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 arrays8
() and this variance is shared between the sexes (). Second, we provide evidence that causal risk variants must include common variants (). Third, we provide evidence that the variance explained by chromosomes is linearly related to the length of the chromosome (), 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 argued6,7,18
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 height17
, 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 study11,12,22
. 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”.