New data and an old puzzle: the negative association between schizophrenia and rheumatoid arthritis

Hong, Lee, S; Byrne, Enda M.; Hultman, Christina M.; Kähler, Anna K.; Vinkhuyzen, Anna A. E.; Ripke, Stephan; Andreassen, Ole A.; Frisell, Thomas; Gusev, Alexander; Hu, Xinli; Karlsson, Robert; Mantzioris, Vasilis X; McGrath, John J.; Mehta, Divya; Stahl, Eli A.; Zhao, Qiongyi; Kendler, Kenneth S.; Sullivan, Patrick F.; Price, Alkes L.; O'Donovan, Michael Conlon; Okada, Yukinori; Mowry, Bryan; Raychaudhuri, Soumya; Wray, Naomi R.; Byerley, William; Cahn, Wiepke; Cantor, Rita M.; Cichon, Sven; Cormican, Paul; Curtis, David; Djurovic, Srdjan; Escott‐Price, Valentina; Gejman, Pablo V.; Georgieva, Lyudmila; Giegling, Ina; Hansen, Thomas Folkmann; Ingason, Andrés; Kim, Yunjung; Konte, Bettina; Lee, Phil H.; McIntosh, Andrew M.; McQuillin, Andrew; Morris, Derek W.; Nöthen, Markus M.; Ó'Dúshláine, Colm; Olincy, Ann; Olsen, Line; Pato, Carlos N.; Pato, Michele T.; Pickard, Ben; Posthuma, Daniëlle; Rasmussen, Henrik Berg; Rietschel, Marcella; Rujescu, Dan; Schulze, Thomas G.; Silverman, Jeremy M.; Thirumalai, Srinivasa; Werge, Thomas; Agartz, Ingrid; Amin, Farooq; Azevedo, Maria Helena Pinto de; Bass, Nick; Black, Donald W.; Blackwood, Douglas; Bruggeman, Richard; Buccola, Nancy G.; Choudhury, Khalid; Cloninger, Robert; Corvin, Aiden; Craddock, Nicholas John; Daly, Mark J.; Datta, Susmita; Donohoe, Gary; Duan, Jubao; Dudbridge, Frank; Fanous, Ayman H.; Freedman, Robert; Freimer, Nelson B.; Friedl, Marion; Gill, Michael; Gurling, Hugh; Haan, Lieuwe de; Hamshere, Marian L.; Hartmann, Annette M.; Holmans, Peter; Kahn, René S.; Keller, Matthew C.; Kenny, Elaine; Kirov, George; Krabbendam, Lydia; Krasucki, Robert; Lawrence, Jacob; Lencz, Todd; Levinson, Douglas F.; Lieberman, Jeffrey A.; Lin, Danyu; Linszen, Don; Magnusson, Patrik K. E.; Maier, Wolfgang; Malhotra, Anil K.; Mattheisen, Manuel; Mattingsdal, Morten; McCarroll, Steven A.; Medeiros, Helena; Melle, Ingrid; Milanova, Vihra; Myin-Germeys, Inez; Neale, Benjamin M.; Ophoff, Roel A.; Pimm, Jonathan; Purcell, Shaun; Puri, Vinita; Quested, Digby; Rossin, L.; Ruderfer, Douglas M.; Sanders, Alan R.; Shi, Jianxin; Sklar, Pamela; Clair, David St; Stroup, T. Scott; Os, Jim van; Visscher, Peter M.; Wiersma, Durk; Zammit, Stanley; Bridges, S. Louis; Choi, Hyon K.; Coenen, Marieke J. H.; Vries, Niek de; Dieud, Philippe; Greenberg, Jeffrey D.; Huizinga, Tom W J; Padyukov, Leonid; Siminovitch, Katherine A.; Tak, Paul P.; Worthington, Jane; Jager, Philip L. De; Denny, Joshua C.; Gregersen, Peter K.; Klareskog, Lars; Mariette, Xavier; Plenge, Robert M.; Laar, Mart van de; Riel, Piet van

doi:10.1093/ije/dyv136

Cited by 53 publications

(53 citation statements)

References 69 publications

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“…Genetic correlation is a key population parameter that describes the shared genetic architecture of complex traits and diseases [1][2][3] . The genetic correlation is the additive genetic covariance between two traits scaled by the square root of the product of the genetic variance for each trait (i.e., the geometric mean of the trait variances).…”

Section: Main Textmentioning

confidence: 99%

Estimation of genetic correlation using linkage disequilibrium score regression and genomic restricted maximum likelihood

Ni¹,

Moser²,

Wray

et al. 2017

Preprint

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20Genetic correlation is a key population parameter that describes the shared genetic architecture 21 of complex traits and diseases. It can be estimated by current state-of-art methods, i.e. linkage 22 disequilibrium score regression (LDSC) and genomic restricted maximum likelihood (GREML). 23The massively reduced computing burden of LDSC compared to GREML makes it an attractive 24 tool for analyses of many traits, achieved through use of GWAS summary statistics rather than 25 individual level genotype data. While both methods generate unbiased estimates of the genetic 26 correlation from genome-wide single nucleotide polymorphism (SNP) data, the accuracy of the 27 LDSC estimates has not been thoroughly studied. In simulation, we show that the accuracy of 28 GREML is generally higher than that of LDSC, which is more obvious when using a smaller 29 number of SNPs (< 500K). When there is a genetic heterogeneity between the actual sample and 30 reference data from which LD scores are estimated, the accuracy of LDSC decreases further. For 31 genomic partitioning analyses, LDSC estimates can be biased. This is also confirmed with real 32 data analyses, showing that GREML estimates based on ~150,000 individuals give a higher 33 accuracy and power than LDSC estimates based on ~400,000 individuals (from combined meta-34 data) in estimating genetic correlation between SCZ and BMI 15 for GREML vs. -0.087 SE=0.019 and p-value=4.91E-06 for LDSC). We show a GREML 36 genomic partitioning analysis reveals that the genetic correlation between SCZ and height is 37 significantly negative for regulatory regions, which cannot be found by non-partitioned GREML 38 or by LDSC (whole genome or partitioned). We conclude that LDSC estimates should be 39 carefully interpreted as there can be uncertainty about homogeneity among combined meta-data 40 sets. We suggest that any interesting findings from massive LDSC analysis for a large number of 41 complex traits should be followed up, where possible, with more detailed analyses with 42.

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Section: Main Textmentioning

confidence: 99%

Estimation of genetic correlation using linkage disequilibrium score regression and genomic restricted maximum likelihood

Ni¹,

Moser²,

Wray

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…At~18,000 cases, a much larger number of loci were significant, and, when the number of cases reached~37,000, 108 independent loci were identified. This polygenic liability is now being linked to schizophrenia chronicity (e.g., Meier et al, 2016) and treatment resistance (e.g., Frank et al, 2015) as well as a variety of other psychiatric and medical conditions (e.g., Lee et al, 2015).…”

Section: (D) Genomic Sources Of Heterogeneitymentioning

confidence: 99%

Meta-Analyses of Genome-Wide Association Data Hold New Promise for Addiction Genetics

Agrawal

Edenberg

Gelernter

2016

J. Stud. Alcohol Drugs

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ABSTRACT. Meta-analyses of genome-wide association study data have begun to lead to promising new discoveries for behavioral and psychiatrically relevant phenotypes (e.g., schizophrenia, educational attainment). We outline how this methodology can similarly lead to novel discoveries in genomic studies of substance use disorders, and discuss challenges that will need to be overcome to accomplish this goal. We illustrate our approach with the work of the newly established Substance Use Disorders workgroup of the Psychiatric Genomics Consortium. (J.

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“…In simulations we observed substantial power gain with this weighting scheme. Assuming 1,000 cases and 50 loci, we compared the power of BUHMBOX implemented with and without weighting correlations (equation (12) in Supplementary Information). Across a wide range of π values we observed that the weighting scheme in BUHMBOX dramatically increased power (Figure 2).…”

Section: Weighting Snps By Their Effect Sizes Increases Powermentioning

confidence: 99%

“…Polygenic risk score approaches 3,10,11 build genetic risk scores (GRSs) for one phenotype and test their association with a second phenotype. Mixed-model approaches 5,6,12 can estimate the genetic covariance between two traits on the observed scale. From the genetic covariance one can also calculate the genetic coheritability and genetic correlation 6 .…”

Section: Introductionmentioning

confidence: 99%

Using genotype data to distinguish pleiotropy from heterogeneity: deciphering coheritability in autoimmune and neuropsychiatric diseases

Han

Slowikowski

et al. 2015

Preprint

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Shared genetic architecture between phenotypes may be driven by a common genetic basis (pleiotropy) or a subset of genetically similar individuals (heterogeneity). We developed BUHMBOX, a well-powered statistical method to distinguish pleiotropy from heterogeneity using genotype data. We observed a shared genetic basis between 11 of 17 tested autoimmune diseases and type I diabetes (T1D, p<10 -12 ) and 11 of 17 tested autoimmune diseases and rheumatoid arthritis (RA, p<10 -7 ). This sharing could not be explained by heterogeneity (corrected p BUHMBOX >0.2 using 6,670 T1D cases and 7,279 RA cases), suggesting that shared genetic features in autoimmunity are due to pleiotropy. We observed a shared genetic basis between seronegative and seropostive RA (p<10 -22 ), explained by heterogeneity (p BUHMBOX =0.008 in 2,406 seronegative RA cases). Consistent with previous observations, we observed genetic sharing between major depressive disorder (MDD) and schizophrenia (p<10 -9 ). This sharing is not explained by heterogeneity (p BUHMBOX =0.28 in 9,238 MDD cases).

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New data and an old puzzle: the negative association between schizophrenia and rheumatoid arthritis

Abstract: 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.

Cited by 53 publications

References 69 publications

Estimation of genetic correlation using linkage disequilibrium score regression and genomic restricted maximum likelihood

Estimation of genetic correlation using linkage disequilibrium score regression and genomic restricted maximum likelihood

Meta-Analyses of Genome-Wide Association Data Hold New Promise for Addiction Genetics

Using genotype data to distinguish pleiotropy from heterogeneity: deciphering coheritability in autoimmune and neuropsychiatric diseases

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