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. 2015 Oct;44(5):1706-21.
doi: 10.1093/ije/dyv136.

New Data and an Old Puzzle: The Negative Association Between Schizophrenia and Rheumatoid Arthritis

S Hong LeeEnda M ByrneChristina M HultmanAnna KählerAnna A E VinkhuyzenStephan RipkeOle A AndreassenThomas FrisellAlexander GusevXinli HuRobert KarlssonVasilis X MantziorisJohn J McGrathDivya MehtaEli A StahlQiongyi ZhaoKenneth S KendlerPatrick F SullivanAlkes L PriceMichael O'DonovanYukinori OkadaBryan J MowrySoumya RaychaudhuriNaomi R WraySchizophrenia Working Group of the Psychiatric Genomics Consortium and Rheumatoid Arthritis Consortium InternationalSchizophrenia Working Group of the Psychiatric Genomics Consortium AuthorsWilliam ByerleyWiepke CahnRita M CantorSven CichonPaul CormicanDavid CurtisSrdjan DjurovicValentina Escott-PricePablo V GejmanLyudmila GeorgievaIna GieglingThomas F HansenAndrés IngasonYunjung KimBettina KontePhil H LeeAndrew McIntoshAndrew McQuillinDerek W MorrisMarkus M NöthenColm O'DushlaineAnn OlincyLine OlsenCarlos N PatoMichele T PatoBenjamin S PickardDanielle PosthumaHenrik B RasmussenMarcella RietschelDan RujescuThomas G SchulzeJeremy M SilvermanSrinivasa ThirumalaiThomas WergeSchizophrenia Working Group of the Psychiatric Genomics Consortium CollaboratorsIngrid AgartzFarooq AminMaria H AzevedoNicholas BassDonald W BlackDouglas H R BlackwoodRichard BruggemanNancy G BuccolaKhalid ChoudhuryRobert C CloningerAiden CorvinNicholas CraddockMark J DalySusmita DattaGary J DonohoeJubao DuanFrank DudbridgeAyman FanousRobert FreedmanNelson B FreimerMarion FriedlMichael GillHugh GurlingLieuwe De HaanMarian L HamshereAnnette M HartmannPeter A HolmansRené S KahnMatthew C KellerElaine KennyGeorge K KirovLydia KrabbendamRobert KrasuckiJacob LawrenceTodd LenczDouglas F LevinsonJeffrey A LiebermanDan-Yu LinDon H LinszenPatrik K E MagnussonWolfgang MaierAnil K MalhotraManuel MattheisenMorten MattingsdalSteven A McCarrollHelena MedeirosIngrid MelleVihra MilanovaInez Myin-GermeysBenjamin M NealeRoel A OphoffMichael J OwenJonathan PimmShaun M PurcellVinay PuriDigby J QuestedLizzy RossinDouglas RuderferAlan R SandersJianxin ShiPamela SklarDavid St ClairT Scott StroupJim Van OsPeter M VisscherDurk WiersmaStanley ZammitRheumatoid Arthritis Consortium International AuthorsS Louis Bridges JrHyon K ChoiMarieke J H CoenenNiek de VriesPhilippe DieudJeffrey D GreenbergTom W J HuizingaLeonid PadyukovKatherine A SiminovitchPaul P TakJane WorthingtonRheumatoid Arthritis Consortium International CollaboratorsPhilip L De JagerJoshua C DennyPeter K GregersenLars KlareskogXavier MarietteRobert M PlengeMart van LaarPiet van Riel
Free PMC article

New Data and an Old Puzzle: The Negative Association Between Schizophrenia and Rheumatoid Arthritis

S Hong Lee et al. Int J Epidemiol. .
Free PMC article

Abstract

Background: A long-standing epidemiological puzzle is the reduced rate of rheumatoid arthritis (RA) in those with schizophrenia (SZ) and vice versa. Traditional epidemiological approaches to determine if this negative association is underpinned by genetic factors would test for reduced rates of one disorder in relatives of the other, but sufficiently powered data sets are difficult to achieve. The genomics era presents an alternative paradigm for investigating the genetic relationship between two uncommon disorders.

Methods: We use genome-wide common single nucleotide polymorphism (SNP) data from independently collected SZ and RA case-control cohorts to estimate the SNP correlation between the disorders. We test a genotype X environment (GxE) hypothesis for SZ with environment defined as winter- vs summer-born.

Results: We estimate a small but significant negative SNP-genetic correlation between SZ and RA (-0.046, s.e. 0.026, P = 0.036). The negative correlation was stronger for the SNP set attributed to coding or regulatory regions (-0.174, s.e. 0.071, P = 0.0075). Our analyses led us to hypothesize a gene-environment interaction for SZ in the form of immune challenge. We used month of birth as a proxy for environmental immune challenge and estimated the genetic correlation between winter-born and non-winter born SZ to be significantly less than 1 for coding/regulatory region SNPs (0.56, s.e. 0.14, P = 0.00090).

Conclusions: Our results are consistent with epidemiological observations of a negative relationship between SZ and RA reflecting, at least in part, genetic factors. Results of the month of birth analysis are consistent with pleiotropic effects of genetic variants dependent on environmental context.

Figures

Figure 1.
Figure 1.
Sample sizes.
Figure 2.
Figure 2.
Genome-wide estimates: (i) SNP heritability; (ii) SNP correlation (using RA + ve only). Estimates are from analyses using as the phenotype residuals after adjusting SNPs in the MHC region unless otherwise stated.
Figure 3.
Figure 3.
Genomic partitioning analyses; (i) Percentage of SNP heritability attributed to each functional annotation class for SZ and RA, compared with the percentage of SNPs attributed to each class. Adjacent to the bars are the P-values for H0: percentage of variation attributed to annotation class = percentage of SNPs attributed to the annotation class; (ii) SNP correlation between SZ and RA based on annotation. Estimates are from analyses using as the phenotype residuals after adjusting SNPs in the MHC region. When the MHC is included, the coding/regulatory correlation is −0.174, s.e. 0.071, P = 0.0075.
Figure 4.
Figure 4.
Relationship between season of birth and association odds ratios of SNPs associated with SZ at P < 0.05 identified in the GWAS of the Psychiatric Genomics Consortium. P1 H0: OR Winter C&R SNPs = OR for Non-Winter C&R SNPs. HA: OR Winter C&R SNPs > OR for Non-Winter C&R SNPs. P2 H0: OR Winter C&R SNPs = OR for Winter All SNPs except C&R. HA: OR Winter C&R SNPs > OR for Winter All SNPs except C&R. C&R = Coding and Regulatory (2820 SNPs). All SNPs except C&R = All SNPs except Coding and Regulatory (44498 SNPs). Winter-born cases (N = 1511) and controls (N = 2036) were born January to April; N = 2962 for non-winter cases and N = 3772 for non-winter controls.

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