Supplemental Information s18

Supplemental Information

Table S1: Breakdown of exclusion-criteria

*We used a non-parametric outlier detection procedure based on density estimation to detect and exclude contrast maps that exceeded criterion values in cortical and subcortical regions. This procedure corresponds to the "Parzen_w" method described in (Fritsch et al., 2012), in which the accuracy of this outlier detection method was shown to be equivalent to other state-of-the-art methods while being faster to compute. No genotype-differences were identified in cortical or subcortical outlier values and controlling for subcortical/cortical outlier variables in the models did not affect any main effects or interactions presented.

Genotyping

DNA purification and genotyping was performed by the Centre National de Génotypage in Paris. DNA was extracted from whole blood samples (~10ml) preserved in BD Vacutainer EDTA tubes (Becton, Dickinson and Company, Oxford, UK) using Gentra Puregene Blood Kit (QIAGEN Inc., Valencia, CA, USA ) according to the manufacturer’s instructions. Genotype information was collected at 582,982 markers using the Illumina HumanHap610 Genotyping BeadChip (Illumina, San Diego, CA, USA) as part of a previous genome-wide association study (GWAS)(Schumann et al., 2010).

Single Nucleotide Polymorphisms (SNPs) with call rates of <98%, minor allele frequency <1% or deviation from the Hardy-Weinberg equilibrium (P ≤ 1×10-4) were excluded from the analyses. Individuals with an ambiguous sex code, excessive missing genotypes (failure rate >2%), and outlying heterozygosity (heterozygosity rate of 3 SDs from the mean) were also excluded. Identity-by-state similarity was used to estimate cryptic relatedness for each pair of individuals using PLINK software (Purcell et al., 2007). Closely related individuals with identity-by-descent (IBD > 0.1875) were eliminated from the subsequent analysis. Population stratification for the GWAS data was examined by principal component analysis (PCA) using EIGENSTRAT software (Price et al., 2006). The four HapMap populations were used as reference groups in the PCA analysis and individuals with divergent ancestry (from CEU) were also excluded.’

References

Fritsch, V., Varoquaux, G., Poline, J. B., & Thirion, B. (2012). Non-parametric density modeling and outlier-detection in medical imaging datasets. In F. Wang (Ed.), Machine learning in medical imaging (Vol. 7588, pp. 210-217). Berlin Heidelberg: Springer-Verlag

Price, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A., & Reich, D. (2006). Principal components analysis corrects for stratification in genome-wide association studies. Nature Genetics, 38(8), 904-909. doi: Doi 10.1038/Ng1847

Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., & Sham, P. C. (2007). PLINK: A tool set for whole-genome association and population-based linkage analyses. American Journal of Human Genetics, 81(3), 559-575. doi: Doi 10.1086/519795

Schumann, G., Loth, E., Banaschewski, T., Barbot, A., Barker, G., Buchel, C., Conrod, P. J., Dalley, J. W., Flor, H., Gallinat, J., Garavan, H., Heinz, A., Itterman, B., Lathrop, M., Mallik, C., Mann, K., Martinot, J. L., Paus, T., Poline, J. B., Robbins, T. W., Rietschel, M., Reed, L., Smolka, M., Spanagel, R., Speiser, C., Stephens, D. N., Strohle, A., Struve, M., & Consortium, I. (2010). The IMAGEN study: reinforcement-related behaviour in normal brain function and psychopathology. Molecular Psychiatry, 15(12), 1128-1139.