A Comprehensive Family-Based Replication Study of Schizophrenia Genes

Supplementary Online Content

Aberg KA, Liu Y, Bukszár J, et al. A comprehensive family-based replication study of schizophrenia genes. JAMA Psychiatry. Published online April 9, 2013. doi:10.1001/jamapsychiatry.2013.288.

eMethods 1. Meta-analysis. eTable 1. The 18 genome-wide association study (GWAS) samples included in our meta-analysis eFigure 1. Number of cases vs controls (A) and genotyped vs imputed SNPs (B) eFigure 2. SNP imputation r2 by study eFigure 3. SNP imputation r2 by lambda eFigure 4. Scree plot PC analyses eFigure 5. Ancestry across studies (A) and per study sample (B) eFigure 6. QQ plot (A) and Manhattan plot (B) GWAS meta-analysis before and after correction with PCs eMethods 2. Family-based replication eTable 2. Number of SNPs selected for various reasons eFigure 7. Ancestral clustering of replication samples eFigure 8. LD between SNPs from Table 1 located on chromosome 6 (A), chromosome 10 (B), and chromosome 22 (C) and from Table 2 located on chromosome 6 (D) eTable 3. Gene-based permutation P values eMethods 3. Validation of MIND eFigure 9. Performance of P value vs MIND-based selection evaluated by simulation (A), cross-validation (B), and results from current replication study (C) eReferences.

This supplementary material has been provided by the authors to give readers additional information about their work.

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eMethods 1. Meta-analysis Study Samples Information about the study samples included in our meta‐analysis is given in eTable 1.

eTable 1.The 18 genome‐wide association study (GWAS) samples included in our meta‐analysis Location Principle Investigator Meta‐study Study label No. of Cases No. of Controls Aberdeen David St Clair, PhD ISC ISC‐Aberdeen 713 689 Bulgaria Mick O’Donovan, PhD ISC ISC‐Cardiff 515 595 Dublin Aiden Corvin, PhD ISC ISC‐Dublin 265 853 Edinburgh, Douglas Blackwood, PhD ISC ISC‐Edinburgh 363 281 Scotland Azores Carlos Pato, PhD ISC ISC‐Azores 331 200 London, Hugh Gurling, PhD ISC ISC‐London 518 478 England United States Patrick Sullivan, MD ‐ CATIE‐US 360 374 Germany Dan Rujescu, PhD SGene SGene‐Germany 413 350 Denmark Thomas Werge, PhD SGene SGene‐Denmark 471 399 United States Pablo Gejman, MD MGS MGS‐US1 1230 1136 United States Pablo Gejman, MD MGS MGS‐US2 1214 1045 Norway Ole Andreassen, PhD SGene SGene‐Norway 251 347 Sweden Patrick Sullivan, MD ISC ISC‐Sweden1 165 164 Sweden Patrick Sullivan, MD ISC ISC‐Sweden2 370 223 Sweden Patrick Sullivan, MD ‐ Sweden3 472 845 Sweden Patrick Sullivan, MD ‐ Sweden4 1009 1139 The Roel Ophoff, PhD SGene SGene‐Netherlands 694 604 Netherlands Ireland Brien Riley, PhD WTCCC2 WT2‐United Kingdom 1831 1046

Total 11185 10768 aLocation is the major sample collection location. Principle investigator for the sample collection is given. Meta‐study refers to “consortium” of which the study sample was part. Study label indicates label used in the supporting information below. Number of cases and number of controls in each study sample separately and combined is given. Abbreviations: ISC, International Schizophrenia Consortium; MGS, Molecular Genetics of Schizophrenia Collaboration; WTCCC2, Wellcome Trust Case Control Consortium 2.

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Quality Control (QC) We obtained the GWAS data in PLINK 1 file format. The QC included careful checks of samples and single‐nucleotide polymorphisms (SNPs). Technical replicates were removed and identification duplications between studies were resolved. Furthermore, using genotype information, identical samples between studies and first‐degree relatives were identified and resolved. In most study samples there were few overlaps (rule: keep case or select one at random). The exception was ISC‐Dublin and WTCCC2, where overlap was substantial (rule: kept ISC‐Dublin). All SNPs were converted to human genome build 18 (hg18) and coded as ACGT on the positive (+) strand. SNPs without rsID that were not in HapMap were excluded from further analysis.

Subjects were removed from a study if:  genome‐wide heterozygosity was > 5 SDs from the mean  chromosome X/chromosome Y genotypes did not match phenotype sex  genotype missing rate of > 0.01  ancestry outliers removed in each sample using the smartpca module of EigenSoft 2 on linkage disequilibrium (LD) pruned SNPs (rule: drop if >4 SDs on any of the principal components [PC] 1‐10) Preimputation SNPs were removed from a study if:  missing rate of > 0.01  minor allele frequency (MAF) of < 0.01  Hardy‐Weinberg equilibrium (HWE) in controls of < 1e‐6 Postimputation SNPs were removed from a study if:  imputation r2 of < 0.5  imputation MAF of < 0.025  passed imputation filters in < 13 of 18 studies (dropped 5.91% of all SNPs).  SNP probes did not map uniquely to both hg18 and hg19 (with reference file including chr*_random regions and PAR handled correctly) (dropped 0.74% of all SNPs).

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After QC a total of 21 953 subjects (11 185 cases and 10 768 controls) remained in the 18 GWAS (eFigure 1a). All subjects were independent with no overlap between cases or controls from different GWAS. The numbers of SNPs before r2 and MAF filtering had been conducted are shown in eFigure 1B. The final analysis set after filtering included 1 085 772 SNPs. eFigure 1. Number of cases vs controls (A) and genotyped vs imputed SNPs (B)

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Imputation In eFigure 2 we show percentiles for imputation r2 by study. The lower r2 values (eg, ISC‐Azores) are from the Affymetix500K or Affymetrix5 chips.

eFigure 2. SNP imputation r2 by study

The yellow, blue, green, and red lines correspond to the 10th, 25th, 75th, and 90th percentiles, respectively. The y‐axis indicates r2 and the x‐axis shows the included GWAS samples.

We explored cutpoints for imputation r2 and MAF. For all samples, a filter of r2 > 0.5 and MAF ≥ 0.05 remove nearly all of the problematical areas for each study (ie, removing the left half and bottom row of each graph) in eFigure 3. As MAF was rounded to nearest 0.05, this effectively means MAF > 0.025.

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eFigure 3. SNP imputation r2 by lambda Each graph shows the observed lambdas broken down by MAF on the y‐axis (rounded to nearest 0.05, 0 to 0.5, in steps of 0.05) and imputation quality r2 on the x‐axis (rounded to nearest 0.1, 0 to 1 step 0.1). The box at the intersection of each (r2, MAF) point represents lambda for the SNPs in a sample meeting with those characteristics. Note that the numbers of SNPs for each box vary widely. White boxes are for lambda missing (no SNPs) or < 0.9 (usually a small number of SNPs). Varying shades of blue/red show increasing lambda (values > 1.6 were set to 1.6).

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Ancestry To describe ancestry across samples, we estimated principal components using a set of SNPs directly genotyped in all studies. Of the 15 364 SNPs genotyped in all samples, we kept autosomal SNPs with MAF > 0.2 in all studies and low missingness (< 0.005). After pruning for SNPs in approximate LD, 4566 SNPs genotyped in 16 928 subjects remained. The scree plot (eFigure 4) tends to flatten out after PC1 and PC2. eFigure 4 shows that of the first 10 PCs, there were significant case‐control differences for PC1 (1.e‐14) and PC4 (1.4e‐5). PC1 taps European continental ancestry. eFigure 5 shows PC2 x PC1 for all samples. The clusters are pretty tight, consistent with European ancestry and having already removed outliers. However, there is evidence of substructure. eFigure 4. Scree plot PC analyses Eigenvalues (y‐axis) are shown for principle components 1 to 25 (x‐axis).

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eFigure 5A. Ancestry across studies Each individual sample is represented by a point. The color scheme shows the study label.

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eFigure 5B shows PC2 X PC1 for each sample with ellipses added to better represent sample density. The density ellipses for most studies are nicely globular, suggesting that the individual samples are relatively homogeneous. The US samples (CATIE, gain, nongain) are a bit more ragged. eFigure 5B. Ancestry per study sample

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Association analyses Logistic regression analyses included PC1‐PC3 (plus any of PC4‐PC10 that were significantly different between cases and controls). These were estimated within each sample based on a subset of SNPs with high MAF (>0.2), low missingness (<0.005), consistent minor allele designation across all studies, and in approximate LD. Fixed‐effects meta‐analysis was

used to combine data across studies. 1000 was 1.021 for the meta‐analysis. eFigure 6A. QQ plot GWAS meta‐analysis

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eFigure 6B. Manhattan plots GWAS meta‐analysis before and after correction with PCs

In summary, the meta‐analysis suggests a number of interesting regions. Also note the sensitivity of major histocompatibility complex (MHC) region chromosome 6 to the PCA ancestry correction.

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eMethods 2. FAMILY‐BASED REPLICATION

Existing information included in MIND data integration Mathematically based Integration of Heterogeneous Data (MIND) empirically “weighs” existing data according to the strength of its disease relevant information. Thus, it does not assign arbitrary weights and data sets that do not contain schizophrenia (SCZ)‐relevant information will not affect the results. Although databases that do not contain SCZ‐relevant information will not affect results, permutation tests can help to select only the informative databases, which will reduce tests and make these analyses more efficient. In our study, 6 of 9 tested databases contained significant information for SCZ. These 6 were SZgene3 database, summarizing the results of 1617 studies reporting on 952 SZC candidate genes (excluding findings from the GWAS used in our meta‐analysis); top regions from a meta‐analysis of 32 independent genome‐wide linkage scans of 3255 pedigrees with 7413 SCZ cases4; a gene expression meta‐analysis of 12 controlled studies across 6 different microarray platforms using postmortem brain tissue from SCZ cases and controls5; the OMIM database of disease genes; human orthologs of mouse genes associated with behavioral phenotypes relevant to neuropsychiatric outcomes 6; and SNPs strongly associated with variation in transcript abundance in cortex (http://eqtl.uchicago.edu).

SNP selection for replication study eTable 2 shows the number of SNPs selected for various reasons. The selection is hierarchical and, for example, part of the SNPs selected after data integration would also have been selected in thet nex step when we considered SNPs with the best P values in the meta‐analysis

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eTable 2. Number of SNP selected for various reasons Reason for selection No. of SNPs 1. SNPs with posterior probability > 0.2 after data integration 4282 2. SNPs with posterior probability > 0.2 without data integration 3254 3. SNPs showing up in both European American and Africian American GWAS 428 4. SNPs in top 200 with P values <.05 and HapMap MAF > 0.05 in African, Asian, and 932 European populations 5. SNPs in ZNF804A 12 6. SNPs in MHC regions 438 7. SNPs on X‐Y just for checking sex 34 Total 9380

We first selected the 4282 SNPs that had a posterior probability of > 0.2 of having a true effect after integrating the 6 informative SCZ‐related data sets. Next, we selected SNPs that were not already selected in step 1 but had the best P values in the meta‐analysis. Without data integration, the SNPs with the top P values are the same as those with the top posterior probabilities. Using the same threshold of posterior probability of > 0.2, this resulted in 3254 additional SNPs. We supplemented this selection with top SNPs after integrating and AA GWAS in the meta ‐analysis (428 additional SNPs). To avoid missing association signals in the replication study due to LD differences across populations, for the 200 genes with the best posterior probabilities we selected 932 additional SNPs. These SNPs all had P values of < .05 in the meta‐analysis and MAF of > 0.05 in subjects from European, Asian, and African ancestry according to HapMap. An additional set of 438 SNPs was selected from the classical and extended MHC regions because our replication study could shed unique light on this region as it controls for population stratification using family‐based tests. Finally, we selected the 12 SNPs in ZNF804A reported in recent studies 7‐21 to be associated with SCZ, and 34 SNPs on the X and Y chromosomes for QC purposes.

QC and analysis of nuclear families A total of 8107 autosomal SNPs of the 9380 selected SNPs were successfully genotyped by Illumina, at their facilities, using the iSelect assay, with a call rate of 99.94%. The samples were nuclear families and sibships from the National Institute of Mental Health repository 22‐25 originally ascertained at Harvard University, Columbia University, and Washington University and from Dr Delisi. Each family had at least 1 subject diagnosed with SCZ, where diagnoses were made using DSM‐III‐R criteria 26 using the diagnostic interview for genetic studies.27 The replication sample was limited to families of a maximum of 2 generations. The following 3 types of families were included: (1) “full nuclear families” consisting of 2 parents and at least 1 affected offspring, (2) “single parent families” consisting of 1 parent and at least 1 affected offspring as well as unaffected full siblings if available, and (3) “sibships” consisting of at least 1 affected individual and affected or unaffected full siblings. If a larger family structure was available, specific individuals were selected to match any of the 3 family types included. Only the most informative family type from each pedigree structure was selected for genotyping. The families consisted of 2 to 7 members with an average size of 3.48 individuals per family. Approximately 31% (n=570) of the families had both parents genotyped, approximately 52% (n=947) had 1 parent genotyped, and approximately 16% (n=294) consisted of full siblings only with no parents genotyped. The average family size among families with 2 parents genotyped was 3.99 individuals and the average family size among the remaining families was 3.24. None of the individuals in the replication sample were previously included in the GWAS studies used in our meta‐analysis. Poor DNA quality caused the genotyping to fail for 31 individuals. For the remaining families, the accuracy of the reported pedigree structures was investigated using PLINK 28 (‐‐rel‐check, check‐sex and ‐‐mendel). Family relations were checked for their accuracy using identity‐by‐state (IBS) sharing. Parent‐offspring relations were confirmed if the IBS sharing was >0.49. Considering that we performed a replication study where the successfully genotyped SNPs investigate were limited to 8132 SNPs (8107 autosomal, 24 on chromosome X and 1 on chromosome Y) and that these SNPs were selected based on their possible susceptibility to SCZ, the IBS sharing between full sibs for these specific

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markers are likely to deviate from 0.5. This is especially true for discordant sibs. Therefore, we allowed for full sibs to have IBS sharing from 0.1 to 0.7. Similarly, the low number of markers investigated makes the IBS sharing between unrelated individuals somewhat unreliable. Therefore, given that the parent‐offspring relation was confirmed, we used a liberal threshold of IBS < 0.3 for unrelated individuals (ie, the parents in each family). None of the included parents in each family had reported any ancestry in common, which was confirmed by the IBS sharing. We found 38 pair‐wise relations with IBS sharing that deviated from our applied thresholds. These families were excluded from any further analysis. In addition, 2 pairs of individuals listed as full siblings showed IBS sharing of 1.0 and were therefore deemed as monozygotic twins. For these families 1 individual from each pair was excluded from further analysis. In addition to the tests for IBS sharing, we used PLINK to conduct mendelian error checking for all nuclear families with both parents successfully genotyped. The mendelian error checking confirmed the conclusions made by the IBS sharing in all cases but one. In this case, which had an IBS sharing around 0.32, the mendelian error checking confirmed that the reported pedigree structure was correct. However, the father of the family carried a deletion on chromosome 4 spanning a large number of SNPs, which was transmitted to the offspring. Thus, the low IBS sharing for this particular relationship falsely indicated an issue with the pedigree structure. This family was excluded from further analysis. For the statistical analysis in UNPHASED used for the replication, information about the individuals’ sex is not included and therefore sex is irrelevant for the statistical analysis. However, to ensure high accuracy of our data (genotype and phenotype data) we still performed the “check‐sex” test in PLINK. We detected 6 discrepancies from the reported data. In 4 families, 3 female members had falsely been coded as male and 1 male member had falsely been coded as female. This miscoding was further supported by genotype information in their offspring and by additional sex‐ specific phenotype information. Furthermore, we detected a switch of the sex between the mother and the father in a single family. Also in this case the discrepancy was supported by the genotype information of their offspring. The sex code of these 6 individuals was corrected without exclusion of any individuals. After quality control of the pedigree structure, 6298 individuals (3286 schizophrenia patients) from 1811 families were investigated. We used UNPHASED 29 that performs genetic association analysis in nuclear families and sibships by implementing a maximum‐likelihood inference on allele effects while allowing for uncertain phase and missing genotypes. Simulations and comparisons with other approaches to analyze nuclear family‐based association data were performed by Dudbridge29. His results show that the power of UNPHASED compares well with other approaches. Furthermore, UNPHASED is robust to population structure when the data are complete, and has only minor loss of robustness when there are missing data. However, to add a second layer of protection against possible stratification effects, we first used PLINK to subdivide subjects into ancestral groups based on the IBS sharing as estimated using the 8107 genotyped SNPs. IBS was estimated using unrelated individuals (ie, all genotyped founders, or if founders were not available, 1 sib per family).

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eFigure 7. Ancestral clustering of replication samples

We used the 2 first dimensions (MDS1 and MDS2) from the multidimensional scaling (MDS) analysis to cluster subjects. eFigure 7 shows 3 well‐distinguished clusters where the major self‐reported ethnicity in each group was African American (1262 individuals, 438 families), European (2740 individuals, 794 families), and Asian (2296 individuals, 579 families). Family‐ based association tests were performed within each ancestral group. The test statistics were then combined across the 3 groups, where the different groups were weighted proportional to their samples size. We preserved the direction of effects (ie, sign) so that an allele being overtransmitted in one group and undertransmitted in another group would have NO effects in this combined analysis. We limited the association testing to markers with a MAF of > 0.05 within each group.

Next, we performed the UNPHASED analyses within each ancestral group and then combined the 3 test statistics to obtain an overall replication P value. This stratified use of UNPHASED mitigates this potential minor loss of robustness against possible stratification effects. Thus, it is unlikely that any differences in allele frequencies between ancestral will account for any detected association. Finally, to avoid overly optimistic P values while still providing some quantification for the combined evidence of a specific SNP across the meta‐analysis and replication study, we combined P values from the 3 ancestral groups with the P value from the meta‐analysis in an unweighted (ie, sample size was ignored) fashion. For various reasons it is difficult to motivate a clear threshold for declaring significance. In our study, the group of selected SNPs replicated as a whole and from this it follows that individual SNPs in the replication study must replicate. A problem is that typical corrections for multiple testing do not incorporate this information. Furthermore, although the unweighted P value provides an indication for the combined evidence for a specific SNP across the meta‐ analysis and replication study, strictly speaking it cannot be interpreted as a P value as we selected on the GWAS P values.

Testing the significance of small P value enrichment in the replication study We used the following statistic to test the significance of small P value enrichment in the replication study

O ,Md X ,Md  , M M  dm  dm )()( m m m m 11  m where M QO   dm )( , , MdMd m and Qd, M and m’(d) are defined as

, Md j ,: j  and   j  dtjdmMrdtjQ ,:)(

where tj is the observed statistic of SNP j, rj is the rank of SNP j in the external data set, and m denotes the number of test units in the external data set. Due to the large number of SNPs, large sample size, and family‐based association tests allowing for missing genotypes, it was computationally not feasible to perform a sufficiently large number of permutations across the entire data set to obtain accurate permutation P values. We therefore attempted to estimate the lower and upper bound for the enrichment of small P values in the replication study assuming no LD as well as very high LD among the SNPs. As an enrichment threshold, we choose SNPs with P < .01, but the calculations in this section were repeated for more liberal P value thresholds with fairly similar results. The statistic Xd, M follows a standard normal distribution for every integer M > 0 and real d > 0 if there is no LD between the SNPs. When we calculated the enrichment P value

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using the R package, it was so small that R returned a 0, meaning that the actual P value was smaller than the precision of floating point numbers in R. For the LD scenario, we simulated the distribution of the test statistic by drawing test statistics from the null distribution fort all SNPs bu assuming these test statistics were in LD (ie, correlated). For this purpose, we assumed LD blocks where all SNPs in a block were in almost perfect LD (r2 = 0.9). As can be seen in the next section, the LD within blocks was on average much lower. When we started a new block each time an SNP was 10 kilobases (kb) apart from the previous SNP, we observed P = 2.0×10‐4. Even in excessive LD scenarios where we only started new a block when a SNP was 100 kb apart from the previous SNP, we still observed P = 6.0×10‐3. In summary, P values from the replication study were extremely unlikely under the null hypotheses assuming no replication.

LD between significant SNPs In eFigure 8A‐D, we give the LD between all SNPs reported in the Table 1 and Table 2 (in the main text) that were within 1 megabase of each other. LD measured as r2 is given in numbers while color coding is used to show LD measured as D’ (darker red indicates higher D’).

eFigure 8A. LD between SNPs from Table 1 located on chromosome 6

The left diagram shows European ancestry, the middle diagram shows Asian ancestry, and the right diagram shows African ancestry. eFigure 8B. LD between SNPs from Table 1 located on chromosome 10

The left diagram shows

European ancestry, the middle diagram shows Asian ancestry, and the right diagram shows African ancestry.

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eFigure 8C. LD between SNPs from Table 1 located on chromosome 22

The left diagram shows European ancestry, the middle diagram shows Asian ancestry, and the right diagram shows African ancestry.

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eFigure 8D. LD between SNPs from Table 2 located on chromosome 6

The diagram shows the LD between markers in the MHC region in samples of European ancestry. Pathway analyses To test if multiple susceptibility alleles with small effects were organized into cohesive pathways, we performed gene set analysis using ConsensusPathDB, a human‐centric meta‐database of functional biological data, compiled from 30 separate public sources of biological interactions 30‐32. The findings reported in this study were obtained using data from the Reactome 33, KEGG (Kyoto Encyclopedia of Genes and Genomes) 34 and Biocarta databases. All SCZ‐associated SNPs with P values < .05 in our analysis were matched to the closest gene ±25 kb. All implicated genes were assembled into a final nonredundant gene list, consisting of 265 genes. For each of the 4601 reference pathways present in ConsensusPathDB, incorporating 9859 known genes, a hypergeometric test was performed to calculate the significance of the overlap between the genes from our SCZ‐associated list and those present in each reference pathway. To account for multiple testing in these pathway analyses, we controlled the false discovery rate (FDR 35) at the 0.01 level meaning that the expected number of false discoveries was 1%. Operationally 36 the FDR was controlled using Q values that are FDRs calculated using the P values of the individual tests as thresholds for declaring significance 37, 38. In general the FDR is fairly robust against correlation tests39‐43. However, because few studies examined its robustness for these specific pathway analyses, some caution is required when interpreting FDR results. In MIND, sources of prior information used to select SNPs through data integration will only affect results to the extent they contain disease relevant information (ie, the extent that genes that have good P values in the sources of prior information also have good P values in the meta‐analysis). As most of the databases we used pertain to genes, there will be a bias in terms of selecting SNPs in genes as opposed to intergenic regions. However, the selection was © 2013 American Medical Association. All rights reserved.

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entirely based on the empirical support for SNPs in those genes. Furthermore, the pathway analyses were performed using the replication results. It is therefore unlikely that our pathway findings will reflect prior notions about SCZ‐ relevant pathways for which there is no empirical support. Gene based analyses For the gene‐based tests we selected genes (±50 kb of flanking sequence) with at least 2 SNPs in each ancestral group. For each group k=1..3 we used the smallest P value in the gene, p(k)smallest, as the test statistic. P values p(k)gene were then assigned to each gene in a group as follows

p )( genek  PrP )( smallestk  )( smallestk | Hp 0 

where H0 is the event that all SNPs are null in the gene, p(k)smallest is the smallest observed P value in the gene, and P(k)smallest is a random variable. The overall gene‐based P value Pgene was calculated by combining the 3 p(k)gene using 2 Fishers’ method where −2 times the sum of the logged p(k)gene has a χ distribution with 6 (2 times the number of groups) degrees of freedom. We obtained Pgene though permutation tests. In nuclear families, the permutations are generated by randomizing the transmission status of the parental haplotypes. In discordant sib pairs, the case‐control labels are randomly shuffled between subjects. Because these permutations are very time consuming, we first prioritized genes using an approximation of Pgene and stopped the permutations once the permutation P values exceeded .05 (P*gene> 0.05). That is, we assume that P(k)smallest= min(P(k)1,…,P(k)n), where P(k)1,…,P(k)n are independent random variables uniformly distributed on (0,1), and n is the number of SNPs in the gene. To calculate Pgene, we need to know the cdf of P(k)smallest, n which is  11  xxF . The correlation between the calculated and simulated P values was 0.94 (or 0.87 after

excluding one gene that was an outlier because it had a notably different value of pgene in the simulation versus theoretical approximation). This suggested that the prioritization approach was sufficient accurate to decide which genes to include in the permutation studies. Results are shown in eTable 3.

eTable 3. Gene‐based permutation P values Gene Asian African European Combined C1orf32 5.00E‐04 2.37E‐01 2.60E‐02 2.90E‐04 MAEL 2.00E‐03 7.70E‐02 2.40E‐02 3.39E‐04 GPA33 3.00E‐03 1.29E‐01 3.60E‐02 1.04E‐03 LGALS14 2.42E‐01 6.11E‐01 5.00E‐04 4.12E‐03 CLC 2.46E‐01 6.28E‐01 5.00E‐04 4.27E‐03 CROCC 8.72E‐01 2.00E‐03 5.50E‐02 5.09E‐03 GUCY2E 6.40E‐02 8.00E‐03 2.01E‐01 5.39E‐03 LOC400696 5.21E‐01 7.02E‐01 5.00E‐04 8.53E‐03 NCR3 2.74E‐01 1.70E‐01 6.00E‐03 1.19E‐02 LST1 3.00E‐01 1.77E‐01 6.00E‐03 1.32E‐02 RAB23 6.30E‐02 2.60E‐01 2.00E‐02 1.35E‐02 SETBP1 4.50E‐01 3.00E‐03 2.52E‐01 1.39E‐02 HSPA14 2.19E‐01 2.28E‐01 1.00E‐02 1.87E‐02 MICB 6.73E‐01 2.52E‐01 3.00E‐03 1.90E‐02 NFKBIL1 2.41E‐01 4.36E‐01 5.00E‐03 1.95E‐02 BAT1 3.48E‐01 3.81E‐01 4.00E‐03 1.96E‐02 ATP6V1G2 3.67E‐01 3.70E‐01 4.00E‐03 2.00E‐02 LOC643220 6.16E‐01 5.40E‐02 2.00E‐02 2.33E‐02 TNF 2.96E‐01 5.42E‐01 6.00E‐03 3.09E‐02 DUSP27 2.50E‐01 1.20E‐01 1.61E‐01 9.93E‐02

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eMethods 3. VALIDATION OF MIND

We studied the ability of MIND to identify SNPs with effects via (1) simulation, (2) cross‐validation, and (3) and replication study in the present paper. Results are shown in panels A, B, and C of eFigure 9. MIND is based on a hierarchical model that evaluates hypotheses in a "primary" data (here the GWAS meta‐analysis) set and uses prior information contained in "auxiliary" data sets to improve inferences. For this purpose, MIND estimates for each SNP in the primary data set, the (compound) posterior probability (cPP) that it is true. For the simulation we simulated 500 studies with 3 auxiliary data sets and a novel data collection consisting of 1 million markers, of which 5500 had a small effect. For each of the 500 simulations, we selected the 5000 SNPs with the smallest P values and the 5000 SNPs with largest compound posterior probabilities after integrating our 6 informative auxiliary data sets. We then binned the selected SNPs in groups of 5000 according to their meta‐analysis P value rank (x‐ axis) and counted the proportion of the markers with effect among the selected markers (y‐axis). The center of the circles in panel A represents the means, and the area of the circles is proportional to the number of correctly identified SNPs. The dots represent the 5th, 25th, 75th, and 95th percentiles. Results show that in the presence of informative external data, MIND does indeed identify a much larger proportion of the markers with effect (the green circle and dots are much higher than the red circle and dots). Furthermore, although the proportion of detected effects decrease as P value ranks increase (indicated by the blue circles and dots), in many instances MIND still successfully identifies SNPs with effects that have P values with ranks up to 100 000 in the GWAS meta‐analysis. eFigure 9. Performance of P value vs MIND‐based selection evaluated by simulation (A), cross‐validation (B), and results from current replication study (C).

All panels: P value–based results are red, cPP‐based results are blue, overlap between both methods is green, and x‐axis is rank of P values in the meta‐analysis. Panels A and B: For each x‐axis interval, the 5th, 25th, 75th, and 95th quantiles of the proportion of SNPs with effect among those selected (A) or proportion of SNPs with cross‐validation P values of less than .01 among those selected (B) are reported. Center of the circles are located at the mean of the proportions, while the area of the circle is proportional to the number of SNPs selected. All P values of less than .01 in the replication study are shown (C).

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The simulation studies show that MIND works as desired. The cross‐validation study was used to examine MIND’s performance in the presence of actual effects sizes, possible artifacts, and LD among markers. For the cross‐validation, we divided the 18 GWAS in a first subset that corresponded with 85% to 90% of the total sample size, and a second subset consisting of the remaining GWAS. We then selected SNPs from the first subset and performed a replication study in the second subset. There were 575 unique ways of subdividing the 18 GWAS studies in 2 subsets. For each of the unique cross‐validation combinations, we selected the 5000 SNPs with the smallest P values and the 5000 SNPs selected with MIND after integrating our 6 informative auxiliary data sets. In contrast to the simulations, we do not know which markers have effects. Instead, the y‐axis in panel B now shows the number of P values below a threshold of .01 in the replication study, which can be viewed as an indicator of the relative success of the 2 strategies to select markers with effects. Other then the y‐axis, panel B displays the same information as panel A. The cross‐validation study results show the very similar pattern as we observed in the simulation study. Thus, MIND identifies a larger proportion of the markers with effect (the green circle and dots are higher than the red circle and dots) and successfully identifies SNPs with effects that have P values with ranks up to 100 000 in the GWAS meta‐analysis (blue circles and dots). The only difference is the extent to which the MIND‐selection outperforms the P value selection. Part of this may be explained by the fact that the replication P values are an imperfect proxy of the proportion of markers with effects, thereby diluting the difference. To avoid results from the replication study reflecting a difference between the number of SNPs selected from the meta‐analysis, we only considered the top 3000 SNPs selected through data integration and top 3000 SNPs without data integration. The dots in panel C represent the replication P value as an indicator of the relative success of the 2 strategies to select markers with effects. Panel C show again the same pattern as panels A and B. Thus, MIND identifies a larger proportion of the markers with effect (the green dots are higher and more abundant than the red and dots) and in many instances MIND is able to replicate SNPs that have P values with ranks over 25 000 in the GWAS meta‐analysis (blue dots).

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