demonstrating parent-of-origin specific associations concordant with known parental expression patterns. Pathway analyses implicated nuclear hormone receptors, particularly retinoic acid and gamma-aminobutyric acid-B2 receptor signaling, among novel mechanisms that regulate pubertal timing in humans. Our findings suggest a genetic architecture involving at least hundreds of common variants in the coordinated timing of the pubertal transition.
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                Author and article information

                Journal
                Journal ID (nlm-journal-id): 9216904
                Journal ID (pubmed-jr-id): 2419
                Journal ID (nlm-ta): Nat Genet
                Journal ID (iso-abbrev): Nat. Genet.
                Title: Nature genetics
                ISSN (Print): 1061-4036
                ISSN (Electronic): 1546-1718
                Publication date Nihms-submitted: 29 November 2015
                Publication date (Electronic): 28 September 2015
                Publication date (Print): November 2015
                Publication date PMC-release: 01 May 2016
                Volume: 47
                Issue: 11
                Pages: 1236-1241
                Affiliations
                [1 ]Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
                [2 ]Stanley Center for Psychiatric Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
                [3 ]Analytic and Translational Genetics Unit, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
                [4 ]Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, USA
                [5 ]Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
                [6 ]Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
                [7 ]MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK
                Author notes
                [8]

                A list of members and affiliations appears in the Supplementary Note.

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                Article
                Manuscript ID: NIHMS719075
                DOI: 10.1038/ng.3406
                PMC ID: 4797329
                PubMed ID: 26414676
                SO-VID: 1a043463-7195-4bea-b7ae-b62d02985e4a
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                Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: http://www.nature.com/authors/editorial_policies/license.html#terms

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                ScienceOpen disciplines: Genetics

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