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High prevalence of a monogenic cause in Han Chinese diagnosed with type 1 diabetes, partly driven by non-syndromic recessive WFS1 mutations.

Running title: Monogenic Diabetes in Chinese diagnosed as T1D

Author:

Meihang Lia, b,d*; Sihua Wangd*; Kuanfeng Xua*; Yang Chena, Qi Fua, Yong Gua, Yun Shia, Mei Zhanga, Min Suna, Heng Chena, Xiuqun Hand, Yangxi Lic.d, Zhoukai Tangd, Lejing Caid, Zhiqiang Lib, Yongyong Shib, Tao Yanga#; Constantin Polychronakosc,d,e#

* Meihang Li, Sihua Wang and Kuanfeng Xu have equal contribution to this paper

# Constantin Polychronakos and Tao Yang will handle correspondence at all stages of refereeing and publication, also post-publication.

a. Department of Endocrinology, the First Affiliated Hospital of Nanjing Medical University, 300 Guangzhou road, Nanjing, Jiangsu province, China.

b. The Biomedical Sciences Institute of Qingdao University (Qingdao Branch of SJTU Bio-X Institutes), Qingdao University, 308 Ningxia road, Qingdao, China;

c. Research Institute of McGill University Health Centre, 1001 Decarie Boulevard, Montreal, QC, Canada.

d. Zhejiang MaiDa Gene Tech Co. Ltd, 68 Xinchi road, Zhoushan, Zhejiang province, China.

e. Honorary Professor, Children’s Hospital of Zhejiang University School of Medicine, 3333 Binsheng Road, Hangzhou 310051, China

Meihang Li, Ph.D: [email protected] Tel: +86-15610081675

Sihua Wang, Master: [email protected] Tel: +86-0580-3695111

Kuanfeng Xu, Ph.D: [email protected] Tel: +86-025-68306530

Yang Chen, MD, Ph.D: [email protected] Tel: +86-025-68306530

Qi Fu, Ph.D: [email protected] Tel: +86-025-68306530

Yong Gu, Ph.D: [email protected] Tel: +86-025-68306530

Page 2 of 40Diabetes

Diabetes Publish Ahead of Print, published online November 11, 2019

mailto:[email protected]






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Yun Shi, Ph.D: [email protected] Tel: +86-025-68306530

Mei Zhang, Ph.D: [email protected] Tel: +86-025-68306530

Min Sun, Ph.D: [email protected] Tel: +86-025-68306530

Heng Chen, Master: [email protected] Tel: +86-025-68306530

Xiuqun Han, Master: [email protected] Tel: +86-0580-3695111

Yangxi Li, Ph.D: [email protected] Tel: +86-0580-3695111

Zhoukai Tang, Master: [email protected] Tel: +86-0580-3695111

Lejing Cai, Master: [email protected] Tel: +86-0580-3695111

Zhiqiang Li, Ph.D: [email protected] Tel: +86-0532-82991039

Yongyong Shi, Ph.D: [email protected] Tel: +86-0532-82991039

Tao Yang, MD, Ph.D: [email protected] Tel: +86-05803695111

Constantin Polychronakos, MD : [email protected] Tel: +1-514-4124400 ext:22866

Word count: Total number of words for main manuscript including abstract is 2152.Number of tables is 1.Number of figures is 1.

Page 3 of 40 Diabetes













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Abstract

It is estimated that ~1% of European-ancestry patients clinically diagnosed with type 1

diabetes (T1D) actually have monogenic forms of the disease. Because of the much

lower incidence of true T1D in East Asians, we hypothesised that the percentage would

be much higher.

To test this, we sequenced the exome of 82 Chinese Han patients clinically diagnosed

as T1D but negative for three autoantibodies. Analysis focused on established or

proposed mongenic diabetes genes.

We found credible mutations in 18 of the 82 autoantibody-negative patients (19.5%).

All mutations had consensus pathogenicity support by five algorithms. As in Europeans,

the most common gene was HNF1A (MODY3), in 6/18 cases. Surprisingly, almost as

frequent were diallelic mutations in WFS1, known to cause Wolfram syndrome but also

described in non-syndromic cases. Fasting C-peptide varied widely and was not

predictive.

Given the 27.4% autoantibody negativity in Chinese and 22% mutation rate, we

estimate that around 6% of Chinese with a clinical T1D diagnosis have monogenic

diabetes.

Our findings support universal sequencing of autoantibody-negative cases as standard

of care in East Asian patients with a clinical T1D diagnosis. Non-syndromic diabetes

with WSF1 mutations is not rare in Chinese. Its response to alternative treatments

should be investigated.


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Introduction：

Genetic risk for diabetes is, in most cases, a complex trait. However, monogenic forms

of diabetes do occur(1). These are often misdiagnosed as either type 1 (T1D) or type 2

(T2D) diabetes, an error of therapeutic consequences, as many of these cases can be

treated with sulfonylureas or GLP-1 agonists, obviating insulin injections and

improving metabolic control (2-4). Patients with pathogenic GCK variants can be

treated only with lifestyle advice.

Correct diagnosis in patients clinically diagnosed as T1D (T1Dclin) is challenging.

Because of the extremely high specificity of T1D autoantibodies, screening for

autoantibody negativity is a meaningful first step. This narrows the search to about 20%

of T1Dclin cases who, however, most still probably have autoimmune T1D (5). Family

history is currently the most important clue, based on autosomal dominant inheritance

(1). However, an “agnostic” testing of autoantibody negative cases in the Search for

Diabetes in Youth cohort found that 50% of cases with documented monogenic diabetes

have no family history (6). A similar testing in the Norwegian Childhood Diabetes

Registry shows convincing evidence of monogenic diabetes in about 4% of

autoantibody negative childhood-onset cases (T1Dclin in their vast majority), (7).

Preserved endogenous insulin secretion has been proposed as a screening criterion (8)

but it may not be reliable in T1Dclin cases. Given the substantial benefit to the

individual patient, and with recent methodological advances in molecular diagnostics,

a case can be made for universal testing of all autoantibody negative T1Dclin cases. To

test this, we undertook sequencing in Chinese autoantibody negative T1Dclin cases.


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T1D is much less common in East Asians (EA) (Chinese, Koreans and Japanese)

compared to Europeans (9). With a lower denominator, we hypothesised that the

positive yield would be higher.

Methods:

Participants

In the process of recruiting cases for the discovery stage of a T1D genome-wide

association study (GWAS), the first in EA(10), we tested for autoantibodies in most of

the 1,121 subjects (newly diagnosed, with BMI < 24kg/m2, and with at least one episode

of ketosis, 586 males and 535 females). Informed consent was obtained from the

patients or parents/guardians, in a protocol approved by the Ethics Committee of the

First Affiliated Hospital of Nanjing Medical University, in conformity with the

Declaration of Helsinki.

Selection of genes

We selected a broad list of monogenic diabetes genes from the literature (Ref. 8) , listed

in Table S1. To cover possible broad phenotypic heterogeneity, we included genes for

neonatal and syndromic diabetes, e.g. WFS1, whose mutations might cause diabetes

without other manifestations of Wolfram syndrome (11) and mitochondrial DNA 2967-

3367.

Pancreatic islet specific autoantibody testing

T1Dclin cases were tested for autoantibodies against GADA, IA-2A, and ZnT8A by


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radioimmunoassay (12). Cases negative for all three were excluded from the GWAS

and recruited for our project.

Whole-exome sequencing (WES).

Out of the 195 autoantibody negative subjects that available, 82 DNA samples that

passed the quality control of the sequencing company, were sequenced: (no significant

degradation and quantity >500ng). Capture was carried out with the Agilent SureSelect

Human All Exon V6 library followed by sequencing on the Illumina hiSeq at a 100x

depth, by the Shanghai Yuanshen company. Variants in known monogenic diabetes

genes, called by either SAMtools or GATK, were filtered to retain only protein-altering

variants (non-synonymous, frameshift, in-frame insertions/deletions, canonical

splicing), and exclude variants with a minor allele frequency (MAF) >0.0001 for

missense, or >0.001 for truncating, in any population in three public databases (1000

genomes, ExAc and Exome Variant Server). For recessive WFS1, the MAF threshold

was 0.005. All results reported here were confirmed by Sanger sequencing. Variants

were evaluated by five pathogenicity-prediction algorithms (Table 1) and classified by

the revised ACMG/AMP (American College of Medical Genetics and

Genomics/Association for Molecular Pathology) guidelines (13). For comparison,

exome data from 866 unselected Han Chinese subjects, similarly sequenced for reasons

other than diabetes, were also annotated.

To detect copy-number variants (CNV), we searched for extended genomic regions

with loss of heterozygosity (LOH) in exome variants. Coverage (read counts) of exons


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in LOH regions were then compared between the patient with LOH vs. all other subjects.

Identification of mitochondrial mutations

The 113 aAb-neg patients whose DNA sample was unsuitable for WES were checked

for mitochondrial polymorphism. Briefly, total DNA was used as template to amplify

a target sequence of mitochodridal DNA (m2966-m3346) containing known Chinese

mutations. PCR used were: Forward: TCAACAATAGGGTTTACGAC and Reverse:

AGGAATGCCATTGCGATTAG, followed by Sanger sequencing.

Statistical analysis

Given the known limitations of pathogenicity-prediction algorithms, we generated

additional support for the variants discovered, and estimates of the false discovery

rate, by comparing our findings with 866 non-diabetic Han Chinese.

First, we compared the prevalence of all variants meeting the filtering criteria between

our cases and the control exomes by the Fischer exact test for each gene separately.

In addition, we estimated the proportion of our positive findings that might be due to

background variant frequency in the population, by applying the expected proportion

(Pexp), of carriers among the 866 controls to our 82 cases and comparing with the

observed proportion (Pobs) (Table S2).

Our cases and the control exomes were sequenced by very similar workflows and at the

same depth. To confirm similar variant yield per individual, we compared yields for


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synonymous variants. To also confirm identity of genetic background, we compared

the two groups on the first two components of principal component analysis of

synonymous variants.

Data and Resource Availability

The datasets generated and/or analyzed during the current study are available from the

corresponding author upon request.

Results:

27.4% of Chinese T1Dclin were antibody negative

By radioimmunoassay, 53.7%, 40.7% and 33.8% of the patients were positive,

respectively, for GADA, ZnT8A, and IA-2A. Negativity for all three RIA was 27.4%

（Fig.S1）.

Monogenic diabetes in 22% of Chinese autoantibody-negative T1Dclin patients

We had genomic DNA suitable for WES from 82 of these autoantibody negative

patients. Among these 82 exomes, the yield of synonymous variants per patient per

gene was not significantly different from that of the 866 Chinese controls (mean + SEM

was 0.72+0.26 vs. 0.82+0.32, p=0.81). Principal component (PCA) analysis, based on

the exome variants, showed complete overlap of the two population samples (Fig S2).

Of our 82 cases, 18 cases (22.0%, CI95 = 14.6%-32.0%) had variants likely to represent

disease-causing mutations (Table 1), a percentage several-fold higher than that reported

with European ancestry cohorts defined by similar, though not identical criteria (7; 14)).

All were predicted to be pathogenic by three or more of the five algorithms. Variants


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in 6 patients met ACMG-AMP (13) criteria for very strong (PVS1) and another four for

strong (PS3 or PS4) evidence of pathogenicity. All 18 had, at the very least, moderate

pathogenicity evidence (PM2). It is important to note that these ratings rely on features

other than computational prediction of pathogenicity and, therefore, they constitute

completely independent evidence. Three additional patients, not counted here, narrowly

missed our pre-determined MAF cut-off (Table S3) but also likely have monogenic

diabetes.

The most common gene mutated was HNF1A (MODY3) with 6/18 patients. Two

mutations were null and one was absent from all databases. All were either truncating

or rated deleterious/disease causing by four or all five of the five algorithms (Table 1).

Some were previously reported with MODY3 (Table S4). Among the 866 controls,

there were only two patients with HNF1A variants meeting the same predetermined

criteria. Thus, Pobs, = 7.3% (CI95 3.9 – 14.4%) of autoantibody negative T1Dclin

Chinese patients have HNF1A mutations. Pexp, derived from the controls, was 0.2%

(CI95 0.08 - 0.4%) with p=6.5x10-6, false discovery q= 0.03.

Somewhat surprisingly, almost as common as MODY3 were diallelic variants of WFS1,

recessively mutated in Wolfram syndrome (WS), in 4 cases not reported to have any

other syndromic features and initially recruited for the T1D GWAS. One was

homozygous and, in another two cases, the two variants were close enough to be

confirmed in trans, by alignment inspection (Fig.S3). All of these variants were rated

deleterious by at least 4 of the 5 algorithms and two have been previously reported in

cases of fully expressed WS (Table S4). Only one of the 866 controls had two missense

mutations, both predicted benign by all 5 algorithms. For 4/82 vs. 0/866, Pobs = 4.9%

(CI95 2.4% - 11.1%) vs. Pexp= 0% (CI95 0 – 0.6%) p = 6.5x10-6, false discovery q=0.

Thus, by our best estimate, 5% of autoantibody negative Chinese T1Dclin cases have


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non-syndromic diabetes due to WFS1 mutations.

One patient was thoroughly examined and found to be normal by fundoscopic eye exam,

audiogram and urine density (Fig.S4), confirming the reported absence of other WS

manifestations (optic nerve atrophy, hearing loss and diabetes insipidus) in that case.

The remaining six patients were mutated in other MODY genes (Table 1) but the

numbers were too small for statistics (Table S2). Two patients had mutations in KLF11

(MODY7) and PAX4 (MODY9), OMIM genes whose role in monogenic diabetes

remains unconfirmed. Only one KLF11 mutation met pathogenicity prediction criteria,

vs. two controls (p=0.25), not providing support for this gene. NEUROD1 is better

established as the cause of MODY6(15) and supported by our finding of a mutation in

one patient (deleterious by all 5 algorithms) vs. 0/866 in the controls. Other well-

established genes found mutated were HNF1B, ABCC8, GCK (each in two patients )

and INS (one patient).

Both HNF1B mutations were complete gene deletions within the known recurrent

microdeletion at 17q22, reported to account for as many as 50% of MODY5 cases (16).

The two patients had LOH over at least 1.4 Mb that encompassed HNF1B among 28

genes (Fig. 1, top). Over the LOH region in each patient, each of 178 exons had

approximately half the read counts of the average of all other patients (Fig.1 bottom),

p=7.5x10-15 and 1x10-16 for patient 17 and 18 respectively. Adjacent to the deletion,

there were no shared haplotypes, indicating independent occurrence in two different

ancestral chromosomes.

Fasting C-peptide, available in 56/82 patients, varied widely and was no different

between patients with or without a mutation (mean 249.5 vs. 280.5, p=0.6699). (Fig.

S5A).


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For the 82 patients (37 females), the median age at diagnosis was 20, (range 1 to 61

years). Patients with mutations were younger, with median age at diagnosis 13.5 vs.

23.2 (p=0.0297) (Fig. S5B).

In a parallel study, 126 T1Dclin, autoantibody negative patients, including 44 whose

DNA sample was unsuitable for WES, were tested for mitochondrial DNA mutations

by Sanger sequencing. Four had the m.3243A>G mutation and two the m.3316A>G,

both previously reported in Chinese diabetes patients (17; 18). Heteroplasmy, estimated

from the sequencing peaks, ranged from 6.2% to 50.4% (Table S5), well within the

described range (19).

The distribution of genetic causes of 18 confirmed cases of monogenic diabetes and 6

cases of mitochondrial diabetes are shown in Supplementary Figure 6.

Discussion

With 27.4% of Chinese T1Dclin being autoantibody negative, and 22.0% of them

having monogenic diabetes, our best estimate for the overall prevalence of

monogenic diabetes in patients diagnosed as T1D is 6%, considerably higher than the

approximately 1% reported in comparable childhood cohorts of European decent (6; 7).

In the UK, 3.6% was reported among patients chosen for preserved C-peptide (8), a

very different population from our T1Dclin cases. Our results show that C-peptide

cannot distinguish monogenic cases presenting with a clinical picture leading to the

diagnosis of T1D.

This percentage is even higher if we add the mitochondrial cases. Such high incidence

is not surprising, given that the denominator (autoimmune T1D) is much less common


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in EA. These findings make a strong argument for universal autoantibody testing of all

Chinese (and, arguably, Korean and Japanese) T1Dclin patients with sequencing of the

autoantibody negative cases. Studies similar to ours in European-ancestry patients may

also reveal monogenic diabetes rates substantially higher than currently reported, if

diallelic WFS1 mutants are included. Despite the low yield and high cost, the benefit

to individual patients may well justify the expense, as most of these monogenic diabetes

cases are amenable to alternative treatments.

Our other important finding is the high prevalence of non-syndromic WFS1 cases,

much more common than Wolfram syndrome and comparable to that reported in

consanguineous families in Lebanon (11), in a non-consangineous population. Non-

syndromic diabetes has also been reported with homozygosity for a non-synonymous

variant frequently found in Ashkenazi Jews (20) but it is clearly not confined to that

variant. The therapeutic implications of this diagnostic reassignment remain to be

seen, but successful use of incretin interventions has been reported (21; 22). It should

definitely be included in all monogenic diabetes panels.

We expect that these preliminary findings will stimulate much larger studies in various

populations, to better define the prevalence of monogenic diabetes in agnostic searches.

Despite the limitations of our study, including small sample size (counterbalanced by

the large effect size and highly significant results) and lack of detailed phenotype data

(a convenience sample collected for a different purpose), we propose that our findings

will apply to the vast majority of T1Dclin cases and justify serious consideration of

agnostic screening of all cases diagnosed as T1D in East Asians.


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Acknowledgements:

Author contributions: Constantin Polychronakos and Tao Yang developed the study

concept and supervised the study. Meihang Li helped with the study design and

interpretation of results and wrote the manuscript. Sihua Wang performed the

bioinformatic analysis of the exome sequencing data. Yangxi Li participated in the

writing and corrections of the manuscript. Kuanfeng Xu summarized clinical

information of T1Dclin patients from 11 hospitals and helped correcting the

manuscript. Yang Chen, Qi Fu, Mei Zhang, Min Sun, Yong Gu and Yun Shi are

clinicians that collected the T1Dclin patients. Heng Chen tested the pancreatic auto-

antibodies. Xiuqun Han, Zhoukai Tang and Lejing Cai carried out the Sanger of all

WES variants. Xiuqun Han also designed the PCR primers for each variant. Zhiqiang

Li and Yongyong Shi collected the control sequencing data from 866 WES for the

selected genes.

The authors wish to thank all patients who consented to participate in the study. For

valuable technical assistance, we thank Min Shen, Yingjie Feng from First Affiliated

Hospital of Nanjing Medical University. The study was supported by Grants from the

National Natural Science Foundation of China (81830023, 81270897 and 81670715)

and the Key Research and Development Program of Science and Technology

Commission Foundation of Jiangsu Province (SBE2017750381); MaiDa Gene

Technology is indebted to the 5313 Leading Talents Project of Zhoushan city, Zhejiang

province for generous funding of this project.


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Conflict of Interest Statement: The authors declared that they have no conflicts of

interest to this work.

Statement of guarantor: Constantin Polychronakos and Tao Yang assume responsibility

for the accuracy of all statements made in this paper and for the integrity of the raw

data and their processing.

Statement: The VCF files generated during analyzed the current study is not publicly

available due to Chinese policy that public sharing of genomic data is not allowed.

Disclosure: Zhejiang MaiDa Gene Tech Co. Ltd. is a publicly funded for-profit

corporation that will be offering genetic testing services.

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Table 1. Genetic variants found in Monogenic Diabetes genes

Patient ID Gene Transcript ID CDS change

AA Change

Hom /Het

Age of diagnosis

(years)Family history

HbA1c % (mmol/mol)

FCP (pmol/L) snp138 maxMAF ExAC GnomAD

Pathogenicity

1 HNF1A NM_000545.5 c.1192C>G p.Q398E Het 20 N NA NA 0 . . D, D, N, D, D

2 HNF1A NM_000545.5 c.865delC p.P289fs Het 12 Y 9.1 (76) NA 0.0007 0.0002 0.00006272NA, NA, NA, NA,

NA

3 HNF1A NM_000545.5 c.686G>A p.R229Q Het 16 N 9.8 (84) 734.8 0 . . D, D, D, D, D

4 HNF1A NM_000545.5 c.1512C>A p.S504R Het 4 N 12.3 (111) 49.56 0.0001 . 0.000008134 D, P, D, D, D

5 HNF1A NM_000545.5 c.956-1G>C .(splicing) Het 12 N 7.4 (57) 6.8 0 . .NA, NA, NA, D,

NA

6 HNF1A NM_000545.5 c. 347C>T p.A116V Het18

N NA 426.24 D, D, D, D, D

c.1096_1097 insAGGACAGCAAG p.Q366fs 0 . .

NA, NA, NA, NA, NA

7 WFS1

NM_006005.3

c.1376T>G p.L459R Het 9 N 15.6 (147)

170.16

0 . . D, D, D, D, D

c.472G>A p.E158K 0.0002 0.00001659 0.0000204 T, D, D, D, D

8 WFS1 NM_006005.3c.985T>A p.F329I

Het 18 NNA

111.7 rs188848517 0.002 0.0001 0.0002 D, D, D, D, D

9 WFS1 NM_006005.3 c.1892C>T p.S631F Hom 5 N NA 69.93 0 . . D, P, D, D, D


18

c.472G>A p.E158K 0.0002 0.00001659 0.0000204 T, D, D, D, D

10 WFS1 NM_006005.3c.985T>A p.F329I

Het 22 NNA NA

rs188848517 0.002 0.0001 0.0002 D, D, D, D, D

11 ABCC8 NM_000352.3 c.1834G>A p.E612K Het 29 Y NA 170.9 0.00003006 0.00001652 0.000008143 T, B, D, D, D

c.1811T>C p.L604P 0 . . T, D, D, D, D

12 ABCC8 NM_000352.3c.793C>T p.R265W

Het 14 N14.2 (132)

NA0 . . D, D, N, D, D

13 INS NM_000207.2 c.94G>A p.G32S Het 7 Y 16 (151) 166.5rs803566

640 . . D, D, D, D, D

14 GCK NM_000162.3 c.665T>A p.V222D Het 3 Y 6.1 (43) 314 0 . . D, D, D, D, D

15GCK NM_000162.3 c.661G>A p.E221K Het 36 N NA NA

rs193922317 0 . . T, D, D, D, D

16NEUROD1 NM_002500.4 c.316G>A p.A106T Het 13 Y NA 399.6 0 . . D, D, D, D, D

17 HNF1B NM_000458.2 WGD WGD Het 25 Y 8.4 (68) NA

NA, NA, NA, NA, NA

18 HNF1B NM_000458.2 WGD WGD Het 14 N 13.7 (126) 480

NA, NA, NA, NA, NA

FCP: Fasting C-peptide, pmol/L. In the Pathogenicity column, the results of evaluation algorithms are indicated in single-letter codes in this order: for SIFT (D: deleterious, T:tolerated); for Polyphen2 (D:Probably damaging, P:Possibly damaging, B:benign); for LRT ( D:Deleterious,N:Neutral); for MutationTaster (D:Disease causing, N:Polymorphism); for LR (D:Deleterious, T:Tolerated). NA: Not available. WGD: whole gene deletion Reference list for previous reports of some mutations is provided in the supplementary Table S4.


19

Figure Legend

Figure 1. Demonstration of the 17q22 microdeletion that encompasses HNF1B in patient 18.

Top: LOH. A plot of the proportion of reads for one of the two alleles calculated as B/(A+B), where A and B are the read counts for each allele (by the Illumina convention, the non-Reference nucleotide is called A if it is A or T, otherwise B). Homozygotes cluster around 0 or 1, heterozygotes around 0.5. Complete LOH can be seen over 1.4 Mb. Only positions with at least one non-Reference allele are shown (all others are homozygous Reference).

Bottom: Copy number over the LOH region in each patient was estimated by comparing read counts at each exon (normalized as counts per million) to the average of all other patients. Divided by that average, intact DNA clusters around 1, heterozygous deletion around 0.5. To harmonize with the conventional display from microarray data, the ratio is plotted as base 2 log (intact DNA is 0, heterozygous deletion is -1). Only exons with 50 or more mapped reads are included. A heterozygous deletion is clearly demonstrated over the LOH, p= 10-16 by paired t-test comparing each exon to the average of all other patients.


Figure 1. Demonstration of the 17q22 microdeletion that encompasses HNF1B in patient 18. Top: LOH. A plot of the proportion of reads for one of the two alleles calculated as B/(A+B), where A and B are the read counts for each allele (by the Illumina convention, the non-Reference nucleotide is called A if it is A or T, otherwise B). Homozygotes cluster around 0 or 1, heterozygotes around 0.5. Complete LOH can be

seen over 1.4 Mb. Only positions with at least one non-Reference allele are shown (all others are homozygous Reference).

Bottom: Copy number over the LOH region in each patient was estimated by comparing read counts at each exon (normalized as counts per million) to the average of all other patients. Divided by that average, intact DNA clusters around 1, heterozygous deletion around 0.5. To harmonize with the conventional display from microarray data, the ratio is plotted as base 2 log (intact DNA is 0, heterozygous deletion is -1). Only exons

with 50 or more mapped reads are included. A heterozygous deletion is clearly demonstrated over the LOH,.p= 10-16 by paired t-test comparing each exon to the average of all other patients.

119x91mm (240 x 240 DPI)


1

High prevalence of a monogenic cause in Han Chinese diagnosed

with type 1 diabetes, partly driven by non-syndromic recessive WFS1

mutations.

ON-LINE SUPPLEMENTARY MATERIAL.

Supplementary table: p. 2-9

Supplementary figure: p. 10-15

Supplementary reference: p. 16-20


2

Table S1. Genes analyzed after WES.

Gene Genbank Reference Sequence

Phenotype OMIM Inheritance

Permanent neonatal diabetes 606176 Dominant (often de novo) or recessive

Transient neonatal diabetes 610374 Dominant (often de novo) or recessive

ABCC8 NM_000352.3

MODY 610374 Dominant

BSCL2 NM_001122955.3 Congenital generalised lipodystrophy, severe insulin resistance and diabetes

269700 Recessive

CEL NM_001807.4 MODY 609812 Dominant

CISD2 NM_001008388.4 Wolfram Syndrome 2 (diabetes mellitus, hearing loss, optic atrophy and defective platelet aggregation).

604928 Recessive

EIF2AK3 NM_004836.6 Wolcott-Rallison syndrome 226980 Recessive

FOXP3 NM_014009.3 Immunodysregulation, polyendocrinopathy, and enteropathy, X-linked syndrome (IPEX)

304790 X-Linked Recessive

GATA4 NM_002052.4 Permanent neonatal diabetes with pancreatic agenesis and congenital heart defects

Not assigned Dominant (often de novo)

GATA6 NM_005257.4 Permanent neonatal diabetes with pancreatic agenesis and congenital heart defects

600001 Dominant (often de novo)

Permanent neonatal diabetes 606176 RecessiveGCK NM_000162.3


GLIS3 NM_001042413.1 Permanent neonatal diabetes with congenital hypothyroidism

610199 Recessive

HNF1A NM_000545.5 MODY 600496 Dominant


http://omim.org/entry/606176



http://www.omim.org/entry/269700










3

HNF1B NM_000458.2 Renal Cysts and Diabetes syndrome (RCAD) 137920 Dominant (often de novo)

HNF4A NM_001287182 MODY 125850 Dominant

IER3IP1 NM_016097.4 microcephaly, epilepsy, and diabetes syndrome (MEDS) 614231 Recessive

IL2RA NM_000417.2 Immunodysregulation, polyendocrinopathy, and enteropathy, X-linked syndrome (IPEX)

606367 Recessive

Permanent neonatal diabetes 606176 Dominant (often de novo) or recessive

Transient neonatal diabetes Not assigned Dominant (often de novo) or recessive

INS NM_000207.2


INSR NM_000208.3 Severe insulin resistance 610549 Dominant

Permanent neonatal diabetes 606176 Dominant (often de novo)

Transient neonatal diabetes 610582 Dominant (often de novo)

KCNJ11 NM_000525.3

MODY Not assigned Dominant

LMNA NM_170707.3 Familial Partial Lipodystropy (FPLD2) 151660 Dominant

MNX1 NM_005515.3 Neonatal diabetes & IUGR Not assigned Recessive

Permanent neonatal diabetes and neurological abnormalities

Not assigned RecessiveNEUROD1 NM_002500.4


NEUROG3 NM_020999.3 Permanent neonatal diabetes with congenital malabsorptive diarrhoea

610370 Recessive

NKX2-2 NM_002509.3 Neonatal diabetes and developmental delay Not assigned Recessive

PAX6 NM_001258462.1 Aniridia and impaired glucose tolerance 106210 Dominant

Permanent neonatal diabetes +/- pancreatic agenesis 260370 RecessivePDX1 NM_000209.3











http://omim.org/entry/610582?search=610582&highlight=610582







4

PPARG NM_015869.4 Familial Partial Lipodystropy (FPLD3) 604367 Dominant

PTF1A NM_178161.2 Permanent neonatal diabetes with cerebellar and pancreatic agenesis

609069 Recessive

RFX6 NM_173560.3 Permanent neonatal diabetes with pancreatic hypoplasia, intestinal atresia, and gallbladder aplasia or hypoplasia

601346 Recessive

SLC2A2 NM_000340.1 Fanconi-Bickel syndrome 227810 Recessive

SLC19A2 NM_006996.2 Thiamine responsive megaloblastic anaemia, diabetes and deafness (TRMA) syndrome

249270 Recessive

TRMT10A NM_152292.4 Diabetes, microcephaly and short stature 616033 Recessive

WFS1 NM_006005.3 Wolfram syndrome (Diabetes insipidus, diabetes mellitus, optic atrophy and deafness, DIDMOAD)

222300 Recessive

ZFP57 NM_001109809.2 Transient neonatal diabetes 601410 Recessive

KLF11 NM_003597.4 Maturity-onset diabetes of the young, type VII 603301 Dominant

Diabetes mellitus, type 2 125853 Dominant

MODY 612225 Not assignedPAX4 NM_006193.2

Diabetes mellitus, ketosis-prone 612227 Dominant, Recessive

BLK NM_001715.2 MODY 613375 Dominant

APPL1 NM_012096.2 MODY 616511 Dominant

Autoimmune disease, multisystem, infantile-onset 615952 DominantSTAT3 NM_139276.2

Hyper-IgE recurrent infection syndrome 147060 Dominant

Mandibular hypoplasia, deafness, progeroid features, and lipodystrophy syndrome

615381 DominantPOLD1 NM_001256849.1

Colorectal cancer 612591 Dominant







http://www.omim.org/entry/616033#2



https://www.omim.org/entry/125853









5

Table S2. Comparison of the frequencies of pathogenic variants (deleterious by at least 3 of the 5 algorithms) between the 82 cases (observed fraction) and the 866 controls (expected fraction).

# pathogenic variants

Gene Cases N=82 Controls N=866 p Pobs (CI95) Pexp (CI95)

WFS1 4 0 4.38E-05 0.049 (0.024 - 0.111) 0.00 (0-0.006)HNF1A 6 2 6.49E-06 0.073 (0.039-0.144) 0.002 (0.0008-0.004)KLF11 1 2 0.255ABCC8 3 2 0.006

GCK 2 0 0.007INS 1 0 0.085

NEUROD1 1 0 0.085


6

Table S3. Missense mutations with MAF<0.0005 likely also pathogenic.

Patient

IDGene Transcript ID CDS change AA Change Hom/Het

Age of

onset

Family

historyHbA1c snp138 maxMAF ExAC GnomAD Pathogenicity Ref

14 HNF4A NM_001287182 c.5C>T p.S2L Het 7 Y NA NA 0.0005 0.0004 0.00005677 NA, NA, NA, NA, NA NA

8 HNF1A NM_000545.5 c.1854C>G p.I618M Het 5 N NA rs193922591 0.0002 . 0.00001626 T, P, N, D, D NA

19 ABCC8 NM_000352.3 c.4135C>T p.R1379C Het 18 N NA rs137852673 0.0004 0.00002663 0.000005143 D, D, D, A, D (1)

In the Pathogenicity column, the results of evaluation algorithms are indicated in single-letter codes in this order: for SIFT (D: deleterious, T:tolerated); for Polyphen2

(D:Probably damaging, P:Possibly damaging, B:benign); for LRT ( D:Deleterious,N:Neutral); for MutationTaster (D:Disease causing, N:Polymorphism, A:

disease_causing_automatic); for LR (D:Deleterious, T:Tolerated). NA: Not available.


7

Table S4. Genetic variants found in Monogenic Diabetes genes (with reference)

Patient ID

Gene Transcript ID CDS changeAA

ChangeHom /Het

RefPatient

IDGene Transcript ID CDS change AA Change

Hom /Het

Ref

1 HNF1A NM_000545.5 c.1192C>G p.Q398E Het NA

2 HNF1A NM_000545.5 c.865delC p.P289fs Het (1-6)c.472G>A p.E158K (21; 22)

3 HNF1A NM_000545.5 c.686G>A p.R229Q Het (7-14)

10 WFS1 NM_006005.3

c.985T>A p.F329I

Het

NA

4 HNF1A NM_000545.5 c.1512C>A p.S504R Het NA 11 ABCC8 NM_000352.3 c.1834G>A p.E612K Het NA

5 HNF1A NM_000545.5 c.956-1G>C splicing Het NA c.1811T>C p.L604P NA

6 HNF1A NM_000545.5 c. 347C>T p.A116V Het NA12 ABCC8 NM_000352.3

c.793C>T p.R265WHet

NA

c.1096_1097insAGGACAGCAAG

p.Q366fs NA 13 INS NM_000207.2 c.94G>A p.G32S Het (1; 15-20)7 WFS1 NM_006005.3

c.1376T>G p.L459R

Het

NA 14 GCK NM_000162.3 c.665T>A p.V222D Het (23; 24)

c.472G>A p.E158K (21; 22) 15 GCK NM_000162.3 c.661G>A p.E221K Het (25-29)8 WFS1 NM_006005.3

c.985T>A p.F329IHet

NA 16NEURO

D1NM_002500.4 c.316G>A p.A106T Het NA

17 HNF1B NM_000458.2 WGD WGD Het (30)9 WFS1 NM_006005.3 c.1892C>T p.S631F Hom NA

18 HNF1B NM_000458.2 WGD WGD Het (30)

NA: Not available. WGD: whole gene deletion


8

Table S5. Genetic variants found in Mitochondrial DNA

Patient ID

Mutation Sanger Sequencing Gender Age of onset BMI HbA1c (%) FCS (pmol/L)

20 m. A3243G

Female 41 19.56 NA NA

21 m. G3316A

Male 46 NA NA 283.20

22 m. A3243G



9

23 m. A3243G


24 m. G3316A

Male 61 16.94 11.4 411.20

25 m. A3243G

Female 24 NA 6.5 795.00


10

Figure S1. Positive rate of auto-antibodies from Total T1Dclin been tested.


11

Figure S2. PCA analysis：In order to show that our 82 cases and 866 Chinese controls are from the same genetic background,

we use the synonymous SNPs of the two exomes to do principal component analysis.As expected,the two population samples show practically complete overlap.


12

Figure S3. Read alignment showing that the two WFS1 mutations (p.Q366fs-p.L459R) are on different read pairs, and therefore in trans.


13

Figure S4. The audiogram (A) and eye fundus photography (B) of one of the patients with diallelic WFS1 mutations.


14

Figure S5. Fasting C-peptide(p=0.6699) (A) and age of diagnosis(p=0.0297) (B) in monogenic forms of diabetes (MFD) vs. the mutation-netative group (non-MFD).


15

Figure S6. Distribution of genetic causes of 18 confirmed cases of monogenic diabetes and 6 cases of mitochondrial diabetes. Data presented as number of cases caused by mutations in each gene.


16

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https://www.ncbi.nlm.nih.gov/pubmed/27321323

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