CDKN2A/B T2D GWAS risk-SNPs impact locus gene expression ... · ABSTRACT Genome-wide association studies link the CDKN2A/B locus with T2D risk, but mechanisms increasing risk remain

CDKN2A/B T2D GWAS risk-SNPs impact locus gene expression and proliferation in

human islets

Yahui Kong1, Rohit B. Sharma

1, Socheata Ly

1, Rachel E. Stamateris

1, William M. Jesdale

2

and Laura C. Alonso1

Diabetes Center of Excellence in the Department of Medicine1, and the Department of

Quantitative Health Sciences2, University of Massachusetts Medical School, Worcester MA

Running title

CDKN2A/B T2D SNPs impact human islet biology

Corresponding author

Laura C. Alonso

774-455-3640 (phone)

508-856-3803 (fax)

AS7-2047, Division of Diabetes

368 Plantation Street, Worcester, MA 01605

[email protected]

Keywords

Aging, ANRIL, beta cell mass, Cdkn2A, Cdkn2B, CDKN2B-AS, insulin secretion, oncogene,

p14, p15, p16, p14ARF

, p15INK4B

, p16INK4A

, pancreatic beta cell, proliferation

Abbreviations

ACTB, beta-actin gene

ANRIL, antisense noncoding RNA in the INK4 locus

ARF, alternate reading frame

CCND2, cyclin D2

CDK, cyclin dependent kinase

CDKN2A, cyclin dependent kinase inhibitor 2, encodes p14ARF

and p16INK4A

CDKN2B, cyclin dependent kinase inhibitor 2, encodes p15INK4B

CDKN2B-AS, cyclin dependent kinase inhibitor 2B antisense

eQTL, expression quantitative trait loci

GAPDH, Glyceraldehyde-3-Phosphate Dehydrogenase

GWAS, genome wide association studies

lncRNA, long non-coding RNA

MTAP, 5-methylthioadenosine phosphorylase

PCNA, proliferating cell nuclear antigen

SNP, single nucleotide polymorphism

Page 1 of 51 Diabetes

Diabetes Publish Ahead of Print, published online February 6, 2018

ABSTRACT

Genome-wide association studies link the CDKN2A/B locus with T2D risk, but mechanisms

increasing risk remain unknown. The CDKN2A/B locus encodes cell cycle inhibitors p14,

p15, and p16, MTAP, and ANRIL, a lncRNA. The goal of this study was to determine

whether CDKN2A/B T2D risk-SNPs impact locus gene expression, insulin secretion, or beta

cell proliferation, in human islets. Islets from non-diabetic donors (n=95) were tested for

SNP genotype (rs10811661, rs2383208, rs564398, rs10757283), gene expression (p14, p15,

p16, MTAP, ANRIL, PCNA, KI67, CCND2), insulin secretion (n=61) and beta cell

proliferation (n=47). Intriguingly, locus genes were co-regulated in islets in two physically

overlapping cassettes: p14-p16-ANRIL, which increased with age, and MTAP-p15, which did

not. Risk-alleles at rs10811661 and rs2383208 were differentially associated with expression

of ANRIL, but not p14, p15, p16 or MTAP, in age-dependent fashion, such that younger

homozygous-risk donors had higher ANRIL expression, equivalent to older donor levels. We

identified several risk-SNP haplotype combinations that may impact locus gene expression,

suggesting possible mechanisms by which SNPs impact locus biology. Risk-allele carriers at

ANRIL coding SNP rs564398 had reduced beta cell proliferation index. In conclusion,

CDKN2A/B locus SNPs may impact T2D risk by modulating islet gene expression and beta

cell proliferation.

Page 2 of 51Diabetes

T2D risk has a strong genetic component. Significant research investment has identified

>100 genomic regions that influence T2D risk in human populations (1–3). Most T2D risk-

SNPs are non-coding, and the mechanism by which they impact local genome biology

remains unclear for most loci (3). Risk alleles may act in multiple ways, interacting with

other genes and polymorphisms in tissue-specific manner. Genome-wide eQTL studies seek

to identify how polymorphisms impact biology at any given locus (1,4–7); however, depth of

information at individual loci is limited in genome-wide studies. Most T2D SNPs influence

risk by impacting islet biology (8), but the cost and inaccessibility of human islets, and poor

utility of non-human models to study the human genome, have slowed progress in clarifying

mechanisms.

SNPs at the CDKN2A/B genomic locus impact risk of T2D, and related diseases such as

gestational diabetes, cystic fibrosis related diabetes and post-transplant diabetes, across

ethnicities and cultures, suggesting a central diabetogenic mechanism (9). Multiple SNPs in

different linkage blocks at the CDKN2A/B locus confer T2D risk (9); mechanisms impacting

risk remain unknown. The CDKN2A/B locus encodes four genes (Figure 1): MTAP,

CDKN2A, CDKN2B and a long-noncoding RNA named ANRIL. CDKN2A and CDKN2B are

well studied, encoding cell cycle inhibitors (p14 and p16 are splice variants of CDKN2A, p15

is encoded at CDKN2B) that impact aging, senescence, and tumorigenesis via regulation of

Rb (retinoblastoma) and p53 (10,11). Three T2D SNPs at this locus, rs10811661, rs2383208

and rs10757283, are non-coding, located downstream of known genes; rs2383208 and

rs10811661 are in one linkage block and rs10757283 is in a separate linkage block

immediately downstream. A fourth SNP, rs564398, about 100,000 bp upstream of these,

falls within exon-2 of ANRIL. These SNPs were identified in large population studies seeking

to identify genomic regions associated with T2D risk (12–14); for more details please see

(9). The three downstream SNPs are mostly associated with T2D risk and not other diseases;

the rs564398 SNP is also associated with coronary heart disease and glaucoma (15). The

absolute magnitude of T2D risk is low with all identified SNPs (at this and other loci); for

example, reported odds ratio for the linkage region containing rs10811661 and rs2383208

ranges from 1.18-1.46 (4,12,14,16,17). Weaker odds ratios were seen for rs564398 1.12-1.26

e.g. (13); intriguingly, multiple studies show that rs564398 is associated with T2D risk in


Caucasian but not Asian populations (18). A risk-risk haplotype of rs10811661/rs2383208

and rs10757283 conferred an odds ratio of 1.24, with stronger association than individual

risk alleles (13). Although each T2D SNP is in linkage disequilibrium with multiple other

SNPs, fine mapping has not identified linked SNPs with greater disease association than

these GWAS-identified SNPs (1,4). The causal SNP in any of these linkage blocks is not yet

known.

In human populations, rs10811661 risk allele is associated with reduced insulin secretory

capacity after oral or IV glucose challenge (16,19–22). Insulin secretory capacity is a

composite endpoint influenced by beta cell mass, insulin production, glucose sensing and

stimulus-secretion coupling (23), factors that cannot currently be effectively separated in

living human subjects. Intriguingly given the aging and senescence roles played by

CDKN2A/B genes, the impact of rs10811661 on T2D risk was influenced by subject age

(18). SNPs at this locus also influence insulin sensitivity and biology of other metabolic

tissues, demonstrating the complexity of even a single genomic locus on T2D biology (9).

Since human studies suggest that CDKN2A/B-locus SNPs impact T2D risk, at least in part,

by reducing insulin secretory capacity, we hypothesized that locus SNPs influence pancreatic

islet biology. Here we present a detailed analysis of CDKN2A/B biology in non-diabetic

human islets. We identified two overlapping co-regulated gene sets: p14-p16-ANRIL, and

p15-MTAP. p14-p16-ANRIL expression, but not p15-MTAP expression, increased with donor

age. Of the four T2D risk-SNPs tested, rs2383208 and rs10811661 risk alleles were

associated with inappropriate high expression of the ANRIL lncRNA in samples from

younger donors. No other SNP-gene interaction was identified, but our data suggest certain

SNP haplotypes that may impact locus gene expression in combinatorial fashion. Finally,

risk-alleles at rs564398 were associated with reduced beta cell proliferation index,

suggesting a functional implication for this SNP, and perhaps the ANRIL lncRNA, in accrual

or maintenance of human beta cell mass.

RESEARCH DESIGN AND METHODS

Human islets


Human islets were obtained from the NIH-NIDDK-supported Integrated Islet Distribution

Program at the City of Hope, or from a collaborative group headed at Vanderbilt (24).

Human islet studies were determined by the University of Massachusetts Institutional

Review Board to not qualify for IRB review or exemption because they do not involve the

use of human subjects. De-identified islet samples from 95 non-diabetic subjects were live-

shipped in Prodo islet transport media transport. Donors (Supplemental Table 1) included 42

females, 48 males and 5 without sex reported, age 40 +/- 16 years, ethnicity 1 Asian, 8 Black

or African American, 14 Hispanic/Latino, 66 White, and 6 unknown. Upon receipt, islets

were plated in islet culture medium (RPMI, 10% FBS, 5 mmol/L glucose,

penicillin/streptomycin) and incubated at 37 degrees, 5% CO2 overnight to recover from

isolation and shipment. Following recovery, 800 IEQ were hand-picked, washed in PBS

containing 100 nmol/L Na3VO4, and flash frozen at -80°C in 200 islet equivalent (IEQ)

aliquots for future DNA and RNA analysis. Additional islets from a subset of donors were

cultured as described below for glucose-stimulated proliferation.

Genotyping

DNA and RNA were extracted from flash-frozen 200 IEQ aliquots using the Norgen

RNA/DNA/Protein purification kit (Norgen Biotek Corp., Ontario, Canada) following the

manufacturer's protocol. Genotyping for four CDKN2A/B SNPs: rs564398 (C/T),

rs10811661 (C/T), rs2383208 (G/A) and rs10757283 (C/T) was performed in duplicate using

commercial (C_2618017_10, C_31288917_10, C_15789011_10, C_31288916_10)

TaqMan® SNP genotyping assays (Thermo Fisher Scientific, Waltham, MA, USA) on

Biorad (C1000 Touch Thermal cycler) or Eppendorf (Realplex cycler) real-time PCR

platforms, using 20ng of DNA in a 10µl reaction volume under conditions recommended by

the manufacturer. SNP determination was confirmed by both allelic discrimination and by

manual CT value assessment for all samples and all SNPs. Minor allele frequencies (MAF)

in our cohort were in agreement with expected MAF based on the 1000 genomes project (25)

(Supplemental Table 2), and the observed haplotype frequency of SNP combinations

predicted similar linkage disequilibrium to 1000 genomes-reported values for these SNPs

(Supplemental Table 3)(26).


Gene expression assays

Total RNA was reverse transcribed using SuperScript IV VILO Master Mix kit (Thermo

Fisher Scientific, Waltham, MA, USA). The expression levels of target genes in human islets

were quantitatively assessed in duplicate using Taqman® validated human gene expression

assays (Thermo Fisher Scientific). Primers/probes used were: ANRIL, Hs04259476_m1; p15,

Hs00793225_m1; p14, Hs99999189_m1; p16, Hs02902543_mH; MTAP, Hs00559618_m1;

KI67, Hs01032443_m1; PCNA, Hs00696862_m1; CCND2, Hs00153380_m1; ACTB,

Hs01060665_g1; GAPDH, Hs02758991_g1. ACTB and GAPDH were used as endogenous

reference to normalize gene expression. Reproducibility of duplicate measurements was

high, as assessed by the R2 of the correlation between duplicates and by the absolute value of

the Relative Percentage Difference between the duplicates (Supplemental Figure 1).

Transcript expression levels were presented as log2-transformed expression (∆CT).

Human islet culture experiments

Human islets cultured overnight in islet culture medium were dispersed to single cells using

single-use–apportioned 0.05% trypsin and plated on uncoated glass coverslips (Fisherbrand)

as described (27–29). Dispersed cells were cultured in islet culture medium containing either

5mM or 15mM glucose for 96 hours, with 20 µg/ml bromodeoxyuridine (BrdU) included for

the entire time. After culture, the islet cells were fixed for 10 min in 4% paraformaldehyde

(Sigma). Immunofluorescence staining was performed after unmasking in 1N HCl for 25

minutes at 37°C for insulin, (Abcam, ab7842 or Dako, A056401-2), BrdU (Abcam, ab6326)

and DAPI as described (27–29). β-Cell proliferation, defined as the percent of insulin-

staining cells that were also BrdU-labeled, was quantified on blinded images (30). Data were

expressed as the proliferation index, calculated as the ratio of %BrdU+ β-cells in 15mM

glucose divided by the %BrdU+ β-cells in 5mM glucose.

Statistics

Univariate analyses were performed using GraphPad Prism and expressed as mean ± SD. P

values were determined by two-tailed Student t test when comparing 2 conditions, with F test

to compare variances, one way ANOVA with Tukey post-test for correction for multiple

comparisons when comparing >2 conditions, or by linear regression when assessing the


relationship between two continuous variables. Multivariable linear models were performed

to examine gene expression (p14, p15, p16, ANRIL, MTAP) simultaneously adjusted for

donor sex, race/ethnicity, age (continuous) and body mass index (continuous); additional

models further adjusted for expression of the other gene products. Missing values were

modeled with a missing indicator, replacing unknown values with sample means for linear

variables. Infrequent or unknown race/ethnicity were grouped in a residual category. RNA

expression associated with SNPs was estimated in linear multivariable models adjusting for

demographics (as above), in two fashions: first, setting the population with no risk alleles as

the common reference group, and second by estimating the linear effect on a per-allele basis

(treating the number of risk alleles as additive). Insulin secretion index was estimated as a

function of demographic variables as described above, and by including each SNP as a

predictor of insulin secretion index. Interpretation of these models can be found in the

Supplemental Materials. P<0.05 was considered significant, although this may be too

generous for the exploratory analyses with multiple comparisons. For the haplotype

hypothesis-generating analyses, the false discovery rate (FDR), calculated by the original

method of Benjamini and Hochberg, was set at 10%, based on our estimation that a

hypothesis with 90% likelihood of being correct warranted experimental follow-up.

RESULTS

CDKN2A/B locus gene expression is coordinately regulated in human islets

To understand the context of how T2D risk-SNPs might impact biology at this locus in

human islets, we first quantified expression of all locus genes (Figure 1A). Validated

Taqman probes were chosen that could independently quantify transcripts including MTAP,

p14 (CDKN2A, ARF), p15 (CDKN2B, INK4B), p16 (CDKN2A, INK4A) and the ANRIL

(CDKN2B-AS1) lncRNA. p14 and p16 are splice variants of CDKN2A, sharing exons 2-3 but

with different first exons; exons 2-3 are in different reading frames and thus p14 and p16

encode entirely different proteins with different functions (31). The ANRIL probe spans

exons 5-6, thus detecting all known isoforms. In this cohort of islet samples from 95 unique

non-diabetic donors (Supplemental Table 1), RNA abundance of p14, p16 and ANRIL were

highly correlated with each other (Figure 1, normalized to ACTB, and Supplemental Figure 2,


normalized to GAPDH). In contrast, abundance of p15, despite being physically located

within the first intron of ANRIL, was poorly (ACTB normalization) or not (GAPDH

normalization) correlated with p14, p16 or ANRIL. On the other hand, p15, but not p14, p16

or ANRIL, was highly correlated with MTAP expression. When the data were examined in

multivariable linear models, integrating donor characteristics such as age into the model,

again p14-ANRIL and p14-p16 were highly correlated, as were p15-MTAP (Supplemental

Table 4). These results suggest two independent but overlapping co-regulatory cassettes at

the CDKN2A/B locus in human islets, with p14-p16-ANRIL in one and p15-MTAP in the

other.

Age-dependent gene expression increase of p14, p16 and ANRIL but not p15 or MTAP

In many tissues, including islets, some CDKN2A/B locus genes increase with advancing age

(9,10,32). In this cohort of human islets, expression of p14, p16 and ANRIL showed a modest

positive correlation with donor age, whereas p15 and MTAP did not (Figure 2A-E). Donor

body mass index (BMI) could potentially confound the impact of age on gene expression;

however, BMI was similar across donor ages (Figure 2F). Furthermore, we observed no

correlation between donor BMI, sex or ethnicity and expression of any CDKN2A/B locus

gene in univariate analysis (Supplemental Figures 3-5). Multivariable linear models

integrating age, sex, race and BMI confirmed a positive correlation between p14, p16 and

ANRIL with donor age, and confirmed a lack of impact of sex, race or BMI on locus gene

expression (Supplemental Table 4). Scatter plots of gene expression versus age showed that

some genes were expressed in very low abundance in islets from juvenile (age < 10 years)

donors, with points falling well below the linear regression curve (Figure 2A, C, D). Focused

analysis of juvenile (<10 years) versus adolescent/adult (>10 years) islets (Figure 2G-K)

revealed that expression of p14, p16 and ANRIL were markedly lower in juvenile islets, but

MTAP and p15 were not, again suggesting altered regulatory characteristics of these two

genes relative to other locus genes. Interestingly, an F test showed that the variances were

reduced in juvenile islets (see standard deviation bars in Figure 2 G-K) for p14 (p<0.0001),

p16 (p<0.001) and ANRIL (p<0.0001) but not for p15 or MTAP (p=ns for both), despite the

much smaller sample size, again suggesting fundamentally different biology of the juvenile

samples. In sum, older age increased expression of p14, p16 and ANRIL, but not p15 or


MTAP, and the youth-associated suppression was exaggerated in islets from very young

donors.

T2D risk-SNPs at rs10811661 and rs2383208 increased ANRIL expression in an age-

defined subset of islet samples

We next tested whether T2D-related SNPs at CDKN2A/B impact locus gene expression.

Validated Taqman genotyping procedures ascertained and confirmed the genotype of all

n=95 preps for four T2D SNPs: rs564398 (hg38 chr9:22029548), rs2383208 (hg38

chr9:22132077), rs10811661 (hg38 chr9:22134095) and rs10757283 (hg38 chr9:22134173).

Measured minor allele frequencies (MAF; Supplemental Table 2) were similar to reported

MAFs for ethnicity-matched populations, supporting genotyping accuracy. SNPs rs2383208

and rs10811661 were tightly linked, with only 2 of 95 samples differing in our cohort,

consistent with the linkage disequilibrium reported in LDpair and HaploReg (26,33,34)

(Supplemental Tables 3 and 5). In raw analysis across the entire cohort, no SNP genotype

correlated with abundance of any locus transcript by univariate (Figure 3) or multivariable

(Supplemental Table 6) analysis. Since donor age impacted expression of p14, p16 and

ANRIL, we assessed whether age interfered with the assessment of SNP effect on gene

expression. Mean age was not significantly different between genotypes for any SNP (data

not shown). However, expressing transcript abundance as a function of age revealed that for

ANRIL, but not for p14 or p16, the age-dependent increase was genotype-dependent, evident

only in protective-allele-carrying samples at rs2383208 (Figure 4) and rs10811661

(Supplemental Figure 6). Homozygous-risk samples had high levels of ANRIL across all ages

>10 years (Figure 4B, flat slope of AA regression line even despite the influence of the

juvenile samples). Age-genotype interaction was not observed for any locus gene for

rs10757283 or rs564398 (not shown). When the samples were reanalyzed using a different

methodology, binning by quartiles, it was again evident that samples with protective-allele at

rs2383208 or rs10811661 showed an age-dependent increase in ANRIL, but homozygous-

risk samples did not. In contrast, for p16 the slope of the age-dependent-gene expression

regression lines (Figure 4A) and binning analysis (Figure 4C) were similar across genotypes.

To test whether ANRIL abundance was inappropriately increased by homozygous-risk at

rs2383208 or rs10811661 in samples from young donors, samples between the ages of 10 (to


exclude juveniles, which were all suppressed independent of genotype) and 50 (defined by

the intersection of the linear regression curves in 4B), was stratified by genotype (Figure 4E-

F). ANRIL, but not p16, abundance was significantly increased in younger homozygous-risk

samples compared with protective-allele carriers. Taken together, T2D homozygous-risk

genotype at rs2383208 or rs10811661 prematurely increased ANRIL expression in islets of

younger donors to older-donor levels.

SNPs may interact with each other to combinatorially influence gene expression

This cohort was not powered to perform subgroup analyses to definitively detect gene

expression impact of SNP haplotype combinations. For example, a sample size analysis

using our ANRIL expression mean and standard deviation for rs2383208 genotypes reveals

that we would require n=54 per subgroup to achieve a power of 80% and type 1 error of 0.05.

Given the diminishing number of samples as we partition by haplotype, we do not approach

this sample size for sub-analyses. Instead, we analyzed our dataset using a false discovery

rate (FDR) approach to prioritize hypotheses to test in future studies such as ex vivo

promoter-enhancer experiments. We estimated that a risk tolerance of 90% likelihood that a

hypothesis was correct would support future experimental investment. We then stratified our

gene expression data by all SNP haplotype combinations and analyzed each comparison for

likelihood of difference, defined by a FDR of 10% (Figure 5 and Supplemental Figure 7). By

these criteria, we determined that genotype at rs564398 and rs10757283 may influence the

impact of rs2383208 and/or rs10811661 on gene expression. For rs564398: in homozygous-

protective rs564398, but not risk-allele-containing samples, protective alleles at rs2383208

and rs10811661 may decrease abundance of p16 compared with homozygous-risk carriers.

For rs10757283: in homozygous-risk rs10757283 samples, but not protective-allele samples,

protective-allele at rs2383208 or rs10811661 may decrease abundance of p15. These

observations suggest that individual SNPs may contribute risk by impacting locus biology

only in the presence of other locus SNP genotypes, support investment in future experiments

to test specific combinations, and help narrow which combinations to target.

CDKN2A/B T2D SNPs did not impact glucose-stimulated insulin secretion


A subset of islet samples (n=61) were tested for insulin secretion stimulation index at their

respective islet isolation centers. Insulin secretion index was positively correlated with BMI

but showed no relationship with donor age, sex, or islet isolation center by univariate

(Supplemental Figure 8) or multivariable (Supplemental Table 7) analysis. When insulin

secretion index was stratified by SNP genotype, contrary to the hypotheses that CDKN2A/B

T2D risk-SNPs impair glucose sensing, insulin production or stimulus-secretion coupling,

samples with T2D risk-alleles did not show evidence for impairment in ex vivo insulin

secretion in this cohort (Figure 6 and Supplemental Table 8).

Risk alleles at rs564398 reduced glucose-induced beta cell proliferation

Since CDKN2A/B locus genes are best known for inhibiting the cell cycle, we assessed

transcript markers of proliferation (KI67, PCNA and CCND2) in this cohort, as well as the

actual rate of S-phase entry in growth-stimulatory culture conditions, by BrdU labeling, in a

subset of samples. Surprisingly, although PCNA and CCND2 showed a high degree of

correlation with each other, KI67 did not correlate with either PCNA or CCND2 (Figure 7A-

C). No SNP genotype was correlated with abundance of PCNA, CCND2 or KI67 (not

shown). Transcript level is only a surrogate for proliferation, and lacks sensitivity in a tissue

with a very low frequency of proliferation events and a mixture of cell types. To measure

actual cell cycle entry in beta cells we cultured a subset (n=47) of islet preparations in low

(5mM) or high (15mM) glucose and quantified nuclear BrdU incorporation in insulin-

positive cells (Supplemental Figure 9). BrdU incorporation rate in 5mM glucose was

nominally correlated with basal PCNA abundance, but not with KI67 or CCND2

(Supplemental Figure 10). As previously observed (27,28,35), glucose increased human beta

cell proliferation (p<0.0001, not shown). To test whether any T2D SNP genotype impacted

beta cell proliferation, the proliferation index (ratio of BrdU+ beta cells in 15mM compared

to 5mM glucose) was stratified by SNP identity. Genotype at rs2383208, rs10811661 and

rs10757283 did not influence the proliferation index (Figure 7D-E). However, genotype at

rs564398 was strongly associated with the human beta cell proliferation index, with

homozygous-protective alleles showing approximately doubled stimulation of proliferation

by 15mM glucose compared with islet samples harboring risk alleles at this SNP (Figure 7F).


DISCUSSION

We have performed a comprehensive analysis of how one T2D GWAS locus associated with

insulin secretory capacity in human populations influences human islet biology. In n=95 islet

samples we quantified locus gene expression, SNP genotype, donor characteristics, beta cell

function (insulin secretion) and beta cell proliferation. We have made several important

observations. First, the locus contains two distinct gene cassettes that are physically

overlapping but have different regulatory characteristics, one age-dependent and the other

not. Juvenile islets have markedly suppressed expression of p14-p16-ANRIL but not p15-

MTAP. Second, individual T2D SNPs at the locus do not substantially alter expression of

locus genes, but subtle age-dependent influences are detected, which, contrary to

expectations, impact the ANRIL lncRNA rather than the most prominent locus product, p16.

SNPs may interact with each other to influence gene expression, increasing the complexity of

genotype interpretation and raising intriguing mechanistic hypotheses. Third, genotype did

not impact insulin secretion index. Finally, risk-allele at rs564398, which is located within a

transcribed exon of ANRIL, decreased the beta cell proliferation index. This work improves

understanding of how CDKN2A/B T2D SNPs impact human islet biology, and suggests the

influence of the locus on human insulin secretory capacity may be effected via beta cell mass

rather than function.

How SNPs influence T2D risk is an important question in genetics today (3). Although the

CDKN2A/B locus is associated with a number of different disease syndromes (36), the SNPs

we selected to study are most strongly related to T2D risk, with the exception of rs564398,

which is also associated with coronary disease and glaucoma (15). CDKN2A/B SNPs are

associated with a range of diabetes-related syndromes beyond T2D, such as gestational

diabetes and transplant-related diabetes (9). Since this locus is active in many different cell

types, and the coronary disease risk region is mostly non-overlapping with the T2D risk

region (36), it is likely that CDKN2A/B impact on diseases other than T2D is mediated by

effects outside the islet. Given the association of rs10811661/rs2383208 with impaired

insulin production capacity, to identify possible T2D risk mechanisms we performed our

study in islets. Our data highlight the complexity of genetic inputs to human metabolism.

Even starting with a genomic locus repeatedly associated with disease risk across ethnicities


and T2D-related syndromes (9), with in vivo evidence that the islet is the risk-mediating

tissue (16,19–21), abundant preclinical locus knowledge in model systems (9,32), and a

fairly large sample size of the relevant tissue, the impact of risk-SNPs is subtle. As with

many studies in human islets, our data illustrate the marked variability from one donor to the

next, which reflects the variability of outbred human populations. We incorporated donor

parameters such as age and BMI in our analyses, but could not measure many potential other

confounding pre-mortem influences such as insulin sensitivity (liver, muscle, brain, fat),

coexisting diseases and medications, environmental effects (diet, stresses, toxins), exercise

history, prenatal events, and others. Islet stress related to donor demise, isolation, shipping

and culture may also introduce variability (37). In this context, subtle effects observed in this

challenging system may reflect large effects in certain sub-populations, or small effects

present uniformly across variable conditions.

The presence of two gene expression cassettes at the locus, only one of which is age-

dependent, suggests interesting biology. p16 is well known to increase with age in many

tissues and organisms (10). Our observation that p15 abundance did not increase with age in

human islets conflicts with published results in other tissues (38,39). The relationship

between MTAP and CDKN2A/B locus genes has not been extensively studied, but these are

contained in the same Hi-C defined topologically associating domain in the human genome

(Supplemental Figure 11) (40,41). Whether co-regulation of p15 and MTAP has functional

importance in the islet remains unknown.

rs2383208/rs10811661 impacted ANRIL abundance in age-dependent fashion. Islets

containing homozygous-risk alleles showed a “premature aging” phenotype, with young risk-

allele islets having ANRIL levels similar to older protective-allele islets. The low MAF for

these SNPs precluded comparison between homozygous-protective and homozygous-risk,

which might have revealed a larger effect size. We do not currently know whether one of

these, or another SNP in linkage with them, is causative. Fine mapping of this region has not

revealed SNPs with greater impact on T2D risk (1,4), but islet ANRIL abundance was not the

endpoint in those studies. These SNPs fall near a known regulatory region downstream of the

3’ end of ANRIL which may regulate ANRIL transcription. Mechanistically, how higher


ANRIL abundance in islets might increase T2D risk is unknown. In other cell types, ANRIL is

pro-proliferative (42), an effect mediated by ANRIL-dependent suppression of locus cell

cycle inhibitors p14, p15 and p16 (43). Our RNA analyses did not detect any hint of negative

correlation between ANRIL and p14/p16 in islets; in fact, the strong positive correlation

between these transcripts calls into question whether ANRIL negatively regulates other locus

genes in human islets.

Beyond SNP regulation of ANRIL abundance, a second observation also points to a role for

ANRIL in human islets: rs564398, which influences the beta cell proliferation index, is a

transcribed polymorphism in this long-noncoding RNA. Although the causative SNP remains

unknown, it is possible that rs564983 itself impacts lncRNA activity. ANRIL is a

complicated gene, with 20 exons and at least 14 reported isoforms (42). Some ANRIL

variants are circular (44). Exon-2 is not contained in all isoforms, but is generally associated

with linear variants (44). Whether rs564398 identity impacts ANRIL isoform production,

splicing, stability, or interaction with other cellular DNA, RNA or protein, to regulate human

beta cell proliferation, remains unknown. Genotype at rs564398 did not correlate with

expression of cell cycle genes, or BrdU incorporation, under basal conditions; rs564398 may

be a marker for beta cell responsiveness to proliferation-inducing conditions rather than

increased proliferation in unstimulated conditions. Importantly, there are many SNPs in

linkage with rs564398, and any of these, or a combination of these, could be influencing

biology instead of rs564398 itself. Also important, while rs564398 has been repeatedly

confirmed to be associated with T2D in Caucasian populations, it has little to no relationship

to T2D risk in Asian populations (45–47). Taken together with the ‘premature aging’

influence of rs10811661/rs2383208 on ANRIL expression, CDKN2A/B SNPs may impact

T2D risk by adversely impacting accrual of beta cell mass during early adulthood.

Combinatorial SNP regulation of gene expression increases the complexity of how genomic

variation may impact cellular function. Whole-genome studies restricting locus analyses to

single ‘lead’ SNPs cannot detect biology related to two or more local SNPs interacting with

each other. The mechanism by which SNPs interact to regulate CDKN2A/B locus gene

expression in human islets is unknown. Interaction between rs10757283 and rs2383208 or


rs10811661 linked SNPs to regulate p15 abundance could be via modulation of transcription

factor occupancy or epigenetic regulation of the enhancer region in which they are located

(48). Intriguingly, our observed interaction between these SNPs is supported by a

complementary LD-block analysis which revealed that a haplotype consisting of

rs2383208/rs10811661 and rs10757283 was associated with T2D risk (13). There are

multiple SNPs in linkage disequilibrium with rs10811661 and also with rs10757283; actual

causal polymorphisms are unknown. A physical or functional interaction between the ANRIL

lncRNA and this enhancer may mediate cooperation between rs564398 and rs2383208,

rs10811661 or linked SNPs to regulate p16 expression. Our study cannot distinguish

between in-cis and in-trans interaction. Focused, cell-type-specific studies are needed to

determine how SNP combinations influence locus gene expression.

This study adds to the body of knowledge debating the relative influence of beta cell mass

versus function on T2D risk. Our study found that CDKN2A/B SNPs did not influence

glucose-stimulated insulin secretion, both in univariate analysis and in multivariable models

incorporating donor age, sex, race and BMI, and islet isolation center. This is perhaps

surprising given the in vivo data linking risk allele at rs10811661 with impaired insulin

secretion (16,22). Since in vivo insulin secretion is a composite outcome of both mass and

function, CDKN2A/B SNPs may impact beta cell mass but not beta cell function. This

concept is in agreement with the widespread assumption that CDKN2A/B SNPs influence

beta cell proliferation because of the known cell cycle inhibitory effects of locus genes p14,

p15 and p16 (49). Our rs564398 data are the first demonstration, to our knowledge, of a

CDKN2A/B locus SNP impacting beta cell proliferation.

Our study has caveats. Multiple cell types are found in human islets. Other than the

proliferation measurements, which were assessed in insulin-positive cells, all other studies

were performed on whole islets. We did not assess the relative proportion of islet cells that

were beta cells, and to the extent that gene expression may be cell type specific, variable

cellular makeup may have influenced results. In addition, there is considerable heterogeneity

even among beta cells (50,51). Repeating our current analyses on sorted beta cells, or on

single cells, exceeds our current resources. The insulin secretion data have caveats; using the


IIDP-reported insulin secretion index introduces technical variability, although benefits from

freshly isolated, pre-shipment tissue. Our confirmation that insulin secretion correlated with

BMI, but not with isolation center, are reassuring in this regard.

In sum, this work provides new information about how CDKN2A/B T2D SNPs impact islet

biology, suggests the ANRIL lncRNA may play a role in human islets, and uncovers a link

between a T2D SNP and beta cell proliferation. Further studies into the CDKN2A/B locus to

develop a mechanistic understanding of how these SNPs impact islet biology to influence

T2D risk could one day open the door for using personalized genomic information to inform

T2D sub-type definitions and therapeutic choice.

ACKNOWLEDGMENTS

LCA devised and planned the experiments. YK performed the majority of the experiments;

SL, RBS and RES also performed experiments. YK and LCA analyzed the data. WMJ

performed the multivariable linear modeling. LCA, the guarantor of this work, wrote the

manuscript; all authors viewed and had the opportunity to edit and approve of the

manuscript. The authors have no conflicts of interest with the work contained in this

manuscript. Human pancreatic islets were provided by the NIDDK-funded Integrated Islet

Distribution Program (IIDP) at City of Hope, as well as Sambra Redick and David Harlan

from the UMass Medical School, and Alvin C. Powers from Vanderbilt University. We are

grateful to Ahmet Rasim Barutcu, from the Broad Institute and Harvard University, for

helpful guidance with the TAD analysis. We would like to thank the Beta Cell Biology

Group at the University of Massachusetts Medical School for many helpful discussions. This

work was supported by NIH/NIDDK: R01-DK095140 (LCA), DK104211 (ACP),

DK106755 (ACP), 2UC4DK098085 (IIDP) and by the American Diabetes Association grant

#1-15-BS-003 (LCA) in collaboration with the Order of the Amaranth.


REFERENCES 1. Morris AP. Fine Mapping of Type 2 Diabetes Susceptibility Loci. Curr Diab Rep

[Internet]. 2014 [cited 2015 Dec 30];14(11). Available from:

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4169615/

2. Morris AP, Voight BF, Teslovich TM, Ferreira T, Segrè AV, Steinthorsdottir V, et al.

Large-scale association analysis provides insights into the genetic architecture and

pathophysiology of type 2 diabetes. Nat Genet. 2012 Sep;44(9):981–90.

3. Fuchsberger C, Flannick J, Teslovich TM, Mahajan A, Agarwala V, Gaulton KJ, et al.

The genetic architecture of type 2 diabetes. Nature. 2016 Aug 4;536(7614):41–7.

4. Gaulton KJ, Ferreira T, Lee Y, Raimondo A, Mägi R, Reschen ME, et al. Genetic fine

mapping and genomic annotation defines causal mechanisms at type 2 diabetes

susceptibility loci. Nat Genet. 2015 Dec;47(12):1415–25.

5. van de Bunt M, Manning Fox JE, Dai X, Barrett A, Grey C, Li L, et al. Transcript

Expression Data from Human Islets Links Regulatory Signals from Genome-Wide

Association Studies for Type 2 Diabetes and Glycemic Traits to Their Downstream

Effectors. PLoS Genet. 2015 Dec;11(12):e1005694.

6. Fadista J, Vikman P, Laakso EO, Mollet IG, Esguerra JL, Taneera J, et al. Global

genomic and transcriptomic analysis of human pancreatic islets reveals novel genes

influencing glucose metabolism. Proc Natl Acad Sci. 2014 Sep 23;111(38):13924–9.

7. Varshney A, Scott LJ, Welch RP, Erdos MR, Chines PS, Narisu N, et al. Genetic

regulatory signatures underlying islet gene expression and type 2 diabetes. Proc Natl

Acad Sci U S A. 2017 Feb 28;114(9):2301–6.

8. Gaulton KJ. Mechanisms of Type 2 Diabetes Risk Loci. Curr Diab Rep. 2017

Sep;17(9):72.

9. Kong Y, Sharma RB, Nwosu BU, Alonso LC. Islet biology, the CDKN2A/B locus and

type 2 diabetes risk. Diabetologia. 2016 Aug;59(8):1579–93.

10. Kim WY, Sharpless NE. The regulation of INK4/ARF in cancer and aging. Cell. 2006

Oct 20;127(2):265–75.

11. Sharpless NE, Sherr CJ. Forging a signature of in vivo senescence. Nat Rev Cancer.

2015 Jul;15(7):397–408.

12. Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren WL, et al. A genome-

wide association study of type 2 diabetes in Finns detects multiple susceptibility variants.

Science. 2007 Jun 1;316(5829):1341–5.


13. Zeggini E, Weedon MN, Lindgren CM, Frayling TM, Elliott KS, Lango H, et al.

Replication of Genome-Wide Association Signals in UK Samples Reveals Risk Loci for

Type 2 Diabetes. Science. 2007 Jun 1;316(5829):1336–41.

14. Saxena R, Voight BF, Lyssenko V, Burtt NP, de Bakker PIW, Chen H, et al. Genome-

wide association analysis identifies loci for type 2 diabetes and triglyceride levels.

Science. 2007 Jun 1;316(5829):1331–6.

15. Cunnington MS, Santibanez Koref M, Mayosi BM, Burn J, Keavney B. Chromosome

9p21 SNPs Associated with Multiple Disease Phenotypes Correlate with ANRIL

Expression. PLoS Genet. 2010 Apr;6(4):e1000899.

16. Grarup N, Rose CS, Andersson EA, Andersen G, Nielsen AL, Albrechtsen A, et al.

Studies of association of variants near the HHEX, CDKN2A/B, and IGF2BP2 genes with

type 2 diabetes and impaired insulin release in 10,705 Danish subjects: validation and

extension of genome-wide association studies. Diabetes. 2007 Dec;56(12):3105–11.

17. Xu M, Bi Y, Xu Y, Yu B, Huang Y, Gu L, et al. Combined effects of 19 common

variations on type 2 diabetes in Chinese: results from two community-based studies. PloS

One. 2010;5(11):e14022.

18. Peng F, Hu D, Gu C, Li X, Li Y, Jia N, et al. The relationship between five widely-

evaluated variants in CDKN2A/B and CDKAL1 genes and the risk of type 2 diabetes: a

meta-analysis. Gene. 2013 Dec 1;531(2):435–43.

19. Hribal ML, Presta I, Procopio T, Marini MA, Stančáková A, Kuusisto J, et al. Glucose

tolerance, insulin sensitivity and insulin release in European non-diabetic carriers of a

polymorphism upstream of CDKN2A and CDKN2B. Diabetologia. 2011 Apr;54(4):795–

802.

20. Hart LM ‘t, Simonis-Bik AM, Nijpels G, Haeften TW van, Schäfer SA, Houwing-

Duistermaat JJ, et al. Combined Risk Allele Score of Eight Type 2 Diabetes Genes Is

Associated With Reduced First-Phase Glucose-Stimulated Insulin Secretion During

Hyperglycemic Clamps. Diabetes. 2010 Jan 1;59(1):287–92.

21. Dimas AS, Lagou V, Barker A, Knowles JW, Mägi R, Hivert M-F, et al. Impact of

Type 2 Diabetes Susceptibility Variants on Quantitative Glycemic Traits Reveals

Mechanistic Heterogeneity. Diabetes. 2014 Jun 1;63(6):2158–71.

22. Jonsson A, Ladenvall C, Ahluwalia TS, Kravic J, Krus U, Taneera J, et al. Effects of

Common Genetic Variants Associated With Type 2 Diabetes and Glycemic Traits on α-

and β-Cell Function and Insulin Action in Humans. Diabetes. 2013 Aug 1;62(8):2978–

83.

23. Brereton MF, Rohm M, Ashcroft FM. β-Cell dysfunction in diabetes: a crisis of

identity? Diabetes Obes Metab. 2016 Sep;18 Suppl 1:102–9.


24. Dai C, Hant Y, Shostak A, Poffenberger G, Hart N, Prasad N, et al. Age-dependent

human β-cell proliferation induced by GLP-1 and calcineurin signaling. J Clin Invest. in

press.

25. 1000 Genomes Project Consortium, Auton A, Brooks LD, Durbin RM, Garrison EP,

Kang HM, et al. A global reference for human genetic variation. Nature. 2015 Oct

1;526(7571):68–74.

26. Machiela MJ, Chanock SJ. LDlink: a web-based application for exploring population-

specific haplotype structure and linking correlated alleles of possible functional variants.

Bioinformatics. 2015 Nov 1;31(21):3555–7.

27. Sharma RB, O’Donnell AC, Stamateris RE, Ha B, McCloskey KM, Reynolds PR, et al.

Insulin demand regulates β cell number via the unfolded protein response. J Clin Invest.

2015 Oct 1;125(10):3831–46.

28. Stamateris RE, Sharma RB, Kong Y, Ebrahimpour P, Panday D, Ranganath P, et al.

Glucose induces mouse beta cell proliferation via IRS2, mTOR and cyclin D2 but not the

insulin receptor. Diabetes. (in press, 2016).

29. Pascoe J, Hollern D, Stamateris R, Abbasi M, Romano LC, Zou B, et al. Free fatty acids

block glucose-induced β-cell proliferation in mice by inducing cell cycle inhibitors p16

and p18. Diabetes. 2012 Mar;61(3):632–41.

30. Alonso LC, Yokoe T, Zhang P, Scott DK, Kim SK, O’Donnell CP, et al. Glucose

infusion in mice: a new model to induce beta-cell replication. Diabetes. 2007

Jul;56(7):1792–801.

31. Robertson KD, Jones PA. Tissue-specific alternative splicing in the human INK4a/ARF

cell cycle regulatory locus. Oncogene. 1999 Jul 1;18(26):3810–20.

32. Krishnamurthy J, Ramsey MR, Ligon KL, Torrice C, Koh A, Bonner-Weir S, et al.

p16INK4a induces an age-dependent decline in islet regenerative potential. Nature. 2006

Sep 28;443(7110):453–7.

33. Ward LD, Kellis M. HaploReg: a resource for exploring chromatin states, conservation,

and regulatory motif alterations within sets of genetically linked variants. Nucleic Acids

Res. 2012 Jan;40(Database issue):D930–4.

34. Gaunt TR, Rodríguez S, Day IN. Cubic exact solutions for the estimation of pairwise

haplotype frequencies: implications for linkage disequilibrium analyses and a web tool

“CubeX.” BMC Bioinformatics. 2007 Nov 2;8:428.

35. Levitt HE, Cyphert TJ, Pascoe JL, Hollern DA, Abraham N, Lundell RJ, et al. Glucose

stimulates human beta cell replication in vivo in islets transplanted into NOD-severe

combined immunodeficiency (SCID) mice. Diabetologia. 2011 Mar;54(3):572–82.


36. Jeck WR, Siebold AP, Sharpless NE. Review: a meta-analysis of GWAS and age-

associated diseases. Aging Cell. 2012 Oct;11(5):727–31.

37. Stiegler P, Stadlbauer V, Hackl F, Schaffellner S, Iberer F, Greilberger J, et al.

Prevention of oxidative stress in porcine islet isolation. J Artif Organs Off J Jpn Soc Artif

Organs. 2010 Apr;13(1):38–47.

38. Menthena A, Koehler CI, Sandhu JS, Yovchev MI, Hurston E, Shafritz DA, et al.

Activin A, p15INK4b Signaling, and Cell Competition Promote Stem/Progenitor Cell

Repopulation of Livers in Aging Rats. Gastroenterology. 2011 Mar;140(3):1009–20.

39. Wang F, Zhang L. p15INK4b regulates cell cycle signaling in hippocampal astrocytes

of aged rats. Aging Clin Exp Res. 2016 Oct 1;28(5):813–21.

40. Zhou X, Maricque B, Xie M, Li D, Sundaram V, Martin EA, et al. The Human

Epigenome Browser at Washington University. Nat Methods. 2011 Nov 29;8(12):989–

90.

41. Wang Y, Zhang B, Zhang L, An L, Xu J, Li D, et al. The 3D Genome Browser: a web-

based browser for visualizing 3D genome organization and long-range chromatin

interactions | bioRxiv. BioRxiv [Internet]. 2017 [cited 2017 Dec 15]; Available from:

https://www.biorxiv.org/content/early/2017/02/27/112268

42. Congrains A, Kamide K, Ohishi M, Rakugi H. ANRIL: molecular mechanisms and

implications in human health. Int J Mol Sci. 2013;14(1):1278–92.

43. Aguilo F, Zhou M-M, Walsh MJ. Long noncoding RNA, polycomb, and the ghosts

haunting INK4b-ARF-INK4a expression. Cancer Res. 2011 Aug 15;71(16):5365–9.

44. Burd CE, Jeck WR, Liu Y, Sanoff HK, Wang Z, Sharpless NE. Expression of linear

and novel circular forms of an INK4/ARF-associated non-coding RNA correlates with

atherosclerosis risk. PLoS Genet. 2010;6(12):e1001233.

45. Wu Y, Li H, Loos RJF, Yu Z, Ye X, Chen L, et al. Common variants in CDKAL1,

CDKN2A/B, IGF2BP2, SLC30A8, and HHEX/IDE genes are associated with type 2

diabetes and impaired fasting glucose in a Chinese Han population. Diabetes. 2008

Oct;57(10):2834–42.

46. Ng MCY, Park KS, Oh B, Tam CHT, Cho YM, Shin HD, et al. Implication of genetic

variants near TCF7L2, SLC30A8, HHEX, CDKAL1, CDKN2A/B, IGF2BP2, and FTO

in type 2 diabetes and obesity in 6,719 Asians. Diabetes. 2008 Aug;57(8):2226–33.

47. Horikawa Y, Miyake K, Yasuda K, Enya M, Hirota Y, Yamagata K, et al.

Replication of genome-wide association studies of type 2 diabetes susceptibility in Japan.

J Clin Endocrinol Metab. 2008 Aug;93(8):3136–41.


48. Pasquali L, Gaulton KJ, Rodríguez-Seguí SA, Mularoni L, Miguel-Escalada I, Akerman

I, et al. Pancreatic islet enhancer clusters enriched in type 2 diabetes risk-associated

variants. Nat Genet. 2014 Feb;46(2):136–43.

49. Hannou SA, Wouters K, Paumelle R, Staels B. Functional genomics of the CDKN2A/B

locus in cardiovascular and metabolic disease: what have we learned from GWASs?

Trends Endocrinol Metab. 2015 Apr;26(4):176–84.

50. Gutierrez GD, Gromada J, Sussel L. Heterogeneity of the Pancreatic Beta Cell. Front

Genet. 2017;8:22.

51. Roscioni SS, Migliorini A, Gegg M, Lickert H. Impact of islet architecture on β-cell

heterogeneity, plasticity and function. Nat Rev Endocrinol. 2016 Dec;12(12):695–709.


FIGURE LEGENDS Figure 1. CDKN2A/B locus genes were expressed coordinately in human islets. (A) Diagram

of the CDKN2A/B locus at 9p21, adapted from the UCSC genome browser GRCh38/hg38

assembly. Vertical arrows show the locations of T2D SNPs tested in this study, by linkage block:

green (rs564398; left-most), blue (rs2383208 and rs10811661; middle two) and red

(rs10757283; right-most). (B-D) Abundance of p14, p16 and ANRIL were highly correlated in

human islet samples. (E-G) p15 abundance did not correlate with p16, and only marginally

correlated with p14 and ANRIL. MTAP expression was marginally correlated with p14, p16 and

ANRIL (H and not shown), but (I) highly correlated with p15 expression. mRNA abundance is

expressed as delta-Ct, normalized to ACTB. Line, p values and R-squared values were calculated

by linear regression; n=95 for all panels. Red lines highlight correlations with higher R-squared

values.

Figure 2. Abundance of p14, p16 and ANRIL, but not p15 or MTAP, was correlated with

donor age and strongly reduced in juvenile islets. (A-E) Consistent with prior observations,

p16 mRNA abundance was positively correlated with donor age (in years). p14 and ANRIL were

also correlated with age, but p15 and MTAP were not. Age accounted for only a small proportion

of the variance in gene expression, even for p16. (F) BMI partitioned equally across donor age in

this cohort (dotted lines demarcate BMI 18-25 (normal weight), 25-30 (overweight) and >30

(obese). (G-K) Islets from juvenile donors (age <10 years) contained markedly less p14, p16 and

ANRIL, but not p15 or MTAP, than older islets. mRNA abundance is expressed as delta-Ct,

normalized to ACTB. Mean +/- SD; p values and R-squared values were calculated by linear

regression (A-F) or by Student’s T-test (G-K). All panels n=92; missing values are due to lack of

donor information for age and BMI (3 samples).

Figure 3. In crude analysis, individual SNP identity did not impact expression of locus

genes in human islets. Risk allele for each SNP, the right-most genotype in each case, is in red.

All comparisons were non-significant by ANOVA with correction for multiple comparisons.

ANRIL showed a trend towards higher abundance in islets with homozygous-risk for rs10811661

(p=0.08) and rs2383208 (p=0.07) compared with protective-allele-carrying samples.


rs108=rs10811661; rs238=rs2383208; rs107=rs10757283; rs564=rs564398. mRNA

abundance is expressed as delta-Ct, normalized to ACTB. Mean +/- SD; n=95 for all sub-panels.

Figure 4. Age interacted with genotype at rs2383208 to determine ANRIL abundance;

young donors with protective alleles had lower ANRIL expression. (A-B) Expressing p16 (A)

or ANRIL (B) abundance as a function of donor age, stratified by genotype, showed that unlike

p16, age-dependence of ANRIL was driven by samples of rs2383208-GG+GA genotype and was

absent in samples with AA (homozygous risk) genotype. (C-D) Binning analysis of the cohort

(non-juvenile samples separated by quartiles) illustrated the age-dependent ANRIL increase in

GG+GA samples but not in homozygous-risk AA samples. Juveniles <10 years of age showed

markedly lower abundance, independent of genotype. (E-F) In younger donors (ages 10-50;

lower threshold defined by juvenile cutoff and upper threshold defined by the intersection of the

regression curves in (B), which is 50.8) homozygous-risk increased ANRIL abundance. mRNA

abundance is expressed as delta-Ct, normalized to ACTB. Statistics by linear regression (A-B),

ANOVA (D-F) with overall ANOVA significance in upper left corner and significant pairwise

comparisons after correction for multiple comparisons labeled. Sample size: (A-D) n=92 (3

samples missing age) and (E-F) n=57 samples between the ages of 10-50.

Figure 5. SNP combinatorial haplotypes may influence locus gene expression. (A)

Schematic showing approximate locations of the T2D SNPs analyzed in this study, relative to

the ANRIL gene. SNP colors, as in Figure 1A, indicate linkage disequilibrium. (B-C): Protective

alleles of rs10811661 (shown) and rs2383208 (Supplemental Fig 4) may decrease expression of

p16 in homozygous-protective rs564398-CC samples. The same comparison for p14 did not

meet FDR<10% (q-value 17%); for ANRIL, FDR>20% (not shown). (D-E): Homozygous risk

alleles at both neighboring SNPs rs10757283 and rs10811661 may collaboratively increase p15

expression. The same comparison for MTAP showed FDR>20%. * indicates FDR<10%, our pre-

determined risk tolerance for future experiments exploring haplotype hypotheses. mRNA

abundance is expressed as delta-Ct, normalized to ACTB. n=95 for all panels. All other inter-

SNP comparisons, both shown and not shown (aside from those in Supplemental Figure 4),

resulted FDR>10% or had insufficient data points to evaluate (defined as n=2 or fewer). FDR,

false discovery rate.


Figure 6. Insulin secretion was similar across SNP genotypes. 61 of the islet preparations

were tested for glucose-stimulated insulin release “stimulation index” at islet isolation centers;

the IIDP-derived insulin secretion index is plotted against donor genotype. No relationship is

evident between T2D SNP genotype and IIDP-reported insulin secretory index.

rs108=rs10811661; rs238=rs2383208; rs107=rs10757283; rs564= rs564398. n=61 for all

SNPs.

Figure 7. Risk allele at rs564398 suppressed glucose induction of beta cell proliferation. (A-

C) RNA abundance of proliferation-related genes PCNA, CCND2 and KI67 in flash-frozen islets

showed a strong correlation between PCNA and CCND2 (A) but not with KI67 (B-C). (D-F)

rs564398, but not rs2383208, rs10811661 or rs10757283 was associated with beta cell

proliferation. Islets containing 1 or 2 T2D risk alleles at rs564398 had lower proliferation index

than islets containing homozygous-protective alleles at rs564398. Dispersed islets were cultured

on glass coverslips for 96 hours in either 5mM glucose (unstimulated) or 15mM glucose

(stimulated) with BrdU present for the whole 96 hours. Cultures were fixed, immunostained,

imaged, blinded and the percent insulin(+) cells that were also BrdU(+) quantified by manual

counting. Plotted is the proliferation index, which is the ratio of 15mM to 5mM. n=95 (A-C) and

n=43 (D-F; 47 preps tested, but 4 preps had 0% BrdU+ in 5mM glucose and thus could not

calculate an index). mRNA abundance is expressed as delta-Ct, normalized to ACTB. Mean +/-

SD; p values are by linear regression (A-C) and ANOVA with correction for multiple

comparisons (D-F).


Figure 1. CDKN2A/B locus genes were expressed coordinately in human islets. (A) Diagram of the CDKN2A/B locus at 9p21, adapted from the UCSC genome browser GRCh38/hg38 assembly. Vertical arrows

show the locations of T2D SNPs tested in this study, by linkage block: green (rs564398; left-most), blue

(rs2383208 and rs10811661; middle two) and red (rs10757283; right-most). (B-D) Abundance of p14, p16 and ANRIL were highly correlated in human islet samples. (E-G) p15 abundance did not correlate with p16, and only marginally correlated with p14 and ANRIL. MTAP expression was marginally correlated with p14,

p16 and ANRIL (H and not shown), but (I) highly correlated with p15 expression. mRNA abundance is expressed as delta-Ct, normalized to ACTB. Line, p values and R-squared values were calculated by linear

regression; n=95 for all panels. Red lines highlight correlations with higher R-squared values.

172x163mm (300 x 300 DPI)


Figure 2. Abundance of p14, p16 and ANRIL, but not p15 or MTAP, was correlated with donor age and strongly reduced in juvenile islets. (A-E) Consistent with prior observations, p16 mRNA abundance was positively correlated with donor age (in years). p14 and ANRIL were also correlated with age, but p15 and

MTAP were not. Age accounted for only a small proportion of the variance in gene expression, even for p16. (F) BMI partitioned equally across donor age in this cohort (dotted lines demarcate BMI 18-25 (normal

weight), 25-30 (overweight) and >30 (obese). (G-K) Islets from juvenile donors (age <10 years) contained markedly less p14, p16 and ANRIL, but not p15 or MTAP, than older islets. mRNA abundance is expressed as delta-Ct, normalized to ACTB. Mean +/- SD; p values and R-squared values were calculated by linear regression (A-F) or by Student’s T-test (G-K). All panels n=92; missing values are due to lack of donor

information for age and BMI (3 samples).

172x163mm (300 x 300 DPI)


Figure 3. In crude analysis, individual SNP identity did not impact expression of locus genes in human islets. Risk allele for each SNP, the right-most genotype in each case, is in red. All comparisons were non-significant by ANOVA with correction for multiple comparisons. ANRIL showed a trend towards higher

abundance in islets with homozygous-risk for rs10811661 (p=0.08) and rs2383208 (p=0.07) compared with protective-allele-carrying samples. rs108=rs10811661; rs238=rs2383208; rs107=rs10757283;

rs564=rs564398. mRNA abundance is expressed as delta-Ct, normalized to ACTB. Mean +/- SD; n=95 for all sub-panels.

172x163mm (300 x 300 DPI)


Figure 4. Age interacted with genotype at rs2383208 to determine ANRIL abundance; young donors with protective alleles had lower ANRIL expression. (A-B) Expressing p16 (A) or ANRIL (B) abundance as a

function of donor age, stratified by genotype, showed that unlike p16, age-dependence of ANRIL was driven

by samples of rs2383208-GG+GA genotype and was absent in samples with AA (homozygous risk) genotype. (C-D) Binning analysis of the cohort (non-juvenile samples separated by quartiles) illustrated the age-dependent ANRIL increase in GG+GA samples but not in homozygous-risk AA samples. Juveniles <10 years of age showed markedly lower abundance, independent of genotype. (E-F) In younger donors (ages 10-50; lower threshold defined by juvenile cutoff and upper threshold defined by the intersection of the

regression curves in (B), which is 50.8) homozygous-risk increased ANRIL abundance. mRNA abundance is expressed as delta-Ct, normalized to ACTB. Statistics by linear regression (A-B), ANOVA (D-F) with overall ANOVA significance in upper left corner and significant pairwise comparisons after correction for multiple comparisons labeled. Sample size: (A-D) n=92 (3 samples missing age) and (E-F) n=57 samples between

the ages of 10-50.

172x163mm (300 x 300 DPI)


Figure 5. SNP combinatorial haplotypes may influence locus gene expression. (A) Schematic showing approximate locations of the T2D SNPs analyzed in this study, relative to the ANRIL gene. SNP colors, as in Figure 1A, indicate linkage disequilibrium. (B-C): Protective alleles of rs10811661 (shown) and rs2383208

(Supplemental Fig 4) may decrease expression of p16 in homozygous-protective rs564398-CC samples. The same comparison for p14 did not meet FDR<10% (q-value 17%); for ANRIL, FDR>20% (not shown). (D-E): Homozygous risk alleles at both neighboring SNPs rs10757283 and rs10811661 may collaboratively increase

p15 expression. The same comparison for MTAP showed FDR>20%. * indicates FDR<10%, our pre-determined risk tolerance for future experiments exploring haplotype hypotheses. mRNA abundance is expressed as delta-Ct, normalized to ACTB. n=95 for all panels. All other inter-SNP comparisons, both

shown and not shown (aside from those in Supplemental Figure 4), resulted FDR>10% or had insufficient data points to evaluate (defined as n=2 or fewer). FDR, false discovery rate.

172x163mm (300 x 300 DPI)


Figure 6. Insulin secretion was similar across SNP genotypes. 61 of the islet preparations were tested for glucose-stimulated insulin release “stimulation index” at islet isolation centers; the IIDP-derived insulin

secretion index is plotted against donor genotype. No relationship is evident between T2D SNP genotype and

IIDP-reported insulin secretory index. rs108=rs10811661; rs238=rs2383208; rs107=rs10757283; rs564= rs564398. n=61 for all SNPs.

172x163mm (300 x 300 DPI)


Figure 7. Risk allele at rs564398 suppressed glucose induction of beta cell proliferation. (A-C) RNA abundance of proliferation-related genes PCNA, CCND2 and KI67 in flash-frozen islets showed a strong correlation between PCNA and CCND2 (A) but not with KI67 (B-C). (D-F) rs564398, but not rs2383208,

rs10811661 or rs10757283 was associated with beta cell proliferation. Islets containing 1 or 2 T2D risk alleles at rs564398 had lower proliferation index than islets containing homozygous-protective alleles at

rs564398. Dispersed islets were cultured on glass coverslips for 96 hours in either 5mM glucose (unstimulated) or 15mM glucose (stimulated) with BrdU present for the whole 96 hours. Cultures were fixed,

immunostained, imaged, blinded and the percent insulin(+) cells that were also BrdU(+) quantified by manual counting. Plotted is the proliferation index, which is the ratio of 15mM to 5mM. n=95 (A-C) and n=43 (D-F; 47 preps tested, but 4 preps had 0% BrdU+ in 5mM glucose and thus could not calculate an

index). mRNA abundance is expressed as delta-Ct, normalized to ACTB. Mean +/- SD; p values are by linear regression (A-C) and ANOVA with correction for multiple comparisons (D-F).

172x163mm (300 x 300 DPI)


16 18 20 22 24

1618

2022

24

Ct value 2

Ct v

alue

1

ACTNp<0.0001R2=0.85

RPD1.13%

Supplemental Figure 1. Taqman gene expression duplicates show high reproducibility. For each CDKN2A/B locus gene, Taqman Ct values of replicate 1 (Ct value 1) and replicate 2 (Ct value 2) are plotted. The RPD values, the absolute value of the Relative Percentage Di�erence, were calculated from the equa-tion RPD=(D1-D2)/(D1+D2)/2X100. RPDs for all genes were well within established goals for biological replicate variability of 10%. Line, p values and R-squared values were calculated by linear regression; n=95 for all panels.

A B GAPDHp<0.0001R2=0.94

RPD0.81%

16 18 20 22 2416

1820

2224

Ct value 2

Ct v

alue

1

C

RPD0.65%

p14p<0.0001R2=0.95

26 28 30 32 34 36

2628

3032

3436

Ct value 2

Ct v

alue

1

RPD1.05%

p15p<0.0001R2=0.92

22 24 26 28 30 32 34

2224

2628

3032

34

Ct value 2

Ct v

alue

1

D

E F G H

I J K

RPD1.05%

p15p<0.0001R2=0.92

22 24 26 28 30 32 34

2224

2628

3032

34

Ct value 2

Ct v

alue

1

RPD0.56%

p16p<0.0001R2=0.97

24 26 28 30 32 34

2426

2830

3234

Ct value 2

Ct v

alue

1

RPD1.30%

ANRILp<0.0001R2=0.87

25 30 35 40

2530

3540

Ct value 2

Ct v

alue

1RPD0.60%

MTAPp<0.0001R2=0.94

22 24 26 28 30 32

2224

2628

3032

Ct value 2

Ct v

alue

1

20 25 30 35 40

2025

3035

40

Ct value 2

Ct v

alue

1

RPD0.60%

KI67p<0.0001R2=0.99

24 26 28 30 32 34

2426

2830

3234

Ct value 2

Ct v

alue

1

RPD0.64%

PCNAp<0.0001R2=0.88

20 22 24 26 28 30

2022

2426

2830

Ct value 2

Ct v

alue

1

RPD0.67%

CCND2p<0.0001R2=0.96


0.0000 0.0005 0.0010

0.00

20.

004

ANRIL

p14

0.000 0.002 0.004

0.01

0.02

p14

p16

0.00 0.01 0.02

0.00

050.

0010

p16

ANR

IL

0.00 0.01 0.02

0.02

0.04

p16

p15

p<0.0001R2=0.42

p<0.0001R2=0.83

p<0.0001R2=0.37

p<0.0001R2=0.52

Supplemental Figure 2. CDKN2A/B locus gene expression in human islets normalized to GAPDH. As observed when normalized to ACTIN, CDKN2A/B locus genes when normalized to islet GAPDH abundance revealed a high degree of correlation between p14-p16-ANRIL (A-C). p15 and MTAP showed little or no correlation with p14, p16 or ANRIL (D-G). However, p15 and MTAP expression correlated with each other. mRNA abundance is expressed as delta-Ct, normalized to GAPDH. Line, p values and R-squared values were calculated by linear regression; n=95 for all panels. Red lines high-light correlations with higher R-squared values.

A B C D

HE F G p<0.01R2=0.08

p=ns

p=ns p=ns

0.00 0.02 0.04

0.02

0.04

p15

MTA

P

0.0000 0.0005 0.0010

0.02

0.04

ANRIL

MTA

P

0.0000 0.0005 0.0010

0.02

0.04

ANRIL

p15

0.000 0.002 0.004

0.02

0.04

p14

p15


A B C

D E

Supplemental Figure 3. Donor BMI did not correlate with locus gene expression. (A-E) Reported donor BMI was compared with islet gene expression by univariate linear regression analysis. No locus gene mRNA abundance was correlated with BMI. mRNA abundance is expressed as delta-Ct, normalized to actin. Statistical analysis was calculated by linear regres-sion; n=92 for all panels. Missing values include the 3 islet samples for which donor BMI was not available.

0 20 40 60

0.00

20.

004

BMI

p14

0 20 40 60

0.02

0.04

BMI

p15

0 20 40 60

0.01

0.02

BMI

p16

0 20 40 60

0.00

050.

0010

BMI

ANR

IL

0 20 40 60

0.02

0.04

BMI

MTA

P


A B C

D E F

Supplemental Figure 4. Locus gene expression did not vary by donor sex. (A-E) No relationship was observed between locus gene expression and donor sex. F: female; M: male. (F) The mean age of male donors was signi�cantly lower than that of females, owing to an unfortunately high number of teenage and young-adult male donors. After age-matching the male and female cohorts (by removing all samples from both sexes with age < 27 years), reanalysis con�rmed that no locus gene expression correlated with donor sex (data not shown). mRNA abundance is expressed as delta-Ct, normalized to actin. Statistical analy-sis was calculated by Student’s t-test; n=42 females and 48 males for all panels. Missing values include the 5 islet samples for which donor sex was not available.

p<0.05

0.00

050.

0010

ANR

IL0.

002

0.00

4

p14

0.01

0.02

0.03

p15

0.01

0.02

p16

0.02

0.04

MTA

P

020

4060

80

Don

or A

ge

F M F M F M

F M F M F M


A B C

D E F

Supplemental Figure 5. Locus gene expression did not di�er by donor ethnici-ty. (A-E) No relationship was observed between locus genes and donor ethnicity. (F) Mean donor age was not di�erent in the ethnicity categories. mRNA abundance is expressed as delta-Ct, normalized to actin. Statistical analysis was calculated by ANOVA; p=ns for all comparisons. Sample sizes are n=1 (Asian), n=8 (black), n=14 (hispanic) and n=66 (white). Missing values include the 6 islet samples for which donor ethnicity was not available.

0.00

000.

0005

0.00

10

ANR

IL0.

000

0.00

20.

004

p14

0.01

0.02

0.03

p15

0.00

0.01

0.02

p16

0.01

0.02

0.03

MTA

P

0

20

40

60

80

Don

or A

ge

Asi

anB

lack

His

pani

cW

hite

Asi

anB

lack

His

pani

cW

hite

Asi

anB

lack

His

pani

cW

hite

Asi

anB

lack

His

pani

cW

hite

Asi

anB

lack

His

pani

cW

hite

Asi

anB

lack

His

pani

cW

hite


p=0.03, R2=0.20p=0.001, R2=0.16

p=0.0002, R2=0.46p=ns

CC+CTAll panels: rs10811661 TT C=protective, T=risk

Supplemental Figure 6. Age interacts with genotype at rs10811661 to determine ANRIL abundance; young donors with protective alleles had lower ANRIL expression. Similar analysis to Figure 4 for rs2383208. (A-B) Expressing p16 (A) or ANRIL (B) abundance as a function of donor age, strati�ed by geno-type, showed that unlike p16, age-dependence of ANRIL was driven by samples of rs10811661-CC+CT geno-type and was absent in samples with TT genotype. (C-D) Binning analysis of the cohort (non-juvenile sam-ples separated by quartiles) illustrated an age-dependent ANRIL increase in CC+CT samples but not in homozygous-risk TT samples. Juveniles <10 years of age showed markedly di�erent biology, independent of genotype. (E-F) In younger donors (ages 10-50; lower threshold de�ned by juvenile cuto� and upper thresh-old de�ned by the intersection of the regression curves in (B), which is 50.8) homozygous-risk increased ANRIL abundance. mRNA abundance is expressed as delta-Ct, normalized to actin. Statistics by linear regres-sion (A-B), ANOVA (D-F) with overall ANOVA signi�cance in upper left corner and signi�cant pairwise com-parisons after correction for multiple comparisons labeled. Sample size: (A-D) n=92 (3 samples missing age) and (E-F) n=57 samples between the ages of 10-50.

<10

15-3

132

-44

45-5

152

-68

<10

15-3

132

-44

45-5

152

-68

p<0.05p<0.05

p<0.01

p=nsp<0.01

<10

15-3

132

-44

45-5

152

-68

<10

15-3

132

-44

45-5

152

-68

p=0.05 p<0.05A C E

B D F p<0.05

p=ns

CC CT TT

CC CT TT

0 20 40 60 800.000

0.005

0.010

0.015

0.020

0.025

Donor age

p16

0 20 40 60 800.0000

0.0005

0.0010

0.0015

Donor age

ANR

IL

0.00

0.01

0.02

p16

0.00

000.

0005

0.00

10

ANR

IL

0.00

0.01

0.02

p16

0.00

000.

0004

0.00

08

ANR

IL


Supplemental Figure 7. Haplotype analysis showing rs2383208 comparisons. Related to the analysis in Figure 5 for rs10811661. (A-B): For p16, in homozygous-protective rs564398-CC samples, homozy-gous-risk rs2383208 increased expression. The same comparison for p14 did not meet FDR<10% (q-value 17%); for ANRIL, FDR>20% (not shown). (C-D): rs10757283 and rs2383208 may collaboratively regulate p15 expression; for MTAP for the same comparison, FDR>20%. * indicates FDR<10%, our pre-determined risk tolerance for future experiments exploring haplotype hypotheses. mRNA abundance is expressed as delta-Ct, normalized to actin. n=95 for all panels. All other inter-SNP comparisons, both shown and not shown, resulted FDR >10% or had insu�cient data points to evaluate (de�ned as n=2 or fewer). FDR, False Discovery Rate.

C D

A B

0.00

0.01

0.02

0.03

p16

rs564398 CC CT TT

*

0.000

0.001

0.002

0.003

0.004

0.005

p14

rs564398 CC CT TT

All panels: rs2383208 GG+GA rs2383208 AA

0.00

0.01

0.02

0.03

0.04

0.05

p15

*

rs10757283 CC CT TT0.00

0.01

0.02

0.03

0.04

MTA

P

rs10757283 CC CT TT


Supplemental Figure 8. Insulin secretion index correlated with donor BMI but was not related to age, sex or isolation center. (A) Insulin secretion index positively correlated with donor BMI. Vertical dotted lines represent demarkations between normal weight (18-25), overweight (25-30) and obese (>30). (B-D) Insulin secretion index was not related to donor age (B), donor sex (C), or isolation center (D). Numerals I-VI refer to the six di�erent islet isolation centers where the IIDP islet samples originated; we do not have insulin secretion data from any non-IIDP samples used in this study. (A-B): n=61; line, p values and R-squared values were calculated by linear regression. (C): n=31 (F), n=30 (M); mean +/- SD, p value by Student’s t-test. (D): n=61 samples from all isolation centers combined; mean +/- SD, ANOVA with Bonfer-roni correction for multiple comparisons.

A B

C D

p<0.01R2=0.13

p=ns

p=nsp=ns

0 20 40 60 800

5

10

15

Donor age

Insu

lin s

ecre

tion

inde

x

10 20 30 40 50 600

5

10

15

Donor BMI

Insu

lin s

ecre

tion

inde

x

F M0

5

10

Insu

lin s

ecre

tion

inde

x

I II IIIIslet isolation center

IV V VI02468

10

Insu

lin s

ecre

tion

inde

x


F F

F

F

5 mM glucose

Insulin BrdU Dapi

15 mM glucose

F

Supplemental Figure 9. Images of dispersed human islet cells cultured for BrdU analysis. Human islets were rested overnight, dispersed using trypsin, and cultured on coverslips for 96 hours in 5mM or 15mM glucose with BrdU included for the entire 96 hours. Coverslips were �xed in paraformaldehyde, immunostained for insulin and BrdU, mounted in Dapi-containing media, and imaged using �uorescent microscopy. Images were blinded and manually counted for total insulin(+) cells and % of insulin(+) BrdU(+) cells to generate the data shown in Figure 7. F, BrdU-staining �broblast. Arrows denote BrdU(+) Insulin(+) cells.


0.0 0.5 1.0 1.5 2.00.00

0.05

0.10

0.15

BrdU 5mM

CC

ND

2

0.0 0.5 1.0 1.5 2.00.00

00.

005

0.01

00.

015

0.02

0

BrdU 5mM

KI67

0.0 0.5 1.0 1.5 2.00.00

00.

002

0.00

40.

006

BrdU 5mM

PCN

A

Supplemental Figure 10. Cell cycle gene expression (on whole islets under basal (5mM) glucose culture conditions, same samples as all previous gene expression data), shown in relation to dispersed islet cell BrdU incorporation in basal (5mM) glucose conditions. PCNA was marginally correlated with BrdU%; KI67 and CCND2 were not. mRNA abundance is expressed as delta-Ct, normalized to actin. BrdU 5mM: % of insulin(+) cells that were also BrdU(+) cells, in 5mM glucose cultures. Statistical analysis was calculated by linear regression; n=46 all panels.

p<0.05R2=0.10 p=ns p=ns


Supplemental Figure 11. Topologically Associated Domain analysis of the human CDKN2A/B locus by Hi-C of lymphoblastoid cell line GM12878. Assmebly: hg19. (A) 25 kb resolution, and (B) 10 kb reso-lution. Data are from Rao et al, Cell 159(7):1665-1680 (2014), and analysis is by the Yue lab website http://promoter.bx.psu.edu/hi-c/view.php and "The 3D Genome Browser: a web-based browser for visual-izing 3D genome organization and long-range chromatin interactions." http://biorxiv.org/content/ear-ly/2017/02/27/112268, Biorxiv, 2017.

A. resolution 25 kb

B. resolution 10 kb


Supplemental Table 1: Donor characteristics and data obtained for each islet preparation

Sex Age BMI Ethinicity Cause of DeathSNP

genotype and RNA

Insulin secretion

Beta cell proliferation

M 42 22.76 Black Anoxia x x xM 40 38.91 White Unknown x x xF 54 22.6 White Cerebrovascular/stroke x xF 51 35.6 White Anoxia x x xF 38 33.1 White Anoxia x x xM 22 32.1 Hispanic Head trauma x xF 47 34.5 Hispanic Cerebrovascular/stroke x xF 39 45.2 White Anoxia x x xM 40 35.4 White Unknown x xM 46 29.3 Unknown Cerebrovasular/stroke x xM 22 40.2 White Head trauma x xM 1.8 18.7 White anoxia xF 0.2 20.8 Hispanic Anoxia xM 48 31.2 White Cerebrovascular/stroke x x

Unknown Unknown Unknown Unknown Unknown xM 57 29.8 White Head Trauma x xF 45 27.4 White Cerebrovascular/stroke x x xF 51 28.7 White Cerebrovascular/stroke x x xM 25 33.8 Hispanic Head Trauma x x xF 50 41.3 Hispanic Cerebrovascular/stroke x xF 49 36.9 White Cerebrovascular/stroke x x xF 52 35.2 White Cerebrovascular/stroke x xF 47 29.9 Black Cerebrovascular/stroke x xF 61 31 Black Cerebrovascular/stroke x xF 32 39.4 White Unknown x x xF 8 16.1 White Cerebrovascular/stroke xF 29 21 White Head trauma x x xM 7 26.6 White Anoxia xM 52 29 White Anoxia x xF 52 31.4 White Cerebrovascular/stroke x x xM 28 32.8 White Cerebrovascular/stroke x x xM 15 23 White Head trauma x x xM 63 38.6 White Anoxia x x xM 35 32 Hispanic Cerebrovascular/stroke x x xM 24 29.4 White Head trauma x xF 36 42.7 White Anoxia x x xM 18 27.9 White Cerebrovascular/stroke x x xM 55 33.4 White Cerebrovascular/stroke x xF 45 32.9 White Cerebrovascular/stroke x x xF 39 22.8 White Cerebrovascular/stroke x xF 56 21.43 White Chronic back pain/stroke xM 19 34.1 Hispanic Head trauma x x xM 25 26 White Cerebrovascular/stroke x x xM 32 27.8 Black Head trauma x x xF 49 31.6 White Cerebrovascular/stroke x xF 58 19.2 White Anoxia x x xM 21 24.8 White Head trauma x x xF 45 26.6 White Cerebrovascular/stroke x x xF 56 33.4 Black Cerebrovascular/stroke x x xM 35 28.5 Asian Head trauma x x xM 30 56.8 White Anoxia x xM 63 21.9 White Cerebrovascular/stroke x x x


F 54 30.1 White Cerebrovascular/stroke x x xM 20 19.8 White Anoxia x x xF 52 26.8 Black Cerebrovascular/stroke x x xM 37 30.5 White Head trauma x x xF 53 31.9 White Cerebrovascular/stroke x x xF 59 28.2 Hispanic Cerebrovascular/stroke x x xF 33 34.2 Black Cerebrovascular/stroke x x xF 40 23 Hispanic Cerebrovascular/stroke x x xM 47 31 White Anoxia x x xM 1.3 22 White Head trauma/Blunt Injury xF 44 34.5 White Cerebrovascular/stroke x x xF 47 36.4 Black Anoxia x x xM 15 24.6 Hispanic Head trauma x xM 49 26.47 White Head trauma/Blunt Injury x xF 59 22 White Cerebrovascular/stroke x x xM 59 26.8 White Anoxia x x xM 68 29.7 White Cerebrovascular/stroke x x xM 60 31.3 White Head trauma x x xM 60 37.9 White Anoxia x x xF 37 25.07 Hispanic Anoxia x

Unknown Unknown Unknown Unknown Unknown xUnknown 29 42.2 Unknown Unknown x

F 40 27.8 White Cerebrovascular/stroke xM 28 32 Hispanic Cerebrovascular/stroke xF 27 26.9 White Cerebrovascular/stroke xM 51 30.2 White Cerebrovascular/stroke xM 52 33.7 White Cerebrovascular/stroke xF 51 23.9 White Unknown xM 30 26.5 Hispanic Unknown xM 30 26.9 White Head trauma xF 63 36.6 White Cerebrovascular/stroke x

Unknown Unknown Unknown Unknown Unknown xM 45 25.49 White Head trauma xM 54 39.2 White Blunt Head Trauma MVA xM 17 32.5 White Unknown xM 51 28.1 Hispanic Unknown x

Unknown 56 26.7 Unknown Unknown xF 35 37 White Cerebrovascular/stroke xM 36 51.9 White Cerebrovascular/stroke xF 40 28.4 White Unknown xF 33 22.3 White Cerebrovascular/stroke xM 35 46.1 White Unknown xM 48 29 White Cerebrovascular/stroke x


Supplemental Table 2: Minor Allele Frequenciesrs2383208 rs10757283 rs10811661 rs564398

G/G C/C C/C C/CG/A C/T C/T C/TA/A T/T T/T T/TG T C CA T T T

All (n=95) 16.3% 46.8% 15.3% 36.8%

Black (n=8) 6.3% 37.5% 0.0% 6.3%

Hispanic (n=13) 10.7% 42.9% 10.7% 28.6%

Caucasian (n=62) 18.9% 50.0% 18.2% 42.4%

Observed minor allele frequency in 1000

GenomesEUR 17.3% 43.9% 16.8% 41.4%

Supplemental Table 2. For each SNP the minor allele, risk allele (in red) and observed minor allele frequency (MAF) in this human islet cohort are described. These MAFs are similar to those reported in the large populations tested in the 1000 genomes project (data are shown for EUR, since we have too few samples in non-white categories for accurate comparison).

Observed minor allele frequency in this

human islet cohort

CDKN2A/B LocusT2D SNPs tested

Minor alleleRisk allele


Supplemental Table 3: Linkage Disequilibrium

Expected LD (R2) (from EUR population)rs564398 rs2383208 rs10811661 rs10757283

rs564398 0.006 0.007 0.016rs2383208 0.006 0.952 0.256rs10811661 0.007 0.952 0.258rs10757283 0.016 0.256 0.258

Observed LD (R2) in our samplesrs564398 rs2383208 rs10811661 rs10757283

rs564398 0.040 0.046 0.001rs2383208 0.040 0.924 0.221rs10811661 0.046 0.924 0.204rs10757283 0.001 0.221 0.204O

bserved in our

samples (n

=95)

Expe

cted

(EUR

popu

latio

n) by

LDpair

Supplemental Table 3. Expected LD (R2) (top chart) were calculated using the LDpair function on the NIH-‐supported LDlink website. The EUR population was selected since the majority of

our samples were Caucasian. The observed LD (R2) in our samples (bottom chart) were calculated using the cubeX web tool at http://www.oege.org/software/cubex/.


Supplemental Table 4: Multivariable linear models testing relationships between donor characteristics and gene expression

Sex Point SE p Point SE p Point SE p Point SE p Point SE pFemale v. Male 0.0869 0.1578 0.5819 0.9949 1.0280 0.3331 0.8965 0.8675 0.3014 0.0410 0.0426 0.3362 0.6714 1.0426 0.5196

Race/ethnicityBlack v. White -0.2308 0.2706 0.3937 -2.7309 11.7634 0.1215 -0.6622 1.4880 0.6563 0.0145 0.0731 0.8428 -2.8995 1.7884 0.1050Other v. White -0.0723 0.2016 0.7198 1.2057 1.3136 0.3587 0.0125 1.1084 0.9910 -0.0177 0.0545 0.7454 -0.0933 1.3322 0.4836

Age, per year 0.0186 0.0051 0.0003 -0.0641 0.0332 0.0535 0.1099 0.0280 <0.0001 0.0030 0.0014 0.0304 -0.0178 0.0337 0.5961

BMI, per kg/m2 -0.0085 0.0107 0.4281 0.0383 0.0697 0.5829 0.0212 0.0588 0.7181 -0.0003 0.0029 0.9063 -0.0108 0.0707 0.8788

Above model, integrating mRNA expression of other locus genesp14 - - - 2.8584 1.1627 0.0140 4.8178 0.3939 <0.0001 0.1584 0.0424 0.0002 -0.0666 1.2224 0.9566p15 0.0209 0.0085 0.0140 - - - -0.0892 0.0533 0.0944 0.0000 0.0039 0.9992 0.5201 0.0900 <0.0001p16 0.1269 0.0104 <0.0001 -0.3209 0.1918 0.0944 - - - 0.0033 0.0074 0.6577 0.1248 0.1980 0.5285

ANRIL 0.8076 0.2164 0.0002 0.0028 2.7079 0.9992 0.6317 1.4258 0.6577 - - - 3.8463 2.7320 0.1592MTAP -0.0005 0.0086 0.9566 0.5005 0.0866 <0.0001 0.0334 0.0529 0.5285 0.0053 0.0038 0.1592 - - -

Supplemental Table 4. Exploratory multivariable model testing for impact of donor characteristics (sex, race, age, BMI) on gene expression confirmed a positive relationship between donor age and islet abundance of p14, p15 (marginal association; inverse relationship), p16 and ANRIL. This model did not uncover an impact of sex, race or BMI on expression of these genes. Integrating expression of other locus genes into the model (lower rows) confirmed a significant positive correlation between p14-ANRIL, p14-p16 and p15-MTAP, as well as p14-p15 (weaker correlation). Point estimate is the difference in gene expression conferred by comparator condition to control condition, or the incremental increase in gene expression conferred by higher amount for linear input variables such as age, BMI and gene expression. SE, standard error of the point estimate. Other: combined all samples of non-white race, since sample size was too small to analyze for those other than black or white. BMI, body mass index.

p14 p15 p16 ANRIL MTAP


Supplemental Table 5: List of SNPs in linkage disequilibrium with SNPs tested in this study

chr pos (hg38) LD(r²) LD(D') variant Ref Alt AFR freq AMR freq ASN freq EUR freq9 22133285 0.93 0.98 rs10965250 G A 0.05 0.14 0.44 0.169 22134069 0.93 0.98 rs10811660 G A 0.05 0.14 0.44 0.169 22134095 0.93 0.98 rs10811661 T C 0.05 0.14 0.44 0.169 22134254 0.93 0.97 rs10811662 G A 0.1 0.15 0.43 0.179 22132730 0.95 0.99 rs10965247 A G 0.05 0.14 0.44 0.169 22132879 0.96 0.98 rs10965248 T C 0.07 0.14 0.44 0.179 22132699 0.97 0.99 rs10965246 T C 0.07 0.14 0.44 0.179 22132077 1 1 rs2383208 A G 0.17 0.14 0.4 0.17

chr pos (hg38) LD(r²) LD(D') variant Ref Alt AFR freq AMR freq ASN freq EUR freq9 22136490 0.82 0.99 rs1333051 A T 0.08 0.09 0.15 0.149 22132077 0.93 0.98 rs2383208 A G 0.17 0.14 0.4 0.179 22132730 0.94 0.97 rs10965247 A G 0.05 0.14 0.44 0.169 22132699 0.96 0.99 rs10965246 T C 0.07 0.14 0.44 0.179 22132879 0.97 1 rs10965248 T C 0.07 0.14 0.44 0.179 22134254 0.99 1 rs10811662 G A 0.1 0.15 0.43 0.179 22133285 1 1 rs10965250 G A 0.05 0.14 0.44 0.169 22134069 1 1 rs10811660 G A 0.05 0.14 0.44 0.169 22134095 1 1 rs10811661 T C 0.05 0.14 0.44 0.16

chr pos (hg38) LD(r²) LD(D') variant Ref Alt AFR freq AMR freq ASN freq EUR freq9 22134303 0.91 0.99 rs7019437 C G 0.23 0.45 0.63 0.419 22134652 0.98 0.99 rs7019778 A C 0.23 0.46 0.64 0.439 22133646 0.99 1 rs10217762 T C 0.2 0.45 0.63 0.439 22133985 0.99 1 rs10757282 T C 0.23 0.45 0.64 0.439 22134173 1 1 rs10757283 C T 0.45 0.47 0.64 0.43

chr pos (hg38) LD(r²) LD(D') variant Ref Alt AFR freq AMR freq ASN freq EUR freq9 22015998 0.81 0.91 rs1101329 C T 0.01 0.21 0.1 0.419 22043613 0.81 0.99 rs1412830 C T 0.01 0.19 0.1 0.379 22021173 0.82 0.91 rs597816 T C 0.01 0.21 0.1 0.419 22007358 0.84 -‐0.99 rs3217977 CA C 0.96 0.75 0.9 0.559 22056360 0.84 -‐0.93 rs7866783 A G 0.98 0.79 0.9 0.599 22011643 0.85 0.98 rs573687 G A 0.01 0.19 0.1 0.389 21999329 0.86 0.95 rs2811713 G A 0.01 0.19 0.18 0.399 22009699 0.86 -‐1 rs2069418 G C 0.98 0.77 0.9 0.559 22049480 0.88 -‐0.94 rs200059580 A ACT 0.99 0.8 0.9 0.599 22015466 0.89 0.98 rs1101330 C A 0.02 0.19 0.1 0.399 22051671 0.89 -‐0.95 rs944801 G C 0.99 0.79 0.9 0.599 22052735 0.89 -‐0.95 rs6475604 T C 0.99 0.8 0.9 0.599 22054041 0.89 -‐0.95 rs7030641 C T 0.99 0.79 0.9 0.599 22003368 0.9 -‐0.99 rs1063192 G A 0.99 0.79 0.82 0.579 22019130 0.9 1 rs523096 A G 0.01 0.23 0.1 0.439 22019674 0.9 1 rs518394 G C 0.01 0.23 0.1 0.439 22022377 0.9 0.99 rs581876 C T 0.01 0.19 0.32 0.399 22026078 0.9 1 rs615552 T C 0.01 0.22 0.1 0.439 22045318 0.9 -‐0.96 rs1360589 C T 0.99 0.79 0.9 0.589 22040766 0.94 -‐0.98 rs1333037 C T 0.99 0.79 0.9 0.589 22036113 0.95 -‐0.99 rs1008878 G T 0.97 0.79 0.9 0.589 22036368 0.95 -‐0.99 rs1556515 C T 0.97 0.79 0.9 0.589 22031006 0.96 -‐1 rs7865618 G A 0.99 0.8 0.9 0.589 22033367 0.96 -‐1 rs2157719 C T 0.99 0.8 0.9 0.589 22028213 0.98 1 rs142048183 CAT C 0.01 0.2 0.1 0.419 22043927 0.98 0.99 rs1412829 A G 0.01 0.21 0.1 0.419 22026595 0.99 1 rs613312 G A 0.01 0.2 0.1 0.419 22026640 0.99 1 rs543830 A T 0.01 0.2 0.1 0.419 22027403 0.99 1 rs599452 G A 0.01 0.2 0.1 0.419 22029081 0.99 1 rs679038 G A 0.02 0.2 0.1 0.419 22032153 0.99 1 rs634537 T G 0.02 0.21 0.1 0.419 22029548 1 1 rs564398 T C 0.01 0.2 0.1 0.41

rs2383208

rs10811661

rs10757283

rs564398

Supplemental Table 5. Multiple SNPs are in linkage disequilibrium with the T2D-‐associated CDKN2A/B SNPs genotyped in this

study. Data in this table include all SNPs in HaploReg with LD(r2) > 0.80, from the EUR population data (chosen because the majority of our samples were Caucasian). T2D SNPs genotyped for this study are in red font. Allele frequencies in AFR, AMR, ASN, EUR are included for reference. Pos, position. Ref, reference allele. Alt, alternate allele.


Supplemental Table 6: Multivariable linear models testing for SNP impact on gene expression

point SE p point SE p point SE p point SE p point SE prs2383208

A/A v. G/A 0.1151 0.1679 0.4931 2.0529 1.7650 0.0565 0.4085 0.9206 0.6572 0.0635 0.0450 0.1585 -0.4870 1.1066 0.6599A/A v. G/G 0.2909 0.5227 0.5779 2.3846 3.3504 0.4766 3.1359 2.8652 0.2738 0.0817 0.1401 0.5598 -3.7195 -3.4444 0.2802

number of risk alleles 0.1234 0.1467 0.4003 1.8169 0.9414 0.0536 0.7264 0.8064 0.3677 0.0573 0.0394 0.1455 -0.8634 0.9693 0.3731

rs10811661T/T v. C/T 0.0473 0.1742 0.7862 2.1538 1.1138 0.0531 0.0192 0.9540 0.9839 0.0566 0.0467 0.2259 -0.5684 1.1453 0.6197T/T v. C/C 0.2730 0.5242 0.6025 2.4398 3.3510 0.4666 3.0297 2.8701 0.2911 0.0806 0.1406 0.5667 -3.7490 3.4458 0.2766

number of risk alleles 0.0729 0.1513 0.6297 1.8850 0.9679 0.0515 0.4498 0.8316 0.5886 0.0519 0.0406 0.2008 -0.9444 0.9963 0.3432

rs10757283T/T v. C/T -0.0318 0.2011 0.8742 1.9756 1.2644 0.1182 0.5558 1.1087 0.6162 -0.0274 0.0548 0.6178 2.5824 1.3227 0.0509T/T v. C/C -0.3073 0.2179 0.1586 -1.5806 1.3704 0.2488 -0.9627 1.2017 0.4231 -0.0600 0.0594 0.3127 1.8335 1.4336 0.2009

number of risk alleles -0.1637 0.1085 0.1316 -1.0181 0.7081 0.1505 -0.5668 0.6011 0.3458 -0.0302 0.0295 0.3058 0.7796 0.7215 0.2799

rs564398T/T v. C/T 0.0252 0.1692 0.8814 0.4790 1.0970 0.6624 0.1187 0.9339 0.8988 -0.0102 0.0459 0.8236 -0.1792 1.1199 0.8729T/T v. C/C 0.2564 0.2379 0.2810 2.2636 1.5427 0.1423 0.8902 1.3133 0.4979 -0.0336 0.0646 0.6032 1.2535 1.5749 0.4261

number of risk alleles 0.1049 0.1133 0.3547 0.9840 0.7350 0.1806 0.3712 0.6248 0.5525 -0.0153 0.0307 0.6183 0.4443 0.7510 0.5541

p14 p15 p16 ANRIL MTAP

Supplemental Table 6. Exploratory multivariable model testing for impact of CDKN2A/B locus T2D SNP genotype, incorporating donor characteristics (sex, race, age, BMI from Supplemental Table 2) on gene expression failed to reveal a significant impact of any genotype on any gene expression. Point estimate is the difference in gene expression relative to protective genotype (individual comparisons) or incremental gene expression for each additional risk allele at that SNP (number of risk alleles). SE, standard error of the point estimate. Risk allele is depicted in red for each SNP.


Supplemental Table 7: Multivariable models testing determinants of insulin secretion indexmean difference in

insulin secretion index p-‐value

Female v. Male -‐0.25 ( -‐1.03 -‐ 0.53 ) 0.5318

age (per year) 0.00 ( -‐0.03 -‐ 0.03 ) 0.8951

African-‐American v. White 0.02 ( -‐1.09 -‐ 1.13 ) 0.974

BMI (per kg/m2) 0.08 ( 0.03 -‐ 0.14 ) 0.0029

Female v. Male -‐0.32 ( -‐1.05 -‐ 0.41 ) 0.3873

age (per year) 0.00 ( -‐0.03 -‐ 0.03 ) 0.9665

African-‐American v. White -‐0.10 ( -‐1.14 -‐ 0.95 ) 0.8588

BMI (per kg/m2) 0.10 ( 0.02 -‐ 0.17 ) 0.0171

Supplemental Table 7. Multivariable model testing for impact of donor characteristics (sex, age, race, BMI) on insulin secretion index confirmed a positive relationship between donor BMI and insulin secretion, but did not uncover an impact of sex, race or age. Integrating islet isolation center into the model using a generalized estimating equation (GEE) approach to adjust for potential clustering of insulin secretion measurements within islet isolation centers (lower rows) confirmed a significant positive correlation between BMI and insulin secretion index (0.10 units of insulin secretion index per BMI unit, p=0.0171). In a multivariable linear model adjusted for sex, age, ethnicity and BMI, mean insulin secretion values from center IV (see Supplemental figure 8) were higher than the mean values from the other centers, but after Bonferroni adjustment none of the between-center mean comparisons were statistically significant (not shown). BMI, body mass index.

95% confidence interval

Isolation center not

includ

ed in m

odel

Isolation center

includ

ed in m

odel


Supplemental Table 8: Multivariable models testing for impact of SNP genotype on insulin secretion index

mean difference in insulin secretion p-‐value




-‐0.32 ( -‐1.16 -‐ 0.52 ) 0.4583 -‐0.25 ( -‐1.03 -‐ 0.53 ) 0.5316 -‐0.25 ( -‐1.03 -‐ 0.53 ) 0.5264 -‐0.24 ( -‐1.04 -‐ 0.55 ) 0.55030.00 ( -‐0.03 -‐ 0.03 ) 0.7976 0.00 ( -‐0.03 -‐ 0.03 ) 0.8704 0.00 ( -‐0.03 -‐ 0.03 ) 0.8717 0.00 ( -‐0.03 -‐ 0.03 ) 0.96850.05 ( -‐1.07 -‐ 1.17 ) 0.9330 0.07 ( -‐1.07 -‐ 1.21 ) 0.9031 0.04 ( -‐1.07 -‐ 1.16 ) 0.9387 0.19 ( -‐1.04 -‐ 1.43 ) 0.75730.08 ( 0.03 -‐ 0.14 ) 0.0030 0.08 ( 0.03 -‐ 0.14 ) 0.0041 0.08 ( 0.03 -‐ 0.14 ) 0.0039 0.09 ( 0.03 -‐ 0.14 ) 0.0021

no risk alleles v. two -‐0.03 ( -‐1.16 -‐ 1.11 ) 0.9655one risk allele v. two 0.17 ( -‐0.93 -‐ 1.27 ) 0.7661

one risk allele v. two 0.20 ( -‐0.74 -‐ 1.13 ) 0.6773

one risk allele v. two 0.18 ( -‐0.71 -‐ 1.07 ) 0.6961

no risk alleles v. two 0.02 ( -‐1.15 -‐ 1.19 ) 0.9692one risk allele v. two 0.45 ( -‐0.42 -‐ 1.32 ) 0.3123

Supplemental Table 8. In multivariable linear models adjusted for sex, age, race and BMI, and for potential clustering of insulin secretion index produced by different islet isolation centers (using a GEE approach), none of the four CDKN2A/B SNP genotypes had any significant association with insulin secretion index. These models confirmed the positive relationship between donor BMI and insulin secretion. BMI, body mass index.

rs10757283

rs10811661

rs2383208

rs564398

Female v. MaleAge (per year)African-‐American v. WhiteBMI (per kg/m2)

rs10757283 rs10811661 rs2383208 rs564398






Documents

CDKN2A/B T2D GWAS risk-SNPs impact locus gene expression ... · ABSTRACT Genome-wide association studies link the CDKN2A/B locus with T2D risk, but mechanisms increasing risk remain