Identification of new susceptibility loci for IgA nephropathy in Han Chinese

Abstract

IgA nephropathy (IgAN) is one of the most common primary glomerulonephritis. Previously identified genome-wide association study (GWAS) loci explain only a fraction of disease risk. To identify novel susceptibility loci in Han Chinese, we conduct a four-stage GWAS comprising 8,313 cases and 19,680 controls. Here, we show novel associations at ST6GAL1 on 3q27.3 (rs7634389, odds ratio (OR)=1.13, P=7.27 × 10^-10 ), ACCS on 11p11.2 (rs2074038, OR=1.14, P=3.93 × 10^-9 ) and ODF1-KLF10 on 8q22.3 (rs2033562, OR=1.13, P=1.41 × 10^-9 ), validate a recently reported association at ITGAX-ITGAM on 16p11.2 (rs7190997, OR=1.22, P=2.26 × 10^-19 ), and identify three independent signals within the DEFA locus (rs2738058, P=1.15 × 10^-19 ; rs12716641, P=9.53 × 10^-9 ; rs9314614, P=4.25 × 10^-9 , multivariate association). The risk variants on 3q27.3 and 11p11.2 show strong association with mRNA expression levels in blood cells while allele frequencies of the risk variants within ST6GAL1, ACCS and DEFA correlate with geographical variation in IgAN prevalence. Our findings expand our understanding on IgAN genetic susceptibility and provide novel biological insights into molecular mechanisms underlying IgAN.

ARTICLE

Received 12 Dec 2014 | Accepted 23 Apr 2015 | Published 1 Jun 2015

Ming Li1,2,*, Jia-Nee Foo3,*, Jin-Quan Wang4,*, Hui-Qi Low3, Xue-Qing Tang1,2, Kai-Yee Toh3, Pei-Ran Yin1,2, Chiea-Chuen Khor3,5,6, Yu-Fen Goh3, Ishak D. Irwan3, Ri-Cong Xu1,2, Anand K. Andiappan7, Jin-Xin Bei3,Olaf Rotzschke7, Meng-Hua Chen8, Ching-Yu Cheng5,6, Liang-Dan Sun9,10, Geng-Ru Jiang11, Tien-Yin Wong5,6, Hong-Li Lin12, Tin Aung5,6, Yun-Hua Liao13, Seang-Mei Saw5,6,14, Kun Ye15, Richard P. Ebstein16, Qin-Kai Chen17,

Wei Shi18, Soo-Hong Chew19, Jian Chen20, Fu-Ren Zhang21, Sheng-Ping Li22, Gang Xu23, E. Shyong Tai24,25, Li Wang26, Nan Chen27, Xue-Jun Zhang9,10, Yi-Xin Zeng28,29, Hong Zhang30, Zhi-Hong Liu4,Xue-Qing Yu1,** & Jian-Jun Liu3,14,31,32,**

IgA nephropathy (IgAN) is one of the most common primary glomerulonephritis. Previously identied genome-wide association study (GWAS) loci explain only a fraction of disease risk. To identify novel susceptibility loci in Han Chinese, we conduct a four-stage GWAS comprising 8,313 cases and 19,680 controls. Here, we show novel associations at ST6GAL1 on 3q27.3 (rs7634389, odds ratio (OR) 1.13, P 7.27 10 10), ACCS on 11p11.2

(rs2074038, OR 1.14, P 3.93 10 9) and ODF1-KLF10 on 8q22.3 (rs2033562, OR 1.13,

P 1.41 10 9), validate a recently reported association at ITGAX-ITGAM on 16p11.2

(rs7190997, OR 1.22, P 2.26 10 19), and identify three independent signals within the

DEFA locus (rs2738058, P 1.15 10 19; rs12716641, P 9.53 10 9; rs9314614,

P 4.25 10 9, multivariate association). The risk variants on 3q27.3 and 11p11.2 show

strong association with mRNA expression levels in blood cells while allele frequencies of the risk variants within ST6GAL1, ACCS and DEFA correlate with geographical variation in IgAN prevalence. Our ndings expand our understanding on IgAN genetic susceptibility and provide novel biological insights into molecular mechanisms underlying IgAN.

1 Department of Nephrology, The First Afliated Hospital, Sun Yat-sen University, Guangzhou , Guangdong 510080, China. 2 Key Laboratory of Nephrology, Ministry of Health and Guangdong Province, Guangzhou, Guangdong 510080, China. 3 Human Genetics, Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore 138672, Singapore. 4 National Clinical Research Center of Kidney Diseases, Jinling Hospital, Nanjing University School of Medicine, Nanjing, Jiangsu 210002, China. 5 Singapore Eye Research Institute, Singapore 169856, Singapore. 6 Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore and National University Health System, Singapore 119228, Singapore. 7 Singapore Immunology Network, Agency for Science, Technology and Research, Singapore 138648, Singapore. 8 Department of Nephrology, General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750004, China. 9 Institute of Dermatology and Department of Dermatology, No.1 Hospital, Anhui Medical University, Hefei, Anhui, 230032, China. 10 State Key Laboratory Incubation Base of Dermatology, Ministry of National Science and Technology, Hefei, Anhui 230032, China. 11 Department of Nephrology, XinHua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200092, China. 12 Department of Nephrology, The First Afliated Hospital, Dalian Medical University, Dalian, Liaoning 116011, China. 13 Department of Nephrology, The First Afliated Hospital, Guangxi Medical University, Nanning 530021, China. 14 Saw Swee Hock School of Public Health, National University of Singapore, National University Health System, Singapore 117549, Singapore. 15 Department of Nephrology, The Peoples Hospital of Guangxi Autonomous Region, Nanning, Guangxi 530021, China. 16 Department of Psychology, National University of Singapore, Singapore 117570, Singapore. 17 Department of Nephrology, the First Afliated Hospital of Nanchang University, Nanchang, Jiangxi 330006, China. 18 Department of Nephrology, Guangdong General Hospital, Guangzhou, Guangdong 510080, China. 19 Department of Economics, National University of Singapore, Singapore 117570, Singapore. 20 Department of Nephrology, Fuzhou General Hospital of Nanjing Military Command, Fuzhou, Fujian 350025, China. 21 Shandong Provincial Institute of Dermatology and Venereology, Shandong Academy of Medical Science, Jinan, Shandong 250000, China. 22 Department of Hepatobiliary Oncology, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong 510080, China. 23 Department of Nephrology, Tongji Hospital, Tongji Medical College of Huazhong University of science & Technology, Wuhan, Hubei 430030, China. 24 Duke-National University of Singapore Graduate Medical School, Singapore 169857, Singapore. 25 Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, National University Health System, Singapore 119228, Singapore. 26 Department of Nephrology, Sichuan Provincial Peoples Hospital, Chengdu, Sichuan 610072, China. 27 Department of Nephrology, RuiJin Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200025, China. 28 State Key Laboratory of oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou 510080, China. 29 Peking Union Medical College, Chinese Academy of Medical Science, Beijing 100730, China. 30 Renal Division, Peking University First Hospital, Peking University, Institute of Nephrology, Beijing 100034, China. 31 School of Biological Sciences, Anhui Medical University, Hefei, Anhui 230032, China. 32 Institute of Dermatology and Department of Dermatology, No.1 Hospital, Anhui Medical University, Hefei, Anhui 230032, China. * These authors contributed equally to this work. ** These authors jointly supervised this work. Correspondence and requests for materials should be addressed toX.-Q.Y. (email: mailto:[email protected]

Web End [email protected] ) or to J.-J.L. (email: mailto:[email protected]

Web End [email protected] ).

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DOI: 10.1038/ncomms8270 OPEN

Identication of new susceptibility loci for IgA nephropathy in Han Chinese

ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms8270

IgA nephropathy (IgAN) is the most common primary glomerulonephritis and a major cause of end-stage renal disease in the Chinese population. It is characterized by the

deposition of IgA in the mesangial area of glomeruli, and up to 40% of the cases progress to end-stage renal diseases within 20 years of disease onset1,2. There is a marked regional difference in frequency of IgAN. It occurs with highest frequency in Asian populations, accounting for 4558.2% of primary glomerular disease, with modest frequency in Caucasians (USA and Europe) and with lower frequency in the African population2,3. These differences, together with evidence of familial clustering, strongly suggest the presence of a substantial genetic contribution to the disease. Previous genome-wide association studies (GWASs) have identied ve loci signicantly associated with IgAN, namely chromosome 1q32 (CFHR3-CFHR1 genes), 6p21 (MHC), 8p23 (DEFA gene cluster), 17p13.1 (TNFSF13) and 22q12 (HORMAD2), providing the rst valuable insights into genetic risk factors underlying disease mechanisms4,5. However, these explain only a fraction of the disease risk and it is clear that more genes and loci remain to be discovered.

In this study, we conduct a four-stage GWAS comprising 8,313 cases and 19,680 controls of Han Chinese ancestry (Supplementary Fig. 1). We identify novel loci on 3q27.3 (ST6GAL1), 11p11.2 (ACCS) and 8q22.3 (ODF1-KLF10), validate a recently reported association on 16p11.2 (ITGAX-ITGAM), and identify three independent signals within the DEFA locus on 8p23. These ndings signicantly expand our understanding of the genetic susceptibility to IgAN.

ResultsGenome-wide discovery analysis. We performed a new GWAS analysis (stage 1) by combining our published GWAS data set5 (consisting 1,434 cases and 4,270 controls) with an additional 6,511 control subjects of Chinese Han ethnicity, including 981 subjects from Guangdong, 523 subjects from Shandong and 5,007 subjects from Singapore (Supplementary Table 1). In each data set, we imputed untyped single-nucleotide polymorphisms (SNPs) using the 1,000-genome multi-ethnic reference panel (Feb 2012, IMPUTE v2) (refs 68). After merging shared SNPs across the data sets and stringent quality control ltering (see Methods), we analysed a total of 3,792,949 autosomal SNPs in 1,434 cases and 10,661 controls. With the expanded sample size and deep imputation of untyped variants, this new GWAS analysis has improved statistical power over the previous GWAS to detect risk variants in the range of odds ratio (OR) o1.3 and minor allele frequency (MAF) o10% (Supplementary Table 2)9.

We analysed genotype dosages taking into account imputation uncertainties10, using a logistic regression model assuming an additive effect for allelic risk and adjusting for population stratication using the rst ve principal components (PCs) as covariates (Supplementary Figs 25). The genomic ination factor after PC correction was low (lGC 1.087; 1.073 for SNPs

with MAF Z5%, l1,000 1.034), suggesting minimal effects of

population stratication in our discovery GWAS. In addition, we examined genotypes of 3,731,832 SNPs imputed with high condence (info score 40.8, with call rates 495% for genotype with probabilities 40.9) using a multivariate linear mixed model implemented in GEMMA11,12 (lGC 1.039; 1.015 for SNPs with

MAF Z5%, l1,000 1.015) (Supplementary Figs 2 and 3),

which gave results that were largely consistent with the logistic regression analysis with PC correction (Tables 1 and 2, Supplementary Data 1).

Our genome-wide discovery analysis provided strong supporting evidence for previously published associations4,5 at 17p13 and 22q12 (Supplementary Table 3, Supplementary Figs 68).

A similar trend of association was also observed at 1q32, even though the result was not statistically signicant (Supplementary Table 3). We also observed strong and consistent evidence for the three independent association signals within the major histocompatibility complex (MHC) region 6p21 that we previously reported (Supplementary Table 3). In addition, we identied a novel independent signal at rs2295119 in the MHC region that remained genome-wide signicant after conditioning on all previously reported human leukocyte antigen (HLA) SNPs (OR 1.359, logistic regression P

unconditioned

3.24 10 11,

Pconditioned 7.52 10 10). This SNP is in linkage

disequilibrium (LD) with rs9277554 (r2 0.556, D0 0.993)

which was found to be independently associated but did not reach genome-wide signicance in our previous study5.

We performed HLA imputation on the expanded GWAS data set using genotyped SNPs within chr6: 2040 Mb (build 37), the SNP2HLA tool13 and the Pan-Asian reference panel14,15. In addition to the associations at the previously reported four-digit HLA alleles (Supplementary Table 4), we also observed strong associations at two-digit alleles DPB1*02 (OR 1.32, logistic

regression P 1.77 10 9) tagged by rs2295119 (r2 0.94), and

DRB1*04 (OR 1.45, logistic regression P 3.16 10 11),

tagged by previously reported SNPs rs660895 (r2 0.44) and

rs1794275 (r2 0.13) (Supplementary Table 4). Detailed HLA

sequencing and typing analyses will be needed to further understand these associations.

Our analysis also suggested three independent association signals within the DEFA gene cluster on 8p23 (each Po10 4, r2o0.1 between each pair of SNPs). After excluding the SNPs within the ve previously identied regions, a notable excess of extremely small P values was observed on the quantilequantile plot compared with the expected null distribution (Supplementary Fig. 9), which suggests the existence of additional associations beyond the ones already identied.

Validation analysis. We rst selected the top 136 independent SNPs exceeding P o1 10 4 in either the PC-adjusted logistic

regression or GEMMA analysis that are not within the known loci (hypothesis free). Finally, by including the three independent SNPs within the 8p23 locus, a total of 139 SNPs were selected for the rst validation study, but only the assays for 122 were successfully designed for multiplex genotyping analysis by Sequenom.

As the initial validation, 115 SNPs were successfully genotyped in 2,651 IgAN cases and 2,907 controls of Han Chinese ethnicity recruited from the Southern region of China (stage 2). We performed logistic regression analysis of the validation samples and combined the association statistics across the discovery (PC-adjusted or GEMMA) and validation samples by a xed-effects meta-analysis (Supplementary Data 1). We then took forward the top 22 SNPs with Po1 10 4 in the meta-analysis

of the combined stages 1 and 2 samples for further validation analysis in an independent set of samples (stage 3) consisting of 2,428 IgAN cases and 4,202 controls recruited from the Northern (1,463 cases and 1,683 controls) and Southern (965 cases and 2,519 controls) regions of China (Supplementary Table 5). Finally, the top eight SNPs with Po5 10 7 in the combined

samples of stages 1 2 3 and showing consistent associations

across all four sample collections were analysed in an additional independent set of 1,800 IgAN cases and 1,910 controls (stage 4) recruited from Northern (704 cases and 805 controls) and Southern (1,096 cases and 1,105 controls) China (Table 1). We then conducted a full meta-analysis of all the stages 14 samples (Supplementary Fig. 1) by analysing the Northern and Southern samples of each of the four stages as independent sample

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Table 1 | Novel SNPs reaching genome-wide signicance and suggestive SNPs approaching genome wide signicance.

SNP/locus I2/Phet

Risk/non-risk allele

P value OR (95% CI)

rs7190997 C/T GWAS logistic 72.0 69.2 4.00E 05 1.207 (1.1031.320)

GWAS gemma 1.94E 05

chr16: 31368178 Validation 1 73.5 66.7 3.35E 15 1.386 (1.2781.503)

ITGAX-ITGAM Validation 2 Southern 71.9 69.3 3.91E 02 1.129 (1.0061.267)

Validation 2 Northern 77.3 76.2 3.01E 01 1.064 (0.9461.196)

Validation 3 Southern 72.9 68.4 1.16E 03 1.239 (1.0891.411)

Validation 3 Northern 77.5 74.7 7.30E 02 1.166 (0.9861.379)

All validation 1.10E 15 1.229 (1.1681.292)

Meta-analysis I2 70.3%, Phet 0.0049 2.26E 19 1.223 (1.1711.278)

rs2074038 T/G GWAS logistic 32.3 28.8 3.36E 05 1.207 (1.1041.319)

GWAS gemma 4.45E 05

chr11:44087989 Validation 1 31.3 28.6 1.87E 03 1.137 (1.0491.233)

ACCS Validation 2 Southern 32.5 28.9 4.08E 03 1.177 (1.0531.315)

Validation 2 Northern 34.8 33.6 3.24E 01 1.054 (0.9501.170)

Validation 3 Southern 30.4 29.1 3.42E 01 1.066 (0.9351.216)

Validation 3 Northern 35.6 32.7 1.03E 01 1.134 (0.9751.318)

All validation 8.76E 06 1.116 (1.0631.171)

Meta-analysis I2 1.26%, Phet 0.408 3.93E 09 1.136 (1.0891.185)

rs2033562 C/G GWAS logistic 51.4 47.7 7.98E 05 1.176 (1.0851.274)

GWAS gemma 4.14E 04

chr8:103547739 Validation 1 50.5 47.7 3.45E 03 1.116 (1.0371.201)

KLF10/ODF1 Validation 2 Southern 50.6 48.8 1.77E 01 1.074 (0.9681.192)

Validation 2 Northern 50.8 47.4 9.15E 03 1.139 (1.0331.256)

Validation 3 Southern 49.8 49.0 5.90E 01 1.033 (0.9181.163)

Validation 3 Northern 50.9 45.0 1.35E 03 1.265 (1.0961.460)

All validation 2.21E 06 1.114 (1.0651.165)

Meta-analysis I2 23.7%, Phet 0.256 1.41E 09 1.128 (1.0851.173)

rs7634389 C/T GWAS logistic 47.4 43.5 6.80E 04 1.151 (1.0611.248)

GWAS gemma 4.33E 05

chr3:186738421 Validation 1 46.2 43.7 8.86E 03 1.105 (1.0261.191)

ST6GAL1 Validation 2 Southern 46.1 44.3 1.80E 01 1.075 (0.9681.194)

Validation 2 Northern 46.6 42.6 1.48E 03 1.176 (1.0641.300)

Validation 3 Southern 47.0 42.8 5.94E 03 1.180 (1.0491.328)

Validation 3 Northern 46.7 43.8 1.09E 01 1.124 (0.9741.297)

All validation 2.48E 07 1.126 (1.0761.178)

Meta-analysis I2 0%, Phet 0.772 7.27E 10 1.132 (1.0881.178)

CI, condence interval; GWAS, genome-wide association study; OR, odds ratio; SNP, single-nucleotide polymorphism.

For the GWAS samples, only the result from logistic regression analyses (GWAS logistic) was used in the xed-effects meta-analysis with the validation samples. P values shown are from logistic regression analyses unless otherwise stated.

Sample Risk allele frequency cases (%)

Risk allele frequency controls (%)

collections (six independent samples in total) to minimize bias resulting from population stratication.

From the combined analysis of a total of 8,313 IgAN cases and 19,680 controls, we identied SNPs at four out of the ve novel loci reaching genome-wide signicance (Po5 10 8), rs7190997

within the ITGAM-ITGAX locus on 16p11.2 (OR 1.22, xed-

effects meta-analysis P 2.26 10 19), rs2074038 at the ACCS

locus on 11p11.2 (OR 1.14, meta-analysis P 3.93 10 9),

rs2033562 near ODF1-KLF10 on 8q22.3 (OR 1.13, meta-analysis

P 1.41 10 9) and rs7634389 at the ST6GAL1 locus on 3q27.3

(OR 1.13, meta-analysis P 7.27 10 10) (Table 1, Fig. 1). The

fth locus rs11264799 (FCRL3) on 1q23.1 remained suggestive (OR 1.14, meta-analysis P 2.00 10 7) (Supplementary

Table 6). We also conrmed all three independent association signals within the DEFA locus at rs2738058 (OR 1.23,

meta-analysis P 1.15 10 19), rs12716641 (OR 1.15, meta-

analysis P 9.53 10 9) and rs9314614 (OR 1.13,

meta-analysis P 4.25 10 9) through a multivariate association

analysis (Table 2, Fig. 2). All the novel associations showed consistent effects across all the independent sample collections

without evidence of heterogeneity and obtained statistically signicant in the validation samples after correction for multiple testing (Bonferroni corrected Po0.05/154 3.25 10 4;

Tables 1 and 2).

To further ensure that none of the novel associations were inuenced by population stratication and/or batch effects among the expanded control samples of stage 1 (genotyped in different arrays), we re-examined the imputation info scores and allele frequencies of all the validated SNPs across the different control data sets and found them to be high quality and very consistent across genotyping arrays and sample collections (Supplementary Table 7). We re-examined our reported SNPs in a subset of samples (1,434 cases and 4,270 controls) that were analysed in our previous GWAS5, on which the imputation was performed as a batch from 444,882 overlapping autosomal SNPs that were genotyped on different Illumina chips and found that the strengths of these associations (ORs) were consistent with the results from the full data set, indicating that the new evidences of these associations were driven by improved statistical power and genetic variation coverage rather than systematic bias due to the

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Table 2 | Three independent signals in the DEFA region. r2 0 between rs9314614 and rs12716641.

SNP Risk allele Risk allele Multivariate analysis*

Risk/non-risk allele frequency cases (%)

frequency controls (%)

P value OR (95% CI) P value OR (95% CI)

rs2738058 chr8:6821617T/CGWAS logistic 73.0 66.6 5.75E 10 1.310 (1.2031.427) 2.06E 08 1.298 (1.1851.422)

GWAS gemma 1.51E 10

Validation 1 72.4 69.0 6.10E 05 1.181 (1.0891.281) 4.88E 03 1.131 (1.0381.232)

Validation 2 Southern 71.9 66.0 1.92E 06 1.327 (1.1811.491) 6.46E 05 1.284 (1.1361.451)

Validation 2 Northern 72.3 67.6 7.08E 05 1.245 (1.1171.386) 6.28E 04 1.219 (1.0881.366)

Validation 3 Southern 72.6 67.3 8.74E 05 1.303 (1.1421.487) 8.58E 04 1.263 (1.1011.450)

Validation 3 Northern 73.4 67.0 1.64E 04 1.354 (1.1561.585) 1.26E 03 1.310 (1.1121.543)

All validation 4.14E 19 1.253 (1.1931.317) 1.67E 17 1.226 (1.1701.285)

Meta-analysis I2 31%, Phet 0.400 2.31E 27 1.267 (1.2141.323) 1.15E 19 1.232 (1.1781.289)

rs9314614 chr8:6697731C/GGWAS logistic 39.7 36.0 3.54E 05 1.191 (1.0971.295) 2.85E 04 1.161 (1.0711.258)

GWAS gemma 6.20E 05

Validation 1 37.7 35.0 3.77E 03 1.121 (1.0381.211) 2.03E 03 1.130 (1.0461.222)

Validation 2 Southern 38.6 35.6 2.10E 02 1.135 (1.0191.264) 3.03E 02 1.127 (1.0111.256)

Validation 2 Northern 40.2 39.1 3.42E 01 1.051 (0.9481.165) 3.72E 01 1.048 (0.9451.163)

Validation 3 Southern 38.4 36.0 1.03E 01 1.108 (0.9801.253) 9.97E 02 1.109 (0.9811.255)

Validation 3 Northern 41.9 36.8 4.85E 03 1.234 (1.0661.428) 6.73E 03 1.225 (1.0581.419)

All validation 2.66E 06 1.118 (1.0671.171) 9.55E 08 1.121 (1.0751.170)

Meta-analysis I2 0%, Phet 0.442 9.48E 10 1.135 (1.0901.182) 4.25E 09 1.129 (1.0841.176)

rs12716641 chr8:6898998T/CGWAS logistic 78.0 74.3 7.80E 07 1.260 (1.1501.381) 4.40E 03 1.153 (1.0451.272)

GWAS gemma 1.79E 06

Validation 1 78.2 73.6 1.22E 08 1.287 (1.1801.404) 2.95E 06 1.244 (1.1351.363)

Validation 2 Southern 78.8 75.2 1.81E 03 1.219 (1.0761.381) 8.47E 02 1.122 (0.9841.278)

Validation 2 Northern 80.4 78.4 5.44E 02 1.129 (0.9981.278) 3.91E 01 1.058 (0.9291.205)

Validation 3 Southern 78.7 75.5 1.00E 02 1.204 (1.0451.386) 1.01E 01 1.131 (0.9761.311)

Validation 3 Northern 80.5 77.4 3.85E 02 1.207 (1.0101.442) 2.65E 01 1.112 (0.9231.339)

All validation 2.34E 13 1.224 (1.1591.291) 1.66E 08 1.157 (1.1001.218)

Meta-analysis I2 0%, Phet 0.432 1.13E 18 1.233 (1.1771.292) 9.53E 09 1.154 (1.0991.212)

CI, condence interval; GWAS, genome-wide association study; OR, odds ratio; SNP, single-nucleotide polymorphism.

Only rs2738058 is in LD (r2 0.71) with the reported SNP rs2738048.For the GWAS samples, only the result from logistic regression analyses (GWAS logistic) was used in the xed-effects meta-

analysis with the validation samples.*Pairwise LD (r2) between rs2738058 and rs9314614 0.001, between rs12716641 and rs2738058 0.074. There is no LD between rs9314614 and rs12716641 (r

2 0).

inclusion of additional control samples that were imputed in separate data sets (Supplementary Table 7). Good genotyping clusters were observed across all genotyping platforms for the reported SNPs (Supplementary Figs 1012) with allele frequencies of imputed SNPs closely matching those genotyped in the validation samples (Tables 1 and 2).

Next, we re-ran the full meta-analysis by dividing the discovery data set into Northern (414 cases and 2,306 controls) and Southern (1,020 cases and 8,355 controls) clusters with adjustment of the top ve principal components re-calculated within each cluster (see Methods) (Supplementary Fig. 5), and the full meta-analysis results were consistent at the top loci (Supplementary Table 8). The association effects were not signicantly different between the combined (discovery and validation) Northern and Southern samples (Phet40.05)

(Supplementary Table 9). In addition, we did another full meta-analysis with lambda GC correction of the PC-adjusted results from the discovery data set. All the novel loci remained genome-wide signicant (Supplementary Table 9). Finally, the associations were not inuenced by age and gender, and similar effects were observed in males and females (Supplementary Table 10).

While this manuscript was under preparation, an independent GWAS on IgAN conducted in Europeans and Asians was published and reported three novel loci ITGAX, VAV3 and CARD9 (ref. 16). Of the two previously reported independent associations within ITGAX locus, our top SNP (rs7190997) at ITGAX shows a high LD with the reported SNP rs11150612 (r2 0.877, D0 0.988). The other reported SNP

rs11574637 as well as SNPs in LD with it are either very rare (MAFo1%) or absent in both our samples and HapMap Asians (http://hapmap.ncbi.nlm.nih.gov/

Web End =http://hapmap.ncbi.nlm.nih.gov/). At the other two novel loci VAV3 and CARD9, we observed a similar direction of association at the reported SNPs16 but the results did not reach statistical signicance in our discovery samples (P40.05; Supplementary

Table 3, Supplementary Fig. 8).

We have also done further investigation of the reported association at the CFH locus4,16 by genotyping rs6677604 in our validation 2 and 3 samples and analysing the Northern and Southern samples of each collection separately in the meta-analysis of the combined discovery and validation samples (Supplementary Table 11). We observed signicant association of the CFH locus (OR 1.19 (95% condence interval

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100

80

60

40

20

0

Recombination rate (cM/Mb)Recombination rate (cM/Mb)

rs7190997

GWAS + Validation 1 + 2 + 3 GWAS + Validation 1 + 2 + 3 GWAS + Validation 1 + 2GWAS + Validation 1 GWAS + Validation 1 + 2

GWAS + Validation 1

GWAS

GWAS

log 10(Pvalue)

log 10(Pvalue)

20

15

10

5

0

10

8

6

4

2

0

Recombination rate (cM/Mb)

10

8

6

4

2

0

r r

0.80.60.40.2

log 10(Pvalue)

log 10(Pvalue)

0.80.60.40.2

100

80

60

40

20

0

31 31.2 31.4 31.6 186.4 186.6 186.8 187

Position on chr16 (Mb)

Position on chr3 (Mb)

100

80

60

40

20

0

r r

10

8

6

4

2

0

0.80.60.40.2

0.80.60.40.2

GWAS + Validation 1 + 2 + 3 GWAS + Validation 1 + 2

GWAS + Validation 1

GWAS

Recombination rate (cM/Mb)

GWAS + Validation 1 + 2 + 3

GWAS + Validation 1 + 2

GWAS + Validation 1

GWAS

100

80

60

40

20

0

43.8 44 44.2 44.4 103.2 103.6

103.4 103.8

Position on chr11 (Mb) Position on chr8 (Mb)

Figure 1 | Recombination plots of the novel loci reaching genome-wide signicance. (a) rs7190997 at 16p11.2, (b) rs7634389 at 3q27.3, (c) rs2074038 at 11p11.2 and (d) rs2033562 at 8q22.3, showing P values obtained in the GWAS discovery (logistic regression) and in the combined analysis of GWAS and validation 1, 2 and 3 samples (xed-effects meta-analysis).

Recombination rat

(cM/Mb)

100 80 60 40 20

0

r

log(P value)

30 25 20 15 10

5 0

0.80.60.40.2

6.6 6.8 7 7.2

Position on chr8 (Mb)

Figure 2 | Recombination plots of the three independent loci at the defensin locus, showing P values obtained in the GWAS discovery (logistic regression) and in the combined analysis of GWAS and validation 1, 2 and 3 samples (xed-effects meta-analysis). The two novel signals are in low linkage disequilibrium with rs2738058, which tags the previously reported SNP rs2738048 and are separated from these SNPs by regions of high recombination rates.

(CI) 1.071.31), meta-analysis P 0.0011) without evidence of

heterogeneity across all the six sample collections (I2 0%,

Cochranes Q test Pheterogeneity 0.70). The effect size in our

samples was, however, smaller than what was previously reported, and a slightly larger effect was observed in our Northern (OR 1.26) than Southern (OR 1.12) samples

(Supplementary Table 11).

Independent associations within the DEFA locus. We discovered three independent signals at the DEFA locus (Table 2, Supplementary Table 12). Of these, only rs2738058 is in LD with the previously reported SNP rs2738048 (r2 0.71) (ref. 5), and

the other two signals at rs12716641 and rs9314614 were located 7677 kb and 124125 kb away from rs2738058 and rs2738048, respectively (Fig. 2). While rs12716641 is located within the DEFA gene cluster, rs9314614 is located in the intron of the long-coding RNA GS1-24F4.2 and separated from the DEFA gene cluster by two recombination hotspots, likely representing an independent novel locus. Furthermore, all three are poorly correlated with rs10086568, an independent association within the DEFA locus reported in ref. 16 (r2o0.1 in our samples and 1,000 genomes Asians, although r2 0.17 with rs12716641

in Europeans). Multiple copy-number variants (CNVs) of DEFA1-A3 exist within this region17 and were previously found to be associated with Crohns disease18 and severe sepsis19. Recently, rs4300027 has been reported to tag the CNVs in Europeans (r2 0.35) (ref. 20). rs4300027 is in moderate LD with

rs2738048 (r2 0.15), rs2738058 (r2 0.12) and rs12716641

(r2 0.28), but not rs9314614 (r2 0.002). This suggests that

the associations at rs2738048/rs2738058 and rs12716641 may

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implicate the role of DEFA1-A3 CNVs in IgAN. Further ne mapping and functional study will be needed to investigate the association of the DEFA1-A3 CNVs with IgAN and its relationship with the complex association patterns at these SNPs in Europeans and Asians.

Functional investigation through eQTL (expression quantitative trait loci) and GRAIL (genetic relationships across implicated loci) analyses. We then investigated potential biological effects of the novel associated SNPs and loci by looking for effects on mRNA expression levels (eQTLs)21,22, ENCODE annotations of associated variants23,24, documented associations with other diseases and known biological functions of nearby genes (Table 3, Supplementary Tables 1315, Supplementary Data 2 and 3). Three out of the four novel loci showed strong associations with the expression levels of the genes ITGAX, ITGAM, ACCS, EXT2 and ST6GAL1 in peripheral blood cells and others, suggesting an important role of regulatory variants of these genes in IgAN risk. All ve SNPs also tag variants that lie in predicted protein

binding sites. Although no strong eQTL effects were observed for rs2033562 near ODF1-KLF10 in blood cells, ENCODE annotations suggest it may play a role in the expression of the nearby UBR5 gene24. Furthermore, although there was no evidence for eQTL effects of rs2738048/rs2738058 and rs12716641, a moderate association with expression of the gene DEFB1 was observed at rs9314614 in monocytes (linear model P 4.22 10 4; Supplementary Tables 13 and 15)22, providing

further support that this SNP could tag a novel independent locus and pathway despite its close proximity to the DEFA cluster.

These variants are also not in strong LD with those previously associated with other traits and diseases in Europeans or Asians (Table 3, Supplementary Table 13)25, and a GRAIL (https://www.broadinstitute.org/mpg/grail/

Web End =https://www.broadinstitute.org/mpg/grail/) 26 analysis of previously reported and novel loci did not identify any major pathways that can account for their associations with IgAN. We anticipate that these loci highlight multiple different pathways including mucosal immune response16 that may jointly inuence IgAN pathogenesis through changes in gene expression levels. Since most of the existing

Table 3 | Functional annotations of novel loci and associated SNPs (details on eQTLs and ENCODE annotations in Supplementary Tables 14 and 15, Supplementary Data 2 and 3)16,2125,3743.

SNP/locus eQTL/ENCODE Other diseases Known functions of genes rs207403811p11.2SNP location: Lies withineither the rst intron or50 UTR of the various

ACCS/PHACS gene isoforms Within the same LD block as the adjacent gene EXT2.

Risk allele is strongly associated with increased ACCS gene expression levels in peripheral blood cells (Po9.81 10 198),

monocytes (P 1.07 10 36), and

B-cells (P 1.46 10 29).

It is also associated with increased EXT2 expression levels in peripheral blood cells (P 2.63 10 53).

This SNP and others in LD are also located at sites predicted with high likelihood to affect chromatin structure and protein binding.

Not previously associated with any other trait.

ACCS encodes a1-aminocyclopropane-1-carboxylate synthase homologue that belongs to the class-I pyridoxal-phosphate-dependent aminotransferase family.

Shown to interact with the protein encoded by FBF1 (Fas (TNFRSF6) binding factor 1), a keratin-binding protein that is required for epithelial cell polarization, apical junction complex assembly andciliogenesis37,38 EXT2 is a glycosyltransferase, which plays a role in heparan sulfate biosynthesis. Heparan sulfate in turn inuences angiogenesis and cell proliferation; mutations in this gene cause multiple exotoses39,40.

IgG glycosylation, Type 2 diabetes, oesophageal cancer.

Drug-induced liver injury.

In moderate LD with rs11710456 in IgG glycosylation in Europeans (r2 0.528) but not Asians

(r2 0.12).

ST6GAL1 encodes ST6 beta-galactosamide alpha-2,6-sialyltranferase, a member of glycosyltransferase family involved in the generation of the cell-surface carbohydrate determinants and differentiation antigens.

Regulates macrophage apoptosis via alpha2-6 sialylation of the TNFR1 death receptor.

May play a regulatory role in innate immune response.

Up-regulated in human cancers41,42.

rs76343893q27.3SNP location: Lies within an intron of the ST6GAL1.

Risk allele is in strong LD (r240.9 in our samples, HapMap Europeans and Asians) with variants associated with decreased expression levels of ST6GAL1 in peripheral blood cells (rs3821819;

P 5.96 10 20) and B-cells

(rs17776120; P 1.61 10 7).

This SNP and others in LD may affect chromatin structure and protein binding.

rs20335628q22.3SNP location: Located in an intergenic region closest to the genes ODF1, KLF10 and UBR5.

No evidence for effects on gene expression levels in peripheral blood cells, monocytes, B-cells. In LD with SNPs with potential regulatory effects on UBR5 expression, chromatin structure and protein binding.

Chronic lymphocytic leukaemia (CLL) Not in LD with CLL-associated SNP.

KLF10 encodes a transcriptional repressor that acts as an effector of transforming growth factor beta signalling and activity of this protein may inhibit the growth of cancers. UBR5 encodes a E3 ubiquitin ligase which interacts with the deubiquitinase DUBA which in turns plays a rolein IL-17 production in T-cells and inammatory response in the small intestine43.

SNP, single-nucleotide polymorphism.

Other recently and previously reported loci are described in Supplementary Table 13.

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NATURE COMMUNICATIONS | DOI: 10.1038/ncomms8270 ARTICLE

expression data sets were generated in healthy individuals that were mostly of European descent21,22, a more careful analysis will be needed to examine the association of these genotypes on the expression levels of these genes in Asian subjects, and to evaluate their biological and clinical relevance in IgAN patients and controls.

At the ST6GAL1 locus, we observed a trend of increasing allele frequencies of risk allele C at rs7634389 from Africans (16%), Europeans (37.5%) to Asians (40%) in HapMap populations (Supplementary Table 16). Similarly, the ACCS SNP rs2074038 risk allele T is absent in Africans, present at moderate frequency in Europe (12%) and highest frequency in Asia (33%). Similar observations were made at two independent variants in the DEFA cluster (Supplementary Table 16). Consistent with previous reports on the geographical distribution of risk variants within 1q32, 22q12 (ref. 1), and more recently, 16p11.2 (ref. 16), these trends of increasing risk allele frequencies suggest that pooled differences in risk allele frequencies of these loci combined may contribute to differences in disease prevalence across different world populations1,4,16.

DiscussionThis study has several advantages over our previous study. Firstly, we have performed deep genome-wide imputation in the discovery samples, leading to the discovery of loci tagged by SNPs that were not previously represented on SNP arrays. Secondly, the addition of 6,391 controls led to a moderate gain in statistical power to detect risk alleles with small effect sizes (ORo1.2). Thirdly, we have expanded our validation data sets to include both Northern and Southern Chinese samples that have provided rm independent replication of the association signals. We used geographic matching as a proxy for genetic matching to control the effects of population stratication in our validation data sets, as has been done in previous studies on the Chinese population27, although ancestry informative markers may also be helpful. We also acknowledge a number of limitations. Despite deep imputation, we expect that the coverage of low frequency and rare variants (minor allele frequencies o5%) is limited given that most of the imputed rare variants in our study had a low information score that did not pass our strict quality control thresholds and were therefore excluded from further analysis. Also, with the current sample size, we had limited power to analyse lower-frequency variants with minor allele frequencies below 5% (Supplementary Table 2)9. Further studies will be needed to directly sequence or genotype rare variants on a larger number of cases and controls to evaluate their role in IgAN risk.

We have conducted the largest study on IgAN in the Han Chinese population to date by analysing a total of 8,313 IgAN cases and 19,680 controls. We have discovered three new loci at 11p11.2, 8q22.3 and 3q27.3, two novel independent associations within the DEFA-DEFB gene cluster and validated the recently reported locus at 16p11.2 (ref. 16). We estimate that these novel association signals explain about 1.7% of the disease variance and5.5% of the variance in combination with the previously published loci4,5. Some risk variants show signicant difference in frequency and may contribute to IgAN prevalence differences across world populations. Our study has signicantly expanded our understanding on the genetic basis of IgAN susceptibility and provided novel insight into the mechanisms underlying the development of IgAN.

Methods

Study subjects. The original genome-wide discovery analysis involved 1,523 cases (from Southern China) and 4,276 controls (972 controls from southern China, 1,228 controls from Northern China and 2,076 Chinese controls from Singapore who share the same ancestral origin as the other Chinese controls)5. To boost the statistical power of the current study, we included 6,511 control subjects of Chinese

Han ethnicity from several of our previous GWAS including 981 subjects from Guangdong, 523 subjects from Shandong and 5,007 subjects from Singapore. For the validation study, three independent casecontrol samples were recruited from China as replication 1 (2,651 IgAN cases and 2,907 controls), replication 2 (2,428 cases and 4,202 controls) and replication 3 (1,800 IgAN cases and1, 910 controls) (Supplementary Note 1).

All the cases were histopathologically diagnosed by biopsy according to the following criteria: (i) immunouorescence showing at least 2 (scale 0 to 3 )

mesangial deposition of IgA, with IgA comprising the dominant immunoglobulin deposited in the glomeruli and (ii) excluding individuals with cirrhosis, Henoch Schnlein purpura nephritis, hepatitis B-associated glomerulonephritis, HIV infection and systemic lupus erythematosus5. In accordance to the Oxford Classication of IgAN, our samples were graded by the four pathological features (mesangial hypercellularity M, endocapillary hypercellularity E, segmental glomerulosclerosis S and tubular atrophy/interstitial brosis T, resulting in a MEST score; Supplementary Note 1)28,29.

The study was approved by the Institutional Review Board at The First Afliated Hospital of Sun Yat-sen University and at the National University of Singapore. Written informed consent was obtained from all of the participants.

Sample genotyping and quality control. Genomic DNA was isolated from whole blood using a Qiagen DNA extraction kit and quantied using a Picogreen assay (Invitrogen). Genotyping analysis of the discovery samples was conducted using Human660-Quad (1,523 cases), Human610-Quad (1,953 southern and 1,228 Northern Chinese controls and the 3,998 Singaporean Chinese controls, 523 Shandong controls), Human 550K (1,022 Singaporean Chinese controls), Human 1M-Duo (930 Singaporean Chinese controls) and Human OmniExpress (1,133 Singaporean Chinese controls) BeadChips (Illumina).We excluded SNPs from the X, Y and mitochondrial chromosomes and focused all further analyses on auto-somal SNPs. We performed identity by descent analysis using PLINK30 and 104 rst degree relative pairs were identied; the relative with a lower sample call rate was excluded. Principal components analysis31 (Eigensoft v3.0: http://genepath.med.harvard.edu/~reich/Software.htm

Web End =http:// http://genepath.med.harvard.edu/~reich/Software.htm

Web End =genepath.med.harvard.edu/Breich/Software.htm ) was done on using a set on 47,462 common SNPs (MAF41%) that were derived from 250,201 genotyped SNPs overlapping across all arrays. These SNPs were pruned to remove SNPs in LD (r240.1, using PLINK --indep-pairwise 50 5 0.1) after exclusion of SNPs in the ve conserved long-range LD regions in Chinese, namely the HLA region on chromosome 6, inversions on chromosomes 8 and 5 and two regions on chromosome 11 (refs. 5,27). After principal components analysis, 16 outliers were identied based on principal components (PCs) 15 and excluded such that 12,095 samples (1,434 cases and 10,661 controls) were left for the nal analysis.

Genotype imputation and quality control. The software IMPUTE version 2 (https://mathgen.stats.ox.ac.uk/impute/impute_v2.html

Web End =https://mathgen.stats.ox.ac.uk/impute/impute_v2.html) was used for imputing genotype data of untyped SNPs in each data set following pre-phasing using SHAPEIT (https://mathgen.stats.ox.ac.uk/genetics_software/shapeit/shapeit.html

Web End =https://mathgen.stats.ox.ac.uk/genetics_software/shapeit/shapeit.html )68,32. We imputed on the basis of only those genotyped SNPs that passed quality control thresholds (call rate 495%, MAF 41%, HardyWeinberg equilibrium (HWE)

P41 10 6 in controls) in each of the data sets. The imputation was performed by

using the multi-ethnic 1,000-genome reference panel (dated March 2012) consisting of 1,092 individuals from Africa, Asia, Europe andthe Americas, which has been shown to outperform imputations with reference panels of matched ancestry7. SNP quality control criteria after imputation are as follows: MAF 41%, Impute info 40.5 if MAF Z5%, Impute info40.8 if MAF o5%, HWE P in controls 41 10 8 and average

maximum posterior probabilities 40.99, leaving 3,792,915 SNPs for genotype dosage analyses in the nal merged data set. For analysis of threshold-selected genotypes of 3,731,832 SNPs using GEMMA (http://www.xzlab.org/software.html

Web End =http://www.xzlab.org/software.html) 11,12, we used the following quality control lters: call rate 40.95 after setting all genotypes with probabilities o0.9 to missing, MAF 40.01, HWE P in controls40.05, test of differential missingness between cases and controls P 41E 8, imputation info 40.8.

HLA imputation and analysis. Imputation of two- and four-digit classical HLA alleles was performed using the SNP2HLA tool (https://www.broadinstitute.org/mpg/snp2hla/

Web End =https://www.broadinstitute.org/ https://www.broadinstitute.org/mpg/snp2hla/

Web End =mpg/snp2hla/ ) and the Pan-Asian reference panel1315 using the same set of directly genotyped SNPs within chr6:2040 Mb (Hg19, build 37) that passed quality control thresholds in each of the data sets as described above. We analysed genotype dosages of all imputed HLA alleles, with most of the reported alleles having high imputation condence (r240.9; Supplementary Table 4).

Genotyping and quality controls in the validation study. Genotyping analysis of the SNPs selected for validation was performed using the MassArray system from Sequenom. Locus-specic PCR and detection primers were designed using the MassArray Assay Design 3.0 software (Sequenom). SNPs that failed Sequenom design or genotyping in validation 2 and 3 samples were genotyped using Taqman assays (Life Technologies). TaqMan reactions were carried out in 5-ml volumes containing 1020 ng DNA according to the manufacturers protocols. Fluorescence data were obtained in the ABI PRISM 7900HT and SDS 2.4 software (Life Technologies) was used to call genotypes. For all SNPs, we examined the clustering patterns of genotypes and selected mass peaks and conrmed that the genotype

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calls were of good quality. All SNPs with call rates o95% and/or HWEPo1 10 6 in controls and all samples with call rates of o90% were removed

from further analysis from each batch (validations 13). After quality control, 130 SNPs from validation 1, 24 SNPs from validation 2 and 10 SNPs from validation 3 (including CFH SNP rs6677604) were left for further analysis.

Association tests. Imputed dosage data were analysed using SNPTEST (https://mathgen.stats.ox.ac.uk/genetics_software/snptest/snptest.html

Web End =https://mathgen.stats.ox.ac.uk/genetics_software/snptest/snptest.html) 10. Genome-wide casecontrol analysis was performed using frequentist tests under a missing data logistic regression model, as implemented in SNPTEST. We assumed an additive model for allelic risk, with the rst ve principal components as covariates to control for population stratication. To evaluate the effects of mixing Northern and Southern Chinese samples in the discovery analysis, we also split the discovery cohort into two clusters based on the rst two principal components30, one of which is predominantly Northern Chinese (414 cases and 2,306 controls), and the other predominantly Southern Chinese (1,020 cases and 8,355 controls). In each cluster, we re-ran the principal components analysis and performed the association analysis adjusted for PCs15 in that cluster, then meta-analysed the PC-adjusted results. The results were found to be similar and hence we kept the results from the rst analysis in which we combined Northern and Southern samples. Finally, we ran Wald tests using a multilvariate linear mixed model to correct for population stratication as implemented in the package GEMMA68. SNPs with P values differing by more than three log10 between the two methods were excluded from further validation.

Lambda 1,000 was calculated as a standardized estimate of the genomic ination regardless of the sample size of the study33,34, using the following formula:

l1,000

1 (1 lobs) (1/n cases 1/ncontrols)/(1/1,000cases 1/1,000 controls).

For the validation studies, we performed the trend test in a logistic regression model, analysing samples from Northern and Southern regions of China in validation 2 as separate sample collections to control for potential confounding by population stratication. To combine the association statistics from the GWAS and the three replication samples, we conducted a xed-effects inverse variance meta-analysis using PLINK (http://pngu.mgh.harvard.edu/~purcell/plink/

Web End =http://pngu.mgh.harvard.edu/Bpurcell/plink/ )30. Tests for independent association signals were carried out using conditional logistic regression analyses implemented in PLINK30. Haplotype analyses were conducted by phasing genotypes of interest using PHASE (http://stephenslab.uchicago.edu/software.html#phase

Web End =http://stephenslab.uchicago.edu/ http://stephenslab.uchicago.edu/software.html#phase

Web End =software.html#phase )35 and analysing phased haplotypes using logistic regression analyses on PLINK30. Regional association plots, centred on the top SNP, were generated using LocusZoom (http://csg.sph.umich.edu/locuszoom/

Web End =http://csg.sph.umich.edu/locuszoom/) 36 to represent results from the logistic regression and xed-effects meta-analysis at each stage of the study.

Furthermore, to evaluate the effects of age and gender on the association results, we entered age and gender as covariates into the logistic regression model and compared the results without adjustment within the same subset of samples with age and gender information available (6,323 cases and 17,349 controls; 84.6%). Each of the four stages were analysed separately and the results combined in a meta-analysis as before. Stratied analysis was performed by analysing males (3,067 cases and 9,993 controls) and females (3,324 cases and 7,371 controls) separately, among 6,391 cases and 17,364 controls with gender information available. Association results were also based on the combined meta-analysis across all four stages. Cochranes Q test was used to test for heterogeneity in effect sizes between males and females, and also between Northern and Southern samples.

Fraction of variance explained by loci. The percentage of the total variance explained was estimated by calculating Nagelkerkes pseudo R2 using the fmsb package (http://cran.r-project.org/web/packages/fmsb/index.html

Web End =http://cran.r-project.org/web/packages/fmsb/index.html), from the result of entering SNP genotypes and affection status into the glm function in R (v 2.15.1).

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Acknowledgements

This work was funded by Guangdong Department of Science & Technology Translational Medicine Center grant (2011A080300002), the Specialized Research Fund for the Doctoral Program of Higher Education of China (20130171130008), the Science and Technology Planning Project of Guangdong Province, China (2013B051000019), Guangzhou Committee of Science and Technology, China.(2012J5100031), Young Scientists Fund of National Natural Science Foundation of China (81200489), Yong Scholars Fund for the Doctoral Program of Higher Education of China (20120171120087), Natural Science Foundation of Guangdong Province, China (2014A030313136) and the Agency for Science & Technology and Research (A*STAR) of Singapore. We thank the staff in The First Afliated Hospital of Sun Yat-sen University for help with sample collection, DNA extraction and clinical data collection, as well as the human genetics genotyping team at the Genome Institute of Singapore for help with the sample genotyping.

Author contributions

J.J.L. and X.Q.Y. organized and designed the study. M.L., J.N.F., K.Y.T., I.D.I., C.C.K. and Y.F.G. conducted and supervised the genotyping of samples. J.J.L, J.N.F. and H.Q.L. contributed to the design and execution of statistical analyses. P.R.Y., R.C.X. and X.Q.T.

contributed to DNA extraction and clinical data collection. P.R.Y., R.C.X., X.Q.T., J.Q.W., A.K.A., J.X.B., O.R., M.H.C., C.Y.C., L.D.S., G.R.J., T.Y.W., H.L.L., T.A., Y.H.L., S.M.S., K.Y., R.P.E., Q.K.C., W.S., S.H.C., J.C., F.R.Z., S.P.L., G.X., E.S.T., L.W., N.C.,X.J.Z., Y.X.Z., H.Z. and Z.H.L. conducted the recruitment of the samples. J.N.F, M.L, J.J.L. and X.Q.Y. drafted the manuscript. All authors contributed to the writing of the manuscript.

Additional information

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Competing nancial interests: The authors declare no competing nancial interests.

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How to cite this article: Li, M. et al. Identication of new susceptibility loci for IgA nephropathy in Han Chinese. Nat. Commun. 6:7270 doi: 10.1038/ncomms8270 (2015).

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