University of Southern California Dissertations and Theses

Variation in CRY2 and MTNR1B have independent effects on insulin secretion in Mexican Americans

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Variation in CRY2 and MTNR1B have independent effects on insulin secretion in Mexican Americans

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Content Variation in CRY2 and MTNR1B have independent effects on
insulin secretion in Mexican Americans
by
Meitong Li
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 Meitong Li

ii
ACKNOWLEDGEMENTS
I would like to express my deepest gratitude to Dr. Richard M. Watanabe, my thesis
supervisor, for his invaluable guidance, unwavering support, and scholarly insights throughout
the entire process of researching and writing this master's thesis. His dedication and expertise
have been instrumental in shaping the quality and depth of this work.
I extend my sincere appreciation to the members of my thesis committee, Dr. Lingyun Ji
and Dr. Farzana Choudhury, for their constructive feedback and valuable suggestions. Their
expertise has significantly enriched the content and academic rigor of this thesis.
I am thankful for the supports by National Institutes of Health Grant DK-61628 and an
American Diabetes Association Distinguished Clinical Scientist Award to T.A.B, which enabled
me to conduct my study and complete this thesis.
My heartfelt thanks go to my friends and family for their unwavering support,
encouragement, and understanding during the challenging phases of my academic journey. Their
love and encouragement have been my source of strength.

iii
TABLE OF CONTENTS
ACKNOWLEDGEMENTS............................................................................................................ ii
LIST OF TABLES......................................................................................................................... iv
ABSTRACT.................................................................................................................................... v
INTRODUCTION .......................................................................................................................... 1
Chapter one: RESEARCH DESIGN AND METHODS. ............................................................... 4
Subject recruitment .................................................................................................................... 4
Clinical Protocols....................................................................................................................... 4
Assays ........................................................................................................................................ 4
Data analysis and interpretation ................................................................................................. 5
Chapter two: RESULTS ................................................................................................................. 7
Chapter three: DISCUSSION ......................................................................................................... 9
REFERENCES ............................................................................................................................. 16

iv
LIST OF TABLES
Table 1. Subject characteristics......................................................................................................12
Table 2. Univariate association results between CRY2 and T2D-related quantitative traits .........13
Table 3. Univariate association results between MTNR1B and T2D-related quantitative traits....14
Table 4. Association results between T2D-related traits and interaction between two SNPs.......15

v
ABSTRACT
Objectives: MTNR1B rs10830963 has been investigated regarding its effects on insulin secretion
based on the previous publications [1]. We hypothesize that the variation in CRY2 rs11605924
may affect type-2 diabetes (T2D)-related quantitative traits and thereby affect insulin secretion.
We further hypothesize the interaction between CRY2 rs11605924 and MTNR1B rs10830963
may have joint effect on insulin secretion or pancreatic beta-cell function as represented by the
disposition index (DI).
Research Design and Methods: We replicated MTNR1B rs10830963 research findings by Ren
et al. [1] and tested whether CRY2 rs11605924 were association with T2D-related quantitative
traits. We also examined the association between T2D-related traits and the interactions between
CRY2 rs11605924 and MTNR1B rs10830963. Data came from the BetaGene study of Mexican
Americans.
Results: MTNR1B rs10830963 is significantly associated with fasting glucose (p = 0.018),
insulinogenic index (p = 0.008), SG (p = 0.036), SI (p = 0.015), DI (p = 2.4×10
-7
), and AIR (p =
4.2×10
-7
) after adjusting for age, sex, and percentage body fat. CRY2 rs11605924 is significantly
associated with DI (p = 0.019) and AIR (p = 0.012) after adjusting with age, sex, and percentage
of body fat. There is no evidence for an association between the interaction between MTNR1B
and CRY2 and any T2D-related quantitative trait.
Conclusion: We conclude that the variation in CRY2 rs11605924 is associated measure of insulin
secretion and pancreatic beta-cell function. There was no evidence for association between T2Drelated traits and interaction between MTNR1B and CRY2 suggesting they have independent

vi
effects on insulin secretion and pancreatic beta-cell function.

1
INTRODUCTION
Diabetes is a major metabolic disease characterized by high blood sugar, which seriously
endangers human health and has multiple causes [2]. There are two types of diabetes, type 1 and
type 2. Type 2 diabetes (T2D) is a chronic metabolic disease characterized by the body's inability
to effectively use insulin to regulate blood sugar levels [3]. The condition usually develops
gradually and is influenced by a combination of genetic and environmental factors. It is
characterized by insulin resistance, in which the body's cells do not respond adequately to
insulin, and a progressive decrease in pancreatic beta cell function, resulting in insufficient
insulin production [4]. Lifestyle factors such as poor diet, sedentary behavior and obesity also
have a significant impact on the development of type 2 diabetes. The disease's increasing
prevalence poses a significant global health burden, so understanding the genetic factors that
underlie the development of type 2 diabetes is critical to advancing prevention and treatment
strategies.
Genome-wide association (GWA) studies have played a key role in advancing our
understanding of the genetic basis of type 2 diabetes (T2D) and T2D-related quantitative traits
[5-11]. GWA studies have identified associations between genetic variations and both T2D and
absolute levels or temporal changes in certain T2D-related quantitative traits. Specifically,
melatonin receptor 1-b (MTNR1B) rs10830963 and cryptochrome circadian clock 2 (CRY2)
rs11605924 have emerged as demonstrating association with T2D and related traits. While
MTNR1B rs10830963 has been investigated regarding its effects on insulin secretion, less is
known about the association between CRY2 rs11605924 and T2D-related quantitative traits and

2
there is a gap concerning the joint impact of CRY2 rs11605924 and MTNR1B rs10830963.
MTNR1B encodes one of two receptors for melatonin, a hormone that regulates sleep-wake
cycles and circadian rhythms [12, 13] and certain variants of MTNR1B are associated with
impaired beta cell function and increased risk of T2D [13]. Its genetic variation has been
extensively studied in relation to T2D risk [14]. CRY2, a key circadian clock protein, regulates
the body's internal clock by responding to light cues. It is involved in the transcriptional
feedback loop, promoting synchronization of biological processes with the day-night cycle [15].
In the evening, CRY2 self-inhibits, contributing to the precision of circadian rhythm regulation.
The CRY2 gene has been the subject of scientific research in the field of T2D, primarily because
of its role in circadian regulation and potential effects on metabolic processes [16]. As a core
component of the circadian clock, CRY2 affects the timing of various physiological functions,
including sleep cycles and metabolism [17]. On this basis, we investigated the genetic variation
of CRY2 (rs11605924) and its potential independent effects on insulin secretion. We aimed to
explore whether CRY2 (rs11605924), is associated with specific T2D-related quantitative traits in
Mexican American. Thus, we firstly hypothesized that genetic variation in CRY2 might affect the
levels of T2D-related quantitative traits or the rate at which some given T2D traits changes over
time, thereby affecting insulin secretion.
MTNR1B and CRY2 are both associated with circadian rhythm regulation and the circadian
clock, which suggests their joint effects may lead to changes in insulin secretion. Studying the
correlation of these two SNPs is critical to understand the genetic influence on insulin secretion
and T2D risk. If two SNPs independently affect specific T2D-related traits, this would suggest

3
that the molecular pathways by which each gene affects metabolic homeostasis may be different.
However, if their effects are dependent, this would imply a potential additive effect on T2D risk.
Therefore, we secondly hypothesized that the interaction between CRY2 rs11605924 and
MTNR1B rs10830963 may have joint effect on insulin secretion. Understanding these genetic
interactions is pivotal for unraveling the nuanced relationships between circadian genes and
glucose homeostasis.
In this thesis, I will replicate previous MTNR1B rs10830963 research findings by Ren [1]
and explore the impact of CRY2 rs11605924 on T2D traits. Additionally, I will investigate
whether the two single nucleotide polymorphism (SNPs) effects on these traits are mutually
independent. I use the data from the BetaGene study to test the above hypothesis [1]. Selection of
T2D-related traits will be determined based on the traits identified in previous studies [1].

4
Chapter one: RESEARCH DESIGN AND METHODS
Subject recruitment:
Subject recruitment from BetaGene study were probands with or without a diagnosis of
gestational diabetes mellitus (GDM) within the 5 years prior to study recruitment and their
family members [18]. All participants were non-diabetic at the time of study and were of
Mexican American ancestry. Detailed information on participant recruitment and study protocols
are previously described [1]. All protocols for BetaGene study were approved by the Institutional
Review Boards of participating institutions and all participants provided informed consent.
Clinical Protocols:
The phenotyping was performed through two separate clinic visits at the University of
Southern California (USC) General Clinical Research Center [1]. The first visit included a
physical examination, DNA collection, and an oral glucose tolerance test (OGTT) with 30-min
blood sampling [18]. Among subjects on the first visit with fasting glucose <126 mg/dL were
invited for a second visit which included a dual-energy X-ray absorptiometry (DXA ) scan for
body composition (percent bodyfat) and an insulin-modified intravenous glucose tolerance test
(IVGTT) performed as previously described [1].
Assays:
Plasma glucose levels are determined through an autoanalyzer employing the glucose
oxidase method (YSI Model 2300). Insulin levels are measured using a two-site
immunoenzymometric assay, exhibiting a 0.1% cross-reactivity with proinsulin and intermediate
split products [18].

5
Data analysis and interpretation:
Glucose and insulin levels were analyzed using the minimal model (MINMOD Milennium
V5.18) [19, 20] to obtain estimates of glucose effectiveness (SG), insulin sensitivity (SI) and
acute insulin response (AIR). The product of SI and AIR was used to compute the disposition
index (DI). Other T2D-related traits utilized in the thesis were introduced in Ren’s paper [1].
Allele frequencies of two SNPs were estimated using SOLAR (Version 2.1.4).
Based on the research of the previous paper, MTNR1B has been shown to be related to some
traits of T2D-related quantitative traits. Firstly, we reproduced and explored the associations
between two specific SNPs, MTNR1B and CRY2 respectively and these traits, as well as other
traits of interest. This aligns our hypothesis that genetic variation in CRY2 might affect the levels
of T2D-related quantitative traits or the rate at which some given T2D traits changes. We tested
two SNPs for association with T2D-related traits using variance components within a likelihood
ratio testing framework, while assuming a dominant genetic model for MTNR1B and an additive
genetic model for CRY2. Covariate-adjusted residuals were created from linear models for
quantitative traits of interest and inverse normal transformation was applied. Then transformed
traits data were tested for association with two SNPs. We used two sets of covariate adjustments
for these analyses: one including age and sex, and the other including age, sex, and body fat
percent. However, since the results between the two-adjustment method were similar, we only
report the results with covariates age, sex, and body fat percent.
Since MTNR1B and CRY2 are both associated with circadian rhythm regulation and the
circadian clock, we next conducted a test of association between T2D-related traits and the

6
interaction between two SNPs using variance components within a likelihood ratio testing
framework. T2D-related Traits significantly associated with MTNR1B were selected for
interaction analysis. We compared a model with MTNR1B rs10830963, and CRY2 rs119605924
as main effects with a second model that included MTNR1B, CRY2 and the interaction between
the two SNPs. As in the univariate analyses, we assumed dominant genetic models for MTNR1B
and additive genetic models for CRY2. We tested for a significant interaction effect using a 1
degree of freedom test.
Descriptive statistics of subjects including basic information and T2D-related traits were
reported as median, interquartile ranges and total numbers. The results from univariate models
and interaction models were reported as beta coefficients, standard error, and p value.

7
Chapter two: RESULTS
Table 1 displays the descriptive statistics of the subjects, including a total of 1831 subjects
in 461 families. According to the World Health Organization (WHO), which defines a normal
BMI within the range of 18.5 to 25 Kg/m2
, the subjects in this study exhibit a relatively high
median BMI of 33.32 Kg/m2
.
The estimated allele frequencies for MTNR1B rs10830963 were 0.78 for the C allele and
0.22 for the G allele. The frequencies for CRY2 rs119605924 were 0.47 for the A allele and 0.53
for the G allele. Thus, dominant genetic models were utilized for the analysis of MTNR1B due to
the relatively low frequency of the minor allele, and additive genetic models were utilized for the
analysis of CRY2.
The univariate association results between MTNR1B rs10830963 and T2D-related
quantitative traits are consistent with those reported in Ren et al. [1] and are shown in Table 3,
MTNR1B rs10830963 is significantly associated with fasting glucose (

= 0.12 ± 0.05, p =
0.018), insulinogenic index (

= -0.14 ± 0.05, p = 0.008), SG (

= -0.13 ± 0.06, p = 0.036), SI
(

= 0.15 ± 0.06, p = 0.015), DI (

= -0.32 ± 0.06, p = 2.4×10
-7
), and AIR (

= -0.35 ± 0.06, p =
4.2×10
-7
).
Results for CRY2 rs119605924 from the univariate models are summarized in Table 2,
CRY2 rs119605924 is significantly associate with DI (

= -0.10 ± 0.04, p = 0.019) and AIR (

= -0.11 ± 0.04, p = 0.012) after adjusting with age, sex, and percentage of body fat. CRY2
rs119605924 is marginally associated with DI when percentage body fat is not included as a
covariate (
 = -0.08 ± 0.04, p = 0.055) and significantly associated with AIR (

= -0.12 ± 0.04,

8
p = 0.004).
The results for the test of the association between the interaction between MTNR1B
rs10830963 and CRY2 rs11605924 and T2D-related quantitative traits is summarized in Table 4.
There was no evidence for an association between MTNR1B and CRY2 and any T2D-related
quantitative trait, suggesting the two variants independently exert their physiologic effects on
these traits.

9
Chapter three: DISCUSSION
The study aimed to replicate previous findings on MTNR1B rs10830963 and explore the
impact of CRY2 rs11605924 on T2D-related quantitative traits. Additionally, the study
investigated whether these variants interact to alter variation in T2D-related quantitative traits.
Our analysis of CRY2 rs11605924, showed a significant decrease in AIR with each copy of the A
allele suggesting an attenuation in response of pancreatic beta cells to elevated blood glucose
levels in those with an A allele. Furthermore, CRY2 rs11605924 was also significantly associated
with disposition index, where the number of A alleles was associated with a decrease in
disposition index suggesting reduced effectiveness in compensating for insulin resistance and
consistent with the observed association with AIR. This association highlights an important role
of CRY2 rs11605924 genetic variation in determining AIR and overall pancreatic β-cell function,
which may significantly alter risk for future T2D.
Our observations align with associations reported by Dupuis et al. [21] and Stamenkovic et
al. [22]. Dupuis et al., reported a significant association between CRY2 rs11605924 and
Homeostatic Model Assessment of Beta-cell function (HOMA-B), an indirect measure of
pancreatic beta-cell function. Our measurements include AIR, DI, SI, and other parameters,
providing a comprehensive assessment of pancreatic beta cell function and insulin sensitivity.
Compared to HOMA-B, which focuses on a single metric, our measurement more accurately
captures the interplay between insulin secretion, glucose utilization, and overall metabolic health,
providing a multifaceted perspective on the complex mechanisms underlying glucose
metabolism and insulin regulation. Additionally, fasting insulin was not significantly associated

10
with CRY2 rs11605924 in both our study and that of Dupui et al, suggesting that examining
fasting insulin alone is not sufficient to understand the role of CRY2 rs11605924.
We replicated our previous associations between MTNR1B rs10830963 and fasting plasma
glucose, insulinogenic index, glucose effectiveness, SI, AIR, and DI [1]. This reaffirms the
significant impact of MTNR1B rs10830963 variation on insulin secretion and glucose
metabolism. Moreover, our study extended the investigation to include additional lipid measures
such as HDL and LDL, although these were not associated with MTNR1B rs10830963,
suggesting the genetic influence of MTNR1B rs10830963 in Mexican Americans might be tied to
glucose metabolism, rather than lipid metabolism. Some literature points to potential interactions
between MTNR1B rs10830963 and specific environmental factors [24]. This suggests that our
observations may be modulated by environmental factors that reduce the potency of MTNR1B
rs10830963. This complexity underscores the need for further investigation into the precise role
of MTNR1B rs10830963 in metabolic processes, particularly in understanding its interactions
with various metabolic pathways and the modifying effects of genetic variations in specific
environmental contexts.
We did not observe an association between the interaction between CRY2 rs11605924 and
MTNR1B rs10830963 and T2D-related traits This suggests CRY2 rs11605924 and MTNR1B
rs10830963 may exert their effects on insulin secretion and pancreatic beta-cell function
independently through different mechanistic pathways.
The study relied on data from the BetaGene study of Mexican Americans, which limits the
generalizability of our findings to other populations. Genetic variants and their associations often

11
exhibit variability across diverse ethnicities or origins due to unique genetic backgrounds and
environmental influences. Therefore, while our observations reveal relationships within Mexican
Americans, different conclusions may be drawn when extrapolating these results to other ethnic
groups.
In conclusion, this study identified genetic associations between CRY2 rs11605924 and
MTNR1B rs10830963 and T2D-related quantitative traits in the Mexican-American population.
The results showed that both genetic variants were associated with measures of insulin secretion
and pancreatic beta-cell function. Given that both genes operate within biologic pathways related
to circadian rhythm, we further assessed whether the two genes interact to exert their effects on
these phenotypes. However, we found no such evidence, suggesting the two genes have
independent effects on these phenotypes. Additional research will be required to better
understand how these independent effects are exerted to alter insulin secretion and pancreatic
beta-cell function. However, the independent associations do suggest a potential effect to
increase risk for future type 2 diabetes in those carrying copies of the risk allele.

12
Table 1: Subject characteristics
Median (IQR) N total
Age (years) 33.48/11.80 2066
Sex (female/male) 1253/828 2081
BMI (kg/m2
) 28.43/7.40 1831
Body fat (%) 33.32/13.60 1835
Fasting glucose (mM) 5.02/0.70 1810
2-h glucose (mM) 7.39/2.50 1683
Fasting insulin (pM) 53.44/43.75 1809
30’ insulin (pM) 450.09/336.00 1809
2-h insulin (pM) 444.74/378 1809
Insulinogenic Index (μU/ml per mM) 23.54/13.70 1682
Cholesterol (mmol/L) 4.57/1.20 1800
HDL cholesterol (mmol/L) 1.21/0.36 1800
LDL cholesterol (mmol/L) 2.75/1.01 1800
Triglycerides (mmol/L) 1.36/0.88 1800
Systolic blood pressure (mm Hg) 118.63/20.00 1817
Diastolic blood pressure (mm Hg) 72.93/12.30 1817
SG (×10-2 min-1
) 1.78/1.00 1212
SI (×10-3 min-1 per pM) 5.04/3.22 1212
AIR (pM × 10 min) 3436.20/2967.00 1212
Disposition index 14263.41/11612.25 1212

13
Table 2: Univariate association results between CRY2 and T2D-related quantitative traits.
Trait Beta SE p-value
BMI (kg/m2
) -0.040 0.035 0.253
Fasting glucose (mM) -0.046 0.036 0.198
2-h glucose (mM) -0.032 0.037 0.375
Fasting insulin (pM) -0.0008 0.036 0.982
30’ insulin (pM) -0.023 0.037 0.539
2-h insulin (pM) 0.007 0.037 0.842
Insulinogenic Index (μU/ml per mM) -0.019 0.037 0.594
Cholesterol (mmol/L) 0.002 0.036 0.956
HDL cholesterol (mmol/L) 0.041 0.036 0.256
LDL cholesterol (mmol/L) 0.023 0.036 0.531
Triglycerides (mmol/L) -0.035 0.036 0.327
Systolic blood pressure (mm Hg) -0.012 0.035 0.722
Diastolic blood pressure (mm Hg) 0.012 0.036 0.737
SG (×10-2 min-1
) -0.033 0.044 0.455
SI (×10-3 min-1 per pM) 0.015 0.044 0.729
AIR (pM × 10 min) -0.110 0.043 0.012
Disposition index -0.100 0.044 0.019
P values significant at the 5% level are bold

14
Table 3: Univariate association results between MTNR1B and T2D-related quantitative traits.
Trait Beta SE p-value
BMI (kg/m2
) -0.065 0.051 0.205
Fasting glucose (mM) 0.12 0.052 0.017
2-h glucose (mM) 0.016 0.054 0.764
Fasting insulin (pM) -0.081 0.052 0.115
30’ insulin (pM) -0.10 0.053 0.054
2-h insulin (pM) 0.038 0.053 0.476
Insulinogenic Index (μU/ml per mM) -0.14 0.054 0.008
Cholesterol (mmol/L) 0.086 0.052 0.096
HDL cholesterol (mmol/L) 0.037 0.052 0.468
LDL cholesterol (mmol/L) 0.049 0.052 0.346
Triglycerides (mmol/L) 7.71×10-6 0.052 0.999
Systolic blood pressure (mm Hg) 0.036 0.051 0.488
Diastolic blood pressure (mm Hg) 0.084 0.052 0.107
SG (×10-2 min-1
) -0.132 0.063 0.036
SI (×10-3 min-1 per pM) 0.154 0.063 0.016
AIR (pM × 10 min) -0.350 0.063 4.21×10-8
Disposition index -0.320 0.062 2.42×10-7
P values significant at the 5% level are bold

15
Table 4: Association results between T2D-related traits and interaction between two SNPs
Trait Beta of interaction SE p-value
Fasting glucose (mM) -0.125 0.045 0.073
Insulinogenic Index (μU/ml per mM ) -0.086 0.047 0.235
SG (×10-2 min-1 per pM) -0.052 0.056 0.559
SI (×10-3 min-1
) 0.013 0.056 0.880
AIR (pM × 10 min) -0.119 0.054 0.157
Disposition index -0.126 0.055 0.140

16
REFERENCES
1. Ren, J., et al., Genetic variation in MTNR1B is associated with gestational diabetes mellitus
and contributes only to the absolute level of beta cell compensation in Mexican Americans.
Diabetologia, 2014. 57(7): p. 1391-9.
2. Karalliedde, J. and L. Gnudi, Diabetes mellitus, a complex and heterogeneous disease, and
the role of insulin resistance as a determinant of diabetic kidney disease. Nephrology Dialysis
Transplantation, 2014. 31(2): p. 206-213.
3. Kaul, K., et al., Introduction to diabetes mellitus. Adv Exp Med Biol, 2012. 771: p. 1-11.
4. Diagnosis and classification of diabetes mellitus. Diabetes Care, 2009. 32 Suppl 1(Suppl 1):
p. S62-7.
5. Bouatia-Naji, N., et al., A variant near MTNR1B is associated with increased fasting plasma
glucose levels and type 2 diabetes risk. Nat Genet, 2009. 41(1): p. 89-94.
6. Scott, L.J., et al., A genome-wide association study of type 2 diabetes in Finns detects
multiple susceptibility variants. Science, 2007. 316(5829): p. 1341-5.
7. Zeggini, E., et al., Replication of genome-wide association signals in UK samples reveals
risk loci for type 2 diabetes. Science, 2007. 316(5829): p. 1336-41.
8. Zheng, J.S., et al., Plasma Vitamin C and Type 2 Diabetes: Genome-Wide Association Study
and Mendelian Randomization Analysis in European Populations. Diabetes Care, 2021. 44(1): p.
98-106.
9. Vaxillaire, M., et al., The common P446L polymorphism in GCKR inversely modulates
fasting glucose and triglyceride levels and reduces type 2 diabetes risk in the DESIR prospective
general French population. Diabetes, 2008. 57(8): p. 2253-7.
10. Zeggini, E., et al., Meta-analysis of genome-wide association data and large-scale
replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet, 2008. 40(5): p.
638-45.
11. Lindgren, C.M., et al., Genome-wide association scan meta-analysis identifies three Loci
influencing adiposity and fat distribution. PLoS Genet, 2009. 5(6): p. e1000508.
12. Zhu, H., et al., The melatonin receptor 1B gene links circadian rhythms and type 2 diabetes
mellitus: an evolutionary story. Ann Med, 2023. 55(1): p. 1262-1286.

17
13. Lyssenko, V., et al., Common variant in MTNR1B associated with increased risk of type 2
diabetes and impaired early insulin secretion. Nat Genet, 2009. 41(1): p. 82-8.
14. Prokopenko, I., et al., Variants in MTNR1B influence fasting glucose levels. Nat Genet,
2009. 41(1): p. 77-81.
15. Lavebratt, C., et al., CRY2 is associated with depression. PLoS One, 2010. 5(2): p. e9407.
16. Angelousi, A., et al., Clock genes alterations and endocrine disorders. Eur J Clin Invest,
2018. 48(6): p. e12927.
17. Li, X., et al., Genetic Variations within the Bovine CRY2 Gene Are Significantly Associated
with Carcass Traits. Animals (Basel), 2022. 12(13).
18. Li, X., et al., Additive effects of genetic variation in GCK and G6PC2 on insulin secretion
and fasting glucose. Diabetes, 2009. 58(12): p. 2946-53.
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236(6): p. E667-77.
20. Pacini, G. and R.N. Bergman, MINMOD: a computer program to calculate insulin
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21. Dupuis, J., et al., New genetic loci implicated in fasting glucose homeostasis and their
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Cardiometabolic Traits. Diabetes Care, 2015. 38(8): p. 1456-66.

Abstract (if available)

Abstract Objectives: MTNR1B rs10830963 has been investigated regarding its effects on insulin secretion based on the previous publications. We hypothesize that the variation in CRY2 rs11605924 may affect type-2 diabetes (T2D)-related quantitative traits and thereby affect insulin secretion. We further hypothesize the interaction between CRY2 rs11605924 and MTNR1B rs10830963 may have joint effect on insulin secretion or pancreatic beta-cell function as represented by the disposition index (DI).

Research Design and Methods: We replicated MTNR1B rs10830963 research findings by Renet al. [1] and tested whether CRY2 rs11605924 were association with T2D-related quantitative traits. We also examined the association between T2D-related traits and the interactions between CRY2 rs11605924 and MTNR1B rs10830963. Data came from the BetaGene study of Mexican Americans.

Results: MTNR1B rs10830963 is significantly associated with fasting glucose (p = 0.018), insulinogenic index (p = 0.008), SG (p = 0.036), SI (p = 0.015), DI (p = 2.4×10-7), and AIR (p =4.2×10-7) after adjusting for age, sex, and percentage body fat. CRY2 rs11605924 is significantly associated with DI (p = 0.019) and AIR (p = 0.012) after adjusting with age, sex, and percentage of body fat. There is no evidence for an association between the interaction between MTNR1Band CRY2 and any T2D-related quantitative trait.

Conclusion: We conclude that the variation in CRY2 rs11605924 is associated measure of insulin secretion and pancreatic beta-cell function. There was no evidence for association between T2D-related traits and interaction between MTNR1B and CRY2 suggesting they have independent effects on insulin secretion and pancreatic beta-cell function.

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Shortcomings of the genetic risk score in the analysis of disease-related quantitative traits

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The association between the PNPLA3 rs738409 variant and hepatic fat in Mexican Americans

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A novel construct to study the pulsatility of insulin secretion in single cells, islets and whole pancreas

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Association of single nucleotide polymorphisms in GCK, GCKR and PNPLA3 with type 2 diabetes related quantitative traits in Mexican-American population

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The associations between ultra-processed food consumption and type 2 diabetes and obesity among young adults

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The impact of global and local Polynesian genetic ancestry on complex traits in Native Hawaiians

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A cross-sectional examination: impact of diet and physical activity on plasma glucose metabolism, insulin sensitivity and pancreatic β-cell function in Hispanic women with histories of gestationa...

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Association testing of polymorphisms in the vitamin D metabolism and signaling pathway with multiple sclerosis across multiple populatuions

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Association of carotid artery stiffness with measures of cognition in older adults

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Ectopic fat and adipose tissue inflammation in overweight and obese African Americans and Hispanics

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The effect of inhibiting the NFkB pathway on myeloid derived suppressor cell cytokine production

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Ocular axial length and prevalence of myopic macular degeneration among Chinese Americans

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Pathogenic variants in cancer predisposition genes and risk of non-breast multiple primary cancers in breast cancer patients

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The effect of renal function on toxicity of E7389 (eribulin) among patients with bladder cancer

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Effects of Rab3D deficiency on Paneth cell morphology and function

Asset Metadata

Creator Li, Meitong (author)

Core Title Variation in CRY2 and MTNR1B have independent effects on insulin secretion in Mexican Americans

School Keck School of Medicine

Degree Master of Science

Degree Program Biostatistics

Degree Conferral Date 2024-05

Publication Date 03/15/2024

Defense Date 03/14/2024

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

Tag Beta cell function,BetaGene,CRY2,genetics,insulin secretion,MTNR1B,OAI-PMH Harvest

Format theses (aat)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Watanabe, Richard M. (committee chair), Choudhury, Farzana (committee member), Ji, Lingyun (committee member)

Creator Email 1940988096@qq.com,meitongl@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC113851031

Unique identifier UC113851031

Identifier etd-LiMeitong-12696.pdf (filename)

Legacy Identifier etd-LiMeitong-12696

Document Type Thesis

Format theses (aat)

Rights Li, Meitong

Internet Media Type application/pdf

Type texts

Source 20240319-usctheses-batch-1129 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright.

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA

Repository Email cisadmin@lib.usc.edu