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ORIGINAL ARTICLE

Association Between Common Variants Near theMelanocortin 4 Receptor Gene and SevereAntipsychotic DrugInduced Weight Gain Anil K. Malhotra, MD; Christoph U. Correll, MD; Nabilah I. Chowdhury, BSc; Daniel J. Muller, MD;Peter K. Gregersen, MD; Annette T. Lee, PhD; Arun K. Tiwari, PhD; John M. Kane, MD;W. Wolfgang Fleischhacker, MD; Rene S. Kahn, MD; Roel A. Ophoff, PhD; Jeffrey A. Lieberman, MD;Herbert Y. Meltzer, MD; Todd Lencz, PhD; James L. Kennedy, MD

Context : Second-generation antipsychotics (SGAs) areincreasingly usedin the treatment of many psychotic andnonpsychotic disorders. Unfortunately, SGAs are oftenassociated with substantial weight gain, with no meansto predict which patients are at greatest risk.

Objective : To identify single-nucleotide polymor-phisms associated with antipsychotic druginducedweight gain.

Design : Pharmacogenetic association study.

Setting : The discovery cohort was from a US generalpsychiatric hospital. Threeadditional cohorts were frompsychiatric hospitals in the United States and Germanyand from a European antipsychotic drug trial.

Participants : The discovery cohort consisted of 139pediatric patients undergoing first exposure to SGAs.The 3 additional cohorts consisted of 73, 40, and 92subjects.

Intervention : Patients in the discovery cohort weretreated with SGAs for 12 weeks. Additional cohorts weretreated for 6 and 12 weeks.

Main Outcome Measures : We conducted a genome-wide association study assessing weight gain associatedwith 12 weeks of SGA treatment in patients undergoing

first exposure to antipsychotic drugs. We next geno-typed 3 independent cohorts of subjects assessed for an-tipsychotic druginduced weight gain.

Results : Our genome-wide associationstudy yielded 20single-nucleotide polymorphisms at a single locus ex-ceeding a statistical threshold of P 105. This locus, nearthemelanocortin4 receptor ( MC4R) gene, overlaps a re-gionpreviously identified by large-scale genome-wide as-sociation studiesof obesityin the general population. Ef-fects were recessive, with minor allele homozygotesgaining extreme amounts of weight during the 12-weektrial. These results were replicated in 3 additional co-horts, with rs489693 demonstrating consistent reces-sive effects; meta-analysis revealed a genome-wide sig-nificant effect ( P=5.59 1012). Moreover, we observedconsistent effects on related metabolic indices, includ-ing triglyceride, leptin, and insulin levels.

Conclusions : These data implicate MC4R in extremeSGA-induced weight gain and related metabolic distur-bances.A priori identification of high-risk subjects couldlead to alternative treatment strategies in this popula-tion.

Arch Gen Psychiatry. 2012;69(9):904-912.Published online May 7, 2012.doi:10.1001/archgenpsychiatry.2012.191

ALTHOUGHSECOND -GENERA-tionantipsychotics (SGAs)are the cornerstone of treatment for many psy-chotic and nonpsychotic

disorders, these medications are associ-ated with substantial weight gain, includ-ing the development of obesity and othercardiovascular risk factors. 1 These medi-cationeffects are importantmediatingfac-tors in the reductionin life expectancy, es-timated to reach 20 to 30 years, in thosewith chronic and severe mental ill-

nesses. 2 Moreover, SGA-induced weightgain frequently leads to medication non-adherenceanddecreasedquality of life, andadequate treatments to prevent or ame-liorate weight gain are lacking. 3

A subgroup of patients experience se-vere weight gain following exposure toSGAs. In a study 4 of the weight and meta-bolic effects of SGAs in a unique cohortof 272 antipsychotic-naive pediatric pa-tients beginning initial treatment with 1of 4 SGAs, we found that approximatelyone-quarter of patients treated with ris-

Author Affiliations are listed atthe end of this article.

ARCH GEN PSYCHIATRY/VOL 69 (NO. 9), SEP 2012 WWW.ARCHGENPSYCHIATRY.COM904

2012 American Medical Association. All rights reserved.


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peridone,quetiapinefumarate, or aripiprazole gained morethan 14% of their baseline weight, with the top quartilegaining between 6.8 and 15.8 kg (15 and 35 lb, respec-tively), in just12weeks of treatment ( Figure1 ). Olanza-pine-treated patients demonstrated a markedly differ-entdistribution,with themajority ofsubjects experiencingextreme weight gain.Theamount of weight gain was notrelated to age, pubertal status, ethnicity, or sex of thesub- jects, and SGA dosage ranges were relatively restricted.

These data are consistent with multiple clinical reportsindicating that a significant proportion of patients gainextreme amounts of weight when treated with SGAs. 5-7

Theuseofpharmacogeneticapproaches to identify pa-tients at risk for severe SGA-induced weight gain couldlead to targeted interventions toameliorate effects inhigh-risk individuals, as well as provide data on the molecu-lar substrates of SGA-induced weightgain. To date, how-ever, pharmacogenetic studies of weight gain have beenrestricted by methodological and technological limita-tions. In particular, prior studies have typically usedsamples of convenience, including patients with vary-ing and often lengthy prior exposure to antipsychotics,considerably confoundingprospectively observed weight

gain. Moreover, nonadherence to treatment, a substan-tial problem in antipsychotic pharmacotherapy, 8 canleadto misclassification of nonadherent subjects as low-riskfor weight gain. Finally, only a modest number of ge-netic loci have been examined, with only one study (inchronically treated adults) using genome-wide associa-tion. 9

Therefore, to our knowledge, we conducted the firstgenome-wide association study (GWAS) of SGA-induced weight gain in patients carefully monitored formedication adherence whowereundergoing initial treat-ment with SGAs. To confirm our results, we next as-sessed 3 independent replication cohorts: (1) a cohortof adult subjects undergoing their first treatment with a

single SGA (clozapine), (2) a cohort of adult subjectstreated with the same SGAs as in our discovery sample,and (3) a cohort of adult subjects in the first episode of schizophrenia and enrolled in a randomized clinical trialof antipsychotic drugs. 10

METHODS

SUBJECTS IN DISCOVERY COHORT

Subjects assessed in the GWAS were enrolled in an observa-tional cohort study assessing the weight and metabolic effectsof SGAs on pediatric psychiatric patients. Participants aged 18to 19 years, and caregivers of all participantsaged4 to 17 years,

provided written informed consent; participants aged 9 to 17yearssigned informed assent to a protocol approved by the in-stitutional review board of the North ShoreLong Island Jew-ish Health System. Detailed methods have been previously re-ported elsewhere. 4

In brief, subjects undergoing initial treatment with an SGAwere included if (1) they were 19 years of age or younger and(2) their prior lifetime exposure to all antipsychotics as a classwas1 week or less. Exclusioncriteria included an activeor pastdiagnosis of an eating disorder, biochemical evidence of thy-roid dysfunction, pregnancy or breastfeeding, and any acutenonpsychiatric medical disorder. The specific choice of anti-

psychotic drug, thedrug dosage, and the titration schedule werebased on clinical indications. To ensure adherence to SGAtreat-ment, antipsychotic plasma levels were measured; individualswith undetectable antipsychotic plasma levels were excluded.

PHENOTYPIC ASSESSMENTSOF DISCOVERY COHORT

Subjects were assessed after 8 hours or more of overnight fast-ing at baseline and weeks 4, 8, and12 of treatment. Height wasmeasured using the stadiometer Seca 214. Weight, body massindex (BMI;calculated as weight in kilogramsdividedby heightin meters squared), and fat mass were assessed by use of im-pedantiometry withtheTanitaBodyCompositionAnalyzerTBF-310. As shown in Figure 1, weight gain profiles after 12 weeksof treatment for 3 SGAs (quetiapine [n=36], risperidone[n= 135], and aripiprazole [n=41])were indistinguishablefromeach other but significantly differed from weight gain profilesafter 12 weeks of treatment for olanzapine (n =45). An omni-bus 2 test of the distributions displayed in Figure 1 (mergingthelowest 2 categories)demonstrateda significanteffectof medi-cation ( 29=24.68, P=.003). When olanzapine was removed,there wasno significant difference acrossthe 3 remainingdrugs( 26=4.42, P=.62), andpairwise comparisonsdemonstrated eachof these medications differed in weightgain distributions com-pared with olanzapine (all P .05) but did not differ from each

other (all P .40). Consequently, subjects taking olanzapinewere excluded from the planned GWAS analysis to maintainhomogeneity of phenotype. 11

Fasting blood samples wereobtained between7 and 11 AM,prior to taking morning medications. Plasma levels were mea-sured at each postbaseline visit (weeks 4, 8, and 12). Glucoseandlipid levels wereanalyzed with theRoche Hitachi747 chem-istry analyzer, and insulin levels were analyzed via the RocheElecsys 2010 immunochemistry analyzer. Plasma levels weremeasured by use of liquid chromatography at the CooperLaboratory (Nathan S. Kline Institute forPsychiatric Research,Orangeburg, New York).

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Figure 1. Distribution of antipsychotic druginduced weight gain inantipsychotic-naive subjects following 12 weeks of treatment withsecond-generation antipsychotic drugs. The y-axis represents the percentageof subjects in each of the 5 weight gain categories; for example, subjectsgaining more than 21% of their baseline weight are indicated in light gray,and subjects gaining more than 14% of their baseline weight are indicated inlight blue. ARI indicates aripiprazole; AVG, the average of quetiapine (QTP),risperidone (RIS), and ARI; OLZ, olanzapine.




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DNA COLLECTION, GENOTYPING, ANDQUALITY CONTROL OF DISCOVERY COHORT

Of 272 individuals reported in Correll et al, 4 245 (90.1%) pro-vided blood samples for DNA extraction. DNA samples weregenotyped on approximately 1 millionsingle-nucleotide poly-morphisms (SNPs) using the Illumina Omni-1Quad platform.All quality-control procedures were performed in SVS version7.3.1 (GoldenHelix Inc), except for cryptic identity and cryp-tic relatedness, which were performedin PLINK version 1.07. 12

Of 245 available samples, 16 (6.5%) were eliminated duringquality control owing to low call rates ( 97%), sex mismatch,cryptic identity, or cryptic relatedness. Eliminationof SNPs witha low call rate ( 95%), a low minor allele frequency ( 2%),or out of Hardy-Weinberg equilibrium ( P 106) resulted in803582 high-quality autosomal SNPs available for analysis. Themean call rate per sample was 99.7%. Of 229 samples passingquality control, 38 were from individuals who were pre-scribed olanzapine, and, therefore, these samples were ex-cluded from the GWAS, and 10 other samples were excludedbecause of demonstrated SGAnonadherence on the partof the10 individuals.A total of 139subjects completedthe full study,with 12-weekBMI changedata andhigh-quality genotype dataavailable for GWAS analysis (eTables 1 and 2, http://www.archgenpsychiatry.com).

STATISTICAL ANALYSISOF DISCOVERY COHORT

Principal components analysis wasperformed using the eigen-strat method 13 implemented in SVSversion7.3.1 using defaultsettings, andthe 10 topprincipal components wereentered intosubsequent GWAS analyses. Principal components analysiscorrected correlation and trend tests were performed to testdominant, recessive, andadditive models, using12-weekchangein BMI as the quantitative dependent measure.

ADDITIONAL COHORTS

To validate the GWAS results from the discovery cohort, weidentified an additional cohort of subjectsundergoing their firstexposure to an SGA with ensured adherenceto medication.Thedetails of this cohorthave been publishedpreviously. 14 In brief,this cohort consists of 73 patients without prior exposure toSGAs who began treatment with the prototypic SGA, cloza-pine. Patientswere 18 to 60 years of age, diagnosed with schizo-phrenia according to DSM-III-R criteria, and were either re-fractoryor intolerant to treatment with typical antipsychotics.Subjects were excluded from our study if they were pregnantand/or breastfeeding, had an organic brain disorder or severehead injuries, had previous medical conditions that requiredtreatment and were not stable, were dependent on a sub-stance, hadmental retardation, or had a severe personality dis-order.Prior to initiating treatmentwith clozapine, subjects un-derwent a medication washout periodof 7 to14 days.Thedosage

of clozapinewastitrated based on clinical indication, andtreat-ment wascontinued forat least 6 weeks.Clozapine plasma lev-els were monitored to ascertain compliance. Patients under-went weight assessment at baseline andat 6 weeksof treatment.

In addition, a second replication cohort of 40 subjects fromthe Charite University of Medicine, Berlin, Germany, were en-rolled in our study. 14 Subjects 18 to 62 years old were diag-nosed with schizophrenia or schizoaffective disorder accord-ing to DSM-IV criteria. Hospital admission was either due tothe first manifestation or a relapse of psychosis with signifi-cantdeterioration. Exclusion criteria were the same as alreadydescribed. Patients were not excluded if they had undergone

previous antipsychotic treatment. Patients underwent 6 weeksof treatment with risperidone, quetiapine, or aripiprazole. Thechoice of antipsychotic drug, the drug dosage, and the titra-tion schedule were based on clinical indications (eTable 1). Asubjectsweight wasassessedat baseline andfollowing 6 weeksof treatment.

Finally,a third replication cohort of patients treated for theirfirst episode of schizophrenia was enrolled as part of the Eu-ropeanUnion First Episode Schizophrenia Trial. (Note that onlya subset of patients enrolled in the larger trial provided DNA

samples.)10

Because there was an insufficient number of non-white subjects available to conduct meaningful covariate analy-ses, only white subjects were included for this report. Patientswere excluded if more than 2 years had passed since the onsetof positive symptomsor if any antipsychotic drughadbeenusedfor more than 2 weeks in the previous year or for 6 weeks atany time. Patients were randomly assigned to 1 of 4 antipsy-chotics: haloperidol, amisulpride,quetiapine, or ziprasidone hy-drochloride (as in the discovery cohort, subjects assigned toolanzapinewere excluded from thepresent study). Patients wereexcluded from pharmacogenetic analyses if nonadherence tomedication was reported. Weight was assessed at baseline andafter 12 weeks of treatment as part of a longer trial; results arereported for a total of 92 subjects meeting the inclusion crite-ria (eTable 1).

GENOTYPING OF ADDITIONAL COHORTS

Genotyping of the second 2 cohorts was completed subse-quent to analysis of the GWAS results from the discovery co-hort and consisted of the 5 SNPs highlighted in Table 1. Asshown in Table 1 , Table 2 , and eFigure 1, the SNPs identi-fied by the GWAS of the discovery cohort were in strong link-age disequilibrium, with D=1 in most instances. Thus, therewas considerable redundancy, which obviated the need to testmore SNPs in the replication cohorts. However, there wassomedifference in minor allele frequenciesacrossthe SNPs inTables 1and 2, with 3 modes (~21%, ~34%, and ~44%) as depicted inTable 2. The 5 SNPs chosen for replication in the first 2 repli-cation cohorts were selected based on their providing a com-prehensive assessment across this frequency distribution. TheSNPs rs1942786 and rs996022, as well as other SNPs in theregion, were not selected because they were in near completelinkage disequilibrium with other selected SNPs, were at verylowallele frequencies, or failed in assay development.No otherSNPs from Tables 1 and 2 were successfully genotyped in thereplication cohorts.

All genotyping was performed using TaqMan SNP geno-typing assays (Applied Biosystems). Two independent research-ers confirmed the calling of genotypes, and 10% of the samplewas regenotyped to ensureconcordance.The concordance ratewas 99.5%, and discordant genotypes were treated as missingdata in the statistical analysis. Sampleswith more than 2 miss-inggenotypecallswere excludedandare notincluded in Table 2.

Finally, genotyping of the European Union First EpisodeSchizophrenia Trial cohortwasperformedafter all prior analy-ses had been completed as part of an ongoing study of anti-psychotic drug efficacy. Genotyping was performed on the Il-lumina Omni-1Quad platform using the already-describedquality-control parameters. For purposes of the present study,only rs469893 was examined. The SNP rs469893 was selectedafter analysis of the replication cohorts revealedthat it was sig-nificant in both replication cohorts ( P=.00014and P=.007, re-spectively). No other SNPs yielded a P value of lessthan .05 inall 3 prior cohorts, and, therefore, none of them were selectedfor follow-up.




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STATISTICAL ANALYSISOF ADDITIONAL COHORTS

Based on the results from the GWAS of the discoverycohort, wetested for recessive effects for each SNP by comparing minor al-lelehomozygoteswithallothersusing t tests,withchangeinweightacross the 6- or 12-week trial as the dependent measure. Effectsofpotentialconfounding variables,includingsex, race, andbase-lineweight,weretestedusinganalysisofcovariance.Meta-analysisof P values derivedfrom t tests wasconductedusing theStoufferz trendtest, anextensionofthe Fishermethodthatpermitsweight-ingfor samplesizesandeffectdirections,asimplementedinMetaP(http://compute1.lsrc.duke.edu/softwares/MetaP/metap.php).

RESULTS

DISCOVERY COHORT

TheGWASof theBMI-changephenotype revealed a strik-ing genotypic effect under the recessive model

(Figure 2 A). Twenty SNPs at a single locus exceeded astatistical threshold of P 105 (Table 1), with no evi-dence of inflation of test statistics due to population ef-fects ( genomic control =1; Figure 2B). This locus, extendingfrom coordinate 55934091 to coordinate 56036944 onchromosome 18, is approximately 190 kilobases (kb)downstream from the MC4R gene and overlaps the re-gion previously identified by large-scale GWAS studiesof obesity andBMIin thegeneral population. 15,16 Foreachof these SNPs, the minor allele homozygotes gained sig-nificantlymore weightduring the12-week trial thaneitherheterozygotes or common allele homozygotes, both of which groupsdidnotdiffer from each other. Results didnot substantially change when drug assignment, sex, orrace were entered into a regression model. Importantly,distribution of drug assignment across the minor allelehomozygotes did not differ from the proportions in thegroup as a whole ( 22=0.86, P=.65). There was also nosignificant correlation between baseline BMI and BMI

Table 1. Association of Top Chromosome 18 SNPs With Antipsychotic DrugInduced Weight Gain in Discovery Cohort

Reference SNP ID Position MAF, % P Value

BMI

Minor AlleleHomozygotes Heterozygotes

Major AlleleHomozygotes

rs8092668 55934091 22.66 1.30 106 3.939 1.856 1.568rs1942879 55939970 33.81 4.63 106 3.201 1.696 1.519rs952044 55949090 33.33 5.57 106 3.201 1.687 1.548rs66723169 55959958 19.15 5.45 106 3.827 1.846 1.613rs12967878a 55977550 19.15 3.60 107 4.153 1.787 1.613rs6567160a 55980115 22.34 8.16 107 4.277 1.952 1.512rs476828b 56003567 28.01 3.29 103 2.674 1.865 1.574rs619825 56010046 45.74 4.62 106 2.625 1.663 1.430rs1942876 56022246 42.20 1.20 107 2.886 1.662 1.454rs996022 56023341 41.84 1.32 107 2.925 1.667 1.454rs12955983 56023969 21.58 4.09 106 3.760 1.900 1.560rs11663816 56027207 21.99 3.17 106 3.760 1.864 1.567rs17175602 56033697 21.63 3.17 106 3.760 1.898 1.554rs489693a 56033767 34.40 2.80 107 3.333 1.797 1.382rs646749a 56034105 44.33 3.09 107 2.884 1.624 1.417rs694780 56034525 44.33 3.09 107 2.884 1.624 1.417rs12957325 56035596 21.28 3.17 106 3.760 1.938 1.538rs12970134a 56035730 21.43 3.26 106 3.760 1.927 1.554rs11660069 56036393 21.63 3.17 106 3.760 1.898 1.554rs603940 56036763 44.33 3.28 106 2.806 1.657 1.428rs581401 56036944 44.33 3.28 106 2.806 1.657 1.428

Abbreviations: BMI, body mass index (calculated as weight in kilograms divided by height in meters squared); ID, identification; MAF, minor allele frequency;SNP, single-nucleotide polymorphism.

aGenotyped in additional cohorts. The SNPs rs12967878 and rs6567160 produced an insufficient number of minor allele homozygotes to test recessive effectsin the additional cohorts.

bProxy SNP for rs17782313.

Table 2. Association of Top SNPs With Antipsychotic DrugInduced Weight Gain in 3 Cohorts

Reference SNP ID Position MAF, %

P Value

Discovery CohortReplication 1

CohortReplication 2

CohortReplication 3

Cohort

rs489693 56033767 34.40 2.80 107 .00014 .007 .042rs646749 56034105 44.33 3.09 107 .00026 .092rs12970134 56035730 21.43 3.26 106 .143 .007

Abbreviations: ID, identification; MAF, minor allele frequency; SNP, single-nucleotide polymorphism.




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(r =0.032, P=.71). Moreover, results did not substan-tially change when baseline BMI was added to a regres-sion, and there was no significant association betweengenotypeat any of the top GWASSNPsand baseline BMI(eg, P=.32 for rs489693).

ADDITIONAL COHORTS

The SNPs emerging from the GWAS are in strong link-age disequilibrium ( D=1 for most SNPs; eFigure 1, up-per panel) but vary in minor allele frequency (Table 1),resultingin variabledegrees of r 2 (eFigure1, lower panel). We genotyped5 SNPs, representing the various levels of minorallele frequencyevident at this locus(Table1, high-lighted SNPs) in the first 2 replication cohorts. How-ever, 2 SNPs (rs6567160 and rs12967878) produced aninsufficientnumber of minor allele homozygotes (n 5)in either cohort to test recessive effects. Results for the

remaining 3 SNPs are displayed in Table 2; the r 2 valuesfor these 3 SNPs in the discovery cohort were of mod-erate strength (rs489693/rs646749, r 2=0.66; rs489693/ rs12970134, r 2=0.52; rs646749/rs12770134, r 2=0.34). Of these 3 SNPs, rs489693 yielded consistent and statisti-cally significant recessive effects across each cohort. Re-sults didnotchangesubstantially when race, sex, or base-line weight wereadded toananalysis ofcovariance model. We next examined rs489693 in a third replication co-hort and, again, obtained statistically significant reces-sive effects that remained significant when sex, baselineweight, and study drug were added using analysis of co-variance. Meta-analysis across all 4 cohorts yielded astrong, genome-wide significant effect (Stouffer z trend,P=5.59 1012). To graphically demonstrate the effects,in Figure 3 , we plot the mean change in weight in eachcohort as a function of genotype at rs489693 (percent-ageweightchangedisplayed ineFigure2).Baseline weightandBMIdidnotsignificantlydifferamong rs489693 geno-type groups in any of the cohorts.

METABOLIC INDICES

Finally, we examined the relationship between rs489693genotype and SGA-induced changes in metabolic indi-ces in our discovery cohort ( Table 3 ). Minor allele ho-mozygotes had significantly greater increases in levels of triglycerides, leptin,andinsulin,in thehomeostasis modelassessment insulin resistance index, andin total fatmasscompared with the group of heterozygotes and com-monallele homozygotes. Othermeasuresapproachedsig-nificance ( P .05 and P .10), includingchanges in totalcholesterol and high-density lipoprotein cholesterol.

COMMENT

To our knowledge, weconducted thefirst GWASofSGA-induced weight gain in an antipsychotic-naive cohort of pediatricsubjects, andwe have identified evidenceof re-

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Figure 2. Genome-wide association study results. A, Manhattan plot displays statistical significance levels (log10 P values) of correlation and trend tests forchange in body mass index (BMI) in the discovery cohort, plotted by chromosomal position for all autosomal single-nucleotide polymorphisms (SNPs). Peakvalues are observed on chromosome 18, between positions 55.934 and 56.037 megabases (Mb), as detailed in Table 1. B, Quantile-quantile plot displaysstatistical significance levels (log10 P values) of correlation and trend tests for change in BMI in the discovery cohort, plotted against expected values under thenull hypothesis. With the exception of the most strongly associated SNPs on chromosome 18, there is no deviation from the diagonal (genomic control=1.00).

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Figure 3. Single-nucleotide polymorphism rs489693 genotype andantipsychotic druginduced weight gain in 4 cohorts of subjects. Replic1indicates the first replication cohort; Replic2, the second replication cohort;Recplic3, the third replication cohort.




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cessive effects at multiple SNPs located at chromosome18q21.32. This peak directly overlaps a region that hasbeen repeatedly identified as a predictor of weight andBMIin healthy individuals (eFigure1), andhasbeen im-plicated in obesity, type 2 diabetes mellitus, and relatedphenotypes. 15,16 The SNP rs489693demonstrated statis-tically significant recessive effects in 3 additional inde-pendent cohorts, with minor allele homozygotes in allcohorts at risk for extreme weightgain followinga short

duration of treatment, and consistent effects on relatedmetabolic indices in our discovery cohort. This locus isapproximately190kb downstream from MC4R, the mela-nocortin4 receptorgene,whichhaspreviously been iden-tified as a candidate for weight-related phenotypes be-causemutations in this gene have been linked to extremeobesity in children and adolescents and Mc4r knockoutmice develop obesity. 17

A major strength of our approach was the assess-ment of subjects undergoing their first exposure to an-tipsychoticdrugtreatment, unlike prior GWASs ofweightchange induced by antipsychotics. 9 Baselineweight vari-ability due to prior treatment with agents known to in-duce substantial weight gain was therefore minimized,

and this provided us with substantially enhanced powerto detect the effects of genetic variation on a complexweight regulation phenotype. Moreover, the use of an-tipsychotic plasma levels to ensure medication compli-ance reduced phenotypic variation due to the nuisance(nongenetic)effects ofmedication nonadherence, therebyenhancing the strength of the genotype-phenotype rela-tionships. This effect may be particularly important inpsychotic disorders, in which noncompliance with treat-ment is estimated to occur in 40% or more of patients.

Althoughsamplesize in thediscovery cohort was smallin comparison with GWASs of complex disease entitiesandquantitativetraits inthegeneralpopulation, theGWASsof pharmacogenetic phenotypes have, in some instances,

demonstrated extremelyrobust effects in small samples.18,19

Although our initial GWAS result did not meet conven-tional thresholds for genome-wide significance, the pos-sibility that our result is a false positive is substantially re-duced by 3 factors: (1) the convergence of results across4 independentcohorts, resulting in a meta-analytic P valueseveral orders of magnitudebeyond genome-wide thresh-olds; (2) the inherent biological plausibility of MC4Rforweight gain; and (3) the high prior probability for asso-ciation with thisgenomic regionbased on numerous pre-viousGWASsofobesity andrelatedphenotypesin thegen-eral population. 15,16,20-26 Similarly,although our discoverysample includedsubjects frommultiple ethnic groups, thelikelihood that the results areartifactsof population strati-

fication is greatlyreduced by several factors: (1) theprin-cipal components analysis correction of theGWASanaly-sis resulted in no evidence of population stratification( genomic control =1.00), (2) the results were replicated in anethnicallyhomogeneousGermansample, and(3)theover-lappingobesitylocus fromgeneralpopulationGWASshasbeen replicated in African-ancestry populations. 27,28

It should be noted, however, that the GWAS signal forantipsychotic-induced weight gainisnotpreciselythe sameas that identified in general population studies. First, ourgenotypic effects followed a recessive pattern (Figure 3);

heterozygotesdidnotsubstantiallydifferfromcommon al-lele homozygotes. Moreover, no SNPs on any chromo-some exceededa statistical thresholdof P 106 for analy-ses testing thedominant or additive models in ourcohort.By contrast, GWAS effects reported in the general popu-lation areadditive,with heterozygotes intermediate to the2 homozygous groups. Second, although this genomic re-gionismarkedbyconsiderablelinkagedisequilibrium,withmultipleSNPsachieving nominalassociations in bothourGWAS and general population studies of obesity, specificSNP effects differ. For example, a proxy for the strongestadditive correlate of general population obesity was notamong the top 20 recessive SNPs in our cohort, although

it was nominally significant (Table 1). Similarly, the SNPin our study (rs489693) has not emerged as the moststronglyassociated SNPin general population studies, ex-cept for a single studyofwaist circumference. 20 Further re-search with larger samples will be needed to test for mul-tiple, independent allelic effects at this locus, as has beenreported in a recent study of obesity. 29

Our results may inform the design of GWASs seek-ing to identify risk alleles for complex phenotypes, suchas obesity, that are mediated bya plethora of genetic andenvironmentalfactors. In GWASs of weight, samplesizes

Table 3. SNP rs489693 Genotype and Metabolic Changes inAntipsychotic-Naive Subjects Following 12 Weeks ofTreatment With Second-Generation Antipsychotics

Metabolic Index, rs489693 Genotype Mean (SEM)2-Tailed P

Value

Fat mass, kgAC/CC 4.87 (0.46) .001AA 10.03 (1.63)

Triglycerides, mg/dLAC/CC 7.29 (5.60) .011AA 51.67 (17.5)

Total cholesterol, mg/dLAC/CC 3.18 (2.25) .066AA 15.73 (5.73)

HDL cholesterol, mg/dLAC/CC 0.03 (0.81) .052AA 2.87 (1.18)

LDL cholesterol, mg/dLAC/CC 1.78 (1.75) .292AA 7.50 (4.70)

Glucose, mg/dLAC/CC 1.56 (0.86) .566AA 3.07 (2.74)

Insulin, IU/mLAC/CC 0.35 (0.75) .043AA 4.91 (1.88)

HOMA-IR indexAC/CC 0.12 (0.17) .033AA 1.23 (0.49)

Leptin, ng/mLAC/CC 3.40 (0.68) .028AA 8.27 (3.10)

Abbreviations: HDL, high-density lipoprotein; HOMA-IR, homeostasismodel assessment insulin resistance; LDL, low-density lipoprotein; SNP,single-nucleotide polymorphism.

SI conversion factors: To convert triglycerides to millimoles per liter, multiplyby 0.0113; to convert total, HDL, and LDL cholesterol to millimoles per liter,multiply by 0.0259; to convert glucose to millimoles per liter, multiply by0.0555; and to convert insulin to picomoles per liter, multiply by 6.945.




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in the thousands were necessary to achieve statisticallysignificant results, presumably because of the vast num-bers of unmeasured (and uncontrolled) environmentalfactors working over variable amounts of time to influ-ence theultimate weightphenotype. In thepresent study,the critical environmental factor predisposing individu-als to weight gain was antipsychotic drug administra-tion over a short period of time. The experimental con-trol of this one factor provided us with sufficient

environmentalhomogeneity to detect genome-wide sig-nificant results ina studyof slightlymore than 100, ratherthan thousands, of subjects. Future studies of complexphenotypes may benefit from consideration of pharma-cological or other environmental challenge para-digms for the detection of susceptibility alleles.

These data have potentialclinical implications. For ex-ample, a priori identification of those subjects at in-creased risk of severe weight gain could lead to alterna-tivetreatments (ie,other thanSGAs),particularlyin patientswithout an Axis I psychotic disorder. Of note, recent datafrom the 2007 National Ambulatory Medical Care sur-vey30 indicate that antipsychotic drugs (most commonlyquetiapine and risperidone) were prescribed in 21.3% of

patient visits for anxiety disorders, with the largest in-crease in new patient visits, despite the fact that there islittle evidence for these drugs efficacy in anxiety. There-fore, it might be plausibleto consider pharmacotherapeu-tic strategies that would not include antipsychotic drugsfor those nonpsychotic individuals who carry the high-risk genotype for weight gain, as well as increased behav-ioral andpsychosocialinterventionsfocused ondietaryandexercise habits. Finally, research on the coadministrationof MC4R agonists, of which several are being devel-oped, 31 in this subset of patients could be informative forthe development of ameliorative strategies.

Submitted for Publication: November 8, 2011; final re-

vision received January 13, 2012; accepted January 16,2012.Published Online: May 7, 2012. doi :10.1001 /archgenpsychiatry.2012.191Author Affiliations: Feinstein Institute for Medical Re-search,Manhasset(DrsMalhotra,Correll,Gregersen, Lee,Kane, and Lencz), Zucker Hillside Hospital, Glen Oaks(Drs Malhotra, Correll, Kane, and Lencz), andNew YorkState Psychiatric Institute/ColumbiaUniversity,NewYork(Dr Lieberman), New York; Centre for Addiction andMental Health, Toronto, Ontario (Ms Chowdhury andDrs Muller, Tiwari, and Kennedy); Medical UniversityInnsbruck, Austria (Dr Fleischhacker); University Medi-cal Center Utrecht, the Netherlands (Drs Kahn and

Ophoff); andVanderbilt UniversityMedicalCenter,Nash-ville, Tennessee (Dr Meltzer).Correspondence: Anil K. Malhotra, MD, Psychiatry Re-search, Zucker Hillside Hospital, 75-59 263rd St, GlenOaks, NY 11004 ([email protected]).Author Contributions: Dr Malhotrahadfull access to allthe data in the study and takes responsibility for the in-tegrity of the data and the accuracy of the data analysis.Drs Correll and Chowdhury contributed equally to thiswork, and Drs Lencz and Kennedy also contributedequally.

Financial Disclosure: Dr Malhotra is a consultant to EliLilly, Shire, and Genomind. He has grant support fromAbbott. Dr Correll has received grants from Bristol-Myers Squibb (BMS), Janssen and Janssen, Otsuka, theNational Alliance for Research on Schizophrenia andDe-pression (NARSAD), theAmericanAcademy ofChildandAdolescent Psychiatry, the Feinstein Institute for Medi-calResearch, andtheNational Institute of MentalHealth(NIMH). He has received consultant fees or honoraria

from Actelion, Alexza, AstraZeneca, Boehringer Ingel-heim, Biotis, BMS, Cephalon, Desitin,Eli Lilly, IntraCel-lular Therapies, MedAvante, Ortho-McNeil/Janssen/ Johnson & Johnson, GlaxoSmithKline, HoffmannLaRoche, Lunbeck, Medicure, Merck, Novartis, Otsuka,Pfizer, Schering-Plough, Sunovion, Takeda, and Vanda.He has been a consultant to AstraZeneca, BMS, Cepha-lon, Lundbeck, Medicure, Otsuka, Supernus, Med-scape, Asante, Physicians Postgraduate Press, the Net-work for ContinuingMedical Education,OptumHealth,PeerView, Veritasime,theAlbertEinsteinCollege ofMedi-cineCenter forContinuous MedicalEducation, theUCLA(University of California, Los Angeles) Center for Con-tinuous Medical Education, andCME LLC. He has been

an advisory board member for Actelion, Alexza, Astra-Zeneca, BMS, IntraCellular Therapies, Lundbeck,MedAvante, Merck, Novartis, Otsuka, Pfizer, Schering-Plough, Sunovion, Takeda, andVanda.He also hasservedon the speakers bureau for AstraZeneca, BMS, Eli Lilly,Merck,Otsuka, andPfizer. He also receivedpayment formanuscript preparation and development of educa-tional presentations from PeerView, Physicians Post-graduate Press, the UCLA Center for Continuous Medi-cal Education, Veritasime, the Albert EinsteinCollege of MedicineCenter for Continuous MedicalEducation, As-ante, CME LLC, Medscape, the Network for Continu-ingMedical Education, andOptumHealth. Dr Muller hasgrants from the Canadian Institutes of Health Research

(CIHR)andNARSAD. Dr Kane receives honorariaor con-sulting fees from AstraZeneca, Boehringer-Ingelheim,BMS, Sunovion, EliLilly, Lundbeck, Intracellular Thera-peutics, Janssen, Johnson & Johnson, Merck, Novartis,Otsuka, Pfizer, Takeda, Wyeth, Vanda, and Roche. Hehas received travel support from BMS, Eli Lilly, Lund-beck,Janssen, Merck,Otsuka, Pfizer, andRoche. He alsoreceives fees for participation in review activities fromOtsuka.Dr Fleischhacker receives research support fromOtsuka, Pfizer, Janssen, Alkermes, and Eli Lilly. He re-ceives honoraria for consulting with Lundbeck, Roche,BMS, Otsuka, Janssen, Pfizer, United BioSource,MedAvante,Sunovion,Merck, Neurosearch,Amgen, andEndo Pharmaceuticals. Hereceivesspeaker honorariafrom

Lundbeck, Sunovion, Janssen, Eli Lilly, Otsuka, AstraZeneca, Roche, and Sunovion. He also holds stock inMedAvante.DrKahn isa consultant for thedataandsafetymonitoring board and receives grants or honoraria fromAstra-Zeneca, BMS, Envivo,Lilly, Janssen-Cilag, Otsuka,Gedeon Richter, Roche, and Sunovion. Dr Liebermanis a consultant to AstraZeneca, Bioline, Cephalon,GlaxoSmithKline, Intracellular Therapies, Eli Lilly, For-est Laboratories, Janssen, Otsuka, Pfizer, Pierre Fabre,Psychogenics, andWyeth. Hehasactive orpendinggrantsfrom Allon, AstraZeneca, BMS, GlaxoSmithKline, For-




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est Laboratories, Janssen, Merck, Novartis, Pfizer, Sun-ovion, Targacept, Wyeth, Eli Lilly, and Roche. He alsohaspatents from Repligenandroyaltiesfrom OxfordPub-lishing, Wiley Publishing, and American PsychiatricPub-lishing. Dr Lencz is a consultant to Eli Lilly. Dr Ken-nedy hasgrant support from CIHR,NIMH, andEliLilly.He is a consultant to sanofi-aventis andDainippon Sumi-tomo. He is also on the speakers bureau for Eli Lilly andhas a patent from Theragenetics.

Funding/Support: This work was supported by Na-tional Institutes of Health grants P50MH080173 (to DrMalhotra), P30MH090590(toDr Kane), an NARSADIn-dependent Investigator Award (to Dr Malhotra), and anNARSAD Young Investigator Award (to Dr Muller).Online-OnlyMaterial: TheeTablesandeFigures areavail-able at http://www.archgenpsychiatry.com.

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