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. Author manuscript; available in PMC: 2019 May 1.
Published in final edited form as: Schizophr Res. 2017 Oct 29;195:286–289. doi: 10.1016/j.schres.2017.10.033

Genetically Determined Schizophrenia is not associated with Impaired Glucose Homeostasis

Renato Polimanti 1, Joel Gelernter 1,2, Dan J Stein 3
PMCID: PMC5924728  NIHMSID: NIHMS917250  PMID: 29092750

Abstract

Here, we used data from large genome-wide association studies to test the presence of causal relationships, conducting a Mendelian randomization analysis, and shared molecular mechanisms, calculating the genetic correlation, among schizophrenia, type 2 diabetes (T2D), and impaired glucose homeostasis. Although our Mendelian randomization analysis was well-powered, no causal relationship was observed among schizophrenia, T2D, and traits related to glucose impaired homeostasis. Similarly, we did not observe any global genetic overlap among these traits. These findings indicate that there is no causal relationships or shared mechanisms between schizophrenia and impaired glucose homeostasis.

Keywords: Psychiatry, Metabolic Disorders, Genetics, Mendelian Randomization, Genetic Correlation

1. Introduction

Although there is a well-established association between schizophrenia and type 2 diabetes (T2D), the mechanisms underlying this association remain unclear. Observational studies on relatively small cohorts including antipsychotic-naïve individuals with first-episode schizophrenia have reported impaired glucose homeostasis, suggesting that the association does not merely reflect the effects of antipsychotic drugs on metabolic regulation (Perry et al., 2016; Pillinger et al., 2017; Steiner et al., 2017). It has been suggested that overlapping inflammatory processes in schizophrenia and T2D may contribute to the association between these (Perry et al., 2016). Such causal hypotheses can be tested by assessing relevant genetic data with methods such as genetic correlation and Mendelian randomization (MR) analyses (Emdin et al., 2017). The Psychiatric Genomics Consortium (PGC), the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, and the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) have conducted genome-wide association studies (GWAS) of cohorts including thousand individuals and identified numerous risk loci associated to schizophrenia, T2D, and impaired glucose homeostasis. Here, we used this publicly-available GWAS data to investigate the extent to which these traits share causal mechanisms. We conducted a two-sample MR (i.e., instrumental variable analysis based on a genetic variable) (Burgess et al., 2013) to test whether schizophrenia causes impaired glucose homeostasis. Linkage Disequilibrium (LD) score regression analysis (Bulik-Sullivan et al., 2015) was used to test the genetic overlap (i.e., shared risk alleles) between schizophrenia and impaired glucose homeostasis.

2. Materials and Methods

We used GWAS summary statistics for schizophrenia from the PGC (https://www.med.unc.edu/pgc/results-and-downloads) (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014), T2D from the DIAGRAM consortium (http://diagram-consortium.org/downloads.html) (Morris et al., 2012), and traits related to impaired glucose homeostasis (i.e., fasting glucose, fasting glucose adjusted for BMI, fasting insulin, fasting insulin adjusted for BMI, fasting proinsulin, hemoglobin A1C, homeostatic model assessment–insulin resistance, oral glucose challenge test) from MAGIC (https://www.magicinvestigators.org/downloads/) (Dupuis et al., 2010; Manning et al., 2012; Saxena et al., 2010; Soranzo et al., 2010; Strawbridge et al., 2011). MR analysis were conducted using the R package MendelianRandomization (https://cran.r-project.org/web/packages/MendelianRandomization/index.html). We used LD-independent SNPs associated with schizophrenia at a genome-wide significance level (p<5*10−8) as the instrumental variable (Supplemental Table 1). The coefficients related to SNP-exposure (schizophrenia associations) and SNP-outcome (associations of T2D and traits related to impaired glucose homeostasis; one for each MR analysis) were combined using an inverse-variance-weighted (IVW) approach to give an overall estimate of the causal effect (Burgess et al., 2013). MR-Egger regression intercept was considered to verify the presence of pleiotropic effects of the SNPs on the outcome (Bowden et al., 2015). To investigate shared molecular mechanisms, we used information about genetic overlap (i.e., shared risk alleles) from LD Hub v1.4.0 (http://ldsc.broadinstitute.org/ldhub/) (Zheng et al., 2017), calculated using the LD regression score method (available at https://github.com/bulik/ldsc) (Bulik-Sullivan et al., 2015). The power calculation for the Mendelian randomization analysis was based on a previously published analytical approach (Brion et al., 2013), considering sample size, the observed association between phenotypes and the proportion of variance explained for the association between the SNP or allele score and the exposure variable.

3. Results

In the MR and LD score regression analyses, no significant result was observed with respect to genetically determined schizophrenia (Table 1). We observed trend results (p<0.1) for homeostatic model assessment–insulin resistance (HOMA-IR, p=0.07) and for the presence of pleiotropy for fasting proinsulin (p=0.055). However, after Bonferroni correction for multiple testing, these findings were non-significant (p=0.63 and p=0.495, respectively). We evaluated the presence of global genetic overlap (i.e., shared risk alleles) of schizophrenia with T2D and traits related to glucose impaired homeostasis considering data from LD score regression analysis: no genetic correlation reached nominal or trend significance (p>0.1). We also conducted a reverse MR, testing whether genetically determined T2D affects schizophrenia risk (i.e., the instrumental variable was extracted from T2D GWAS; Supplemental Table 2): a non-significant outcome was observed (IVW: Estimate=0.065, SE=0.091, p= 0.49; MR-Egger intercept: Estimate=−0.042, SE=0.044, p=0.341). The present study was adequately powered to detect a causal association between schizophrenia and T2D. According to a recent meta-analysis (Perry et al., 2016), individuals with first-episode psychosis have a 5-fime increased risk to meet the criteria for impaired glucose tolerance than controls (odds ratio = 5.44, 95% CI = 2.63–11.27). Genome-wide significant risk loci for schizophrenia (i.e., variants considered for the instrumental variable) explain 3.4% of disease variation (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014). According to these parameters, our Mendelian randomization analysis theoretically had more than 95% statistical power to detect an association between schizophrenia and impaired glucose tolerance at a Bonferroni-corrected significance (p = 5.6*10−3), considering the sample size of the T2D dataset (12,171 cases and 56,862 controls) (Morris et al., 2012).

Table 1.

Results of the MR analysis (IVW and MR-Egger) and LD Score regression analysis considering genetic information regarding schizophrenia, T2D, and traits related to glucose impaired homeostasis.

Trait IVW MR-Egger intercept LD score regression
Estimate SE P value Estimate SE P value rg SE P value
T2D −0.010 0.053 0.855 −0.030 0.030 0.312 −0.028 0.055 0.618
FG 0.002 0.009 0.859 −0.001 0.004 0.744 −0.038 0.042 0.366
FGadj <0.001 0.009 0.961 −0.003 0.005 0.575 −0.034 0.037 0.353
FI 0.003 0.008 0.687 <0.001 0.004 0.914 −0.039 0.058 0.500
FIadj 0.006 0.007 0.444 −0.002 0.004 0.607 0.012 0.053 0.823
proI −0.011 0.018 0.527 0.016 0.008 0.055 −0.011 0.083 0.893
HbA1C <0.001 0.008 0.958 −0.001 0.004 0.812 0.007 0.066 0.915
HOMA-IR 0.019 0.010 0.070 −0.004 0.005 0.396 −0.106 0.065 0.104
2hrGlu −0.060 0.047 0.202 −0.004 0.023 0.860 −0.003 0.0664 0.968
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Abbreviations: T2D, type 2 diabetes; FG, fasting glucose; FGadj, FG adjusted for BMI; FI, fasting insulin; FIadj, FI adjusted for BMI; proI, fasting proinsulin; HbA1C, hemoglobin A1C; HOMA-IR, homeostatic model assessment–insulin resistance; 2hrGlu, oral glucose challenge test.

4. Discussion

In contrast with results from observational studies, our findings from analyses of genetic information indicate that schizophrenia is not causally related to impaired glucose homeostasis and that there is no major genetic overlap between these traits. The genetic contribution of schizophrenia to impaired glucose homeostasis was investigated previously, with conflicting results (Liu et al., 2013; Padmanabhan et al., 2016). There are more data and methods available now to test these hypotheses, and accordingly, we applied MR and LD score regression to large GWAS data, providing a deeper investigation of this topic. Our power analysis indicated that our Mendelian randomization study is well-powered (i.e., >95% of statistical power). This is in accordance with previous studies that used these GWAS data to successfully investigate other causal associations using similar analytic approaches (Hartwig et al., 2016; Polimanti et al., 2017; van ‘t Hof et al., 2017; Wang et al., 2017). Thus, it is unlikely that our negative result is due to a lack of statistical power. MR and genetic correlation approaches were developed to investigate causal relationships and shared molecular mechanisms, respectively (Pasaniuc and Price, 2017). Genetic investigations are less biased by confounders than observational studies (Emdin et al., 2017). Accordingly, we hypothesize that the results observed in epidemiological studies are affected by some unidentified variables. Nevertheless, our approach has a number of important limitations. Our MR findings may be limited by the dose–response linear relation assumed and by differences in the age of onset between SCZ, T2D and traits related to glucose impaired homeostasis. That said, the information obtained from genetic data may be useful in developing novel studies of the mechanisms involved in the glucose impaired homeostasis observed in patients with schizophrenia.

Supplementary Material

supplement

Acknowledgments

Funding

This study was supported by grants from the National Institutes of Health (R01 DA12690, R01 AA017535, P50 AA012870, U01 MH109532, and R21 AA024404), the VA Connecticut MIRECC, and a NARSAD Young Investigator Award from the Brain & Behavior Research Foundation. The funding sources had no role in the study design, data analysis, and results interpretation of the present study.

We acknowledge the public-use data from the genome-wide association studies conducted by the Psychiatric Genomics Consortium (PGC), the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC), and the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium. The funding source had no role in the design and conduct of the study; collection, management, analysis and interpretation of the data; the preparation, review or approval of the manuscript; and decision to submit the manuscript for publication.

Footnotes

Contributors

R.P., J.G., D.J.S. were involved in the design of the study. R.P. carried out all analysis. R.P. wrote the manuscript which was subsequently revised by all authors. All authors contributed to and have approved the final manuscript.

Conflict of Interest

Dr. Stein is a consultant to Biocodex, Lundbeck, Novartis, and Servier and is a speaker for Eli Lilly and Company, GlaxoSmithKline, Lundbeck, and Servier. The other authors declare no conflict of interest.

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References

  1. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44(2):512–525. doi: 10.1093/ije/dyv080. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Brion MJ, Shakhbazov K, Visscher PM. Calculating statistical power in Mendelian randomization studies. Int J Epidemiol. 2013;42(5):1497–1501. doi: 10.1093/ije/dyt179. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Bulik-Sullivan B, Finucane HK, Anttila V, Gusev A, Day FR, Loh PR, ReproGen C, Duncan L, Perry JR, Patterson N, Robinson EB, Daly MJ, Price AL, Neale BM Psychiatric Genomics C, Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control C. An atlas of genetic correlations across human diseases and traits. Nat Genet. 2015;47(11):1236–1241. doi: 10.1038/ng.3406. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37(7):658–665. doi: 10.1002/gepi.21758. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Dupuis J, Langenberg C, Prokopenko I, Saxena R, Soranzo N, Jackson AU, Wheeler E, Glazer NL, Bouatia-Naji N, Gloyn AL, Lindgren CM, Magi R, Morris AP, Randall J, Johnson T, Elliott P, Rybin D, Thorleifsson G, Steinthorsdottir V, Henneman P, Grallert H, Dehghan A, Hottenga JJ, Franklin CS, Navarro P, Song K, Goel A, Perry JR, Egan JM, Lajunen T, Grarup N, Sparso T, Doney A, Voight BF, Stringham HM, Li M, Kanoni S, Shrader P, Cavalcanti-Proenca C, Kumari M, Qi L, Timpson NJ, Gieger C, Zabena C, Rocheleau G, Ingelsson E, An P, O’Connell J, Luan J, Elliott A, McCarroll SA, Payne F, Roccasecca RM, Pattou F, Sethupathy P, Ardlie K, Ariyurek Y, Balkau B, Barter P, Beilby JP, Ben-Shlomo Y, Benediktsson R, Bennett AJ, Bergmann S, Bochud M, Boerwinkle E, Bonnefond A, Bonnycastle LL, Borch-Johnsen K, Bottcher Y, Brunner E, Bumpstead SJ, Charpentier G, Chen YD, Chines P, Clarke R, Coin LJ, Cooper MN, Cornelis M, Crawford G, Crisponi L, Day IN, de Geus EJ, Delplanque J, Dina C, Erdos MR, Fedson AC, Fischer-Rosinsky A, Forouhi NG, Fox CS, Frants R, Franzosi MG, Galan P, Goodarzi MO, Graessler J, Groves CJ, Grundy S, Gwilliam R, Gyllensten U, Hadjadj S, Hallmans G, Hammond N, Han X, Hartikainen AL, Hassanali N, Hayward C, Heath SC, Hercberg S, Herder C, Hicks AA, Hillman DR, Hingorani AD, Hofman A, Hui J, Hung J, Isomaa B, Johnson PR, Jorgensen T, Jula A, Kaakinen M, Kaprio J, Kesaniemi YA, Kivimaki M, Knight B, Koskinen S, Kovacs P, Kyvik KO, Lathrop GM, Lawlor DA, Le Bacquer O, Lecoeur C, Li Y, Lyssenko V, Mahley R, Mangino M, Manning AK, Martinez-Larrad MT, McAteer JB, McCulloch LJ, McPherson R, Meisinger C, Melzer D, Meyre D, Mitchell BD, Morken MA, Mukherjee S, Naitza S, Narisu N, Neville MJ, Oostra BA, Orru M, Pakyz R, Palmer CN, Paolisso G, Pattaro C, Pearson D, Peden JF, Pedersen NL, Perola M, Pfeiffer AF, Pichler I, Polasek O, Posthuma D, Potter SC, Pouta A, Province MA, Psaty BM, Rathmann W, Rayner NW, Rice K, Ripatti S, Rivadeneira F, Roden M, Rolandsson O, Sandbaek A, Sandhu M, Sanna S, Sayer AA, Scheet P, Scott LJ, Seedorf U, Sharp SJ, Shields B, Sigurethsson G, Sijbrands EJ, Silveira A, Simpson L, Singleton A, Smith NL, Sovio U, Swift A, Syddall H, Syvanen AC, Tanaka T, Thorand B, Tichet J, Tonjes A, Tuomi T, Uitterlinden AG, van Dijk KW, van Hoek M, Varma D, Visvikis-Siest S, Vitart V, Vogelzangs N, Waeber G, Wagner PJ, Walley A, Walters GB, Ward KL, Watkins H, Weedon MN, Wild SH, Willemsen G, Witteman JC, Yarnell JW, Zeggini E, Zelenika D, Zethelius B, Zhai G, Zhao JH, Zillikens MC, Global BC, Borecki IB, Loos RJ, Meneton P, Magnusson PK, Nathan DM, Williams GH, Hattersley AT, Silander K, Salomaa V, Smith GD, Bornstein SR, Schwarz P, Spranger J, Karpe F, Shuldiner AR, Cooper C, Dedoussis GV, Serrano-Rios M, Morris AD, Lind L, Palmer LJ, Hu FB, Franks PW, Ebrahim S, Marmot M, Kao WH, Pankow JS, Sampson MJ, Kuusisto J, Laakso M, Hansen T, Pedersen O, Pramstaller PP, Wichmann HE, Illig T, Rudan I, Wright AF, Stumvoll M, Campbell H, Wilson JF, Anders Hamsten M, Bergman RN, Buchanan TA, Collins FS, Mohlke KL, Tuomilehto J, Valle TT, Altshuler D, Rotter JI, Siscovick DS, Penninx BW, Boomsma DI, Deloukas P, Spector TD, Frayling TM, Ferrucci L, Kong A, Thorsteinsdottir U, Stefansson K, van Duijn CM, Aulchenko YS, Cao A, Scuteri A, Schlessinger D, Uda M, Ruokonen A, Jarvelin MR, Waterworth DM, Vollenweider P, Peltonen L, Mooser V, Abecasis GR, Wareham NJ, Sladek R, Froguel P, Watanabe RM, Meigs JB, Groop L, Boehnke M, McCarthy MI, Florez JC, Barroso I Consortium D, Consortium G, on behalf of Procardis C, investigators. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet. 2010;42(2):105–116. doi: 10.1038/ng.520. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Emdin CA, Khera AV, Natarajan P, Klarin D, Zekavat SM, Hsiao AJ, Kathiresan S. Genetic Association of Waist-to-Hip Ratio With Cardiometabolic Traits, Type 2 Diabetes, and Coronary Heart Disease. JAMA. 2017;317(6):626–634. doi: 10.1001/jama.2016.21042. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Hartwig FP, Bowden J, Loret de Mola C, Tovo-Rodrigues L, Davey Smith G, Horta BL. Body mass index and psychiatric disorders: a Mendelian randomization study. Sci Rep. 2016;6:32730. doi: 10.1038/srep32730. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Liu Y, Li Z, Zhang M, Deng Y, Yi Z, Shi T. Exploring the pathogenetic association between schizophrenia and type 2 diabetes mellitus diseases based on pathway analysis. BMC Med Genomics. 2013;6(Suppl 1):S17. doi: 10.1186/1755-8794-6-S1-S17. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Manning AK, Hivert MF, Scott RA, Grimsby JL, Bouatia-Naji N, Chen H, Rybin D, Liu CT, Bielak LF, Prokopenko I, Amin N, Barnes D, Cadby G, Hottenga JJ, Ingelsson E, Jackson AU, Johnson T, Kanoni S, Ladenvall C, Lagou V, Lahti J, Lecoeur C, Liu Y, Martinez-Larrad MT, Montasser ME, Navarro P, Perry JR, Rasmussen-Torvik LJ, Salo P, Sattar N, Shungin D, Strawbridge RJ, Tanaka T, van Duijn CM, An P, de Andrade M, Andrews JS, Aspelund T, Atalay M, Aulchenko Y, Balkau B, Bandinelli S, Beckmann JS, Beilby JP, Bellis C, Bergman RN, Blangero J, Boban M, Boehnke M, Boerwinkle E, Bonnycastle LL, Boomsma DI, Borecki IB, Bottcher Y, Bouchard C, Brunner E, Budimir D, Campbell H, Carlson O, Chines PS, Clarke R, Collins FS, Corbaton-Anchuelo A, Couper D, de Faire U, Dedoussis GV, Deloukas P, Dimitriou M, Egan JM, Eiriksdottir G, Erdos MR, Eriksson JG, Eury E, Ferrucci L, Ford I, Forouhi NG, Fox CS, Franzosi MG, Franks PW, Frayling TM, Froguel P, Galan P, de Geus E, Gigante B, Glazer NL, Goel A, Groop L, Gudnason V, Hallmans G, Hamsten A, Hansson O, Harris TB, Hayward C, Heath S, Hercberg S, Hicks AA, Hingorani A, Hofman A, Hui J, Hung J, Jarvelin MR, Jhun MA, Johnson PC, Jukema JW, Jula A, Kao WH, Kaprio J, Kardia SL, Keinanen-Kiukaanniemi S, Kivimaki M, Kolcic I, Kovacs P, Kumari M, Kuusisto J, Kyvik KO, Laakso M, Lakka T, Lannfelt L, Lathrop GM, Launer LJ, Leander K, Li G, Lind L, Lindstrom J, Lobbens S, Loos RJ, Luan J, Lyssenko V, Magi R, Magnusson PK, Marmot M, Meneton P, Mohlke KL, Mooser V, Morken MA, Miljkovic I, Narisu N, O’Connell J, Ong KK, Oostra BA, Palmer LJ, Palotie A, Pankow JS, Peden JF, Pedersen NL, Pehlic M, Peltonen L, Penninx B, Pericic M, Perola M, Perusse L, Peyser PA, Polasek O, Pramstaller PP, Province MA, Raikkonen K, Rauramaa R, Rehnberg E, Rice K, Rotter JI, Rudan I, Ruokonen A, Saaristo T, Sabater-Lleal M, Salomaa V, Savage DB, Saxena R, Schwarz P, Seedorf U, Sennblad B, Serrano-Rios M, Shuldiner AR, Sijbrands EJ, Siscovick DS, Smit JH, Small KS, Smith NL, Smith AV, Stancakova A, Stirrups K, Stumvoll M, Sun YV, Swift AJ, Tonjes A, Tuomilehto J, Trompet S, Uitterlinden AG, Uusitupa M, Vikstrom M, Vitart V, Vohl MC, Voight BF, Vollenweider P, Waeber G, Waterworth DM, Watkins H, Wheeler E, Widen E, Wild SH, Willems SM, Willemsen G, Wilson JF, Witteman JC, Wright AF, Yaghootkar H, Zelenika D, Zemunik T, Zgaga L, Replication DIG, Meta-analysis C, Wareham NJ, McCarthy MI, Barroso I, Watanabe RM, Florez JC, Dupuis J, Meigs JB, Langenberg C Multiple Tissue Human Expression Resource C. A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nat Genet. 2012;44(6):659–669. doi: 10.1038/ng.2274. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Morris AP, Voight BF, Teslovich TM, Ferreira T, Segre AV, Steinthorsdottir V, Strawbridge RJ, Khan H, Grallert H, Mahajan A, Prokopenko I, Kang HM, Dina C, Esko T, Fraser RM, Kanoni S, Kumar A, Lagou V, Langenberg C, Luan J, Lindgren CM, Muller-Nurasyid M, Pechlivanis S, Rayner NW, Scott LJ, Wiltshire S, Yengo L, Kinnunen L, Rossin EJ, Raychaudhuri S, Johnson AD, Dimas AS, Loos RJ, Vedantam S, Chen H, Florez JC, Fox C, Liu CT, Rybin D, Couper DJ, Kao WH, Li M, Cornelis MC, Kraft P, Sun Q, van Dam RM, Stringham HM, Chines PS, Fischer K, Fontanillas P, Holmen OL, Hunt SE, Jackson AU, Kong A, Lawrence R, Meyer J, Perry JR, Platou CG, Potter S, Rehnberg E, Robertson N, Sivapalaratnam S, Stancakova A, Stirrups K, Thorleifsson G, Tikkanen E, Wood AR, Almgren P, Atalay M, Benediktsson R, Bonnycastle LL, Burtt N, Carey J, Charpentier G, Crenshaw AT, Doney AS, Dorkhan M, Edkins S, Emilsson V, Eury E, Forsen T, Gertow K, Gigante B, Grant GB, Groves CJ, Guiducci C, Herder C, Hreidarsson AB, Hui J, James A, Jonsson A, Rathmann W, Klopp N, Kravic J, Krjutskov K, Langford C, Leander K, Lindholm E, Lobbens S, Mannisto S, Mirza G, Muhleisen TW, Musk B, Parkin M, Rallidis L, Saramies J, Sennblad B, Shah S, Sigurethsson G, Silveira A, Steinbach G, Thorand B, Trakalo J, Veglia F, Wennauer R, Winckler W, Zabaneh D, Campbell H, van Duijn C, Uitterlinden AG, Hofman A, Sijbrands E, Abecasis GR, Owen KR, Zeggini E, Trip MD, Forouhi NG, Syvanen AC, Eriksson JG, Peltonen L, Nothen MM, Balkau B, Palmer CN, Lyssenko V, Tuomi T, Isomaa B, Hunter DJ, Qi L, Shuldiner AR, Roden M, Barroso I, Wilsgaard T, Beilby J, Hovingh K, Price JF, Wilson JF, Rauramaa R, Lakka TA, Lind L, Dedoussis G, Njolstad I, Pedersen NL, Khaw KT, Wareham NJ, Keinanen-Kiukaanniemi SM, Saaristo TE, Korpi-Hyovalti E, Saltevo J, Laakso M, Kuusisto J, Metspalu A, Collins FS, Mohlke KL, Bergman RN, Tuomilehto J, Boehm BO, Gieger C, Hveem K, Cauchi S, Froguel P, Baldassarre D, Tremoli E, Humphries SE, Saleheen D, Danesh J, Ingelsson E, Ripatti S, Salomaa V, Erbel R, Jockel KH, Moebus S, Peters A, Illig T, de Faire U, Hamsten A, Morris AD, Donnelly PJ, Frayling TM, Hattersley AT, Boerwinkle E, Melander O, Kathiresan S, Nilsson PM, Deloukas P, Thorsteinsdottir U, Groop LC, Stefansson K, Hu F, Pankow JS, Dupuis J, Meigs JB, Altshuler D, Boehnke M, McCarthy MI, Replication DIG, Meta-analysis C Wellcome Trust Case Control C, Meta-Analyses of G, Insulin-related traits Consortium I, Genetic Investigation of ATC, Asian Genetic Epidemiology Network-Type 2 Diabetes C, South Asian Type 2 Diabetes C. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet. 2012;44(9):981–990. doi: 10.1038/ng.2383. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Padmanabhan JL, Nanda P, Tandon N, Mothi SS, Bolo N, McCarroll S, Clementz BA, Gershon ES, Pearlson GD, Sweeney JA, Tamminga CA, Keshavan MS. Polygenic risk for type 2 diabetes mellitus among individuals with psychosis and their relatives. J Psychiatr Res. 2016;77:52–58. doi: 10.1016/j.jpsychires.2016.02.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Pasaniuc B, Price AL. Dissecting the genetics of complex traits using summary association statistics. Nat Rev Genet. 2017;18(2):117–127. doi: 10.1038/nrg.2016.142. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Perry BI, McIntosh G, Weich S, Singh S, Rees K. The association between first-episode psychosis and abnormal glycaemic control: systematic review and meta-analysis. Lancet Psychiatry. 2016;3(11):1049–1058. doi: 10.1016/S2215-0366(16)30262-0. [DOI] [PubMed] [Google Scholar]
  14. Pillinger T, Beck K, Gobjila C, Donocik JG, Jauhar S, Howes OD. Impaired Glucose Homeostasis in First-Episode Schizophrenia: A Systematic Review and Meta-analysis. JAMA Psychiatry. 2017;74(3):261–269. doi: 10.1001/jamapsychiatry.2016.3803. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Polimanti R, Agrawal A, Gelernter J. Schizophrenia and substance use comorbidity: a genome-wide perspective. Genome Med. 2017;9(1):25. doi: 10.1186/s13073-017-0423-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Saxena R, Hivert MF, Langenberg C, Tanaka T, Pankow JS, Vollenweider P, Lyssenko V, Bouatia-Naji N, Dupuis J, Jackson AU, Kao WH, Li M, Glazer NL, Manning AK, Luan J, Stringham HM, Prokopenko I, Johnson T, Grarup N, Boesgaard TW, Lecoeur C, Shrader P, O’Connell J, Ingelsson E, Couper DJ, Rice K, Song K, Andreasen CH, Dina C, Kottgen A, Le Bacquer O, Pattou F, Taneera J, Steinthorsdottir V, Rybin D, Ardlie K, Sampson M, Qi L, van Hoek M, Weedon MN, Aulchenko YS, Voight BF, Grallert H, Balkau B, Bergman RN, Bielinski SJ, Bonnefond A, Bonnycastle LL, Borch-Johnsen K, Bottcher Y, Brunner E, Buchanan TA, Bumpstead SJ, Cavalcanti-Proenca C, Charpentier G, Chen YD, Chines PS, Collins FS, Cornelis M, GJC, Delplanque J, Doney A, Egan JM, Erdos MR, Firmann M, Forouhi NG, Fox CS, Goodarzi MO, Graessler J, Hingorani A, Isomaa B, Jorgensen T, Kivimaki M, Kovacs P, Krohn K, Kumari M, Lauritzen T, Levy-Marchal C, Mayor V, McAteer JB, Meyre D, Mitchell BD, Mohlke KL, Morken MA, Narisu N, Palmer CN, Pakyz R, Pascoe L, Payne F, Pearson D, Rathmann W, Sandbaek A, Sayer AA, Scott LJ, Sharp SJ, Sijbrands E, Singleton A, Siscovick DS, Smith NL, Sparso T, Swift AJ, Syddall H, Thorleifsson G, Tonjes A, Tuomi T, Tuomilehto J, Valle TT, Waeber G, Walley A, Waterworth DM, Zeggini E, Zhao JH, Illig T, Wichmann HE, Wilson JF, van Duijn C, Hu FB, Morris AD, Frayling TM, Hattersley AT, Thorsteinsdottir U, Stefansson K, Nilsson P, Syvanen AC, Shuldiner AR, Walker M, Bornstein SR, Schwarz P, Williams GH, Nathan DM, Kuusisto J, Laakso M, Cooper C, Marmot M, Ferrucci L, Mooser V, Stumvoll M, Loos RJ, Altshuler D, Psaty BM, Rotter JI, Boerwinkle E, Hansen T, Pedersen O, Florez JC, McCarthy MI, Boehnke M, Barroso I, Sladek R, Froguel P, Meigs JB, Groop L, Wareham NJ, Watanabe RM consortium G, investigators M. Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge. Nat Genet. 2010;42(2):142–148. doi: 10.1038/ng.521. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511(7510):421–427. doi: 10.1038/nature13595. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Soranzo N, Sanna S, Wheeler E, Gieger C, Radke D, Dupuis J, Bouatia-Naji N, Langenberg C, Prokopenko I, Stolerman E, Sandhu MS, Heeney MM, Devaney JM, Reilly MP, Ricketts SL, Stewart AF, Voight BF, Willenborg C, Wright B, Altshuler D, Arking D, Balkau B, Barnes D, Boerwinkle E, Bohm B, Bonnefond A, Bonnycastle LL, Boomsma DI, Bornstein SR, Bottcher Y, Bumpstead S, Burnett-Miller MS, Campbell H, Cao A, Chambers J, Clark R, Collins FS, Coresh J, de Geus EJ, Dei M, Deloukas P, Doring A, Egan JM, Elosua R, Ferrucci L, Forouhi N, Fox CS, Franklin C, Franzosi MG, Gallina S, Goel A, Graessler J, Grallert H, Greinacher A, Hadley D, Hall A, Hamsten A, Hayward C, Heath S, Herder C, Homuth G, Hottenga JJ, Hunter-Merrill R, Illig T, Jackson AU, Jula A, Kleber M, Knouff CW, Kong A, Kooner J, Kottgen A, Kovacs P, Krohn K, Kuhnel B, Kuusisto J, Laakso M, Lathrop M, Lecoeur C, Li M, Li M, Loos RJ, Luan J, Lyssenko V, Magi R, Magnusson PK, Malarstig A, Mangino M, Martinez-Larrad MT, Marz W, McArdle WL, McPherson R, Meisinger C, Meitinger T, Melander O, Mohlke KL, Mooser VE, Morken MA, Narisu N, Nathan DM, Nauck M, O’Donnell C, Oexle K, Olla N, Pankow JS, Payne F, Peden JF, Pedersen NL, Peltonen L, Perola M, Polasek O, Porcu E, Rader DJ, Rathmann W, Ripatti S, Rocheleau G, Roden M, Rudan I, Salomaa V, Saxena R, Schlessinger D, Schunkert H, Schwarz P, Seedorf U, Selvin E, Serrano-Rios M, Shrader P, Silveira A, Siscovick D, Song K, Spector TD, Stefansson K, Steinthorsdottir V, Strachan DP, Strawbridge R, Stumvoll M, Surakka I, Swift AJ, Tanaka T, Teumer A, Thorleifsson G, Thorsteinsdottir U, Tonjes A, Usala G, Vitart V, Volzke H, Wallaschofski H, Waterworth DM, Watkins H, Wichmann HE, Wild SH, Willemsen G, Williams GH, Wilson JF, Winkelmann J, Wright AF, Wtccc, Zabena C, Zhao JH, Epstein SE, Erdmann J, Hakonarson HH, Kathiresan S, Khaw KT, Roberts R, Samani NJ, Fleming MD, Sladek R, Abecasis G, Boehnke M, Froguel P, Groop L, McCarthy MI, Kao WH, Florez JC, Uda M, Wareham NJ, Barroso I, Meigs JB. Common variants at 10 genomic loci influence hemoglobin A(1)(C) levels via glycemic and nonglycemic pathways. Diabetes. 2010;59(12):3229–3239. doi: 10.2337/db10-0502. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Steiner J, Berger M, Guest PC, et al. Assessment of insulin resistance among drug-naive patients with first-episode schizophrenia in the context of hormonal stress axis activation. JAMA Psychiatry. 2017 doi: 10.1001/jamapsychiatry.2017.1983. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Strawbridge RJ, Dupuis J, Prokopenko I, Barker A, Ahlqvist E, Rybin D, Petrie JR, Travers ME, Bouatia-Naji N, Dimas AS, Nica A, Wheeler E, Chen H, Voight BF, Taneera J, Kanoni S, Peden JF, Turrini F, Gustafsson S, Zabena C, Almgren P, Barker DJ, Barnes D, Dennison EM, Eriksson JG, Eriksson P, Eury E, Folkersen L, Fox CS, Frayling TM, Goel A, Gu HF, Horikoshi M, Isomaa B, Jackson AU, Jameson KA, Kajantie E, Kerr-Conte J, Kuulasmaa T, Kuusisto J, Loos RJ, Luan J, Makrilakis K, Manning AK, Martinez-Larrad MT, Narisu N, Nastase Mannila M, Ohrvik J, Osmond C, Pascoe L, Payne F, Sayer AA, Sennblad B, Silveira A, Stancakova A, Stirrups K, Swift AJ, Syvanen AC, Tuomi T, van ‘t Hooft FM, Walker M, Weedon MN, Xie W, Zethelius B, Mu TC, Ongen H, Malarstig A, Hopewell JC, Saleheen D, Chambers J, Parish S, Danesh J, Kooner J, Ostenson CG, Lind L, Cooper CC, Serrano-Rios M, Ferrannini E, Forsen TJ, Clarke R, Franzosi MG, Seedorf U, Watkins H, Froguel P, Johnson P, Deloukas P, Collins FS, Laakso M, Dermitzakis ET, Boehnke M, McCarthy MI, Wareham NJ, Groop L, Pattou F, Gloyn AL, Dedoussis GV, Lyssenko V, Meigs JB, Barroso I, Watanabe RM, Ingelsson E, Langenberg C, Hamsten A, Florez JC Consortium D, Consortium G, Consortium CA, Consortium CD. Genome-wide association identifies nine common variants associated with fasting proinsulin levels and provides new insights into the pathophysiology of type 2 diabetes. Diabetes. 2011;60(10):2624–2634. doi: 10.2337/db11-0415. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. van ‘t Hof FN, Vaucher J, Holmes MV, de Wilde A, Baas AF, Blankensteijn JD, Hofman A, Kiemeney LA, Rivadeneira F, Uitterlinden AG, Vermeulen SH, Rinkel GJ, de Bakker PI, Ruigrok YM. Genetic variants associated with type 2 diabetes and adiposity and risk of intracranial and abdominal aortic aneurysms. Eur J Hum Genet. 2017 doi: 10.1038/ejhg.2017.48. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Wang Q, Polimanti R, Kranzler HR, Farrer LA, Zhao H, Gelernter J. Genetic factor common to schizophrenia and HIV infection is associated with risky sexual behavior: antagonistic vs. synergistic pleiotropic SNPs enriched for distinctly different biological functions. Hum Genet. 2017;136(1):75–83. doi: 10.1007/s00439-016-1737-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Zheng J, Erzurumluoglu AM, Elsworth BL, Kemp JP, Howe L, Haycock PC, Hemani G, Tansey K, Laurin C, Early G, Pourcain BS, Warrington NM, Finucane HK, Price AL, Bulik-Sullivan BK, Anttila V, Paternoster L, Gaunt TR, Evans DM, Neale BM Lifecourse Epidemiology Eczema C. LD Hub: a centralized database and web interface to perform LD score regression that maximizes the potential of summary level GWAS data for SNP heritability and genetic correlation analysis. Bioinformatics. 2017;33(2):272–279. doi: 10.1093/bioinformatics/btw613. [DOI] [PMC free article] [PubMed] [Google Scholar]

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