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      Quantifying the polygenic contribution to variable expressivity in eleven rare genetic disorders

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          Abstract

          Rare genetic disorders (RGDs) often exhibit significant clinical variability among affected individuals, a disease characteristic termed variable expressivity. Recently, the aggregate effect of common variation, quantified as polygenic scores (PGSs), has emerged as an effective tool for predictions of disease risk and trait variation in the general population. Here, we measure the effect of PGSs on 11 RGDs including four sex-chromosome aneuploidies (47,XXX; 47,XXY; 47,XYY; 45,X) that affect height; two copy-number variant (CNV) disorders (16p11.2 deletions and duplications) and a Mendelian disease (melanocortin 4 receptor deficiency ( MC4R)) that affect BMI; and two Mendelian diseases affecting cholesterol: familial hypercholesterolemia (FH; LDLR and APOB) and familial hypobetalipoproteinemia (FHBL; PCSK9 and APOB). Our results demonstrate that common, polygenic factors of relevant complex traits frequently contribute to variable expressivity of RGDs and that PGSs may be a useful metric for predicting clinical severity in affected individuals and for risk stratification.

          Abstract

          Rare genetic disorders (RGDs) often exhibit significant clinical variability among affected individuals. Here, Oetjens et al. systematically study the contribution of common genetic variation to variable expressivity of RGDs and find it is frequently influenced by polygenic factors identified in genome-wide association studies of relevant traits.

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          USING THE CORRECT STATISTICAL TEST FOR THE EQUALITY OF REGRESSION COEFFICIENTS

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            The personal and clinical utility of polygenic risk scores

            Initial expectations for genome-wide association studies were high, as such studies promised to rapidly transform personalized medicine with individualized disease risk predictions, prevention strategies and treatments. Early findings, however, revealed a more complex genetic architecture than was anticipated for most common diseases - complexity that seemed to limit the immediate utility of these findings. As a result, the practice of utilizing the DNA of an individual to predict disease has been judged to provide little to no useful information. Nevertheless, recent efforts have begun to demonstrate the utility of polygenic risk profiling to identify groups of individuals who could benefit from the knowledge of their probabilistic susceptibility to disease. In this context, we review the evidence supporting the personal and clinical utility of polygenic risk profiling.
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              The Geisinger MyCode Community Health Initiative: an electronic health record-linked biobank for Precision Medicine research

              Purpose Geisinger Health System (GHS) provides an ideal platform for Precision Medicine. Key elements are the integrated health system, stable patient population, and electronic health record (EHR) infrastructure. In 2007 Geisinger launched MyCode®, a system-wide biobanking program to link samples and EHR data for broad research use. Methods Patient-centered input into MyCode® was obtained using participant focus groups. Participation in MyCode® is based on opt-in informed consent and allows recontact, which facilitates collection of data not in the EHR, and, since 2013, the return of clinically actionable results to participants. MyCode® leverages Geisinger’s technology and clinical infrastructure for participant tracking and sample collection. Results MyCode® has a consent rate of >85% with more than 90,000 participants currently, with ongoing enrollment of ~4,000 per month. MyCode® samples have been used to generate molecular data, including high-density genotype and exome sequence data. Genotype and EHR-derived phenotype data replicate previously reported genetic associations. Conclusion The MyCode® project has created resources that enable a new model for translational research that is faster, more flexible, and more cost effective than traditional clinical research approaches. The new model is scalable, and will increase in value as these resources grow and are adopted across multiple research platforms.
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                Author and article information

                Contributors
                mtoetjens@geisinger.edu
                dhledbetter@geisinger.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                25 October 2019
                25 October 2019
                2019
                : 10
                : 4897
                Affiliations
                ISNI 0000 0004 0394 1447, GRID grid.280776.c, Geisinger Health System, ; Danville, PA USA
                Author information
                http://orcid.org/0000-0001-8934-4210
                Article
                12869
                10.1038/s41467-019-12869-0
                6814771
                31653860
                e77553ed-bd64-4294-89db-3546a802772d
                © The Author(s) 2019

                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
                : 14 March 2019
                : 3 October 2019
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                © The Author(s) 2019

                Uncategorized
                medical genetics,rare variants,quantitative trait,genetics research
                Uncategorized
                medical genetics, rare variants, quantitative trait, genetics research

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