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Variants in MTNR1B and CDKAL1 contributes independent additive effects to GDM-related traits in Mexican Americans

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Variants in MTNR1B and CDKAL1 contributes independent additive effects to GDM-related traits in Mexican Americans

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Content Variants in MTNR1B and CDKAL1 Contribute Independent Additive Effects to GDM-Related
Traits in Mexican Americans
by
HAORAN ZHANG
A Thesis Presented to the
FACULTY OF THE USC KECK SCHOOL OF MEDICINE
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(BIOSTATISTICS)
May 2024
Copyright 2024 HAORAN ZHANG

Dedication
To my committees, my parents, and my partner. Thank you for all of your support.
ii

Acknowledgements
We express my most sincere gratitude to the families who participated in Betagene study. We knowledge
the University of Southern California General Clinical Research Center as well as the faculties and staffs
who maintained the biomysql3 database and web server.
iii

Table of Contents
Dedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
0.0.1 Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
0.0.2 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
0.0.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii
0.0.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
0.0.5 Key Words . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
0.0.6 Abbreviation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x
Chapter 1: Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Chapter 2: Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Chapter 3: Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Chapter 4: Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
Chapter 5: Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
iv

List of Tables
4.1 Descriptive statistics of the sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
4.2 Univariate association of MTNR1B rs10830963 assuming a additive model . . . . . . . . . . 9
4.3 Univariate association of CDKAL1 rs7754840 assuming a additive model . . . . . . . . . . . 11
4.4 Multiplicative interaction between rs10830963 and rs7754840 on T2DQTs . . . . . . . . . . 12
4.5 Test for rate of change in genotypes assuming dominant genetic models . . . . . . . . . . 13
4.6 Univariate association between CDKAL1 rs7754840 and rate of change in genotypes
assuming dominant genetic models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
5.1 Test for interaction between rs7754840 and BFP on the effect of T2DQTs . . . . . . . . . . 20
v

List of Figures
4.1 Association between MTNR1B rs10830963 and rates of change in AIR (a), IVGTT 30min
∆insulin (b), DI (c), DI30 (d), and IGI (e) assuming a dominant genetic model. The rates of
change for CC genotype are denoted by circles and that for CG/GG genotype are denoted
by triangles in the graphs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4.2 Association between CDKAL1 rs7754840 and rates of change in AIR (a), IVGTT 30min
∆insulin (b), DI (c), DI30 (d), and IGI (e) after adjusting for age and sex assuming a
dominant genetic model. The rates of change for CC/CG genotype are denoted by circles
and that for GG genotype are denoted by triangles in the graphs. . . . . . . . . . . . . . . 15
5.1 Association between MTNR1B rs10830963 and rates of change in AIR (a), IVGTT 30min
∆insulin (b), DI (c), DI30 (d), and IGI (e) after adjusting for age, sex, and BFP assuming a
dominant genetic model. The rates of change for CC genotype are denoted by circles and
that for CG/GG genotype are denoted by triangles in the graphs. . . . . . . . . . . . . . . . 25
5.2 Association between CDKAL1 rs7754840 and rates of change in AIR (a), IVGTT 30min
∆insulin (b), DI (c), DI30 (d), and IGI (e) after adjusting for age, sex, and BFP assuming a
dominant genetic model. The rates of change for CC/CG genotype are denoted by circles
and that for GG genotype are denoted by triangles in the graphs. . . . . . . . . . . . . . . 25
vi

Abstract
Type 2 diabetes is a complicated public health problem worldwide. In this study, we will investigate the
effect of MTNR1B(rs10830963) and CDKAL1(rs7754840), two variations in genes that are possibly associated
with Type 2 Diabetes among Mexican-American population.
0.0.1 Hypothesis
In Mexican-American population, variants of CDKAL1 shows similar effect with variants of MTNR1B regarding the association between the absolute level of some characteristic traits related to Type 2 Diabetes,
as well as the rate of change. CDKAL1 possibly interacts with MTNR1B on their effect with T2DQTS.
0.0.2 Method
With a valid sample size up to 1895, we tested the univariate association between MTNR1B, CDKAL1 and 16
traits related to Type 2 Diabetes respectively using cross-sectional linkage method. We then tested if there
is any interaction between MTNR1B and CDKAL1 on their association with T2DM traits. This followed a
test for rate of change in the same phenotypes over a 3-5 years follow-up study of up to 380 observations
within the original sample.
vii

0.0.3 Results
After adjusting for age and sex, MTNR1B was associated with acute insulin response(AIR, p= 7.64 · 10−5
),
disposition index(DI, p= 3.41 · 10−6
), insulinogenic index(IGI, p=0.07), fast glucose(p=0.013), glucose effectiveness(SG, p=0.009), cholesterol(p=0.052). The testing for slope yielded that MTNR1B was associated
with acute insulin response(AIR, p<0.001), 30-minute IVGTT insulin(p<0.001), 30-minute disposition index(DI30, p<0.001), insulinogenic index(IGI, p<0.001). CDKAL1 was associated with acute insulin response(AIR, p= 2.77·10−5
), 30-minute IVGTT insulin(p= 9.15·10−4
), disposition index(DI, p= 0.004), insulinogenic index(IGI, p= 0.0002), fast insulin(p=0.02), 2-hour insulin(p=0.097), glucose effectiveness(SG,
p=0.056), and insulin sensitivity index(SI, p=0.011). The testing for slope yielded that CDKAL1 was associated with acute insulin response(AIR, p<0.001), disposition index(DI, p<0.001), 30-minute disposition
index(DI30, p<0.001), insulinogenic index(IGI, p<0.001).
If we adjust for age, sex and body fat percentage(BFP), MTNR1B was associated with acute insulin response (AIR, p= 4.94·10−5
), disposition index (DI, p=0.004), insulinogenic index (IGI, p=0.042), fast glucose
(p=0.026), glucose effectiveness(SG, p=0.023), and cholesterol(p=0.059). The testing for slope yielded that
MTNR1B was associated with acute insulin response(AIR, p<0.001), 30-minute IVGTT insulin(p<0.001),
disposition index(DI30, p<0.001), 30-minute disposition index(DI30, p<0.001), insulinogenic index(IGI,
p<0.001). CDKAL1 was associated with acute insulin response(AIR, p= 3.39 · 10−5
), 30-minute IVGTT
insulin(p= 1.334·10−3
), disposition index(DI, p= 4.202·10−4
), insulinogenic index(IGI, p= 5.045·10−4
),
fast insulin(p=0.055), glucose effectiveness(SG, p=0.02), and insulin sensitivity index(SI, p=0.084). The testing for slope yielded that CDKAL1 was associated with acute insulin response(AIR, p<0.001), disposition
index(DI, p<0.001), 30-minute disposition index(DI30, p<0.001), and 30-minute IVGTT insulin(p<0.001).
The test for multiplicative interaction between MTNR1B and CDKAL1 on the effect of T2DM related
traits showed that after adjusting for age and sex, the interaction term was associated with fast insulin
viii

(p=0.041), 2h insulin(p=0.055), and cholesterol(p=0.079). When adjusting for age, sex and body fat percentage, the interaction term was associated with fast insulin(p=0.013), and 2h insulin(p=0.055). The effect on
acute insulin response(AIR, p=0.11) and cholesterol(p=0.109) remained marginal.
0.0.4 Conclusion
We conclude that from our association study, MTNR1B and CDKAL1 show similar association with type 2
diabetes and its related traits, more to primary traits, insulin secretion, and secondary metabolism. There
is no evidence of multiplicative interaction between MTNR1B and CDKAL1 on their association with type
2 diabetes and its related traits, and both genes provide additive independent effect on type 2 diabetes
related traits.
0.0.5 Key Words
Association study, Beta cell function, CDKAL1, Cross-section, Families, Genetics, Insulin secretion, Longitudinal study, MTNR1B
ix

0.0.6 Abbreviation
AIR Acute insulin response to glucose
CDKAL1 Cdk5 regulatory associated protein 1-like 1
DI Disposition index
DXA Dual-energy x-ray absorptiometry
FSIGT (Frequently-sampled) intravenous glucose
tolerance test
GDM Gestational diabetes mellitus
IGI Insulinogenic Index
IFG Impaired fasting glucose
MAF Minor allele frequency
MTNR1B Melatonin receptor 1B
SI
Insulin sensitivity index
SG Glucose effectiveness
SNP Single-nucleotide polymorphism
T2DQT Type 2 diabetes-related quantitative trait
USC University of Southern California
x

Chapter 1
Introduction
Type 2 diabetes is a complex and prevalent metabolic disorder that affects millions of individuals worldwide. It is characterized by chronic high blood sugar levels, resulting from the body’s inability to properly
regulate insulin, a hormone responsible for controlling blood sugar. While lifestyle factors, such as diet and
physical activity, play a significant role in the development of type 2 diabetes, genetics also plays a crucial
part in determining an individual’s susceptibility to the disease. GWAS have shown that loci including
MTNR1B is associated with insulin secretions[1, 2]. Also, CDKAL1 is another known loci that influences
insulin secretions and thus is associated with Type 2 Diabetes[3]. We then want to study how the variants
in MTNR1B and CDKAL1 change the physiology and trajectory of diabetes. By observing how rs10830963
and rs7754840 within two genes affect insulin secretion, it’s rational for us to also seek an interaction between the two variants. However, most studies only tested for the association between specific phenotypes
and the absolute values of T2DQTs while ignoring the trend effect those phenotypes could contribute to
the rate of change in T2DQTs over time. Therefore, We want to test if genetic variation is associated with
not only the absolute value of T2DQTs, but the rate of change as well.
To test our hypothesis, we replicated the results of Ren et al[4] for investigating rs10830963 and GDM
by testing the univariate association between rs10830963 and T2DQTs with an additive genetic model. It
followed a test on the rate of change with a dominant model. Furthermore, previous studies have shown
1

that variants in CDKAL1 were associated with insulin secretion of European ancestry by reducing proinsulin to insulin conversion[5.]. Likewise, we tested the association between rs7754840 and T2DQTs using
the same method in our analysis on rs10830963. Previous studies further reported that variation in MTNR1B
interacted with other loci, which revealed significant joint association over insulin secretion[5.]. Then,
there may exist some biological interactions between MTNR1B and CDKAL1. We tested if multiplicative
interaction exists in the two variants.
2

Chapter 2
Method
We used the database from BetaGene study, a family based study of Mexican-Americans collected by
Watanabe et al to perform our analysis. The baseline studies for BetaGene have been outlined in previous
research[6]. This research encompasses Mexican American participants classified as either probands diagnosed with gestational diabetes mellitus (GDM) within the past 5 years, according to the criteria of the
Third International GDM Workshop, along with their family members. Alternatively, non-GDM probands
are included, characterized by maintaining normal glucose levels during pregnancy in the preceding 5
years. The initial BetaGene sample comprises 2,157 individuals from 526 families with available genotype
data. In the subsequent phase, BetaGene II successfully recalled and phenotyped 374 individuals approximately 3–5 years after the baseline testing. Individuals who developed type 2 diabetes during the follow-up
period or had fasting glucose levels exceeding 7 mmol/l (126 mg/dl) were excluded from the study. Ethical
approval for all BetaGene protocols has been granted by the Institutional Review Boards of participating
institutions, and written informed consent was obtained from all participants before their involvement.
Clinical protocol: phenotyping is conducted through a two-step process at the General Clinical Research
Center of the University of Southern California. The initial visit (Visit 1) encompasses a thorough physical examination, DNA collection, and a 75-gram 2-hour intravenous glucose tolerance test (IVGTT) with
3

blood sampling at 30-minute intervals. Subsequently, participants exhibiting fasting glucose levels below 7mmol/l (126 mg/dl) are extended an invitation for a second visit. This subsequent visit involves a
dual-energy X-ray absorptiometry scan to ascertain the percentage of body fat and an insulin-modified
intravenous glucose tolerance test (IVGTT).
Assays: Plasma glucose levels were assessed utilizing the glucose oxidase method on an auto-analyzer (YSI
Model 2300; Yellow Springs Instruments, Yellow Springs, OH, USA). The measurement of insulin was conducted through a two-site immunoenzymometric assay (TOSOH) characterized by <0.1% cross-reactivity
with proinsulin and intermediate split products.
Molecular analysis: Only two SNPs, rs10830963 and rs7754840 were genotyped in this study using the
Applied Biosystems TaqMan system. [7] 1895 out of 3406 observations were adults satisfying our requirements thus were included in our study. The Goodness-of-Fit test for Hardy-Weinberg Equilibrium showed
a p-value<0.05. We estimated the MAF of rs10830963 in MTNR1B was about 0.22 in our sample and the
MAF of rs7754840 in CDKAL1 was about 0.31. More descriptive data would be shown in the result part of
this paper.
Phenotype Definition: We calculated two measures of insulin response to glucose:The acute insulin response(AIR) is defined as the area under the insulin curve in the first 10-minute time period, and the
difference between the 30 min and fasting plasma insulin concentration from IVGTT is defined as 30min
∆insulin. Moreover, two quantitative traits are computed from existing values:
DI = SI × AIR DI30 = SI × 30 min∆ insulin
4

Also, the rate of change of the follow-up study over a 3-5 years time period is defined as
y =
yFollow-up − yBaseline
Follow-up years
5

Chapter 3
Analysis
The association of rs10830963(C/G) in MTNR1B and rs7754840(C/G) in CDKAL1 with T2DQTs was examined separately, employing additive allele models with the C allele as the reference group. For the further
test of slope, we used dominant allele models due to the relative small sample size. The risk allele of
rs10830963 was G and that of rs7754840 was C. The minor allele frequency (MAF) for rs10830963(G) in
MTNR1B was 0.2215, and that of rs7754840(C) in CDKAL1 equaled 0.31. 16 traits were categorized into
primary traits (related to insulin secretion: AIR, insulin, DI, DI30, and IGI) and secondary traits (including body composition: BMI, BFP; blood lipids: cholesterol, HDL, triglycerides; and IVGTT results: fasting
insulin, 2h insulin, fasting glucose, 2h glucose). A significance level of 0.1 was set, while Bonferroni correction was performed. All T2DQTs underwent transformation to univariate normal using inverse normal
transformation with R v4.2.0. Since prior evidence showed that body fat was associated with the two
SNPs independently, two separate adjustments were performed: one for age and sex, and another for age,
sex, and BFP, respectively before our analysis in case that BFP might be a confounder. Residuals were
regressed during adjustments. In each adjustment, the association between SNPs and T2DQTs was tested
using variance-component association analysis implemented in SOLAR v8.5.1[8], with the C allele as the
reference via estimated kinship. We also tested the multiplicative interaction between MTNR1B and CDKAL1 on their effect of T2DQTs. 1 degree-of-freedom likelihood ratio tests were performed for all the
6

tests stated above for effect of SNPs and for interaction. A supplementary test for the rate of change in
all primary traits was conducted, utilizing the dominant genetic model for both rs10830963 in MTNR1B
and rs7754840 in CDKAL1 due to a smaller overall sample size. Rate of change tests were executed using
SOLAR v8.5.1, and results were visualized in R v4.2.0 by generating figures with the actual mean values
of T2DQTs from the baseline study and their projections into a five-year follow-up time point calculated
with the predicted slope.
7

Chapter 4
Results
Descriptive statistics for the data is shown in table 4.1, including the baseline and the follow-up study.
We estimated the MAF of rs10830963 in MTNR1B was about 0.22 in our sample and the MAF of rs7754840
in CDKAL1 was about 0.31. After adjusting for age and sex, rs10830963 in MTNR1B is associated with
AIR (p=7.64 · 10−5
), DI (p=3.41 · 10−6
), IGI (p=0.07), fast glucose (p=0.013), SG (p=0.009) and cholesterol
(p=0.052). 30-minute insulin secretion, including 30min ∆insulin (p=0.811) and DI30 (p=0.674) are not associated with genetic variant. After adjusting for age, sex and BFP, rs10830963 in MTNR1B is associated
with AIR (p=4.94 · 10−5
), DI (p=1.45 · 10−5
), IGI (p=0.042), fast glucose (p=0.026), SG (p=0.023) and cholesterol (p=0.059). 30-minute insulin secretion, including 30min ∆insulin (p=0.566) and DI30 (p=0.671) are
not associated with genetic variant. These results were similar to what Ren had reported in his study[4].
Now we look at rs7754840 in CDKAL1. After adjusting for age and sex, rs7754840 is associated with
AIR (p=2.77 · 10−5
), 30min ∆insulin (p=9.15 · 10−4
), DI (p=0.004), IGI (p=2 · 10−4
), fast insulin (p=0.02), 2h
insulin(0.097), SG (p=0.056), and SI
(p=0.011). After adjusting for age, sex and BFP, rs7754840 is associated
with AIR (p=3.39·10−5
), 30min ∆insulin (p=1.33·10−3
), DI (p=4.20·10−4
), IGI (p=5.05·10−4
), fast insulin
(p=0.055), SG (p=0.02), and SI
(p=0.084). The result of univariate association in rs10830963 and rs7754840
are shown in table 4.2 and 4.3 respectively.
8

Table 4.1: Descriptive statistics of the sample
Follow-up subgroup Cross-sectional Traits Baseline Follow-up
n meana
sda meana
sd mean sda p
b
Sex 1895 734/1161 106/304
Age 1895 36.470 9.9225 35.044 8.165 39.768 8.368
BMI, kg/m2 1869 29.544 5.832 29.355 6.055 29.955 5.955 8.275 × 10−9
BFP, % 1835 33.320 8.672 34.494 8.440 35.034 8.043 3.654 × 10−5
Fast Glu, mmol/l 1226 5.158 0.717 5.098 0.680 5.148 0.578 0.037
2h Glu, mmol/l 1226 4.403 0.735 4.333 0.660 4.452 0.681 0.001
Fast Ins, mmol/l 1226 56.903 48.890 54.750 42.508 57.630 48.765 0.166
30 min insulin, mmol/l 1225 770.273 443.782 761.338 383.109 855.654 405.966 1.047 × 10−5
2h Ins, mmol/l 1226 59.504 66.613 54.029 49.275 69.078 83.378 7.903 × 10−5
IGI, µ U/ml per mmol/l 1703 23.986 18.981 23.210 16.337 27.231 32.450 0.004
DI30 1225 3253.429 2023.340 3230.304 1446.144 3071.569 1250.666 0.068
Chol, mmol/l 1831 4.575 0.924 4.468 0.855 4.625 0.889 7.535 × 10−7
HDL, mmol/l 1831 1.207 0.289 1.218 0.287 1.219 0.326 0.431
Trig, mmol/l 1831 1.358 1.166 1.381 1.169 1.554 1.021 1.077 × 10−4
SG × 10−2
/min 1226 0.018 0.007 0.018 0.006 0.017 0.006 0.115
SI × 10−3 min−1
(pmol/l)−1 1226 5.036 2.737 5.117 2.812 4.258 2.293 1.407 × 10−11
AIR, (pmol/l)×10 min 1220 3455.052 3029.190 3456.181 3288.561 3479.519 3417.632 0.677
DI 1220 14340.276 11539.457 14035.066 9353.477 11896.133 8108.152 5.237 × 10−9
amean and standard deviations are based on raw phenotype data without adjustments
b
paired-sample t-test comparing means of baseline vs. follow-up
Table 4.2: Univariate association of MTNR1B rs10830963 assuming a additive model
Adjustments Age+Sex Age+Sex+BFP
Trait n β(se)a p n β(se)a p
Primary Traits
30 min insulin, mmol/l 1124 0.012(0.052) 0.811 1114 -0.03(0.052) 0.566
IGI, µU/ml per mmol/l 1561 -0.079(0.043) 0.07 1540 -0.088(0.043) 0.042
DI30 1124 0.022(0.051) 0.674 1114 0.022(0.051) 0.671
AIR, (pmol/l)×10 min 1121 -0.204(0.051) 7.64 · 10−5
1111 -0.211(0.052) 4.94 · 10−5
DI 1121 -0.239(0.051) 3.41 · 10−6
1111 -0.224(0.051) 1.45 · 10−5
Secondary traits
BMI, kg/m2
1702 0.037(0.041) 0.368 1672 0.002(0.042) 0.962
BFP, % 1672 0.051(0.043) 0.233 1672 0.051(0.042) 0.234
Fast Glu, mmol/l 1125 0.129(0.052) 0.013 1115 0.117(0.052) 0.026
2h Glu, mmol/l 1125 -0.048(0.052) 0.363 1115 -0.046(0.053) 0.382
Fast Ins, mmol/l 1125 0.002(0.052) 0.974 1115 -0.038(0.052) 0.462
2h Ins, mmol/l 1125 0.043(0.052) 0.411 1115 -0.004(0.053) 0.932
Chol, mmol/l 1676 0.082(0.042) 0.052 1652 0.08(0.043) 0.059
HDL, mmol/l 1676 -0.002(0.042) 0.956 1652 -0.002(0.042) 0.95
Trig, mmol/l 1676 0.034(0.042) 0.42 1652 0.038(0.042) 0.372
SG × 10−2
/min 1125 -0.135(0.052) 0.009 1115 -0.119(0.052) 0.023
SI × 10−3 min−1
(pmol/l)−1
1125 0.015(0.052) 0.77 1115 0.049(0.052) 0.341
a β values are based on inverse normal transformed data adjusting for desired covariates
9

The results of the test for a multiplicative interaction between rs10830963 and rs7754840 on the association between T2DQTs are shown in table 4.4. After adjusting for age and sex, the interaction is significant
only for cholesterol (p=0.078). After adjusting for age, sex and BFP, the interaction is significant only for
fast insulin (p=0.056), while that for cholesterol is marginal (p=0.105). Therefore, there is no evidence for
a multiplicative interaction between rs10830963 and rs7754840.
It follows the test of association between the two SNPs and the rate of change in T2DQTs. We focused
on the rate of change in primary traits and the results are shown in Table 4.5. After adjusting for age
and sex, there is a significant trend effect between rs10830963 and AIR(p<0.001). Individuals homozygous
for the C allele has a rate of change in AIR of 717.32 pmol/l × 10 min per year, presence of the G allele
contributes to an additional -734.75 pmol/l × 10 min in the rate of change in AIR per year. The trend effect
between rs10830963 and 30min ∆insulin is significant(p<0.001). Individuals homozygous for the C allele
has a rate of change in 30min ∆insulin of 58.076 pmol/l per year, presence of the G allele contributes to
an additional -110.84 pmol/l in the rate of change in 30min ∆insulin per year. The trend effect between
rs10830963 and DI30 is significant(p<0.001). Individuals homozygous for the C allele has a rate of change
in DI30 of -267.9 per year, presence of the G allele contributes to an additional 172.66 in the rate of change in
30min ∆insulin per year. The trend effect between rs10830963 and IGI is significant(p<0.001). Individuals
homozygous for the C allele has a rate of change in IGI of 7.5387 per year, presence of the G allele contributes to an additional -6.277 in the rate of change in IGI per year. The trend effect between rs10830963
and DI is not significant (p=0.188).
After adjusting for age, sex and BFP, the trend effect between rs10830963 and AIR(p<0.001) is significant, Individuals homozygous for the C allele have a rate of change in AIR of 666.6 pmol/l × 10 min per
year, presence of the G allele contributes to an additional -754.56 pmol/l × 10 min in the rate of change in
AIR per year. The association between rs10830963 and 30min ∆insulin is significant(p<0.001). Individuals
homozygous for the C allele has a rate of change in 30min ∆insulin of 60.063 pmol/l per year, presence of
10

Table 4.3: Univariate association of CDKAL1 rs7754840 assuming a additive model
Adjustments Age+Sex Age+Sex+BFP
Trait n β(se) pa n β(se) pa
Primary traits
30 min insulin, mmol/l 1178 0.153(0.046) 9.151 · 10−4 1166 0.149(0.046) 1.33 · 10−3
IGI, µU/ml per mmol/l 1629 0.140(0.038) 0.0002 1607 0.135(0.038) 5.045 · 10−4
DI30 1176 0.005(0.045) 0.907 1166 0.0075(0.0459) 0.869
AIR, (pmol/l)×10 min 1171 0.193(0.046) 2.77 · 10−5 1161 0.192(0.046) 3.39 · 10−5
DI 1171 0.131(0.046) 0.004 1161 0.164(0.046) 4.202 · 10−4
Secondary traits
BMI, kg/m2 1786 0.0505(0.037) 0.173 1753 0.049(0.037) 0.195
BFP % 1753 0.0237(0.038) 0.532 1753 0.0237(0.038) 0.532
Fast Glu, mmol/l 1177 0.018(0.045) 0.688 1167 0.011(0.045) 0.811
2h Glu, mmol/l 1177 0.0338(0.0459) 0.46 1167 0.036(0.046) 0.436
Fast Ins, mmol/l 1177 0.107(0.046) 0.02 1167 0.089(0.046) 0.055
2h Ins, mmol/l 1177 0.0767(0.046) 0.097 1167 0.050(0.046) 0.288
Chol, mmol/l 1755 -0.012(0.038) 0.603 1728 -0.014(0.038) 0.712
HDL, mmol/l 1755 -0.0506(0.0379) 0.182 1728 -0.054(0.038) 0.155
Trig, mmol/l 1755 -0.0046(0.037) 0.902 1728 0.0009(0.038) 0.97
SG × 10−2
/min 1177 0.0883(0.0461) 0.0558 1167 0.107(0.046) 0.02
SI × 10−3 min−1
(pmol/l)−1 1177 -0.116(0.046) 0.011 1167 -0.079(0.046) 0.084
a β values are based on inverse normal transformed phenotype data adjusting for desired covariates
the G allele contributes to an additional -84.65 pmol/l in the rate of change in 30min ∆insulin per year. The
association between rs10830963 and DI is significant(p<0.001). Individuals homozygous for the C allele
has a rate of change in DI of -587.5 per year, presence of the G allele contributes to an additional 597.07
in the rate of change in DI per year. The association between rs10830963 and DI30 is significant(p<0.001).
Individuals homozygous for the C allele has a rate of change in DI30 of -249.18 pmol/l per year, presence of
the G allele contributes to an additional 331.92 pmol/l in the rate of change in DI per year. The association
between rs10830963 and IGI is significant(p<0.001). Individuals homozygous for the C allele has a rate of
change in IGI of 3.4166 per year, presence of the G allele contributes to an additional -3.645 in the rate of
change in IGI per year.
For the trend test between rs7754840 and primary T2DQTs, we used a dominant genetic model with
CC/CG alleles as the reference group. After adjusting for age and sex, there is a significant trend effect
between rs7754840 and AIR(p<0.001). Those with C allele has a rate of change in AIR of 782.92 pmol/l × 10
11

Table 4.4: Multiplicative interaction between rs10830963 and rs7754840 on T2DQTs
Trait Age+Sex Age+Sex+BFP
β(se) pa β(se) pa
Primary traits
30 min insulin -0.061(0.099) 0.422 -0.095(0.076) 0.216
IGI -0.024(0.063) 0.702 -0.023(0.064) 0.71
DI30 0.016(0.077) 0.83 0.0066(0.077) 0.931
AIR -0.101(0.074) 0.175 -0.119(0.075) 0.114
DI -0.064(0.075) 0.39 -0.067(0.075) 0.371
Secondary traits
BMI -7.66*10-5(0.061) 0.998 0.001(0.063) 0.98
Fat % 0.006(0.082) 0.91 0.006(0.064) 0.91
Fast Glu -0.027(0.075) 0.721 -0.034(0.076) 0.652
2h Glu -0.012(0.076) 0.87 -0.011(0.077) 0.883
Fast Ins -0.153(0.075) 0.041 -0.188(0.076) 0.013
2h Ins -0.146(0.076) 0.055 -0.148(0.077) 0.055
Chol -0.112(0.064) 0.079 -0.103(0.064) 0.109
HDL 0.025(0.06) 0.685 0.0411(0.063) 0.514
Trig -0.052(0.062) 0.405 -0.048(0.063) 0.444
SG -0.021(0.077) 0.78 -0.018(0.077) 0.808
SI 0.079(0.076) 0.298 0.097(0.077) 0.209
a β values are based on inverse normal transformed phenotype data adjusting for desired covariates
min per year, no presence of C allele results in a 1085.37 pmol/l × 10 min decrease in the rate of change in
AIR per year. The trend effect between rs7754840 and DI is significant(p<0.001). Those with C allele has a
rate of change in DI of 1199.2 per year, no presence of C allele results in a -2918.26 decrease in the rate of
change in DI per year. The trend effect between rs7754840 and DI30 is significant(p<0.001). Those with C
allele has a rate of change in DI of -247.92 per year, no presence of C allele results in a 258.31 increase in
the rate of change in DI30 per year. The trend effect between rs7754840 and IGI is significant (p=0.0389).
Those with C allele has a rate of change in IGI of 4.3042 per year, no presence of C allele results in a 1.1856
increase in the rate of change in IGI per year.
Now if we adjust for age, sex and BFP, there is a significant trend effect between rs7754840 and
AIR(p<0.001). Those with C allele has a rate of change in AIR of 631.37 pmol/l × 10 min per year, no
presence of C allele results in a -917.835 pmol/l × 10 min decrease in the rate of change in AIR per year.
12

Table 4.5: Test for rate of change in genotypes assuming dominant genetic models
Adjustments Age+Sex Age+Sex+BFP
β SE p Corrected pα β SE p Corrected pα
rs10830963
30 min insulin, mmol/l -110.84 10.50 8.71 × 10−23 4.35 × 10−22 -84.65 8.89 3.09 × 10−19 1.55 × 10−18
IGI, µU/ml per mmol/l -6.277 0.369 9.37 × 10−48 4.68 × 10−47 -3.645 0.3836 3.45 × 10−19 1.73 × 10−18
DI30 172.66 40.625 2 × 10−5 1 × 10−4 331.92 42.785 8.13 × 10−14 4.65 × 10−13
AIR, (pmol/l)×10 min -734.75 52.770 8.55 × 10−35 4.27 × 10−34 -754.56 50.452 1.21 × 10−38 6.05 × 10−38
DI 53.914 174.08 0.756 1 597.07 172.84 6.142 × 10−4 3.07 × 10−3
rs7754840
30 min insulin, mmol/l 17.446 10.627 0.101 0.505 36.300 10.023 3.328 × 10−4 1.66 × 10−4
IGI, µU/ml per mmol/l 1.1856 0.4431 7.76 × 10−3 0.0389 -0.1833 0.4456 0.6807 1
DI30 258.31 42.763 4.16 × 10−9 2.08 × 10−8 127.115 42.592 0.003 0.015
AIR, (pmol/l)×10 min -1085.37 60.921 3.47 × 10−51 1.74 × 10−50 -917.835 56.571 1 × 10−44 5 × 10−44
DI -2918.26 213.662 6.037 × 10−44 3.02 × 10−43 -3458.86 216.08 1.259 × 10−43 6.3 × 10−43
a Bonferroni’s correction was performed for multiple comparison
The trend effect between rs7754840 and 30min ∆insulin is significant(p<0.001). Those with C allele has a
rate of change in 30min ∆insulin of -4.289 pmol/l per year, no presence of C allele results in a 36.3 pmol/l
increase in the rate of change in 30min ∆insulin per year. The trend effect between rs7754840 and DI is
significant(p<0.001). Those with C allele has a rate of change in DI of 1239.2 per year, no presence of C
allele results in a 3458.86 decrease in the rate of change in DI per year. The trend effect between rs7754840
and DI30 is significant(p<0.001). Those with C allele has a rate of change in DI of -161.99 per year, no
presence of C allele results in a 127.115 increase in the rate of change in DI30 per year.
Figure 4.1 showed the association between MTNR1B rs10830963 and rates of change among primary
traits after adjusting for age and sex. The x-axis denoted timeline and the y-axis denoted the value of
specific traits. Each two connected points showed the actual baseline value of each trait and the predicted
value of the trait after five years assuming linear trend. Actual baseline values were obtained and the fiveyear follow-up were computed by projection with predicted slopes based on regression models. Consistent
with previous results, rs10830963 was associated with the rates of change of AIR, 30min ∆insulin, DI30
and IGI. DI however, despite its significant association in the univariate cross-sectional study, the slopes
seemed parallel for the rate of change, which indicated that changing the phenotype could alter the risk
13

(a) (b) (c)
(d) (e)
Figure 4.1: Association between MTNR1B rs10830963 and rates of change in AIR (a), IVGTT 30min ∆insulin
(b), DI (c), DI30 (d), and IGI (e) assuming a dominant genetic model. The rates of change for CC genotype
are denoted by circles and that for CG/GG genotype are denoted by triangles in the graphs.
Table 4.6: Univariate association between CDKAL1 rs7754840 and rate of change in genotypes assuming
dominant genetic models
Adjustments Age+Sex Age+Sex+BFP
β SE p Corrected pα β SE p Corrected pα
30 min insulin, mmol/l 17.446 10.627 0.101 0.505 36.300 10.023 3.328 × 10−4 1.66 × 10−4
IGI, µU/ml per mmol/l 1.1856 0.4431 7.76 × 10−3 0.0389 -0.1833 0.4456 0.6807 1
DI30 258.31 42.763 4.16 × 10−9 2.08 × 10−8 127.115 42.592 0.003 0.015
SI 0.2176 0.0687 0.0016 0.008 -0.1517 0.0653 0.02 0.1
AIR, (pmol/l)×10 min -1085.37 60.921 3.47 × 10−51 1.74 × 10−50 -917.835 56.571 1 × 10−44 5 × 10−44
DI -2918.26 213.662 6.037 × 10−44 3.02 × 10−43 -3458.86 216.08 1.259 × 10−43 6.3 × 10−43
a Bonferroni’s correction was performed for multiple comparison
of diabetes. Adjusting for age, sex and BFP yielded similar results and the graphs could be found in the
Appendix.
Figure 4.2 showed the association between CDKAL1 rs7754840 and rates of change among primary
traits after adjusting for age and sex. Actual baseline values were obtained and the five-year follow-up
were computed by projection with predicted slopes based on regression models. rs7754840 was associated
with the rates of change of AIR, DI, DI30 and IGI.
14

(a) (b) (c)
(d) (e)
Figure 4.2: Association between CDKAL1 rs7754840 and rates of change in AIR (a), IVGTT 30min ∆insulin
(b), DI (c), DI30 (d), and IGI (e) after adjusting for age and sex assuming a dominant genetic model. The
rates of change for CC/CG genotype are denoted by circles and that for GG genotype are denoted by
triangles in the graphs.
The plots inferred that with copies of C allele, ones with higher DI has better β cell function, and
lower risk of GDM. Those with no copies of C allele had relatively lower β cell function. The slopes of
the charts gave us clues about how insulin secretion and β cell function change over time. Moreover, the
slopes of DI and DI30 showed completely opposite patterns, while DI30=30min ∆ insulin·SI
. We check
the rate of change for SI
. Table 4.6 showed the univariate association between CDKAL1 rs7754840 and
rate of change in genotypes assuming dominant genetic models. We observed statistically significant
associations between rs7754840 and rate of change in SI
. Therefore, we may imply that the inconsistent
patterns observed on DI and DI30 were caused by the insignificant association between rs7754840 and
rate of change in 30min ∆insulin. The difference in the slope of rate of change between AIR and 30min
∆insulin may also contribute to this phenomenon as AIR showed a decreasing slope while 30min ∆insulin
showed an increasing slope. Adjusting for age, sex and BFP yielded similar results and the graphs could
be found in the Appendix.
15

Chapter 5
Discussion
Identified by genome-wide studies, MTNR1B and CDKAL1 played important roles in the risk of T2Ds. Variation in MTNR1B have been shown to be associated with Insulin secretion[4], while variation in CDKAL1
was less studied. We replicated among Mexican-Americans [4]. In our cross-section univariate analysis,
rs10830963 was associated with AIR, DI, IGI, fast glucose, SG, and cholesterol while rs7754840 was associated with 30min ∆insulin, IGI, AIR, DI, fast insulin, SG, and SI
. In the longitudinal analysis on primary
traits, AIR, 30min ∆insulin, DI30, and IGI were significantly associated with rs10830963 for both adjustments. DI was only significant while adjusting for age, sex and BFP. rs7754840 was associated with IGI,
DI30, AIR, and DI while adjusting for age and sex. After adjusting for age, sex and BFP, rs7754840 was
associated with 30min ∆insulin, DI30, AIR, and DI.
Variation in MTNR1B is an important locus associated with type 2 diabetes related traits, including
insulin secretion, fasting glucose, beta cell compensation, etc by previous genome-wide studies[4, 2, 9].
These studies collected cross-sectional samples worldwide, mainly from orthern European, Caucasian and
Asian ancestry. In European descent, MTNR1B was shown to be associated with fasting glucose and beta
cell compensation[2]. In the meta-analysis by Chen and colleagues, rs10830963 in MTNR1B significantly
increases the risk of GDM in Europeans, Asians, and Americans. G allele carriers in European population
were further confirmed a higher risk of GDM. They concluded that the odds ratios of the association of
16

MTNR1B rs10830963 variants with GDM risk varied between (1.17, 2.43) in dominant genetic models, (1.07,
5.49) in recessive models, and (1.10, 1.82) in allele models[10]. Chao et al. conducted a study with an multiethnic sample including Caucasians, African–Americans and Hispanics while concluding that variants
of MTNR1B, especially rs10830963, was associated with an increase risk of impaired fasting glucose. They
further conducted a follow-up study with 274 subjects over 3.0 ± 2.2 years interval. His follow-up study
concluded that variants in MTNR1B showed a trend effect toward higher risk of transitioning from normal
glucose tolerance to IFG which led to significant higher risk to develop type 2 diabetes[11]. These studies
collected data from the baseline studies and the follow-up studies then comparing the results to see if they
were consistent. They were working on risk categories instead of treating them as continuous data.
Previous studies have shown that GDM predicted markedly increased rates of type 2 diabetes among
pregnant women, and relative risk increased substantially with each additional affected pregnancy[12].
Many studies focused on the association between variation in CDKAL1 and GDM, but the association
between variation in CDKAL1 and GDM wasn’t strong. Researches failed to detect a significant difference
in CDKAL1 and allele frequencies between GDM and non-GDM groups. Using a dominant model, Amr
reported a p-value of 0.145 and an OR of 2.729(0.678–10.976) after sampling 98 Egyptian pregnant women
in a case-control study[13]. Despite the insignificant conclusion, the relatively small sample size restricted
this study while resulting in a large odds ratio confidence interval. Another test on Filipino pregnant
women also suggested no association between CDKAL1 gene variant rs7754840 and GDM development
with a dominant OR of 1.21 and p-value 0.62[14]. Association between GDM and rs7754840 in CDKAL1
also showed an insignificant result among Bangladeshi women with a dominant OR of 1.25 and p-value
0.12515. In the meta-analysis by Amir et al. which combined 11 studies involving 6,119 Asian, Caucasian
and mixed ethnicity patients with GDM and 7,188 healthy controls, marginal association was identified
between CDKAL1 and GDM with additive genetic model[16].
17

Despite previous case-control studies concluded insignificant associations between rs7754840 in CDKAL1 and GDM, variants in CDKAL1 were possibly associated with T2DQTs, which increasing susceptibility to develop T2D in a long term period. In the meta-analysis by Chen and colleagues, CDKAL1 was shown
to be associated with impaired insulin secretion and glucose-related traits, further analysis yielded that C
allele showed a significant association with decreased insulin secretion In central China population[10].
Study on Finnish men reported a significant association between rs7754840 and insulin secretion. Carriers
of the GC and CC genotypes of rs7754840 had 11% and 24% lower first-phase insulin release in an IVGTT
compared with that in carriers of the GG genotype. Moreover, rs7754840 was associated with type 2 diabetes and IGI in Finnish men[17]. Same result was concluded in our study, where AIR, 30min ∆insulin,
DI, and IGI were associated with rs7754840 assuming an additive model. In another study among Korean
women on the association between rs7754840 and GDM also reported significant association between
fasting insulin , HOMA-IR, and HOMA-β, while fasting glucose remained insignificant in this study[3]. In
our study, insulin secretion including AIR, 30min ∆insulin, and fast insulin showed the same significant
association, while fasting glucose yielded no association. The two research results are consistent.
What was unique in our study was that we not only tested the cross-sectional association between
rs7754840 and T2DQTs, but also tested the rate of change of T2DQTs in a follow-up study. Few studies
have analyzed variants of CDKAL1 to test the trend effect of CDKAL1 variants. Soo’s study compared the
results of stage 1(baseline) and stage 2(follow-up) and concluded significant associations on the association
of CDKAL1 on fasting insulin, insulin secretion and insulin resistance[3]. In our study, we showed that
after adjusting for age and sex, rs7754840 was associated with rate of change in AIR, DI, DI30, and IGI. This
result suggested an interesting feature as two essential traits related to insulin secretion expressed different
results. Presence of C allele resulted in a positive rate of change in insulin secretion(782.92pmol/l × 10min,
p<0.001), and two copies of G allele lowered the rate of change to -305.66pmol/l × 10min. However, the
rate of change in 30min ∆insulin showed an insignificant result. Furthermore, both the absolute value and
18

rate of change of IGI computed by 30min ∆insulin resulted in a significant association with rs7754840 as
well. Taking together the significant association over the absolute value of 30min ∆insulin we observed in
the baseline cross-sectional analysis, we may assume that other covariates should be adjusted in the model.
Given that rs7754840 may be associated with obesity-related indicators[18], we repeated the test adjusting
for age, sex, and BFP and observed a substantially higher rate of change in 30min ∆insulin with two copies
of G allele. The rate of change with presence of C allele remained insignificant. With this adjustments,
two copies of G alleles wasn’t associated with a lower rate of change in IGI, which differed from the result
adjusting for only age and sex. If we look at the absolute value in measures of beta cell compensation in
the cross-sectional analysis, which DI revealed significant association with rs7754840 but DI30 didn’t. We
may infer that rs7754840 was only associated with rate of change in first phase insulin secretion. We can
also infer that the effect of CDKAL1 might be related to intravenous glucose stimulated insulin secretion
and not oral glucose stimulated insulin secretion.
We not only tested the association between T2DQTs and rs10830963 and rs7754840 respectively, but
also tested the multiplicative interaction effects. Alena Stančáková investigated the effect of MTNR1B and
CDKAL1 among Finland population respectively and stated that MTNR1B and CDKAL1 were significantly
associated with early-phase insulin release[19]. The multiplicative interaction test reported significant
association with fast insulin, 2h insulin, and cholesterol when adjusting for age and sex, and the association
between AIR and cholesterol became marginal when considering BFP as another covariate. Therefore,
rs10830963 and rs7754840 may have some marginal interaction effect on insulin resistance.
From the univariate study with two different types of adjustments, BFP may modify the effect in the
association between SNPs and T2DQTs. Observing the analysis of rs10830963, two kinds of adjustments
yielded similar results. Traits that are significant in one adjustments remained significant in another.
However, BFP modified the results of the univariate association analysis of rs7754840, and it contributed
a larger portion of variation to rs7754840 and secondary traits. Among them, 2h insulin was significant if
19

Table 5.1: Test for interaction between rs7754840 and BFP on the effect of T2DQTs
β(se)a βm(se)b p
c
Primary traits
30 min insulin, mmol/l 0.055(0.040) 0.175
IGI, µU/ml per mmol/l 0.064(0.036) 0.079
IGI+Interactionc
0.064(0.036) 0.131(0.038) 0.0006
DI30 -0.021(0.0455) 0.63
AIR, (pmol/l)×10 min -0.0336(0.044) 0.442
DI -0.090(0.044) 0.04
DI+Interactiond
-0.090(0.044) 0.164(0.046) 0.0005
Secondary traits
BMI, kg/m2
0.029(0.028) 0.296
Fast Glu, mmol/l 0.022(0.043) 0.613
2h Glu, mmol/l 0.0076(0.044) 0.863
Fast Ins, mmol/l 0.0043(0.052) 0.914
2h Ins, mmol/l 0.029(0.040) 0.475
Chol, mmol/l 0.013(0.037) 0.729
HDL, mmol/l 0.0123(0.036) 0.736
Trig, mmol/l -0.0003(0.036) 0.993
SG × 10−2
/min -0.105(0.044) 0.018
SG+Interactionc
-0.105(0.044) 0.117(0.046) 0.004
SI × 10−3 min−1
(pmol/l)−1
-0.052(0.042) 0.22
aβi denotes the value of slope and se of the interaction term(rs7754840×BFP)
bβm denotes the value of slope and se of the main effect(rs7754840)
c
p value is computed based on phenotype values adjusted by age and sex
dTraits with significant results in the test of interaction
we adjust for age and sex, but if we add BFP into the adjustment, the p-value shifted to 0.288. Therefore,
we performed a test for multiplicative interaction between rs7754840 and BFP on the association with
T2DQTs. The results are shown in table 5.1. Only age and sex were adjusted in this test for interaction.
The interaction between rs7754840 and BFP is significant for DI (p=0.04), IGI (p=0.079), and SG (p=0.018)
under an α = 0.1 criteria. For those three significant traits, a further 2 degree freedom test on both
the main effect and the interaction term was performed. After adjusting for age and sex, the 2 degree
of freedom test yielded significant association over DI (p=5 · 10−4
), IGI (p=6 · 10−4
) and SG (p=0.004).
Therefore, level of body fat may be a significant effect modifier between rs7754840 and T2DQTs.
Overall, The follow-up study is limited by the relatively small sample size. We thus conduct the test
with dominant genetic models. Also, we predicted the value of T2DQTs by projecting the baseline value
20

with predicted slope on a 5-year follow-up. The result of our test may not reflect the actual long-term
association due to this insufficiency. Assuming simple linear models is also a subtle step in this study.
From the result we can conclude that rs10830963 is associated with AIR, DI, IGI, fast glucose, SG, and
cholesterol cross-sectionally. rs7754840 is associated with AIR, 30min ∆insulin, DI, IGI, fast insulin, 2h
insulin(marginal), SG, and SI
. Furthermore, rs10830963 is associated with the rate of change of insulin
secretion, which may be a natural response to insulin sensitivity. The rate of change in 30min ∆insulin
showed an insignificant association with rs7754840, which may be due to simplicity of our model. Body
fat played an important role as a covariate in our study, which the association should be researched in
further studies.
In conclusion, we tested the univariate association between rs10830963 in MTNR1B, rs7754840 in CDKAL1 and T2DQTs respectively among Mexican-American population collected in the BetaGene database.
We further analyzed the rate of change of the two SNPs as well as their interactions. From our analysis,
rs10830963 was significantly associated with AIR, DI, IGI, fast glucose, SG, and cholesterol. rs7754840 was
associated with 30min ∆insulin, IGI, AIR, DI, fast insulin, SG, and SI
. In the longitudinal analysis on primary traits, AIR, 30min ∆insulin, DI30, and IGI were significantly associated with rs10830963. rs7754840
was associated with 30min ∆insulin, DI30, AIR, and DI. We can summarize from the results that the rates
of change of T2DQTs for rs10830963 might be caused by changes of insulin sensitivity instead of longitudinal trend effects. The effect of rs7754840 might be related to first phase insulin secretion and intravenous
glucose stimulated insulin secretion instead of oral glucose stimulated insulin secretion.
21

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23

Appendix
24

(a) (b) (c)
(d) (e)
Figure 5.1: Association between MTNR1B rs10830963 and rates of change in AIR (a), IVGTT 30min ∆insulin
(b), DI (c), DI30 (d), and IGI (e) after adjusting for age, sex, and BFP assuming a dominant genetic model.
The rates of change for CC genotype are denoted by circles and that for CG/GG genotype are denoted by
triangles in the graphs.
(a) (b) (c)
(d) (e)
Figure 5.2: Association between CDKAL1 rs7754840 and rates of change in AIR (a), IVGTT 30min ∆insulin
(b), DI (c), DI30 (d), and IGI (e) after adjusting for age, sex, and BFP assuming a dominant genetic model.
The rates of change for CC/CG genotype are denoted by circles and that for GG genotype are denoted by
triangles in the graphs.
25

Abstract (if available)

Abstract Type 2 diabetes is a complicated public health problem worldwide. In this study, we will investigate the effect of MTNR1B(rs10830963) and CDKAL1(rs7754840), two variations in genes possibly associated with Type 2 Diabetes among the Mexican-American population.

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Creator Zhang, Haoran (author)

Core Title Variants in MTNR1B and CDKAL1 contributes independent additive effects to GDM-related traits in Mexican Americans

Contributor Electronically uploaded by the author (provenance)

School Keck School of Medicine

Degree Master of Science

Degree Program Biostatistics

Degree Conferral Date 2024-05

Publication Date 05/17/2024

Defense Date 05/01/2024

Publisher Los Angeles, California (original), University of Southern California (original), University of Southern California. Libraries (digital)

Tag association study,Beta cell function,CDKAL1,cross-section,Families,genetics,insulin secretion,longitudinal study,MTNR1B,OAI-PMH Harvest

Format theses (aat)

Language English

Advisor Watanabe, Richard (committee chair), Conti, David (committee member), Mancuso, Nicholas (committee member)

Creator Email hzhang74@usc.edu,zhanghaoran990722@gmail.com

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