Common polygenic variation contributes to risk of schizophrenia and bipolar disorder

Abstract

Schizophrenia is a severe mental disorder with a lifetime risk of about 1%, characterized by hallucinations, delusions and cognitive deficits, with heritability estimated at up to 80%. We performed a genome-wide association study of 3,322 European individuals with schizophrenia and 3,587 controls. Here we show, using two analytic approaches, the extent to which common genetic variation underlies the risk of schizophrenia. First, we implicate the major histocompatibility complex. Second, we provide molecular genetic evidence for a substantial polygenic component to the risk of schizophrenia involving thousands of common alleles of very small effect. We show that this component also contributes to the risk of bipolar disorder, but not to several non-psychiatric diseases.

Figure 1. Association results across the MHC region

Results are shown as –log10 (P-value) for genotyped SNPs. The most associated SNP is shown as a blue diamond. The colour of the remaining markers reflects r² with rs3130375, light pink, r² > 0.1, red, r² > 0.8. The recombination rate from the CEU HapMap (second Y-axis) is plotted in light blue.

Figure 2. Replication of the ISC-derived polygenic component in independent SCZ and BPD samples

Variance explained in the target samples based on scores derived in the entire ISC for five significance thresholds (p_T < 0.1, 0.2, 0.3, 0.4 and 0.5, plotted left to right in each study). The y-axis indicates Nagelkerke’s pseudo-R²; the number above each set of bars is the P-value for the p_T < 0.5 target sample analysis. Numbers for cases/controls: MGS-EA 2687 / 2656; MGS-AA 1287 / 973; O’Donovan 479 / 2938; STEP-BD 955 / 1498; WTCCC 1829 / 2935; CAD 1926 / 2935; CD 1748 / 2935; HT 1952 / 2935; RA 1860 / 2935 ; T1D 1963 / 2935 ; T2D 1924 / 2935.

Figure 3. Observed and simulated profiles of target sample variance explained

Panel A shows the observed variance explained (R², black line). Panel B shows a subset of models that produced results consistent with the observed data. All yielded similar estimates of the total variance explained by the SNPs that tag the causal variants, V_M, with a mean value of 34%. The seven models were: (% SNPs, Mean GRR/variance explained (V) per causal allele, LD, frequency model) M₁: 6.25%, GRR=1.05, r² =1, empirical; M₂: 25%, GRR=1.025, r² =1, empirical; M₃: 12%, GRR=1.05, r² <1, uniform; M₄: 32%, GRR=1.04, r² <1, U-shaped; M₅: 11%, V=0.00006, r² =1, empirical; M₆: 25%, GRR(Exponential)=1.025, r² <1, uniform; M₇: 100%, GRR(Exponential)=1.012, r² <1, uniform. Panel C shows four inconsistent models with fewer variants of larger effect.

Figure 4. Analysis stratified by score allele frequency

Panel A shows the observed data for the ISC/MGS-EA comparison. The y-axis is the target sample pseudo R², scaled within each figure as a proportion of the maximum value observed. The two other plots show results for simulated data: the common variant model, with a uniform frequency distribution for causal risk-increasing alleles (panel B) and a multiple rare variant (MRV) model, in which the collective frequency of rare variants at a locus that all reside on the same haplotypic background with respect to the genotyped SNP was bounded at a maximum of 5% (panel C).