Volume 3 Supplement 7

Genetic Analysis Workshop 16

Open Access

Epistatic interactions of CDKN2B-TCF7L2 for risk of type 2 diabetes and of CDKN2B-JAZF1 for triglyceride/high-density lipoprotein ratio longitudinal change: evidence from the Framingham Heart Study

  • Ping An1Email author,
  • Mary Feitosa1,
  • Shamika Ketkar1,
  • Avril Adelman1,
  • Shiow Lin1,
  • Ingrid Borecki1 and
  • Michael Province1
BMC Proceedings20093(Suppl 7):S71

DOI: 10.1186/1753-6561-3-S7-S71

Published: 15 December 2009

Abstract

Fifteen known type 2 diabetes (T2D) gene variants were assessed for their associations with T2D status in 228 T2D families from the Framingham Heart Study (FHS) Original, Offspring, and Children Cohorts. Bayesian approach was used to test single-single-nucleotide polymorphism (SNP) association followed by logistic regression. Bayesian and logic regression approaches were used to test multiple SNP association searching for the best combinations of variants followed by logistic regression reconfirmation. The significant variants for T2D risk were also tested for their main and interacting effects on triglyceride (TG)/high-density lipoprotein (HDL) ratio change derived from four point measures across time. This slope phenotype was made available using mixed model growth curve approach from 155 T2D families in the FHS Offspring Cohort.

Results

CDKN2B rs10811661 (p = 0.042), TCF7L2 rs4506565 (p = 0.004), and JAZF1 rs864745 (p = 0.04) were individually associated with risk of T2D (OR = 1.0-2.0; effect size <1%). CDKN2B and TCF7L2 were found with significant main (p = 0.02, 0.01) and interacting (p = 0.05) effects for increased (OR = 3.0) risk of T2D. CDKN2B and JAZF1 were found with significant main (p = 0.0002 and 0.034) and interacting (p = 0.001) effects on increased (β = 0.42) TG/HDL ratio longitudinal change. These interacting effects were independent of effects of age and sex with effect sizes of 0.3-0.4% for risk of T2D or TG/HDL ratio longitudinal change.

Conclusion

These synthetic approaches allowed for successful detection of CDKN2B and TCF7L2 interacting effect for T2D risk and CDKN2B and JAZF1 interacting effect on TG/HDL ratio increase over time among T2D families in the FHS. These interacting effects were consistent in conferring risk of T2Dor progressive insulin resistance with modest effect sizes.

Background

Fifteen replicated gene variants for type 2 diabetes (T2D) have been assessed [1]. While more variants remain to be uncovered, challenge exists in better understanding of the interplay among these variants underneath complex T2D pathophysiology and pathogenesis. These variants were either common with negligible effect or rare with relatively larger effect size [1]. In reality, it is very likely that the individual main effect of each variant is undetectable, but collectively their effect emerges. Concurrent increased triglyceride (TG) and decreased high-density lipoprotein (HDL) were characteristics of subjects with insulin resistance, mostly seen in patients with T2D. The inverse relationship and combined information of these two measures, TG/HDL ratio, represents a single inherited phenotype [2] as a surrogate for insulin resistance [3]. In this analysis, we hypothesized that multiple variants jointly confer T2D susceptibility, and we wanted to examine main and interacting effects of the reported variants in the Offspring Cohort of the Framingham Heart Study (FHS) using different statistical analysis approaches.

Methods

Subjects

All participants in the FHS were Caucasians. Their characteristics were described elsewhere [4]. In brief, participants with diabetes comprised 7.59% (28/369), 9.91% (242/2,441), and 2.38% (95/3,997) of the Original, Offspring, and Children Cohorts, respectively. Average age of diagnosis of diabetes at study visit were 66, 57, and 46 years in the three cohorts, respectively. From a total of 6,807 members in 1,157 families, 228 families had members with diabetes (3,217 members, 366 diabetes cases); 46% (1,489/3,217) were men. In the Offspring Cohort, 155 families had members with diabetes (727 members) who had valid fasting TG and HDL measures at 4 time points. In the Offspring Cohort families with member with diabetes, the average age was 33.8, 46.4, 53.3, and 60.2 years, average BMI was 25.2, 26.2, 27.6, and 28.3 kg/m2, average TG was 83.7, 108.5, 134.0, and 135.5 mg/dl, average HDL was 51.6, 52.6, 51.6, and 54.5 mg/dl, and average TG/HDL ratio was 1.9, 2.5, 3.1, and 3.0, at Visit 1, 3, 5, and 7, respectively. Every study subject provided written informed consent. The study was approved by Boston University Institutional Review Board.

Genotyping

Affymetrix 100 k single-nucleotide polymorphisms (SNPs) and genotype annotation resources were described elsewhere [4]. Identical variants or variants in the same intron/exon regions were identified using Affymetrix 500 k SNPs.

Statistical analysis

Covariates included age, age2, and sex. BIMBAM (Bayesian imputation-based association mapping) [5] was used to assess single-SNP effect according to Bayes Factor (BF) and p-value (based on 10,000 permutations). This was followed by logistic-GEE (generalized estimating equations) single-SNP test under general and additive assumptions. BIMBAM output corrected p for multiple testing. BIMBAM was further used to identify the best multiple-SNP combination with the highest BF. LOGREG [6] was also used to find the best multiple SNP combination as well as parameter estimates. Findings from these two approaches were compared. The best interacting variants may be tested for main and interacting effects using the logistic-GEE approach. Further, TG/HDL ratios at the four visits in the families in the Offspring Cohort with member with diabetes were used to derive TG/HDL ratio change (slope) via the mixed growth curve model. The mixed sandwich estimator approach was used to assess associations between the identified interacting variants and the slope phenotype.

Results

Single-SNP test results are presented in Table 1 (BIMBAM) and Table 2 (logistic-GEE, general model). Three variants were found to significant under additive assumption: CDKN2B rs10811661 (p = 0.0011), TCF7L2 rs4506565 (p = 0.0043), and JAZF1 rs864745 (p = 0.0402). Multiple SNP test results are also given in Table 1. LOGREG results, consistent with the BIMBAM finding, suggested almost the same best interacting variants. Interaction between CDKN2B rs10811661 and TCF7L2 rs4506565 was significantly detected using the logistic-GEE model (OR = 3.0, p = 0.02 and 0.01 and 0.05 for main and interacting effects). For the slope trait, single-SNP test results were non-significant for CDKN2B rs10811661 (p = 0.4621), but significant for TCF7L2 rs4506565 (p = 0.0379) and JAZF1 rs864745 (p = 0.0349) in the mixed sandwich estimator additive model. Interaction between CDKN2B rs10811661 and TCF7L2 rs4506565 variants were significant (β = 1.6667, p = 0.0002, 0.0341, and 0.0012 for main and interacting effects), and it was associated with high slope values, which may be expressed as an increase in insulin resistance across ages.
Table 1

Search results for best single and multiple SNP associations with T2D status

Method

Gene

Chr

Position

RAFa

Bayes Factor

Rank

P b

BIMBAM

       

rs10811661

CDKN2A/2B

9p

22124094

T, 0.82

0.978

1

0.0045

rs4506565

TCF7L2

10q

114748339

T, 0.36

0.591

2

0.0154

rs864745

JAZF1

7p

28147081

A, 0.52

0.041

3

0.0397

rs17030946

THADA

2p

43587013

T, 0.10

0.035

4

0.0771

rs7961581

TSPAN8-LGR5

12q

69949369

T, 0.70

-0.161

5

0.1298

rs10946398

CDKAL1

6p

20769013

C, 0.33

-0.284

6

0.2382

rs5215

KCNJ11

11p

17365206

C, 0.36

-0.292

7

0.0944

rs2793823

ADAM30

1p

120239241

G, 0.89

-0.366

8

0.4292

rs10282940

SLC30A8

8q

118257007

G, 0.89

-0.381

9

0.4606

rs5018648

WFS1

4p

6343719

G, 0.61

-0.457

10

0.2901

rs8050136

FTO

16q

52373776

A, 0.41

-0.457

11

0.4008

rs1801282

PPARG

3p

12368125

G, 0.11

-0.488

12

0.8047

rs564398

CDKN2A/2B

9p

22019547

G, 0.39

-0.517

13

0.5119

rs4402960

IGF2BP2

3q

186994381

G, 0.67

-0.601

14

0.4523

rs4607103

ADAMTS9

3p

64686944

C, 0.74

-0.613

15

0.8136

SNP pair 1

--

--

--

--

1.76

1, 2

--

SNP pair 2

--

--

--

--

1.064

1, 3

--

LOGREG

Gene

Chr

Position

RAF

Beta

Mode c

Logic

rs864745

JAZF1

7p

28147081

A, 0.52

0.03 (tree 1)

Rec

and

rs10811661

CDKN2A/2B

9p

22124094

T, 0.82

0.09 (tree 2)

Rec

and

rs4506565

TCF7L2

10q

114748339

T, 0.36

0.09 (tree 2)

Dom

and

rs5215

KCNJ11

11p

17365206

C, 0.36

0.09 (tree 2)

Dom

or

aRAF, risk allele and frequency

bPermutation-based p-values for single-SNP association tests with T2D status using BIMBAM

cRec, recessive coding; Dom, dominant coding

Table 2

Single-SNP association test results for T2D status confirmed by regression analysis

SNP

Gene

Chr

Position

RAF

OR (95% CI)

R2

P a

rs2793823

ADAM30

1p

120239241

G, 0.89

2.0 (0.3-13.0)

2.2 (0.4-14.1)

0.0007

0.4762

0.3905

rs17030946

THADA

2p

43587013

T, 0.10

1.2 (0.3-5.2)

0.8 (0.5-1.2)

0.0044

0.793

0.2617

rs4607103

ADAMTS9

3p

64686944

C, 0.74

1.1 (0.6-2.1)

1.1 (0.6-2.1)

0.0002

0.6784

0.3905

rs1801282

PPARG

3p

12368125

G, 0.11

1.2 (0.1-28.7)

0.9 (0.6-1.3)

0.0004

0.8895

0.625

rs4402960

IGF2BP2

3q

186994381

G, 0.67

1.2 (0.8-2.0)

1.2 (0.7-1.9)

0.0014

0.3728

0.5386

rs5018648

WFS1

4p

6343719

G, 0.61

1.1 (0.7-1.6)

1.0 (0.6-1.4)

0.0017

0.8127

0.8432

rs10946398

CDKAL1

6p

20769013

C, 0.33

1.2 (0.7-2.1)

1.3 (0.9-2.1)

0.0014

0.4013

0.1858

rs864745

JAZF1

7p

28147081

A, 0.52

1.5 (1.0-2.3)

1.1 (0.8-1.6)

0.0043

0.0427 b

0.6146

rs10282940

SLC30A8

8q

118257007

G, 0.89

1.3 (0.4-4.5)

1.3 (0.4-4.3)

0.0006

0.6785

0.7141

rs10811661

CDKN2A/2B

9p

22124094

T, 0.82

2.1 (1.0-4.4)

1.3 (0.6-2.9)

0.0061

0.0424

0.4428

rs564398

CDKN2A/2B

9p

22019547

G, 0.39

1.1 (0.7-1.9)

1.0 (0.8-1.4)

0.0007

0.5892

0.7728

rs4506565

TCF7L2

10q

114748339

T, 0.36

2.0 (1.2-3.1)

1.4 (0.9-2.0)

0.0045

0.0041

0.0932

rs5215

KCNJ11

11p

17365206

C, 0.36

1.0 (0.6-1.7)

0.7 (0.5-1.0)

0.0039

0.9201

0.0619

rs7961581

TSPAN8-LGR5

12q

69949369

T, 0.70

1.1 (0.7-1.8)

1.3 (0.8-2.2)

0.0024

0.6417

0.2377

rs8050136

FTO

16q

52373776

A, 0.41

1.1 (0.7-1.7)

0.9 (0.7-1.2)

0.001

0.6854

0.6667

aLogistic-GEE regression p for genotypes homozygous (line 1) and heterozygous (line 2) for a risk allele with genotype homozygous for a non-risk allele as reference.

bBold font indicates significant p-values.

Discussion

Our finding of TCF7L2 intron 3 variant for T2D is in line with a previous FHS genome-wide association study report [7] and a good number of replicating reports across large and independent study cohorts. Evidence of CDKN2B near/promoter and JAZF1 intron 1 variants for T2D risk was also found. We thought the T2D families in FHS may contain an enriched genetic component for SNP association assessment of T2D gene variants. Currently, functions of these two genes are yet not clear, but as is the case for TCF7L2 and other confirmed T2D genes [1], they may be directly or indirectly involved in pancreatic β-cell failure, insulin resistance, and lipid toxicity. Our finding of interactions among the three variants for T2D risk was interesting and novel, which may help disentangle underlying gene pathways and further our understanding of T2D etiology and pathophysiology.

Correction for multiplicity to minimize the false-positive declaration rate seems to be less sensitive for this analysis for two reasons. First, we aimed to identify and evaluate the best combination of SNPs from confirmed T2D variants. Second, the FHS T2D status information was obtained at the initial visit of the study, and 'new' T2D cases may have remained in the 'non-diabetic' control group after decades of follow-up. This would bias our association findings towards reduction of type 1 error. The results of BIMBAM were consistent with those of logistic-GEE analyses for the single-SNP main effect; likewise, BIMBAM and logic regression results for multiple-SNP joint/interacting effect were also consistent. These synthetic approaches apparently worked well in association detection for both main and joint effects of candidate gene variants with prior knowledge.

This 500 k SNP genotype data allowed us to examine 15 of the 18 confirmed exact variants (genotypic imputation was not applied). While reproducibility may be cohort-specific and sensitive to sample heterogeneity and size, it was a bit surprising that we could only identify three meaningful variants. It is very possible that this was due to the presence in the control groups of persons with diabetes who had not received a clinical diagnosis. We did not use a transmission-disequilibrium test-like FBAT program for this analysis simply because we wanted to maximize the sample size by using all available family information. We did not correct the data for potential effect of body max index because obesity coexisting with T2D is very likely rooted in the same pathway genes among most T2D patients, at least in most Western countries.

We were excited to find that the identified interacting variants also affected the TG/HDL ratio. Using the ROC/AUC (receiver operator characteristic/area under the curve) model, we found that it was an imperfect predictor of T2D with best sensitivity and specificity of approximately 75%. Applying a mixed growth curve approach to model longitudinal multiple measures of the TG/HDL ratio proved useful. The derived slope phenotype represented the TG/HDL ratio change across decades of observation time. This approach is advantageous in handling missing data points, as well as remote values, especially in intervention and interaction studies. Genetic heritability of the slope phenotype was 20%, and these three variants were individually and interactively associated with an increase of the slope phenotype. Previously, it was thought that the TCF7L2 variant was only associated with β-cell function failure but not insulin resistance [1]. Our current finding suggests that TCF7L2, together with JAZF1 variants, may indirectly impart insulin resistance through joint modulation of lipid metabolism.

In conclusion, using the FHS T2D family data and different statistical approaches, we found variants in the TCF7L2, CDKN2B, and JAZF1 genes were independently and interactively associated with increased T2D risk and the TG/HDL ratio change across ages. For the remaining T2D risk conferring gene variants, we did not find significant association evidence in this analysis.

List of abbreviations used

AUC: 

Area under the curve

BF: 

Bayes factor

BIMBAM: 

Bayesian imputation-based association mapping

GAW16: 

Genetic Analysis Workshop 16

GEE: 

Generalized-estimating equations

HDL: 

High-density lipoprotein

TG: 

Triglyceride

T2D: 

Type 2 diabetes

FHS: 

Framingham Heart Study

ROC: 

Receiver operator characteristic

SNP: 

Single-nucleotide polymorphism

Declarations

Acknowledgements

The Genetic Analysis Workshops are supported by NIH grant R01 GM031575 from the National Institute of Health and General Medical Sciences.

This article has been published as part of BMC Proceedings Volume 3 Supplement 7, 2009: Genetic Analysis Workshop 16. The full contents of the supplement are available online at http://www.biomedcentral.com/1753-6561/3?issue=S7.

Authors’ Affiliations

(1)
Division of Statistical Genomics and Department of Genetics, Washington University School of Medicine (Campus Box 8506)

References

  1. Lango H, UK Type 2 Diabetes Genetics Consortium, Palmer CN, Morris AD, Zeggini E, Hattersley AT, McCarthy MI, Frayling TM, Weedon MN: Assessing the combined impact of 18 common genetic variants of modest effect sizes on type 2 diabetes risk. Diabetes. 2008, 57: 3129-3135. 10.2337/db08-0504.PubMed CentralView ArticlePubMedGoogle Scholar
  2. Mahaney MC, Blangero J, Comuzzie AG, VanderBerg JL, Stern MP, MacCluer JW: Plasma HDL cholesterol, triglycerides, and adiposity. A quantitative genetic test of the conjoint trait hypothesis in the San Antonio Family Heart Study. Circulation. 1995, 92: 3240-3248.View ArticlePubMedGoogle Scholar
  3. Kannel WB, Vasan RS, Keyes MJ, Sullivan LM, Robins SJ: Usefulness of the triglyceride-high-density lipoprotein versus the cholesterol-high-density lipoprotein ratio for predicting insulin resistance and cardiometabolic risk (from the Framingham Offspring Cohort). Am J Cardiol. 2008, 101: 497-501. 10.1016/j.amjcard.2007.09.109.PubMed CentralView ArticlePubMedGoogle Scholar
  4. Cupples LA, Arruda HT, Benjamin EJ, D'Agostino RB, Demissie S, DeStefano AL, Dupuis J, Falls KM, Fox CS, Gottlieb DJ, Govindaraju DR, Guo CY, Heard-Costa NL, Hwang SJ, Kathiresan S, Kiel DP, Laramie JM, Larson MG, Levy D, Liu CY, Lunetta KL, Mailman MD, Manning AK, Meigs JB, Murabito JM, Newton-Cheh C, O'Connor GT, O'Donnell CJ, Pandey M, Seshadri S, Vasan RS, Wang ZY, Wilk JB, Wolf PA, Yang Q, Atwood LD: The Framingham Heart Study 100 K SNP genome-wide association study resource: overview of 17 phenotype working group reports. BMC Med Genet. 2007, 8 (Suppl 1): S1-10.1186/1471-2350-8-S1-S1.PubMed CentralView ArticlePubMedGoogle Scholar
  5. Servin B, Stephens M: Imputation-based analysis of association studies: candidate regions and quantitative traits. PloS Genetics. 2007, 3: e114-10.1371/journal.pgen.0030114.PubMed CentralView ArticlePubMedGoogle Scholar
  6. Ruczinski I, Kooperberg C, Le Blanc ML: Logic regression. J Comput Graph Stat. 2003, 12: 475-511. 10.1198/1061860032238.View ArticleGoogle Scholar
  7. Meig JB, Manning AK, Fox CS, Florez JC, Liu C, Cupples LA, Dupuis J: Genome-wide association with diabetes-related traits in the Framingham Heart Study. BMC Med Genet. 2007, 8 (Suppl 1): S16-10.1186/1471-2350-8-S1-S16.View ArticleGoogle Scholar

Copyright

© An et al; licensee BioMed Central Ltd. 2009

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Advertisement