Genetic association study between the detected risk variants based upon type II
diabetes GWAS and psychotic disorders in the Japanese population
Kajio and Kondo et al.
Supplementary text:
Subjects
Two independent sets of samples were used in this study. In the screening
analysis involving the first-set, we examined 1,032 SCZ patients (48.3% female; mean
age±standard deviation (SD), 46.8±14.8 years), 1,012 BD patients (51.8% female; mean
age±SD, 50.7±14.3 years, BD type I=621, BD type II=380, schizoaffective disorder
(SA)=7, unknown type of diagnosis=4) and 993 healthy controls (51.1% female; mean
age±SD, 49.7±14.0 years).
We used an independent second sample set for replication analysis. This
sample consisted of 1,808 patients with SCZ (45.1% female; mean age±SD, 49.8±14.8
years), 821 patients with BD (54.6% female; mean age±SD, 48.2±14.4; 387 BD type I,
344 BD type II, 89 SA, 1 unknown type of diagnosis) and 2,321 healthy controls
(57.2% female; mean age±SD, 42.3±14.2 years).
SNP selection and quality control (QC)
We selected 37 SNPs from T2D GWAS data published before September
20111, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 (NOTE: SNPs reported by Shu et al.13 were not included
because the discovery sample only included females). The selection criteria were as
follows: possible risk SNPs located on autosomal chromosomes with 1) a P value of less
than 10-7, if the GWAS was conducted in a non-Asian population, or 2) a P value less
than 5  10-6, if the GWAS was conducted in an Asian population and 3) a minor allele
frequency (MAF) above 5% in the JPT HapMap database (rs7903146 in TCF7L2 was
included, although its MAF in the JPT HapMap database is less than 5%, because
Hansen et al. reported a significant association of this SNP with SCZ14). In addition, we
only included the most significant SNPs if the ‘proxy’ SNPs were located in the same
gene.
All of the SNPs were genotyped using the Sequenom iPLEX GOLD system
(Sequenom, San Diego, CA). In the optimization step, we designed primers for all of
the SNPs and verified the genotype clustering. Four SNPs did not show acceptable
genotyping calls, and we designed new primers for proxy SNPs (N=4) to replace these
SNPs. Thus, we determined the genotype distribution for a total of 37 SNPs in the final
genotyping stage (Supplementary Table 1). The quality control (QC) procedures were
based on the missing rate per person (less than 10%) and per SNP (less than 5%) and
the existence of Hardy-Weinberg Equilibrium (HWE) with a P value of more than
0.0001 by visual inspection.
Polygenic component analysis and sign test
To increase the statistical power for our results in the first-set screening
samples, we conducted a polygenic component analysis proposed by International
Schizophrenia Consortium15. This comparison is aimed to check the overall enrichment
of the ‘risk alleles’ based upon T2D GWASs (even if the SNPs did not show significant
association) in the BD, SCZ and Psychosis samples. Nagaekelke’s pseudo R 2 was
calculated by the logistic regression analysis using R (www.r-project.org) with
covariating for ‘non-missing SNPs’ (Supplementary Table S3). In addition, we assessed
a sign test to check if the direction of the effect size was same (e.g. if the OR of the
original study was positive, we expected the OR of our association analysis would be
positive), that also provided more statistical power than the SNP-based analysis
(Supplementary Table S4).
Power analysis
The power analysis16 showed that our sample size displayed more than 99%
power (for BD, SCZ and psychosis) to detect an odds ratio of 1.5 under an additive
genetic model, assuming a prevalence of approximately 1%, a minor allele frequency of
25% (average in the controls from the screening sample) and a type I error rate of
0.00156 (=0.05/32), though it displayed lower power (BD and SCZ: 21%, psychosis:
33%) when the effect size was assumed to be 1.2.
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