Medical

Communication

Biosci. Biotech. Res. Comm. 10(1): 236-240 (2017)

Association of gene variants with longitudinal

changes in high density lipoprotein cholesterol among

Tehranian people: A latent growth curve model

Mahdi Akbarzadeh

, Abbas Moghimbeigi

*, Nathan Morris

, Maryam S Daneshpour

Ali Reza Soltanian

and Hossein Mahjub

Ph.D Candidate in Biostatistics, Department of Biostatistics, School of Public Health, Hamadan University

of Medical Sciences, Hamadan, Iran

Associate Professor of Biostatistics, Modeling of Noncommunicable Disease Research Canter, Department of

Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran

Associate Professor of Biostatistics, Department of Epidemiology and Biostatistics, Case Western Reserve

University, Cleveland, OH, USA

Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid

Beheshti University of Medical Sciences; Tehran, Iran

Associate Professor of Biostatistics, Modeling of Noncommunicable Disease Research Canter, Department of

Biostatistics, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran

Professor of Biostatistics, Research Center for Health Sciences and Department of Biostatistics, School of

Public Health, Hamadan University of Medical Sciences, Hamadan, Iran

ABSTRACT

High density lipoprotein cholesterol (HDL-C) particles remove fat molecules from cells which need to export fat molecules. The aim

of this study was to assess the association of SNP related to fat mass and obesity associated gene (FTO) with HDL-C change in a

subset of participated families in Tehran Lipid and Glucose Study (TLGS). In this study 914 individual (with age>3) including 126

families with size 8.49±3.10 were selected from participants in TLGS. Genomic DNA was extracted from peripheral blood using

standard salting-out method. HDL-C measured in four time point from March 1999 to December 2011 with a 3-year follow-up

period. We examine the association via a latent growth curve model. To achieve the study target we used the latent growth curve

model (LGCM) in R in TLGS family data. Adjusted association by sex and age between some FTO genes and changes of HDL-C

overtime are signi cant. Our  ndings provide basis for searching for genes affecting change in HDL-C.

KEY WORDS: HIGH DENSITY LIPOPROTEIN CHOLESTEROL, GENETIC INFLUENCE, LATENT GROWTH CURVE

236

ARTICLE INFORMATION:

*Corresponding Author: moghimb@yahoo.com

Received 17

Dec, 2016

Accepted after revision 17

March, 2017

BBRC Print ISSN: 0974-6455

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Online Contents Available at: http//www.bbrc.in/

Mahdi Akbarzadeh et al.

BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS SIMULATION STUDY OF CONTROLLING WATER CONING IN OIL RESERVOIRS 237

INTRODUCTION

High-density lipoproteins (HDL) are one of the  ve

major groups oflipoproteins. Lipoproteins are complex

particles composed of multiple proteins which trans-

port all fat molecules (lipids) around the body within

the water outside cells. They are typically composed

of 80-100 proteins per particle and transporting up

to hundreds of fat molecules per particle. Unlike the

larger lipoprotein particles which deliver fat molecules

to cells, HDL particles remove fat molecules from cells

which need to export fat molecules. The fats carried

include cholesterol, phospholipids, and triglycerides;

amounts of each are quite variable. Increasing concen-

trations of HDL particles are strongly associated with

decreasing accumulation of atherosclerosis within the

walls of arteries. This is important because atherosclero-

sis eventually results insudden plaque ruptures,cardio-

vascular disease,strokeand othervascular diseases. HDL

particles are sometimes referred to as “good cholesterol”

because they can transport fat molecules out of artery

walls, reduce macrophage accumulation, and thus help

prevent or even regress atherosclerosis. However, studies

have shown that HDL-lacking mice still have the ability

to transport cholesterol to bile, suggesting that there are

alternative mechanisms for cholesterol removal. Also

heritability of low HDL-C is demonstrated, (Asselbergs

etal. 2012). Qureshi etal. have shown FTO genes have

association with HDL-C in Pakistani people, (Qureshi

etal. 2016).

In both epidemiological and clinical studies, as well

as the meta-analyses thereof, low plasma levels of high-

density lipoprotein (HDL) cholesterol (HDL-C) identi ed

individuals at increased risk of major coronary events.

In line with a causally protective effect, HDLs exert a

broad spectrum of potentially anti-atherogenic proper-

ties. Moreover, atherosclerosis was decreased or even

reverted in several animal models by transgenic over-

expression or exogenous application of apolipoprotein

(apoA-I) the most abundant protein of HDL,(Zhang etal.

2008).

However, to date, drugs increasing HDL-C, such as

 brates, niacin, and inhibitors of cholesteryl ester trans-

fer protein (CETP), have failed to consistently and signif-

icantly reduce the risk of major cardiovascular events,

especially when combined with statins. Moreover, muta-

tions in several human genes as well as targeting of

several murine genes modulate HDL-C levels without

changing cardiovascular risk and atherosclerotic plaque

load, respectively, in the opposite direction as expected

from the inverse correlation of HDL-C levels and car-

diovascular risk in epidemiological studies. Because of

these controversial data, the pathogenic role and, hence,

suitability of HDL as a therapeutic target has been

increasingly questioned. Because of the frequent con-

founding of low HDL-C with hypertriglyceridemia, it has

been argued that low HDL-C is an innocent bystander

of (postprandial) hypertriglyceridemia or another culprit

related to insulin resistance or in ammation, (Gordon

etal. 1977; Silventoinen etal. 2007). Genetic associa-

tion of HDL-C is demonstrated in some population,(Kim

& Lee 2013; Nirengi etal. 2016; Bandarian etal. 2016).

In the Framingham study, high density lipoprotein

is shown as a protective factor against coronary heart

disease,(Gordon et al. 1977). In some studies on Ira-

nian population, low HDL-C is reported as a risk fac-

tor of coronary artery disease(Shari etal. 2009; Shari

et al. 2008; Hatmi et al. 2007).Also Akbarzadeh et al.

have shown some genes association with HDL-C in the

Iranian people without any results about the trend of

HDL-C(Akbarzadeh etal. 2011; Alavi Majd etal. 2010).

Among Iranian people as well as Tehranian people the

topic has been proven (Zarkesh etal. 2012).

An advanced, powerful and  exible framework to

model the latent variables is structural equation mod-

eling (SEM). In recent years, in genetic analysis of longi-

tudinal family data, SEM has made signi cant progress.

One of these models is Morris and his colleagues that it

would be in addition to the relationships between vari-

ables over time, taking into account the simultaneous

equations to analyze genetic family data(Morris et al.

2010). The model can be able to run a wide range of

genetic models with latent variables under SEM, and in

recent months the model R package, strum, has been

released(Song etal. 2015).

According to our knowledge, up to this point about

the relationship between HDL-C changes related genes

in Iran has not been investigated. The aim of this study

was to assess the association of SNP in the gene fat mass

and HDL-C-associated gene (FTO) with HDL-C change

in a subset of participated families in Tehran Lipid and

Glucose Study (TLGS). To achieve this target we used the

latent growth curve model (LGCM) in STRUM R package

in TLGS family data.

MATERIAL AND METHODS

Population and sample: The TLGS is an ongoing longitu-

dinal large-scale community-based study, with a 3-year

follow-up period, designed to estimate the prevalence

of non-communicable disorders (NCD) and included a

representative sample of residents of 13 districts of Teh-

ran, capital of Iran. The TLGS has been implemented in

a multistage strati ed (district) cluster (families) random

sampling technique, to select more than 15000 people

aged > 3 years, from March 1999 to December 2011.The

phases II, III, and IV were prospective follow-up studies

Mahdi Akbarzadeh et al.

238 GENETIC ASSOCIATION ON HIGH-DENSITY LIPOPROTEINS CHANGES BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS

and were performed from 2002 to 2004, 2006 to 2008,

and 2009 to 2011, respectively, (Azizi etal. 2002; Azizi

et al. 2009). A total of 14761 individuals (valid case)

were selected from the total participating cases in TLGS,

including 3980 families, with an average number of 3.38

individuals, among the phase I (baseline).

This family-based study was conducted on families

who participated in TLGS, with at least two members

affected with syndrome metabolic (Mets).The design of

TLGS includes two major components, a cross-sectional

prevalence study of CVD and associated risk factors and

a prospective 20-year follow up in  ve phases(14). In

the study 914 individual including 126 families with size

8.49±3.10 person from participated people in TLGS. Par-

ticipants provided informed consent and the study was

approved by the institutional ethics committees of the

Research Institute for Endocrine Sciences af liated to

Shahid Beheshti University of Medical Sciences, Tehran,

Iran. Information on age, sex, demographic and medica-

tion usage for treatment diabetes, hypertension and lipid

disorders were collected with a standardized question-

naire.

SNP Selection and Typing: Genomic DNA was

extracted from peripheral blood using standard salting-

out method(Koshy etal. 2016). 5 selected polymorphisms

(rs708272, rs1864163, rs1558902, rs7202116, rs9939609)

studied by Tetra ARMS method, by determining through

the NCBI site. MAF among for the  ve markers is mini-

mum 0.039 and maximum 0.363. Which is as follows:

Our T-ARMS assay with different inner allele speci c

primers to produce allele-speci c PCR products. Two

outer primers will produce a PCR product to be used as

an internal control for reaction. For all studied SNPs,

the PCR reaction was optimized in a 12.5 μl total vol-

ume containing 1.5 μ DNA template, 6.25 μl Master Mix

containing (MgCl2, Smart Taq polymerase (Cinna Gene

Co; Iran) and BSA 0.1% (TaKaRa; Japan) and 2 μl primer

containing (outers and inners ) and 2.75 μl water.

Details of the primers information and  nal frag-

ments are mentioned in table 1. The optimized thermo

cycling conditions were as follows: 5 min at 95 °C; 10

cycles of 45 sec at 94°C, 30 sec at 63°C, and 35 sec at

72°C; followed by 25 cycles of 45 sec at 94°C, 30 sec at

61°C, and 30 sec at72°C; and  nal extension at72°C for 5

minutes. When the PCR products were separated by size

via agarose gel electrophoresis, each genotype generated

a special band. Accuracy of results was con rmed by

direct sequencing of 10% samples using outer primers.

Statistical Model: An advanced, powerful and  ex-

ible framework to model the latent variables is structural

equation modeling (SEM).Structural equation modeling

evaluates the relationships among manifest variables,

manifest-latent variables and latent variables. Moreo-

ver the latent growth curve modeling (LGCM) has been

recently developed in longitudinal study (Bollen& Cur-

ran 2006). One of advantage of the model is that allows

for individual changes to be analyzed in the SEM frame-

work and includes additional latent factors in the lin-

ear model for testing a linear, quadratic, cubic, or spline

growth trajectory (Xitao & Xiaotao 2005).One advan-

tage of this model compared to the routine analytical

methods for longitudinal data is that modeling latent

variables over time and also the intercept and slop mod-

eled as latent variables.

The recent presented SEM is capable to model a broad rela-

tionships between latent variables based linkage or association

analysis. In strum a novel score test developed. This method

is a computationally rapid test of association with many SNPs

FIGURE 1. The path diagram of the latent growth curve model p: polygenic

effects; e: random effects; I: intercept; S: slope; sqrtHDL i: Squared meas-

urement of the HDL-C at ith time point; sex and age: gender and age of

individuals.

Mahdi Akbarzadeh et al.

BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS GENETIC ASSOCIATION ON HIGH-DENSITY LIPOPROTEINS CHANGES 239

in GWAS data (Song etal. 2015). In this new score test, we

 rst assessed the null model  t without any SNPs in the model

to con rm the appropriateness of the model for the data. In

the research, HDL-C measured in 4 time point. We examined

association of 5 markers of FTO genes with change of HDL-C

in Tehranian pedigrees using strum R package.

The graphical representations of analysis model with

latent variable for longitudinal HDL-C are shown for the

latent growth curve model in  gure 1. Variable sqrtHDL-

C is the main trait values corrected for the use of nor-

mality. Nodes marked with: “p” are polygenic effects,

and “e” are random effects. “I” is the intercept and S is

the slope.

After removing the SNPs with no variation in the data

set or with no score test results, the remaining 18 SNPs

were tested for association with the main trait by coding

additively as 0, 1, or 2 based on the minor allele count.

RESULTS AND DISCUSSION

The genomic in ation factors were 1.01 and 1.02 for

each model. The MAF (minor allele frequency) of all

SNPs are greater than 5% and they are informative for

association analysis. Also the CFI (comparative  t index)

of the conceptual model was 99% shown that the model

had good  tness. The association between 5 FTO markers

and slop of HDL-C was signi cant (ie, P value<1.0e-5).

Table 1 shows the characteristics and P values.

Changing in HDL-C in human is certainly under

genetic in uence(Kim & Lee 2013; Nirengi etal. 2016;

Bandarian etal. 2016). Actually, the changes of HDL-C

is a latent variable. A preferable understanding way to

the change discovery is measuring by natural indica-

tor such as HDL-C. The study is the  rst to simultane-

ously analyze level and change in HDL-C using a popu-

lation-based longitudinal study of individual including

families. We found a sigini cant genetic in uence on

change in HDL-C for four known markers. The modeling

assumes that long-term (15 year) individual HDL-C tra-

jectories in the four waves of young adults aged 3–83

years at baseline is adequately described by individual

initial level and rate of change.

CONCLUSION

Our  ndings provide basis for searching for genes

affecting change in HDL-C. We assume that the genetic

in uence on change in HDL-C has a polygenic origin

with  nite known markers. However, trying to identify-

ing genetic variants for HDL-C change may be require

a whole human genome wide scan in the target popula-

tion.

ACKNOWLEDGMENTS

We express appreciation to the participations and TLGS

staff for their guidance in the preparing dataset. We

thank the reviewers for their insightful and helpful com-

ments.

REFERENCES

Akbarzadeh, Mahdi, H A Majd, Maryam Sadat Daneshpour,

Yadolah Mehrabi, and Freydoon Azizi. (2011). Analyzing of

Multivariate Two Levels Haseman-Elston Regression and Its

Application in Genetic Linkage of HDL-C, Triglycerides and

Waist in 91 Iranian Families with Metabolic Syndrome. Koo-

mesh 12 (3). Semnan University of Medical Sciences and

Health Services: Pe266-Pe271.

Alavi Majd, H, M Akbarzadeh, Y Mehrabi, M S Daneshpour,

and F Azizi. (2010). Genetic Linkage Analysis of HDL-C in 91

Iranian Families with Metabolic Syndrome with Haseman-

Elston Regression Methods. HBI_Journals 7 (4): 243–47.

Asselbergs, Folkert W, Yiran Guo, Erik P A van Iperen, Suthesh

Sivapalaratnam, Vinicius Tragante, Matthew B Lanktree, Leslie

A Lange, Berta Almoguera, Yolande E Appelman, and John

Barnard. (2012). Large-Scale Gene-Centric Meta-Analysis

across 32 Studies Identi es Multiple Lipid Loci. The American

Journal of Human Genetics 91 (5). Elsevier: 823–38.

Azizi, Fereidoun, Arash Ghanbarian, Amir Abbas Momenan,

Farzad Hadaegh, Parvin Mirmiran, Mehdi Hedayati, Yadollah

Mehrabi, and Saleh Zahedi-Asl. (2009). Prevention of Non-

Communicable Disease in a Population in Nutrition Transition:

Tehran Lipid and Glucose Study Phase II. Trials 10 (January):

5. doi:10.1186/1745-6215-10-5.

Azizi, Fereidoun, Maziar Rahmani, Habib Emami, P Mirmiran,

R Hajipour, M Madjid, J Ghanbili, (2002). Cardiovascular Risk

Factors in an Iranian Urban Population: Tehran Lipid and Glu-

cose Study (Phase 1). Sozial- Und Präventivmedizin 47 (6).

Birkhäuser Verlag: 408–26. doi:10.1007/s000380200008.

Bandarian, Fatemeh, Maryam Sadat Daneshpour, Mehdi

Hedayati, Mohsen Naseri, and Fereidoun Azizi. (2016). Identi-

 cation of Sequence Variation in the Apolipoprotein A2 Gene

and Their Relationship with Serum High-Density Lipoprotein

Cholesterol Levels.Iranian Biomedical Journal 20 (2). Pasteur

Institute of Iran: 84.

Bollen, KA, and PJ Curran.( 2006). Latent Curve Models: A

Structural Equation Perspective. John Wiley & Sons, Inc.

Tab le 1. SNPs associated with HDL-C in

analysis models

SNP Al MAF P value

rs708272 G/T 0.1232 3.320E-16

rs1558902 G/T 0.1102 2.045E-13

rs7202116 C/T 0.1851 1.489E-06

rs9939609 T/T 0.1448 2.276E-06

rs6696438 C/T 0.1476 4.462E-06

Al: major/minor alleles; MAF: minor allele frequency

Mahdi Akbarzadeh et al.

240 GENETIC ASSOCIATION ON HIGH-DENSITY LIPOPROTEINS CHANGES BIOSCIENCE BIOTECHNOLOGY RESEARCH COMMUNICATIONS

Gordon, Tavia, William P Castelli, Marthana C Hjortland, Wil-

liam B Kannel, and Thomas R Dawber. (1977). High Density

Lipoprotein as a Protective Factor against Coronary Heart Dis-

ease: The Framingham Study. The American Journal of Medi-

cine 62 (5). Elsevier: 707–14.

Hatmi, Z N, S Tahvildari, A Gafarzadeh Motlag, and A Sabouri

Kashani. (2007). Prevalence of Coronary Artery Disease Risk

Factors in Iran: A Population Based Survey BMC Cardiovascu-

lar Disorders 7 (1). BioMed Central: 1.

Kim, Jongwoo, and Seon Yeong Lee (2013 )Obesity and High-

Density Lipoprotein Cholesterol (HDL-C): The Recent Related

Research Trend Including New Generation Drugs for HDL-C

The Korean Journal of Obesity 22 (2): 67–72.

Koshy, Linda, A L Anju, S Harikrishnan, V R Kutty, V T Jissa,

Irin Kurikesu, Parvathy Jayachandran, A Jayakumaran Nair, A

Gangaprasad, and G M Nair (2016). Evaluating Genomic DNA

Extraction Methods from Human Whole Blood Using End-

point and Real-Time PCR Assays. Molecular Biology Reports.

Springer, 1–12.

Morris, Nathan J, Robert C Elston, and Catherine M Stein. (2010).

A Framework for Structural Equation Models in General Pedi-

grees. Human Heredity 70 (4): 278–86. doi:10.1159/000322885.

Nirengi, Shinsuke, Mami Fujibayashi, Kokoro Tsuzaki, Sachiko

Furuno, Akihiko Uchibe, Yasuharu Kawase, Kazuhiko Kotani,

and Naoki Sakane. (2016) ACTN3 Gene R577X Polymorphism

Associated with High-Density Lipoprotein Cholesterol and

Adiponectin in Rugby Players. Endocrine Practice. American

Association of Clinical Endocrinologists.

Qureshi, Sarah Anwar, Amir Mumtaz, and Saleem Ullah Sha-

hid. (2016)A Common Variant, rs3751812, in Fat Mass and

Obesity Associated (FTO) Gene Is Associated with Serum High

and Low Density Lipoprotein Cholesterol in the Pakistani Sub-

jects. Nutrition. Elsevier.

Shari , F1, S N Mousavinasab, M Saeini, and M Dinmoham-

madi. (2009). Prevalence of Metabolic Syndrome in an Adult

Urban Population of the West of Iran. Experimental Diabetes

Research 2009. Hindawi Publishing Corporation.

Shari , F, S N Mousavinasab, R Soruri, M Saeini, and M Din-

mohammadi (2008) High Prevalence of Low High-Density

Lipoprotein Cholesterol Concentrations and Other Dyslipi-

demic Phenotypes in an Iranian Population Metabolic Syn-

drome and Related Disorders 6 (3). Mary Ann Liebert, Inc. 140

Huguenot Street, 3rd Floor New Rochelle, NY 10801-5215 USA:

187–95.

Silventoinen, Karri, Meike Bartels, Daniëlle Posthuma, G Fred-

eriek Estourgie-van Burk, Gonneke Willemsen, Toos C E M van

Beijsterveldt, and Dorret I Boomsma (2007) Genetic Regulation

of Growth in Height and Weight from 3 to 12 Years of Age:

A Longitudinal Study of Dutch Twin Children Twin Research

and Human Genetics 10 (2). Cambridge Univ Press: 354–63.

doi:https://doi.org/10.1375/twin.10.2.354.

Song, Yeunjoo E, Catherine M Stein, and Nathan J Mor-

ris. (2015) Strum: An R Package for Structural Modeling of

Latent Variables for General Pedigrees.BMC Genetics 16 (1):

35. doi:10.1186/s12863-015-0190-3.

Xitao, Fan, and Fan Xiaotao (2005) Power of Latent Growth

Modeling for Detecting Linear Growth: Number of Measure-

ments and Comparison with Other Analytic Approaches. The

Journal of Experimental Education 73 (2). Taylor & Francis:

121–39.

Zarkesh, Maryam, Maryam Sadat Daneshpour, Bita Faam,

Mohammad. (2012) Heritability of the Metabolic Syndrome

and Its Components in the Tehran Lipid and Glucose Study

(TLGS). Genetics Research 94 (6): 331–37. doi:10.1017/

S001667231200050X.

Zhang, Da-Wei, Rita Garuti, Wan-Jin Tang, Jonathan C Cohen,

and Helen H Hobbs (2008) Structural Requirements for PCSK9-

Mediated Degradation of the Low-Density Lipoprotein Recep-

tor Proceedings of the National Academy of Sciences 105 (35).

National Acad Sciences: 13045–50.