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      The Transcription Factor Nfatc2 Regulates β-Cell Proliferation and Genes Associated with Type 2 Diabetes in Mouse and Human Islets

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          Abstract

          Human genome-wide association studies (GWAS) have shown that genetic variation at >130 gene loci is associated with type 2 diabetes (T2D). We asked if the expression of the candidate T2D-associated genes within these loci is regulated by a common locus in pancreatic islets. Using an obese F2 mouse intercross segregating for T2D, we show that the expression of ~40% of the T2D-associated genes is linked to a broad region on mouse chromosome (Chr) 2. As all but 9 of these genes are not physically located on Chr 2, linkage to Chr 2 suggests a genomic factor(s) located on Chr 2 regulates their expression in trans. The transcription factor Nfatc2 is physically located on Chr 2 and its expression demonstrates cis linkage; i. e., its expression maps to itself. When conditioned on the expression of Nfatc2, linkage for the T2D-associated genes was greatly diminished, supporting Nfatc2 as a driver of their expression. Plasma insulin also showed linkage to the same broad region on Chr 2. Overexpression of a constitutively active (ca) form of Nfatc2 induced β-cell proliferation in mouse and human islets, and transcriptionally regulated more than half of the T2D-associated genes. Overexpression of either ca-Nfatc2 or ca-Nfatc1 in mouse islets enhanced insulin secretion, whereas only ca-Nfatc2 was able to promote β-cell proliferation, suggesting distinct molecular pathways mediating insulin secretion vs. β-cell proliferation are regulated by NFAT. Our results suggest that many of the T2D-associated genes are downstream transcriptional targets of NFAT, and may act coordinately in a pathway through which NFAT regulates β-cell proliferation in both mouse and human islets.

          Author Summary

          Genome-wide association studies (GWAS) and linkage studies provide a powerful way to establish a causal connection between a gene locus and a physiological or pathophysiological phenotype. We wondered if candidate genes associated with type 2 diabetes in human populations, in addition to being causal for the disease, could also be intermediate traits in a pathway leading to disease. In addition, we wished to know if there were any regulatory loci that could coordinately drive the expression of these genes in pancreatic islets and thus complete a pathway; i.e. Driver → GWAS candidate expression → type 2 diabetes. Using data from a mouse intercross between a diabetes-susceptible and a diabetes-resistant mouse strain, we found that the expression of ~40% of >130 candidate GWAS genes genetically mapped to a hot spot on mouse chromosome 2. Using a variety of statistical methods, we identified the transcription factor Nfatc2 as the candidate driver. Follow-up experiments showed that overexpression of Nfatc2 does indeed affect the expression of the GWAS genes and regulates β-cell proliferation and insulin secretion. The work shows that in addition to being causal, GWAS candidate genes can be intermediate traits in a pathway leading to disease. Model organisms can be used to explore these novel causal pathways.

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          Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis.

          By combining genome-wide association data from 8,130 individuals with type 2 diabetes (T2D) and 38,987 controls of European descent and following up previously unidentified meta-analysis signals in a further 34,412 cases and 59,925 controls, we identified 12 new T2D association signals with combined P<5x10(-8). These include a second independent signal at the KCNQ1 locus; the first report, to our knowledge, of an X-chromosomal association (near DUSP9); and a further instance of overlap between loci implicated in monogenic and multifactorial forms of diabetes (at HNF1A). The identified loci affect both beta-cell function and insulin action, and, overall, T2D association signals show evidence of enrichment for genes involved in cell cycle regulation. We also show that a high proportion of T2D susceptibility loci harbor independent association signals influencing apparently unrelated complex traits.
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            The mystery of missing heritability: Genetic interactions create phantom heritability.

            Human genetics has been haunted by the mystery of "missing heritability" of common traits. Although studies have discovered >1,200 variants associated with common diseases and traits, these variants typically appear to explain only a minority of the heritability. The proportion of heritability explained by a set of variants is the ratio of (i) the heritability due to these variants (numerator), estimated directly from their observed effects, to (ii) the total heritability (denominator), inferred indirectly from population data. The prevailing view has been that the explanation for missing heritability lies in the numerator--that is, in as-yet undiscovered variants. While many variants surely remain to be found, we show here that a substantial portion of missing heritability could arise from overestimation of the denominator, creating "phantom heritability." Specifically, (i) estimates of total heritability implicitly assume the trait involves no genetic interactions (epistasis) among loci; (ii) this assumption is not justified, because models with interactions are also consistent with observable data; and (iii) under such models, the total heritability may be much smaller and thus the proportion of heritability explained much larger. For example, 80% of the currently missing heritability for Crohn's disease could be due to genetic interactions, if the disease involves interaction among three pathways. In short, missing heritability need not directly correspond to missing variants, because current estimates of total heritability may be significantly inflated by genetic interactions. Finally, we describe a method for estimating heritability from isolated populations that is not inflated by genetic interactions.
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              A simple regression method for mapping quantitative trait loci in line crosses using flanking markers.

              The use of flanking marker methods has proved to be a powerful tool for the mapping of quantitative trait loci (QTL) in the segregating generations derived from crosses between inbred lines. Methods to analyse these data, based on maximum-likelihood, have been developed and provide good estimates of QTL effects in some situations. Maximum-likelihood methods are, however, relatively complex and can be computationally slow. In this paper we develop methods for mapping QTL based on multiple regression which can be applied using any general statistical package. We use the example of mapping in an F(2) population and show that these regression methods produce very similar results to those obtained using maximum likelihood. The relative simplicity of the regression methods means that models with more than a single QTL can be explored and we give examples of two lined loci and of two interacting loci. Other models, for example with more than two QTL, with environmental fixed effects, with between family variance or for threshold traits, could be fitted in a similar way. The ease, speed of application and generality of regression methods for flanking marker analysis, and the good estimates they obtain, suggest that they should provide the method of choice for the analysis of QTL mapping data from inbred line crosses.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS Genet
                PLoS Genet
                plos
                plosgen
                PLoS Genetics
                Public Library of Science (San Francisco, CA USA )
                1553-7390
                1553-7404
                9 December 2016
                December 2016
                : 12
                : 12
                : e1006466
                Affiliations
                [1 ]Department of Biochemistry, University of Wisconsin, Madison, Wisconsin, United States of America
                [2 ]Department of Biostatistics & Medical Informatics, University of Wisconsin, Madison, Wisconsin, United States of America
                [3 ]Department of Statistics, University of Wisconsin, Madison, Wisconsin, United States of America
                [4 ]Sage Bionetworks, Seattle, Washington
                [5 ]Institute for Systems Biology, Seattle, Washington
                [6 ]Department of Chemistry, University of Wisconsin, Madison, Wisconsin, United States of America
                UCLA School of Medicine, UNITED STATES
                Author notes

                The authors have declared that no competing interests exist.

                • Conceptualization: MPK ADA.

                • Data curation: MPK PKP MER DSS KLS ATB SIY NL GMS KWB BSY.

                • Formal analysis: MPK PKP MER DSS ATB SIY CJB NL ECN CLP SPS GMS.

                • Funding acquisition: MPK PKP CJB ECN CLP NSB LMS KWB CK ADA.

                • Investigation: MPK PKP MER DSS KLS ATB SIY NL CJB SPS MAKe GMS MAKl.

                • Methodology: MPK PKP MER DSS KLS ATB SIY NL ECN CLP GMS KWB BSY CK ADA.

                • Project administration: MPK NSB LMS KWB BSY CK ADA.

                • Resources: MER DSS KLS ATB NSB LMS KWB BSY CK ADA.

                • Supervision: MPK NSB LMS KWB BSY CK ADA.

                • Validation: MPK PKP MER DSS ATB CJB SPS BSY.

                • Visualization: MPK PKP DSS ATB CJB SPS BSY.

                • Writing – original draft: MPK ADA.

                • Writing – review & editing: MPK PKP MER DSS NL CJB SPS MAKe GMS BSY CK ADA.

                Author information
                http://orcid.org/0000-0002-7405-5552
                http://orcid.org/0000-0002-9922-4877
                http://orcid.org/0000-0002-7108-3718
                http://orcid.org/0000-0003-3783-2807
                http://orcid.org/0000-0001-6077-4081
                http://orcid.org/0000-0002-9359-7808
                http://orcid.org/0000-0002-4223-9947
                http://orcid.org/0000-0002-6652-8639
                http://orcid.org/0000-0002-4914-6671
                http://orcid.org/0000-0002-8774-9377
                Article
                PGENETICS-D-16-01598
                10.1371/journal.pgen.1006466
                5147809
                27935966
                fe200667-92e2-45ff-8b6c-c02381155279
                © 2016 Keller et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 20 July 2016
                : 4 November 2016
                Page count
                Figures: 6, Tables: 0, Pages: 26
                Funding
                This work was supported by the JDRF (3-PDF-2014-195-A-N) to PKP, and (2-SRA-2015-57-Q-R) to ADA, the NIH (DK101573, DK101573-02S1, DK066369, DK58037) to ADA, (GM074244) to KWB, (GM102756) to CK, (1P01GM081629 and U54DK093467) to LMS, (P50GM076547 and 1R01GM077398-01A2), and NSF (DBI-0640950) to NSB, and (T32-HL 07936) to CJB, and by the Clinical and Translational Science Award (CTSA) program, through the NIH National Center for Advancing Translational Sciences (NCATS), grant UL1TR000427. GMS was supported by the NIH Genomic Sciences Training Program 5T32HG002760. CLP was supported by an American Cancer Society Research Scholar Grant. The Luxembourg Centre for Systems Biomedicine and the University of Luxembourg provided additional support to CLP and NSB. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Computational Biology
                Genome Analysis
                Genome-Wide Association Studies
                Biology and Life Sciences
                Genetics
                Genomics
                Genome Analysis
                Genome-Wide Association Studies
                Biology and Life Sciences
                Genetics
                Human Genetics
                Genome-Wide Association Studies
                Biology and Life Sciences
                Genetics
                Gene Expression
                Gene Regulation
                Biology and Life Sciences
                Genetics
                Gene Expression
                Biology and Life Sciences
                Genetics
                Genetic Loci
                Medicine and Health Sciences
                Endocrinology
                Diabetic Endocrinology
                Insulin
                Biology and Life Sciences
                Biochemistry
                Hormones
                Insulin
                Medicine and Health Sciences
                Endocrinology
                Endocrine Physiology
                Insulin Secretion
                Biology and Life Sciences
                Physiology
                Endocrine Physiology
                Insulin Secretion
                Medicine and Health Sciences
                Physiology
                Endocrine Physiology
                Insulin Secretion
                Biology and Life Sciences
                Cell Biology
                Cell Processes
                Cell Cycle and Cell Division
                Biology and Life Sciences
                Genetics
                Genomics
                Animal Genomics
                Mammalian Genomics
                Custom metadata
                All relevant data are within the paper and its Supporting Information files. In addition, all raw expression data has been deposited to the Gene Expression Omnibus (GEO) at NCBI (GSE73697 and GSE76477). Gene expression and diabetes-related clinical phenotypes & data, and eQTL data can be accessed via the authors' interactive database ( http://diabetes.wisc.edu/).

                Genetics
                Genetics

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