Ripke S, O'Dushlaine C, Chambert K, Moran JL, Kähler AK, Akterin S, Bergen SE, Collins AL, Crowley JJ, Fromer M, Kim Y, Lee SH, Magnusson PK, Sanchez N, Stahl EA, Williams S, Wray NR, Xia K, Bettella F, Borglum AD, Bulik-Sullivan BK, Cormican P, Craddock N, de Leeuw C, Durmishi N, Gill M, Golimbet V, Hamshere ML, Holmans P, Hougaard DM, Kendler KS, Lin K, Morris DW, Mors O, Mortensen PB, Neale BM, O'Neill FA, Owen MJ, Milovancevic MP, Posthuma D, Powell J, Richards AL, Riley BP, Ruderfer D, Rujescu D, Sigurdsson E, Silagadze T, Smit AB, Stefansson H, Steinberg S, Suvisaari J, Tosato S, Verhage M, Walters JT, , Levinson DF, Gejman PV, Kendler KS, Laurent C, Mowry BJ, O'Donovan MC, Owen MJ, Pulver AE, Riley BP, Schwab SG, Wildenauer DB, Dudbridge F, Holmans P, Shi J, Albus M, Alexander M, Campion D, Cohen D, Dikeos D, Duan J, Eichhammer P, Godard S, Hansen M, Lerer FB, Liang KY, Maier W, Mallet J, Nertney DA, Nestadt G, Norton N, O'Neill FA, Papadimitriou GN, Ribble R, Sanders AR, Silverman JM, Walsh D, Williams NM, Wormley B, , Arranz MJ, Bakker S, Bender S, Bramon E, Collier D, Crespo-Facorro B, Hall J, Iyegbe C, Jablensky A, Kahn RS, Kalaydjieva L, Lawrie S, Lewis CM, Lin K, Linszen DH, Mata I, McIntosh A, Murray RM, Ophoff RA, Powell J, Rujescu D, van Os J, Walshe M, Weisbrod M, Wiersma D, , Donnelly P, Barroso I, Blackwell JM, Bramon E, Brown MA, Casas JP, Corvin AP, Deloukas P, Duncanson A, Jankowski J, Markus HS, Mathew CG, Palmer CN, Plomin R, Rautanen A, Sawcer SJ, Trembath RC, Viswanathan AC, Wood NW, Spencer CC, Band G, Bellenguez C, Freeman C, Hellenthal G, Giannoulatou E, Pirinen M, Pearson RD, Strange A, Su Z, Vukcevic D, Donnelly P, Langford C, Hunt SE, Edkins S, Gwilliam R, Blackburn H, Bumpstead SJ, Dronov S, Gillman M, Gray E, Hammond N, Jayakumar A, McCann OT, Liddle J, Potter SC, Ravindrarajah R, Ricketts M, Tashakkori-Ghanbaria A, Waller MJ, Weston P, Widaa S, Whittaker P, Barroso I, Deloukas P, Mathew CG, Blackwell JM, Brown MA, Corvin AP, McCarthy MI, Spencer CC, Bramon E, Corvin AP, O'Donovan MC, Stefansson K, Scolnick E, Purcell S, McCarroll SA, Sklar P, Hultman CM, Sullivan PF.
Genome-wide association analysis identifies 13 new risk loci for schizophrenia. Nat Genet.
Comments on News and Primary Papers
Comment by: Sven Cichon
Submitted 30 August 2013
Posted 30 August 2013
This paper is an important addition to the psychiatric genetics literature. One important message of it is that increasing the GWAS sample size in a complex neuropsychiatric phenotype such as schizophrenia identifies more common risk loci.
In the first-wave schizophrenia mega-analysis two years ago (Schizophrenia Psychiatric Genome-Wide Association Study Consortium, 2011), five risk loci at genomewide significance were detected, and it was speculated that more would follow with larger sample sizes. In fact, by performing a meta-analysis of a new Swedish schizophrenia sample (about 5,000 patients and 6,000 controls) and the first-wave PGC sample (about 9,000 patients and 12,000 controls) plus follow-up/replication in large, independent samples, the authors now find 22 genomic loci at genomewide significance. This is another important step forward in schizophrenia genetics.
It is reassuring (and important for the scientific community to know) that there is support for some of the previously reported loci. As expected, the findings are getting much more consistent now with the increasing power of the samples. Interestingly, some loci pop up now at genomewide significance that were known from bipolar disorder or combined phenotype analyses (schizophrenia + bipolar), such as CACNA1C and CACNB2. Together with the finding that there is an enrichment of smaller p values in genes encoding calcium channel subunits, there is now growing evidence that calcium signaling is involved in both bipolar disorder and schizophrenia. Calcium signaling is a crucial neuronal process and relevant in a number of human diseases, as the authors nicely review. Importantly, calcium channel complexes may be useful for clinical translation (e.g., as drug targets).
Variation in another gene that was implicated in bipolar disorder before, NCAN, was found at genomewide significance in schizophrenia in the study by Ripke et al. (in fact, an association with schizophrenia was already reported earlier by Mühleisen et al., 2012). It seems that another "theme" of disease-relevant genes may be neurodevelopmental effects in both bipolar disorder and schizophrenia. The different lincRNAs implicated now among the 22 genomewide significant findings may also point in this direction.
What I find particularly interesting are the considerations regarding the much debated genetic architecture of schizophrenia. The authors’ simulations, although certainly still not perfectly exact, substantiate previous calculations that common SNPs make substantial contributions to the risk for schizophrenia.
To my knowledge, for the first time there are estimates regarding the absolute number of common SNPs that contribute to the etiology of schizophrenia: The authors estimate "6,300 to 10,200 independent and mostly common SNPs." While it is difficult to deduce the number of genes or functional units covered by these SNPs, several thousand are well possible. Many of these loci/genes will fall into the same biological pathways, and the identification of a subset of these will probably suffice to identify the most important biological processes. The authors estimate that the top 2,000 loci might be sufficient, and maybe it is even fewer. At the same time, they estimate that such a number of identified loci is not completely out of reach. Sixty thousand patients and 60,000 controls should have enough statistical power to identify between 400 and 1,100 common loci at genomewide significance. Psychiatrists will agree that a great collaborative effort will be required to recruit such a large number of patients. But it is not impossible. The next wave of the PGC schizophrenia group is already moving in this direction.
Schizophrenia Psychiatric Genome-Wide Association Study (GWAS) Consortium (2011) Genome-wide association study identifies five new schizophrenia loci. Nat Genet. 43:969-76. Abstract
Mühleisen TW, Mattheisen M, Strohmaier J, et al. (2012) Association between schizophrenia and common variation in neurocan (NCAN), a genetic risk factor for bipolar disorder. Schizophr Res. 138:69-73. Abstract
View all comments by Sven CichonComment by: Ole A. Andreassen, SRF Advisor, Martin Tesli
Submitted 6 October 2013
Posted 7 October 2013
I recommend the Primary Papers
The recent genomewide association study (GWAS) by Ripke and co-workers is a very important contribution to our knowledge of the genetic underpinnings of schizophrenia. By adding ~5,000 schizophrenia cases and ~6,000 healthy controls from Sweden to the Psychiatric Genomics Consortium (PGC) results from 2011 (PGC, 2011), the authors identified 22 gene loci (including 13 novel) at genomewide significance. These findings confirm that previous schizophrenia GWAS have been statistically underpowered, and that increasing the sample size is a successful approach to bridge the gap between the high heritability estimates in schizophrenia and low variance explained by currently identified genetic variants.
With increasing sample size, consistency is also enhanced. Reassuringly, of the 100 most significant SNPs in the Sweden/PGC meta-analysis, 90 percent had the same sign. Moreover, previously reported signals from immune-related genes in the MHC region on chromosome 6 were confirmed, as well as genes encoding calcium channel subunits, miR-137, and targets of miR-137. Additionally, the authors found enrichment in an extended set of genes with predicted miR-137 target sites. The results also pointed to long intergenic noncoding RNAs (lincRNAs), as 13 out of 22 identified regions contain lincRNAs. Interestingly, there is evidence that lincRNAs have functions related to epigenetic regulation.
Using two newly developed methods—genomewide complex trait analysis (GCTA) and applied Bayesian polygenic analysis (ABPA)—the authors estimated that common variants account for 52 percent and 78 percent, respectively, of the phenotypic variation in schizophrenia. These numbers are, although imprecisely, in accordance with the high heritability estimates from meta-analyses on twin studies (81 percent) (Sullivan et al., 2003) and large epidemiological studies (64 percent) (Lichtenstein et al., 2009), and indicate that a substantial proportion of the genetic risk for schizophrenia can be explained by common variants identified in GWAS. With the ABPA method, the authors also estimated that between 6,300 and 10,200 SNPs explain 50 percent of the variance in schizophrenia susceptibility. This clearly shows the high polygenicity in schizophrenia.
The authors suggest that 60,000 schizophrenia cases and 60,000 controls are warranted to identify ~800 markers at genomewide significance level. With the combined effort from the PGC, this might be achievable, and a larger proportion of the hidden heritability will undoubtedly be revealed. However, when using the PGC schizophrenia sample as the discovery set and the Swedish sample as the test set, explained variance (Nagelkerke pseudo R2) was only ~0.06 (contrasted by an impressive significance level of 2 x 10-114). This is slightly disappointing, as the explained variance with similar methodology was found to be ~0.03 in a study by Purcell and co-workers in 2009 (Purcell et al., 2009). By increasing the sample size five times (~6,000 vs. ~30,000 individuals), explained variance has only increased from 3 to 6 percent. Although a further increase in the PGC sample will provide important information on risk variants, a refinement of the method is probably also needed to discover larger parts of the hidden heritability for the highly polygenic disorder schizophrenia. Such methods might include Bayesian models for weighing SNPs based on prior evidence, for example, related to pleiotropic effects (Andreassen et al., 2013) and genic annotation (Schork et al., 2013). A combination of large numbers and methodological refinement might prove particularly potent, both in terms of explaining more of the variance and identifying underlying molecular pathways.
PGC. Genome-wide association study identifies five new schizophrenia loci. Nat Genet . 2011 Oct ; 43(10):969-76. Abstract
Andreassen OA, Thompson WK, Schork AJ, Ripke S, Mattingsdal M, Kelsoe JR, Kendler KS, O'Donovan MC, Rujescu D, Werge T, Sklar P, , , Roddey JC, Chen CH, McEvoy L, Desikan RS, Djurovic S, Dale AM. Improved detection of common variants associated with schizophrenia and bipolar disorder using pleiotropy-informed conditional false discovery rate. PLoS Genet . 2013 Apr ; 9(4):e1003455. Abstract
Lichtenstein P, Yip BH, Björk C, Pawitan Y, Cannon TD, Sullivan PF, Hultman CM. Common genetic determinants of schizophrenia and bipolar disorder in Swedish families: a population-based study. Lancet . 2009 Jan 17 ; 373(9659):234-9. Abstract
Purcell SM, Wray NR, Stone JL, Visscher PM, O'Donovan MC, Sullivan PF, Sklar P. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature . 2009 Aug 6 ; 460(7256):748-52. Abstract
Schork AJ, Thompson WK, Pham P, Torkamani A, Roddey JC, Sullivan PF, Kelsoe JR, O'Donovan MC, Furberg H, Schork NJ, Andreassen OA, Dale AM. All SNPs are not created equal: genome-wide association studies reveal a consistent pattern of enrichment among functionally annotated SNPs. PLoS Genet . 2013 Apr ; 9(4):e1003449. Abstract
Sullivan PF, Kendler KS, Neale MC. Schizophrenia as a complex trait: evidence from a meta-analysis of twin studies. Arch Gen Psychiatry . 2003 Dec ; 60(12):1187-92. Abstract
View all comments by Ole A. Andreassen
View all comments by Martin Tesli