Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs.

Schizophrenia is a complex disorder caused by both genetic and environmental factors. Using 9,087 affected individuals, 12,171 controls and 915,354 imputed SNPs from the Schizophrenia Psychiatric Genome-Wide Association Study (GWAS) Consortium (PGC-SCZ), we estimate that 23% (s.e. = 1%) of variation in liability to schizophrenia is captured by SNPs. We show that a substantial proportion of this variation must be the result of common causal variants, that the variance explained by each chromosome is linearly related to its length (r = 0.89, P = 2.6 × 10(-8)), that the genetic basis of schizophrenia is the same in males and females, and that a disproportionate proportion of variation is attributable to a set of 2,725 genes expressed in the central nervous system (CNS; P = 7.6 × 10(-8)). These results are consistent with a polygenic genetic architecture and imply more individual SNP associations will be detected for this disease as sample size increases.

Schizophrenia is a severe mental disorder with lifetime risk ~1% and heritability of ~0.7 to 0.8[1-3]. Of all complex genetic diseases, the genetic architecture of schizophrenia perhaps has received the most speculation and debate[4,5] and the relative importance of common causal variants remains controversial[6,7]. Genome-wide association (GWA) studies of schizophrenia have discovered associated variants[8-10] that together explain only a small fraction of the heritability[11]. Here, new methods[12,13] for estimation of the variation explained by GWA genotypes are applied to PGC-SCZ data[14]. We use only cases and controls that are ‘unrelated’ in the classical sense and calculate the variance explained by autosomal SNPs. The variance estimate is derived from the average genome-wide similarity between all pairs of individuals determined using all SNPs. Genetic variation is estimated when case-case pairs and control-control pairs are on average more similar genome-wide than case-control pairs. We partition[15] this genomic variation by chromosome, by sex, by functional annotation and by minor allele frequency.

RESULTS

Genomic variation captured by common SNPs

The PGC-SCZ includes data from the International Schizophrenia Consortium (ISC)[8], the Molecular Genetics of Schizophrenia (MGS) study[9] and other samples (OTH) (Supplementary Table 1). Using a linear mixed model (Online Methods) we estimated the proportion of variance in liability to schizophrenia explained by SNPs (h2) in each of these three independent data subsets (Table 1). We use the notation h2 because the estimates represent a lower bound of narrow sense heritability; it is a lower bound because only variation due to association with the SNPs can be estimated. Preliminary analyses were conducted using non-imputed genotypes of the ISC and MGS subsets (Supplementary Table 2). The estimates of h2 for the PGC-SCZ subsets of ISC, MGS and OTH were each greater than the estimate from the total combined PGC-SCZ sample of h2 = 23% (s.e. 1%) (Table 1). We investigated this result by conducting bivariate analyses considering cases and controls from one subset as trait 1 and those from a different subset as trait 2 (Table 2); the two independent subsets are related through the coefficients of genome-wide similarity calculated from SNPs between individuals (Online Methods equation 2). The estimated correlation coefficients based on SNP genome-wide similarities are less than 1, consistent with several explanations. Subsets may be more homogeneous both phenotypically, for example because of similar and consistent diagnostic criteria, and genetically, because linkage disequilibrium (LD) between causal variants and analysed SNPs may be higher within than between subsets. Alternatively, subtle artefacts could generate non-random differences in allele frequencies between sets of cases and sets of controls from the same study. However, our preliminary analyses using genotyped SNPs for ISC and MGS and extreme QC (Supplementary Table 2) suggest that this is unlikely to be a major contributor. Furthermore, the correlations between data sets from the bivariate analyses are high (~0.8) demonstrating that the same genetic signals can explain variance in schizophrenia liability in different case-control samples collected; given that these samples were collected independently with genotyping conducted at different laboratories, it is difficult to envision artefacts that could generate such high correlations. Hence, we conclude that the PGC-SCZ estimate of h2 represents the lower bound of variance in liability that would be explained by common SNPs in a large phenotypically and genetically homogeneous sample with no genotyping artefacts.

Table 1

Estimated proportion of variance in liability to schizophrenia explained by SNPs (h2).

Dataset	Cases	Controls	h2 (SE)
ISC	3220	3445	0.27 (0.02)
MGS	2571	2419	0.31 (0.03)
OTH	3296	6307	0.27 (0.02)
ISC+MGS	5791	5864	0.25 (0.01)
PGC-SCZ	9087	12171	0.23 (0.01)

Estimates based on 915354 imputed SNPs; SE standard error of h2. PGC-SCZ is comprised of independent subsets ISC, MGS and OTH

Table 2

Bivariate analyses of PGC-SCZ subsets

Subset 1/Subset 2	Cases Subsets 1/2	Controls Subsets 1/2	Subset 1 h2 (SE)	Subset 2 h2 (SE)	r (SE)
ISC/MGS	3220/2571	3445/2419	0.26 (0.02)	0.29 (0.03)	0.84 (0.09)
ISC/OTH	3220/3296	3445/6307	0.26 (0.02)	0.27 (0.02)	0.89 (0.07)
MGS/OTH	2571/3296	2419/6307	0.30 (0.03)	0.26 (0.02)	0.79 (0.08)
ISC+MGS/OTH	5791/3296	5864/6307	0.24 (0.01)	0.26 (0.02)	0.87 (0.06)
Male/Female	6031/3056	5884/6287	0.24 (0.01)	0.25 (0.02)	0.89 (0.06)

Estimates based on 915354 imputed SNPs; h2 estimate of proportion of variance in liability to schizophrenia explained by SNPs; SE standard error of h2. r correlation of liabilities explained by SNPs between subset 1 and subset 2. PGC-SCZ is comprised of independent subsets ISC, MGS and OTH.

Partitioning of genomic variation by chromosome

Cryptic population stratification has been proposed as a confounding factor in GWA studies[7]. A consequence of population stratification is that segments of ancestry specific chromosomes segregate together in the population. In this situation, variance attributed to causal variants on one chromosome can be predicted by SNPs from segments derived from the same ancestral population on other chromosomes. To investigate whether population stratification could contribute to our results (over and above the ancestry principal component scores included as covariates in the analyses), we performed two kinds of analyses: one in which the similarity matrix for each chromosome was fitted separately (22 analyses estimating one additive genetic variance component per analysis) and a joint analysis which fitted 22 similarity matrices simultaneously (estimating 22 additive genetic variance components in a single analysis) (Online Methods). A higher total variance explained by the 22 individually estimated variances compared to the 22 simultaneously estimated variances would provide evidence of stratification. The total variance explained was 26% for chromosomes fitted separately compared to a total of 23% when fitted together, demonstrating little evidence of population stratification (Figure 1a). The estimates of variance explained by each chromosome are linearly related with the length of the chromosome (correlation = 0.89, p = 2.6 × 10−8), consistent with a highly polygenic model and remarkably similar to results for human height[12].

Figure 1

Genomic partitioning of schizophrenia. a) By chromosome: Estimated proportion of the variance in liability to schizophrenia explained bySNPs on individual chromosomes from a joint analysis of all chromosomes simultaneously or separate analyses for each chromosome The sum of the h2 is 0.23 for the joint analysis and 0.26 for the separate analyses. b) By annotation; the total variance explained by SNPs ( h2 ) in CNS+ genes, other genes and by those not in genes totals 0.23. Of this, a proportion 0.31 is attributed to SNPs in brain genes, which is greater than expected by chance (p = 7.6 × 10−8) given that the brain genes cover 0.20 of the length of the genome (Mb) and represent 0.21 of the SNP count (N SNPs). Error bars represent the 95% confidence intervals of the estimates. c) By MAF bin from analyses fitting MAF bins jointly or each separately d) By MAF bin compared to simulation under a rare variants only model. The variance explained by SNPs in each MAF bin (when MAF bins are fitted in separate analyses) as a proportion of the variance explained by all SNPs. Error bars represent 95% confidence intervals, for the simulations (right graph) these are calculated using the standard deviation across simulation replicates.

Genomic variation by sex

Sex differences have been described for almost all features of schizophrenia (prevalence, incidence, age of onset, clinical presentation, course, response to treatment)[16]. To assess if the variance in liability tagged by SNPs on autosomes differs between the sexes we undertook a bivariate analysis considering male cases and controls as one trait and female cases and controls as the other trait; the two independent subsets are related through the coefficients of similarity calculated from SNPs (Online Methods equation 2). The correlation in liabilities explained by SNPs between the sexes was very high (0.89 s.e. 0.06, not significantly different from 1) (Table 2) implying that the majority of additive genetic variance is shared between the sexes. We also investigated variance explained by genotyped SNPs on the X chromosome for the ISC and MGS data sets and concluded that the variance explained by the X chromosome is consistent with expectation given its length (Supplementary Table 3).

Partitioning of genomic variation by functional annotation

To assess if functional annotation of SNPs is associated with the variance they explain we partitioned the variance explained by SNPs into three components by creating similarity matrices from SNPs in “CNS+” genes, other genes and no genes (Online Methods). The CNS+ genes were the four sets identified by Raychaudhuri et al.[28] and comprised the genes in their brain-expressed (specifically, genes with differential CNS expression), neuronal activity, learning and synapse sets. We find that the variance attributable to the CNS+ genes is significantly greater than the proportion of the genome that they represent (31% s.e. 2% vs 20%, p = 7.6 × 10−8) (Figure 1b; Supplementary Table 4).

Partitioning of genomic variation by minor allele frequency of SNPs

It has been argued (e.g.[6,7,18]) that the low proportion of variance explained by previous GWA studies of schizophrenia implies that common variants are unimportant to the etiology of schizophrenia. To evaluate this hypothesis we undertook an analysis partitioning the variance tagged by SNPs into five components defined by minor allele frequency (MAF) (Online Methods). For close relatives (who are excluded from our analyses), estimated similarities based upon SNPs with different MAF will be similar. However, very distant relatives inherit chromosome segments from distant common ancestors. If a SNP is more recent than the common ancestor then the relationship between the individuals will not be reflected by the SNP; low MAF SNPs tend to be younger than high MAF SNPs. The variance explained by SNPs with MAF < 0.1 was 2% (s.e. 1%) from a joint analysis of all five MAF bins in the total PGC-SCZ data (Supplementary Table 5, Figure 1c). This low contribution to the total variance explained is likely to partly reflect under-representation of SNPs with low MAF in the analysis (minimum MAF 0.01) relative to those in the genome. The other four MAF bins explain approximately equal proportions of the variance, ~5% (s.e. 1%) each. Analyses of the PGC-SCZ subsets were consistent with these results (Supplementary Table 5). Based on the known relationship between allele frequencies and LD[19], it is highly unlikely that the estimates of h2 reported here are caused predominantly by rare causal variants[20]. We performed simulations conditional on PGC-SCZ data and confirmed that a rare variants only model could not explain our results. For example, in an analysis of PGC-SCZ data using only SNPs with MAF > 0.4, 11% (s.e. 1%) of the variance in liability was explained, which is nearly half of the variance explained by all SNPs. However, in simulations which attributed 50% of variation in liability to SNPs with MAF < 0.1, SNPs with MAF > 0.4 explained only 5% (s.e. 0.3%) of the variance, which is only 10% of the variation explained by all SNPs (Figure 1c,d; Supplementary Tables 5–6). Furthermore, our simulation strategy is a best case scenario in favour of the rare variants only model since our simulation extends the frequency of “rare” variants to MAF of 0.1 generating higher LD between the common genotyped SNPs and causal variants than would be expected under a more usual MAF definition of “rare”. Our results are consistent with analyses of the ISC data[8,20]. In the Supplementary Note we contrast our methods to the risk profiling methods used by the ISC and the efficient mixed model association expedited (EMMAX) method of Kang et al[21].

DISCUSSION

We draw four important conclusions from these results. First, from direct queries of the genome, we quantify the lower limit of the genetic contribution to schizophrenia; approximately one quarter of the variance in liability is directly tagged by common variants represented across the current generation of GWA arrays[8] (Table 1) and this variance is shared between the sexes (Table 2). Second, we provide evidence that causal risk variants must include common variants (Figure 1d). Third, we provide evidence that the variance explained by chromosomes is linearly related to the length of the chromosome (Figure 1b), consistent with a highly polygenic model (many risk loci). Fourth, we find that the CNS+ gene set explains significantly (p = 7.6 × 10−8) more variation relative to the proportion of the genome it represents. Together our results provide guidance for the future of genetic studies in schizophrenia. Some have argued[6,7,18] that common variants play little role in the etiology of schizophrenia and that the GWA approach for schizophrenia has been misconceived. Our results refute this conjecture that common variants play little role in the etiology of schizophrenia and that the GWA approach for schizophrenia has been misconceived by demonstrating that at least one quarter of variation in liability to schizophrenia is tagged by SNPs and that common causal variants must be responsible for most of this signal. Therefore, larger sample sizes are likely to achieve the statistical power necessary to detect additional effects (over those detected to date) with genome-wide significance. For example, a GWA for height[17], considered as a model complex trait, identified 180 robustly associated loci in a total sample size of 180,000 individuals and the identified variants were concentrated in pathways biologically associated with growth. Sample sizes of ~50,000 schizophrenia cases and 50,000 controls are needed to afford the same power to detect variants that explain the same proportion of phenotypic variance and gain insight into biological pathways achieved in the height study[11,12,22]. Our results imply that the GWA approach applied to larger case-control samples will deliver important results for schizophrenia.

In conclusion, we estimate that about one quarter of variation in liability to schizophrenia, or approximately one third of genetic variation in liability, is tagged when considering all genotyped and imputed SNP simultaneously. The remaining ‘missing’ heritability most likely reflects imperfect LD between causal variants and the genotyped and imputed SNPs. The current generation of genotyping chips may explain only ~70% of the total variance attributable to common SNPs (MAF > 0.1) and explains less of variance attributable to uncommon and rare variants (Supplementary Figure 1). From the analyses we have performed we cannot estimate a frequency distribution of the allele frequency of causal variants, but the most likely cause of low LD between causal variants and SNPs is that many causal variants have low MAF. Nevertheless, from the results presented we can conclude that common causal variants in LD with genotyped and imputed SNPs must contribute to genetic variation for liability to schizophrenia in the population. Hence, causal risk variants for schizophrenia range across the entire “allelic spectrum”.

METHODS

See Online Methods for full details.