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      Item-level analyses reveal genetic heterogeneity in neuroticism

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          Abstract

          Genome-wide association studies (GWAS) of psychological traits are generally conducted on (dichotomized) sums of items or symptoms (e.g., case-control status), and not on the individual items or symptoms themselves. We conduct large-scale GWAS on 12 neuroticism items and observe notable and replicable variation in genetic signal between items. Within samples, genetic correlations among the items range between 0.38 and 0.91 (mean r g = .63), indicating genetic heterogeneity in the full item set. Meta-analyzing the two samples, we identify 255 genome-wide significant independent genomic regions, of which 138 are item-specific. Genetic analyses and genetic correlations with 33 external traits support genetic differences between the items. Hierarchical clustering analysis identifies two genetically homogeneous item clusters denoted depressed affect and worry. We conclude that the items used to measure neuroticism are genetically heterogeneous, and that biological understanding can be gained by studying them in genetically more homogeneous clusters.

          Abstract

          Neuroticism can be assessed as a composite score of individual items. Here, Nagel et al. perform genetic association studies for 12 neuroticism items and the sum-score and demonstrate genetic heterogeneity at the item-level.

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          Most cited references26

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          Genome-wide association studies for complex traits: consensus, uncertainty and challenges.

          The past year has witnessed substantial advances in understanding the genetic basis of many common phenotypes of biomedical importance. These advances have been the result of systematic, well-powered, genome-wide surveys exploring the relationships between common sequence variation and disease predisposition. This approach has revealed over 50 disease-susceptibility loci and has provided insights into the allelic architecture of multifactorial traits. At the same time, much has been learned about the successful prosecution of association studies on such a scale. This Review highlights the knowledge gained, defines areas of emerging consensus, and describes the challenges that remain as researchers seek to obtain more complete descriptions of the susceptibility architecture of biomedical traits of interest and to translate the information gathered into improvements in clinical management.
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            Genome-wide association study implicates immune activation of multiple integrin genes in inflammatory bowel disease

            Genetic association studies have identified 215 risk loci for inflammatory bowel disease 1–8, which have revealed fundamental aspects of its molecular biology. We performed a genome-wide association study of 25,305 individuals, and meta-analyzed with published summary statistics, yielding a total sample size of 59,957 subjects. We identified 25 new loci, three of which contain integrin genes that encode proteins in pathways identified as important therapeutic targets in inflammatory bowel disease. The associated variants are correlated with expression changes in response to immune stimulus at two of these genes (ITGA4, ITGB8) and at previously implicated loci (ITGAL, ICAM1). In all four cases, the expression increasing allele also increases disease risk. We also identified likely causal missense variants in the primary immune deficiency gene PLCG2 and a negative regulator of inflammation, SLAMF8. Our results demonstrate that new common variant associations continue to identify genes relevant to therapeutic target identification and prioritization.
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              Connecting genetic risk to disease end points through the human blood plasma proteome

              Genome-wide association studies (GWAS) with intermediate phenotypes, like changes in metabolite and protein levels, provide functional evidence to map disease associations and translate them into clinical applications. However, although hundreds of genetic variants have been associated with complex disorders, the underlying molecular pathways often remain elusive. Associations with intermediate traits are key in establishing functional links between GWAS-identified risk-variants and disease end points. Here we describe a GWAS using a highly multiplexed aptamer-based affinity proteomics platform. We quantify 539 associations between protein levels and gene variants (pQTLs) in a German cohort and replicate over half of them in an Arab and Asian cohort. Fifty-five of the replicated pQTLs are located in trans. Our associations overlap with 57 genetic risk loci for 42 unique disease end points. We integrate this information into a genome-proteome network and provide an interactive web-tool for interrogations. Our results provide a basis for novel approaches to pharmaceutical and diagnostic applications.
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                Author and article information

                Contributors
                d.posthuma@vu.nl
                s.vander.sluis@vu.nl
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                2 March 2018
                2 March 2018
                2018
                : 9
                : 905
                Affiliations
                [1 ]GRID grid.484519.5, Department of Clinical Genetics, , Section Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, VU Medical Centre, ; Amsterdam, 1081 HV The Netherlands
                [2 ]ISNI 0000 0004 1754 9227, GRID grid.12380.38, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, , VU University Amsterdam, ; Amsterdam, 1081 HV The Netherlands
                Author information
                http://orcid.org/0000-0003-3115-8532
                http://orcid.org/0000-0001-7582-2365
                Article
                3242
                10.1038/s41467-018-03242-8
                5834468
                29500382
                cc2bf337-9d84-4a28-b746-8150f0c0816a
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 19 July 2017
                : 29 January 2018
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