# For the GWAS stage, B-cell NHL cases which small numbers (N<20) were excluded from analysis.

Transcription

1 Supplementary Table 1a. Subtype Breakdown of all analyzed samples Stage GWAS Singapore Validation 1 Guangzhou Validation 2 Guangzhou Validation 3 Beijing Total No. of B-Cell Cases 253 # 168^ 294^ 713^ 1428 B-Cell NHLs Subtype Follicular lymphoma Diffuse large B-cell Lymphoma Marginal zone lymphoma MALT Mantle cell lymphoma Unspecified B-cell NHLs CLL/SLL No. of non B-cell (T/NK-cell) Cases No. of Controls Table above lists all samples which had passed QC filters and were selected for analysis. # For the GWAS stage, B-cell NHL cases which small numbers (N<20) were excluded from analysis. ^ For the validation Stages, the unspecified NHLs and Burkitt s lymphomas were excluded from analysis. MALT: Mucosa associated lymphoid tissue lymphoma, CLL/SLL: Chronic lymphocytic leukemia/small lymphocytic lymphoma

2 Supplementary Table 1b. Age and gender information for all samples analyzed Age and gender details of study participants Stage/Details GWAS Singapore Validation 1 Guangzhou Validation 2 Guangzhou Validation 3 Beijing Cases Controls Cases Controls Cases Controls Cases Controls Total # Average age in years No. of males # No. of females # Females to males ratio 1 : : : : : : : : 1.88 Table above lists the age and gender information for all samples that were recruited for this study. # 20 controls were without age/gender information, and excluded from calculations.

3 Supplementary Table 2a. List of SNPs (P < ) in the GWAS stage and the validation 1 association analysis data CHR SNP Minor allele cases controls GWAS P value GWAS OR cases controls Validation P-value Validation OR Meta-P Meta-OR I 2 P Heterogeneity 8 rs273429# G E E E rs # G E E E rs # A E E rs # A E E rs # A E E rs G E E rs A E E rs G E E rs G E E rs A E E rs C E E rs G E rs A E E rs A E E rs G E E rs G E E rs A E E rs G E E rs G E E rs A E E rs A E E rs A E E rs31549 G E rs A E rs G E rs C E rs A E rs A E rs G E

4 Supplementary 2a continued. CHR SNP Minor allele cases controls GWAS P value GWAS OR cases controls Validation P-value Validation OR Meta-P Meta-OR I 2 P Heterogeneity 5 rs G E rs A E rs G E rs G E rs G E E rs A E E rs A E rs A E rs A E rs G E rs A E E rs A E rs G E rs A E rs C E rs A E rs A E rs A E Meta-analysis was performed using inverse variance weight under the fixed-effect model as described in online methods. Only SNPs fulfilling the selection criteria of replication P < 0.06 with no evidence of heterogeneity (P Heterogeneity > 0.10, I 2 < 20) in the primary meta-analysis were selected for validation. # These SNPs were selected for validation.

5 Supplementary Table 2b. List of SNPs (P < 1.0 x 10-4 ) in the GWAS stage that failed Sequenom primer design and genotyping CHR SNP Minor allele cases controls GWAS P value GWAS OR Remarks 2 rs C E Failed sequenom Design 2 rs C E Failed sequenom Design 4 rs A E Failed sequenom Design 6 rs C E Failed sequenom Design 8 rs A E Failed sequenom Design 11 rs G E Failed sequenom Design 16 rs A E Failed sequenom Design 3 rs A E Failed Genotyping 5 rs G E Failed Genotyping 8 rs A E Failed Genotyping 12 rs A E Failed Genotyping 16 rs A E Failed Genotyping SNP PCR primers with either high dimerization potential or high interference to other SNP PCR primers were considered as failed design as determined automatically with the Sequenom MassArray Assay Designer software. SNP with genotyping call rate < 95%, poor cluster plots or zero extension (no amplification of SNP allele) were considered as failed genotyping and are listed above. All primers used were ordered from and synthesized by Integrated DNA technologies (IDT) as according to Sequenom protocol requirements.

6 Supplementary Table 3. Summary statistics for remaining 4 candidate SNPs in 2 nd and 3 rd validation SNP Cases Controls Study/Stage rs rs rs rs cases controls Trend P OR (95% CI) Validation 2 -Guangzhou ( ) Validation 3 -Beijing Validation 2 -Guangzhou ( ) Validation 3 -Beijing ( ) Validation 2 -Guangzhou ( ) Validation 3 -Beijing ( ) Validation 2 -Guangzhou ( ) Validation 3 -Beijing ( ) Statistical tests were performed using Cocharn-Armitage trend test. As can been seen, no evidence of association for these markers were observed and they were not further analyzed. rs was not brought forward for the 3 rd validation in the Beijing Cohort.

7 Supplementary Table 4.Stratified analysis for rs Supplementary Table 4. Summary of statistic for rs in GWAS and all validation for DLCBL, FL and Non-B Cell NHL Subtype/stage Cases Controls cases controls Trend P OR (95% CI) Meta-P Meta-OR (95% CI) DLBCL + FL P heterogeneity (I 2 ) GWAS ( ) Validation ( ) (Guangzhou) Validation ( ) (Guangzhou) Validation ( ) (Beijing) DLBCL GWAS ( ) Validation ( ) (Guangzhou) Validation ( ) (Guangzhou) Validation ( ) (Beijing) FL GWAS ( ) Validation ( ) (Guangzhou) Validation ( ) (Guangzhou) Validation ( ) (Beijing) Non B-cell NHL GWAS ( ) Validation ( ) (Guangzhou) Validation ( ) (Guangzhou) Validation ( ) (Beijing) Combined a DLBCL + FL ( ) 0.40 (0) DLBCL ( ) 0.21 (33) * FL ( ) 0.88 (0) Non B-cell ( ) 0.25 (25)

8 a Includes all samples from GWAS and Validations 1-3 * Meta analysis P value under the random effects model for DLBCL, results remains at genome-wide significance.

9 Supplementary Table 5. Look-up of previously reported DLBCL and FL susceptibility loci Subtype Chr SNP Closest Gene Minor Trend OR Study by allele cases controls P value FL 6 rs HLA Class II A Smeby et al 1 FL 6 rs STG/PSORS1 A Skibola et al 2 DLBCL 1 rs T Conde et al 3 DLBCL 1 rs G DLBCL 2 rs TRPM8 T DLBCL 3 rs C* DLBCL 3 rs FGD5 T DLBCL 3 rs C* DLBCL 3 rs # DLBCL 3 rs CLSTN2 G DLBCL 4 rs Q9ULE4_HUMAN C DLBCL 4 rs Q9ULE4_HUMAN G DLBCL 6 rs C6orf98 A FL 6 rs HLA Class II G FL 6 rs HLA Class II G DLBCL 8 rs T DLBCL 8 rs A DLBCL 8 rs NP_ A DLBCL 8 rs C DLBCL 9 rs NFIB A* DLBCL 10 rs C DLBCL 10 rs C DLBCL 11 rs OR5B17 C DLBCL 11 rs T DLBCL 11 rs A DLBCL 12 rs GRIP1 T DLBCL 12 rs SH2B3 T DLBCL 12 rs A DLBCL 12 rs GRIP1 C* DLBCL 13 rs # DLBCL 14 rs SMOC1 A DLBCL 14 rs T DLBCL 14 rs SMOC1 T DLBCL 14 rs SAMD4A G* DLBCL 15 rs Q6ZSY1_HUMAN G DLBCL 15 rs C DLBCL 15 rs # DLBCL 17 rs CCDC46 C* DLBCL 18 rs A DLBCL 19 rs T* DLBCL 20 rs Q4G0G3_HUMAN T DLBCL 21 rs C DLBCL 21 rs A DLBCL 22 rs # DLBCL 1 rs # IL Rothman et al 4 DLBCL 6 rs TNF A

10 ; minor allele frequency, OR; per-allele odds ratio Total number of FL; 51, DLBCL; 148, Controls; 1438 # We were unable to impute these SNPs. * These SNPs have reversed minor allele as compared to study by Conde et al 3

11 Supplementary Table 6. NHL incidence rates across different parts of China Subpopulation Region Age-standardized (world) incidence (per 100,000; male/female) Reference Year obtained Northern China Beijing 3.4 / 2.5 CI5Vol8 a Tianjin 2.7 / 1.6 CI5Vol8 a Harbin city 3.1 / 1.9 CI5Vol9 b Central China Shanghai 4.3 / 3.0 CI5Vol8 a Shanghai 5.5 / 3.5 CI5Vol9 b Jiashan, Zhejiang 3.5 / 1.5 CI5Vol8 a Jiashan, Zhejiang 3.4 / 1.6 CI5Vol9 b Wuhan 4.3 / 2.2 CI5Vol8 a Southern China Guangzhou 6.7 / 4.2 CI5Vol9 b Hong Kong 8.3 / 5.4 CI5Vol8 a Hong Kong 8.1 / 5.3 CI5Vol9 b CI5: Cancer incidence in 5 continent. Data were obtained from: a CI5Vol8 : Cancer incidence in 5 continent volume VIII, IARC Scientific Publication No 155, Edited by D.M. Parkin, S.L. Whelan, J. Ferlay, L. Teppo and D.B. Thomas b CI5Vol9 : Cancer incidence in 5 continent volume IX, IARC Scientific Publication No 160, Edited by M. P. Curado, B. Edwards, H. R. Shin, H. Storm, J. Ferlay, M. Heanue and P. Boyle.

12 Supplementary Figure 1. Flowchart describing our GWAS study approach

13 Supplementary Figure 2a. Principal components analysis (PCA) plot of GWAS stage study participants, including the additional 1,189 population controls. The Singapore genome variation project (SGVP) 5 reference panels were used.

14 Supplementary Figure 2b. Principal components analysis (PCA) plot of GWAS stage study participants without the Singapore genome variation project (SGVP) reference panel.

15 Supplementary Figure 3. Manhattan plot for the combined B-cell NHL analysis comprising 253 cases 1438 controls

16 Supplementary Figure 4. Quantile-quantile (QQ) plot

17 Supplementary Figure 5. Illumina beadchip genotyping cluster plot for rs Norm R rs Norm Theta

18 References 1. Smedby, K.E. et al. GWAS of follicular lymphoma reveals allelic heterogeneity at 6p21.32 and suggests shared genetic susceptibility with diffuse large B-cell lymphoma. PLoS Genet 7, e (2011). 2. Skibola, C.F. et al. Genetic variants at 6p21.33 are associated with susceptibility to follicular lymphoma. Nat Genet 41, (2009). 3. Conde, L. et al. Genome-wide association study of follicular lymphoma identifies a risk locus at 6p Nat Genet 42, (2010). 4. Rothman, N. et al. Genetic variation in TNF and IL10 and risk of non-hodgkin lymphoma: a report from the InterLymph Consortium. The Lancet Oncology 7, (2006). 5. Teo, Y.Y. et. al. Singapore Genome Variation Project: a haplotype map of three Southeast Asian populations. Genome Res 11, (2009).