Joint analysis of three genome-wide association studies of esophageal squamous cell carcinoma in Chinese populations

Abstract

We conducted a joint (pooled) analysis of three genome-wide association studies (GWAS) ^1-3 of esophageal squamous cell carcinoma (ESCC) in ethnic Chinese (5,337 ESCC cases and 5,787 controls) with 9,654 ESCC cases and 10,058 controls for follow-up. In a logistic regression model adjusted for age, sex, study, and two eigenvectors, two new loci achieved genome-wide significance, marked by rs7447927 at 5q31.2 (per-allele odds ratio (OR) = 0.85, 95% CI 0.82-0.88; P=7.72x10⁻²⁰) and rs1642764 at 17p13.1 (per-allele OR= 0.88, 95% CI 0.85-0.91; P=3.10x10⁻¹³). rs7447927 is a synonymous single nucleotide polymorphism (SNP) in TMEM173 and rs1642764 is an intronic SNP in ATP1B2, near TP53. Furthermore, a locus in the HLA class II region at 6p21.32 (rs35597309) achieved genome-wide significance in the two populations at highest risk for ESSC (OR=1.33, 95% CI 1.22-1.46; P=1.99x10⁻¹⁰). Our joint analysis identified new ESCC susceptibility loci overall as well as a new locus unique to the ESCC high risk Taihang Mountain region.

Esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma are distinct diseases with different etiologies. ESCC remains the more common type of esophageal carcinoma in economically-developing countries as well as globally. Approximately half of the world's 500,000 new ESCC cases annually occur in China where the disease is a major public health problem. Three genome wide association studies (GWAS) have examined ESCC^1-3 and two subsequent analyses used combinations of the three studies^{4, 5} to report as many as 12 loci associated with ESCC risk. Four additional loci have been reported based on an interaction with alcohol consumption, a known risk factor for ESCC⁴. Two of the GWAS^{1, 2} drew subjects primarily from the Taihang Mountain region of Henan and Shanxi provinces where ESCC occurs at very high rates. Total mortality due to ESCC and gastric cardia cancer can exceed 20% in the highest risk communities in this region⁶. The third GWAS³ drew subjects from a range of locations in China including a substantial collection from Beijing, where the population is comprised of people who originate from all provinces. The contribution of lifestyle risk factors for ESCC differs widely between locations in China, with alcohol consumption being a notable example. Heavy consumption of alcoholic beverages is a known cause of ESCC⁷, but historically was uncommon in the high incidence regions of China⁸. Therefore, alcoholic beverages played little role in the extraordinarily high rates, leaving the high incidence largely unexplained. To discover additional novel ESCC susceptibility alleles, we conducted a joint analysis of the three original GWAS and followed promising signals in an independent set of new cases and controls.

Supplementary Table 1 describes the subjects from the three underlying GWAS, which include additional subjects that were scanned after the first round publications plus the additional subjects used for replication of the top hits. In our joint analysis we investigated for the first time the individual GWAS data drawn from three studies in China, which consisted of 5,337 ESCC cases and 5,787 controls of Han Chinese ethnicity. The NCI GWAS used the Illumina 660W-Quad SNP microarray, the Henan GWAS used the Illumina 610-Quad SNP microarray, while the Beijing GWAS used the Affymetrix GeneChip Human Mapping 6.0 set. Supplementary Figure 1 shows eigenvector plots from a principal components analysis (PCA) of 25,676 uncorrelated genotyped SNPs (r² < 0.01 in our combined control set). The results show distinct clusters corresponding to different source populations. However, only the first two eigenvectors were significant in the base cancer risk model and were thus used to adjust for population stratification in the final estimates for our joint analysis. To explore untyped common variants, we assessed variants based on the imputation of 40.5 million variants using version 3 of the 1000 Genomes data as the reference for each of the three data sets before combining them as described in the ONLINE METHODS and Supplementary Table 2. After filtering out SNPs with MAF < 1% or imputation information criteria (INFO) < 0.3, we advanced 7,556,215 SNPs to the association analysis. The inflation factor λ for the joint analysis is 1.01 for all SNPs, which indicates that population stratification should not be a concern. Supplementary Figure 2 provides a quantile-quantile plot for the joint case-control comparison with all SNPs and after exclusion of SNPs within 500 Kb of loci previously reported to be associated with ESCC. Supplementary Figures 3-5 provide individual quantile-quantile plots for each of the three underlying GWAS data sets. Fourteen promising SNPs based on the joint analysis (see ONLINE METHODS) were genotyped in an additional 9,654 ESCC cases and 10,058 controls divided between subjects from Henan Province and Beijing.

The joint analysis identified two new genome-wide significant loci at 5q31.2 (Figure 1a) and 17p13.1 (Figure 1b) associated with risk of ESCC in the pooled data of individual genotypes of all three GWAS and two replication studies (Table 1 and Supplementary Table 3). At 5q31.2, rs7447927, a synonymous SNP located in transmembrane protein 173 (TMEM173), had a combined per allele odds ratio (OR) and 95% confidence interval (95% CI) of 0.85 (0.82-0.88), P=7.72x10⁻²⁰. At 17p13.1, rs1642764, an intronic SNP located in ATPase, Na+/K+ transporting, beta 2 polypeptide (ATP1B2), which is just telomeric to the tumor suppressor gene, TP53, had a combined per allele OR (95%CI) of 0.88 (0.85-0.91), P=3.10x10⁻¹³. No statistically significant heterogeneity was observed across the three pooled GWAS and two replication studies for either locus (Table 1). Furthermore, the associations were confirmed independently in the follow-up sets collected from both high- and low-risk populations.

Figure 1. Association results, recombination, and linkage disequilibrium plots for the 5q31.2 and 17p13.1 regions identified in stage 1 (joint analysis of three independent GWAS), for the two replications sets (Beijing and Henan), and the joint estimate from all data.

(a) 5q31.2:138,701,120–138,885,920 and (b) 17p13.1:7,435,040–7,644,641. Association results from a trend test in –log10 Pvalues (y axis, left; gray diamonds, stage I association result; sky blue diamonds, Beijing replication result; purple diamonds, Henan replication results; red diamonds, joint result) of the SNPs are shown according to their chromosomal positions (x axis). Linkage disequilibrium structure based on the 1000 Genomes CHB data (n=91) was visualized by snp.plotter software. The line graph shows likelihood ratio statistics (y axis, right) for recombination hotspot by SequenceLDhot software based on the background recombination rates inferred by PHASE v2.1 using the 1000 Genomes CHB data. Physical locations are based on NCBI human genome build 37. Gene annotation was based on the NCBI RefSeq genes from the UCSC Genome Browser.

Table 1.

Association between SNPs at three loci and risk for esophageal squamous cell carcinoma in a joint analysis of three independent genome-wide association studies of Han Chinese subjects.

NCBI dbSNP 137 identifier (Reference Allele, Effect Allele)	Cytoband	Nearest Gene	Study	Controls	Cases	Effect Allele Frequency in Controls	Effect Allele Frequency in Cases	OR	(95% CI)	P-value	Pheterogeneity
All China
rs7447927 (C,G)	5q31.2	TMEM173	3 scan analysis (Stage 1)	5786	5336	0.460	0.451	0.87	(0.82-0.92)	4.07E-06
rs7447927 (C,G)			Henan Replication	4802	4486	0.489	0.438	0.80	(0.75-0.86)	2.70E-11
rs7447927 (C,G)			Beijing Replication	5079	4797	0.431	0.397	0.87	(0.82-0.92)	2.92E-06
rs7447927 (C,G)			Combined	15667	14619			0.85	(0.82-0.88)	7.72E-20	1.31E-01
rs1642764(C,T)	17p13.1	ATP1B2,TP53,p53	3 scan analysis (Stage 1)	5786	5336	0.499	0.456	0.84	(0.79-0.89)	3.91E-08
rs1642764(C,T)			Henan Replication	4634	4694	0.484	0.470	0.93	(0.87-0.99)	2.67E-02
rs1642764(C,T)			Beijing Replication	5054	4777	0.520	0.483	0.86	(0.81-0.92)	1.55E-06
rs1642764(C,T)			Combined	15474	14807			0.88	(0.85-0.91)	3.10E-13	7.70E-02
rs35597309(G,A)	6p21.32	HLA class II genes	3 scan analysis (Stage 1)	5787	5336	0.072	0.094	1.32	(1.18-1.47)	6.17E-07
rs35597309(G,A)			Henan Replication	4659	4597	0.067	0.085	1.23	(1.09-1.38)	1.00E-03
rs35597309(G,A)			Beijing Replication	5079	4786	0.075	0.080	1.05	(0.94-1.17)	3.59E-01
rs35597309(G,A)			Combined	15525	14719			1.19	(1.12-1.27)	1.18E-07	1.50E-02
Taihang mountains only
rs35597309(G,A)	6p21.32	HLA class II genes	NCI Scan	2707	2021	0.077	0.109	1.43	(1.23-1.67)	4.27E-06
rs35597309(G,A)			Henan Scan	1082	1375	0.063	0.093	1.55	(1.22-1.97)	3.50E-04
rs35597309(G,A)			Henan Replication	4659	4597	0.067	0.085	1.23	(1.09-1.38)	1.00E-03
rs35597309(G,A)			Combined	8448	7993			1.33	(1.22-1.46)	1.99E-10	1.22E-01

One of these new loci showed significant heterogeneity (p=0.015) among the three studies and the associations are also reported using only the two GWAS and one replication set that primarily used subjects from populations with high incidence in the Taihang Mountains of north central China.

On further analysis, we observed an additional susceptibility locus that showed geographic differences such that the significant association was observed only in the two GWAS^{1, 2} which included subjects from populations at the highest risk for ESCC. In joint analyses using all three GWAS, a SNP at 6p21.32 showed a nearly genome-wide significant association, however, statistically significant heterogeneity among studies (P=0.015) was evident when subjects from the Beijing GWAS and replication subjects were included (i.e., among the three pooled GWAS and two replication studies as shown in Table 1). The test for heterogeneity became non-significant when the Beijing GWAS and Beijing replication subjects were excluded (Table 1). Table 1 also shows that when the analyses were restricted to subjects from the high incidence regions, rs35597309, located in the HLA Class II gene region between HLA-DRB1 and HLA-DQA1, had a per allele OR (95% CI) of 1.33 (1.22-1.46), P=1.99 x 10^-10 (Supplementary Figure 6). This heterogeneity between high- and low-risk regions was also evident when data from the three separate GWAS were examined (Supplementary Table 4). Further genotyping and possibly sequencing are necessary to map the susceptibility alleles across the HLA Class II region due to its complex structure defined by long-range haplotypes.

Finally, our joint analysis observed a promising association with rs61271866 (P=5.18 x 10⁻⁸), an intergenic SNP at 9p21.3 that includes the cyclin-dependent kinase inhibitor 2B (CDKN2B)-CDKN2A gene cluster (Supplementary Table 3). Variants in this region have been associated with risk of melanoma⁹, childhood acute lymphoblastic leukemia¹⁰, chronic lymphocytic leukemia¹¹, and glioma¹², as well as ESCC¹³ in a prior study using a subset of the samples examined here. This SNP showed heterogeneity among individual studies in the GWAS (Supplementary Table 4) and between the joint Stage 1 and replication phase results (Supplementary Table 3). The heterogeneity picture for the 9p21.3 locus is more varied as compared to the 6p21.32 HLA Class II locus, indicating that validation of this finding will require additional work.

For these four SNPs (rs7447927, rs1642764, rs35597309, and rs61271866), we tested for interactions by use of alcohol (Supplementary Table 5) or tobacco (Supplementary Table 6). Because of the substantial differences in the degree of alcohol and tobacco use by population, we did these tests separately for each of the three underlying studies. In total this constituted 24 tests and we found one that was nominally significant (rs35597309 and tobacco in the NCI GWAS, Supplementary Table 6), but the difference did not replicate in the two other studies and thus, is most likely due to chance. We further note that the number of GWAS subjects with these covariate data from Henan was limited.

Here we report a new finding of an association between rs7447927, a synonymous SNP in TMEM173, and ESCC risk. TMEM173 (also known as STING) facilitates innate immune reactions to viruses and bacteria through the production of type 1 interferon¹⁴. The only previous GWAS hit at this locus (rs13181561) was demonstrated to be associated with the modulation of interferon-α responses to the smallpox vaccine¹⁵ and is highly correlated (r²=0.956 in 1000 Genomes data for CHB population) with rs7447927. rs13181561 is also tagged as an expression quantitative trait locus in lymphoblastoid cells (Supplementary Table 7) that alters expression of genes in segment AC135457.2. This genomic region includes the sodium-dependent vitamin C transporter (SLC23A1), which is critical for vitamin C transport¹⁶. Interestingly, low vitamin C has been implicated in risk of ESCC¹⁷.

The new susceptibility locus at 17p13.1 is marked by rs1642764, an intronic SNP located within the ATP1B2 gene; this variant resides in an LD block that includes the 3’ region of TP53 (Figure 1b). Alteration of TP53 regulation or function could be a plausible explanation for the observed association between rs1642764 and risk of ESCC. It is noteworthy that no prior cancer GWAS has reported a conclusive association with a variant in or around TP53. Recently, a candidate gene study of genotyped and imputed SNPs across TP53 reported a strong association of a SNP with a low minor allele frequency, rs78378222, with glioma risk (P=6.86 x 10⁻²⁴ with a MAF=0.013 in a population of European ancestry)¹⁸. We also observe that TP53 is frequently inactivated in ESCC¹⁹. Our target SNP in this region is in LD with several other SNPs, notably rs1050541 (r²=0.575), which alters a binding site for RAD21 (Supplementary Table 7). RAD21 is a key component of the cohesion complex, which binds to DNA and is essential for mitosis, homologous DNA repair, and enhancer activities and may be relevant to cancer²⁰. Alternatively, this SNP falls between recombination hotspots that includes the SHBG gene, and variants bracketing this SNP are known to be associated with sex hormone binding globulin regulation and serum testosterone concentration²¹. Esophageal cancer is male predominant in low incidence populations, but this has typically been attributed to greater tobacco smoking and alcohol consumption by men compared to women. Recent studies have suggested that hormonal factors may play a role in the development of ESCC^{22, 23}.

In published GWAS, HLA Class II genetic variants in close proximity (<20 kb) of rs35597309 on 6p21.32 have been associated with multiple cancers including nasopharyngeal carcinoma²⁴, hepatocellular carcinoma²⁵, lung cancer in never smokers²⁶, and familial chronic lymphocytic leukemia²⁷ as well as autoimmune diseases, including Crohn's disease²⁸ and lupus ²⁹. This SNP resides between HLA-DRB1 and HLADQA1, both MHC class 2 genes that function in antigen presentation. The HLA region is large, complex, and shows unusually long-range LD that makes the interpretation of GWAS hits in this region difficult. rs35597309 is in perfect LD (r²=1.0) with 34 other SNPs within 2 MB (Supplementary Table 7) including two missense mutations in HLADQA1 and a host of putative protein binding sites, enhancers, and regulatory motifs. But the LD with top hits in this region from previously reported studies of cancer was low; r²<0.1 for rs2860580 (nasopharyngeal carcinoma), rs9272105 (hepatocellular carcinoma), rs2395185 (lung cancer), and rs674313 (chronic lymphocytic leukemia) in 1000 Genomes data for CHB+JPT populations.

Our results provide evidence for an association between variants at 6p21.32 and risk of ESCC, although the association was restricted to the studies which examined subjects from the high incidence Taihang Mountain region. While it is plausible that our results could be due to a gene-environment interaction between HLA genotypes and an uncharacterized risk factor specific to the Taihang Mountains, it is also possible that chance could account for this finding. The result was, however, confirmed in the Henan replication set (P=0.001). We know that populations in this region suffer from very high rates of ESCC, which is likely multi-factorial and could involve immune challenges. It is also important to note that the Han Chinese population is genetically diverse and this difference in association may be due to true differences in the genomic structure of the HLA regions between subjects from the Taihang Mountains and other parts of China.

But we note that the allele frequency is similar between the Henan and Beijing replication sets (Table 1). Future work should use genomic methods specifically designed to investigate the HLA region to explore these results. We also note that a region at 6p22.1 that is linked to other HLA alleles has been associated with risk of Barrett's esophagus, the precursor lesion for esophageal adenocarcinoma³⁰.

Supplementary Table 8 shows the individual GWAS and joint analysis results for the 12 main effect GWAS loci reported in five previous publications^1-5. Joint analysis of the pooled data from the three GWAS did not strengthen all previously reported loci. We observed strong associations for SNPs in loci harboring PLCE1, CASP8, RUNX1, and CHEK2, but no signal for four loci. The Beijing GWAS showed geographic differences in associations for some SNPs³ and this variation may be attributed, in part, to differences in environmental exposures and habits, such as alcohol consumption. A previous analysis by Wu et al. ⁴ using partial data from two of the studies also showed substantial heterogeneity between GWAS. Of 18 hits identified in analyses of both main effects and alcohol interactions in the Beijing data, only four replicated in subjects from the Shanxi Upper Gastrointestinal Cancer Genetics Study¹.

In conclusion, we present the joint analysis of the individual genotype data from three previously published GWAS in China and have established two new loci associated with risk of ESCC across all three studies, and two promising signals, the most notable, in the HLA class II region. The latter appears to be present only in subjects from the high incidence Taihang Mountain region of China. Environmental factors have previously been shown to be of varied relevance for ESCC in different Chinese populations and this may have led to differential GWAS findings. Here we find additional evidence for distinct results among these populations. Etiologic heterogeneity may play an important role in interpreting GWAS results and should be considered as GWAS are extended to understudied populations with distinct lifestyles. Lastly, further work is needed to fine-map the regions to identify the optimal alleles for laboratory studies that will provide an understanding of the basic biology underlying the ESCC susceptibility alleles and their interaction with environmental factors.

ONLINE METHODS

Subject selection and genotyping

This study pooled the individual genotype data of subjects from three independent GWAS of esophageal squamous cell carcinoma in Han Chinese populations, which were part of three earlier reports from the NCI¹, Henan², and Beijing study groups³. The numbers of subjects differs in some cases from those listed in the original publications because additional subjects were genotyped using the same platform subsequent to the original publications; these subjects were included in the joint analysis of the individual genotype data. For the NCI GWAS, subjects came from four prospective cohort studies and one large case-control study as reported in Abnet et al.¹ and all subjects used in replication in the original paper were subsequently genotyped using the Illumina 660W-Quad microarray. For the Henan scan, subjects were collected from many hospitals in Henan Province and a smaller ‘genetically-matched’ subset² (1,076 cases, 713 controls) was selected for this joint analysis. Furthermore, 299 cases and 370 controls were added who had subsequently been genotyped using the Illumina 610-Quad SNP microarray after the first publication. For the Beijing scan, subjects were collected from four different localities as previously reported in Wu et al.⁴ and all subjects were included in the current study. These subjects had been genotyped using the Affymetrix GeneChip Human Mapping 6.0 set. Subjects included in the replications from both Henan² and Beijing³ were identified and recruited using the same approaches as for subjects included in the GWAS from each of these respective sites and as described in the initial publications from each of their GWAS. A description of the included subjects is given in Supplementary Table 1.

GWAS data

The details of the analytic pre-processing for the three GWAS were included in each of the primary papers. In addition to the quality control procedures performed in the previous primary publications for all three studies, SNPs with a call rate < 95% or Hardy-Weinberg proportion test P-value < 0.000001 or minor allele frequency < 1% were further removed prior to imputation for the current analysis (Supplementary Table 2). We also searched for potential duplicates or first degree relatives across all three GWAS with glu ibds module (http://code.google.com/p/glu-genetics/) using the set of 25,676 independent SNPs with pair-wise r²<0.01 estimated from the GWAS control population. A total of nine pairs of duplicates were found; all were between Henan² and the NIT component of the NCI scan¹, which enrolled subjects in the same high incidence area of Henan Province. As a result, we excluded nine individuals (one from each pair) from the Henan study for the joint analysis. Application of standardized QC procedures for subjects and for SNPs resulted in the exclusion of an additional small proportion of subjects such that the final numbers of subjects in the current analysis are slightly different from those reported previously.

Imputation analysis

Imputation was conducted separately for each scan using IMPUTE2 software version 2.2.2 (http://mathgen.stats.ox.ac.uk/impute/impute_v2.html) and version 3 of the 1000 Genomes Project data as the reference set. First, the genomic coordinates were lifted over from NCBI human genome build 36 to build 37 using the UCSC lift over tool (http://hgdownload.cse.ucsc.edu/downloads.html). The few loci that failed to be lifted over were also excluded from the imputation. Second, the strand of the inference data was aligned with the 1000 Genomes data by simple allele state comparison or allele frequency matching for A/T and G/C SNPs. We implemented a 4-Mb sliding window to impute across the genome, resulting in 744 jobs running in parallel on the NIH BIOWULF cluster (http://biowulf.nih.gov/). A pre-phasing strategy with SHAPEIT software version 1 (http://www.shapeit.fr/) was adopted to improve the imputation performance. The phased haplotypes from SHAPEIT were fed directly into IMPUTE2. Imputed loci with INFO < 0.3 or MAF < 0.01 were excluded from further association analysis. To technically validate our imputation findings, we optimized TaqMan assays for rs7447927, rs1642764, and rs35597309. We genotyped a set of 892 samples from NCI and another set of 752 samples from Beijing. For the NCI set, the concordance rates between the imputed genotypes (using a posterior probability threshold of 0.95) and TaqMan genotypes were 99.3%, 96.5%, and 99.1%, respectively and for the Beijing set, the concordance rates between the imputed genotypes (using a posterior probability threshold of 0.95) and TaqMan genotypes were 96.7%, 90.4%, and 99.6%, respectively.

Association analysis

The imputed genotypes were merged using GTOOL software version 0.7.5 (http://www.well.ox.ac.uk/~cfreeman/software/gwas/gtool.html) and the association testing was performed using SNPTEST software version 2.2 (https://mathgen.stats.ox.ac.uk/genetics_software/snptest/snptest.html), with adjustment for age, sex, study, and the top two eigenvectors, which controls for population stratification. In the joint analysis baseline model (not including SNP effects) adjusting for age, sex, study, and all top ten eigenvectors (EVs), the top two eigenvectors were significantly associated with case status (P-value < 0.05), and were, therefore, included in the final joint analysis association models used to test SNP effects across all three studies. In a sensitivity analysis, we also conducted the association analyses in each of the three GWAS separately, followed by meta-analyses that used the fixed effect inverse variance method to combine the beta estimates and standard errors from each scan. In this second approach, we generated the set of eigenvectors based on each GWAS and identified significant eigenvectors to control for population stratification in each individual GWAS (EV1 for NCI; EV1, EV5 and EV7 for Beijing; no EV was needed for Henan). The meta-analysis (Supplementary Table 4) produced very similar results to our current joint analysis (Table 1), so we presented results for Stage 1 from the joint analysis as our primary analysis. The P for heterogeneity was calculated using Cochran's Q, which is distributed as a chi-square statistic with (n-1) degrees of freedom where n is the number of sets included in the meta-analysis. For exploring the gene and environment interaction with use of alcohol or tobacco, we performed stratified analyses for the four novel SNP associations and assessed the risk heterogeneity between drinkers and nondrinkers (Supplementary Table 5), and between smokers and nonsmokers (Supplementary Table 6). To evaluate population stratification, we examined QQ plots before and after eigenvector adjustment for the joint analysis (Supplementary Figure 7), for Beijing (Supplementary Figure 8), and for NCI (Supplementary Figure 9). No figure is shown for Henan as no adjustment was required. Further, we examined the association with risk for the four novel SNP associations we report here before and after eigenvector adjustment (Supplementary Table 9).

Recombination hotspot inference

Likelihood ratio statistics for recombination hotspots were estimated by SequenceLDhot software based on background recombination rates inferred by PHASE v2.1 using the 1000 Genomes CHB data.

Replication genotyping and analysis

After SNPs from previously reported ESCC risk loci were excluded, the top SNPs with p values less than 1.0 x 10⁻⁵ (n=14) from our Stage 1 analysis were selected for replication testing in both Beijing and Henan (Supplementary Table 3). However, when the imputation was updated with the addition of more covariate data on subjects, the new Stage 1 p-value for rs4252725 was only 1.0 x 10⁻⁴. At that point, primers for those top 14 SNPs from the initial analysis had already been designed and validated, so we proceeded to test all 14 of these SNPs. Therefore, we reported replication results for all the SNPs that we advanced to replication despite the updated analysis of our initial results and the appearance of a shift in our criterion. All SNPs genotyped in samples from the additional Beijing subjects used optimized TaqMan assays, whereas the Henan replication subjects were genotyped using Sequenom (11 SNPs) and TaqMan (three SNPs) assays. Three Sequenom assays failed genotyping and one (rs7822239) was repeated using TaqMan because it was nominally significant (P = 0.02) in the Beijing replication set. Samples with completion rates less than 80% in either replication were excluded from association analysis. Association analyses used log additive models with a trend effect and were adjusted for sex and age. Replication and Stage 1 results were combined using a fixed effect meta-analysis.

In silico bioinformatics analysis

Using 1000 Genomes CHB data, we identified all SNPs with r²>0.8, 0.8, 0.5 (because no SNPs passed the 0.8 threshold), respectively, for the lead SNP in each of the three novel regions we identified. We then used HaploReg³¹ and RegulomeDB³² to explore potential functional annotations within the ENCODE data in the genome surrounding our lead SNPs (Supplementary Table 7).