Genomewide association for schizophrenia in the CATIE study: results of stage 1

Abstract

Little is known for certain about the genetics of schizophrenia. The advent of genomewide association has been widely anticipated as a promising means to identify reproducible DNA sequence variation associated with this important and debilitating disorder. A total of 738 cases with DSM-IV schizophrenia (all participants in the CATIE study) and 733 group-matched controls were genotyped for 492,900 single-nucleotide polymorphisms (SNPs) using the Affymetrix 500K two-chip genotyping platform plus a custom 164K fill-in chip. Following multiple quality control steps for both subjects and SNPs, logistic regression analyses were used to assess the evidence for association of all SNPs with schizophrenia. We identified a number of promising SNPs for follow-up studies, although no SNP or multimarker combination of SNPs achieved genomewide statistical significance. Although a few signals coincided with genomic regions previously implicated in schizophrenia, chance could not be excluded. These data do not provide evidence for the involvement of any genomic region with schizophrenia detectable with moderate sample size. However, a planned genomewide association study for response phenotypes and inclusion of individual phenotype and genotype data from this study in meta-analyses hold promise for eventual identification of susceptibility and protective variants.

Figures

Figure 1

Results of single-marker association tests of case-control status for 492,900 SNPs. Panel A. shows the QQ plot (77) of the observed p-values, −log10 (p), versus those expected by chance, −log10(iL+1), where p is the asymptotic p-value from the additive test that the SNP coefficient is zero, i is the rank for each SNP p-value (1=smallest, L=largest), and L is the number of SNPs. The dashed lines show the expected 95% probability interval for ordered p-values. Panel B depicts −log10 (p) for the 26,738 SNPs with p-values < 0.05 in the context of the human genome in order to make spatial clustering more evident.