Accepted Manuscript

Association of genetic variants with macronutrient intake inCircassian and Chechan populations in relation to diabetes

R. Dajani, S.J. Shbailat, A. Arafat, S. Sobar, H. Bitar, R. Tayyem,Z. Wei, H. Hakonarson

PII: S2214-5400(18)30030-6DOI: doi:10.1016/j.mgene.2018.03.003Reference: MGENE 415

To appear in: Meta Gene

Received date: 4 December 2017Revised date: 21 February 2018Accepted date: 4 March 2018

Please cite this article as: R. Dajani, S.J. Shbailat, A. Arafat, S. Sobar, H. Bitar, R.Tayyem, Z. Wei, H. Hakonarson , Association of genetic variants with macronutrientintake in Circassian and Chechan populations in relation to diabetes. The address forthe corresponding author was captured as affiliation for all authors. Please check ifappropriate. Mgene(2017), doi:10.1016/j.mgene.2018.03.003

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Title: Association of genetic variants with macronutrient intake in Circassian and

Chechan populations in relation to diabetes.

Authors:

Dajani Ra†

, Shbailat SJa, Arafat A

b, Sobar S

c, Bitar H

d, Tayyem R

e, Wei Z

f, Hakonarson

Hg,h

aDepartment of Biology and Biotechnology, The Hashemite University, Zarqa, Jordan

bNational Center for Diabetes, Endocrinology and Genetics, Amman, Jordan

cCell Therapy Center, University of Jordan, Amman, Jordan

dFaculty of Medicine, University of Jordan, Amman, Jordan

e Faculty of Agriculture, University of Jordan, Amman, Jordan

fNew Jersey Institute of Technology, New Jersey, USA

gCenter for Applied Genomics, The Children’s Hospital of Philadelphia, Philadelphia,

Pennsylvania 19104, USA.

hDepartment of Pediatrics, The Perelman School of Medicine, University of

Pennsylvania, Philadelphia, Pennsylvania, 19104, USA.

†Correspondence should be addressed to RD

Department of Biology and Biotechnology, The Hashemite University, Zarqa, Jordan

email: rdajani@hu.edu.

Telephone: 00962798859335

Running title:

Macronutrient intake in relation to diabetes in ethnic populations

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Abstract

Background and Aims: Type two Diabetes (T2D) is a complex disease including

environmental and genetic factors. Macronutrient intake variation can be partly related

to genetics. We propose in this study to analyse GWAS datasets for the Circassian and

Chechan populations in order to determine associations between the GWAS data and

nutrition intake in relation to diabetes.

Methods and Results: We analyzed a total of 36 traits including the

macro/micronutrient intake in association with Genome wide association studies data

for 34 persons with diabetes Chechans and Circassians. Within the Circassian

population, there was a statistically significant association between carbohydrate and

calorie intake and T2D associated SNPs on Histone deacetylase 9 gene- and between

Vitamin B2 intake and SNPs on a second locus of potential interest on chromosome 11

near LOC101928989 and teneurin transmembrane protein 4. Caffeine intake was also

associated with significant SNPs unrelated to T2D. On the other hand, only calorie

intake correlated with the occurrence of significant SNPs in Chechans, none of them

were related to T2D.

Conclusion: we have identified a genetic association between intake of carbohydrate,

calorie, vitamin B2 and caffeine in the Circassian population and calorie intake in the

Chechan population. Three of these intake traits (carbohydrate, calorie and vitamin B2)

were correlated with T2D development in Circassians. The association between

macronutrient intake and diabetes development can shed light on causative variants for

the pathogenesis of T2D.

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Keywords

Circassian; Chechan; SNPs; Diabetes; ethnic

Introduction

Diabetes is among the most common non-communicable diseases globally. The

International Diabetes Federation estimates that there are currently 194 million people

aged 20 to 79 with diabetes worldwide and that this will increase to 333 million by 2025

(1). Diabetes is the fifth main cause of death in Jordan. According to a 2007 study by

the Heart and Capillary Disease Prevention directorate (HCDP) of the Ministry of

Health in Jordan, diabetes afflicts 16% of Jordanians over the age of 18. Another 23.8%

of Jordanians over 18 years old are on the brink of becoming persons with diabetes,

while the diabetes prevalence in Jordan is 30.5% among both children and adults (2).

Despite extensive research efforts, the genetic basis of T2D remains largely unknown

(3).

Over the past decade, Genome Wide Association Studies (GWAS) have been

used to link regions of the human genome with complex diseases (4, 5). Researchers

have linked haplotypes to type 2 diabetes, coronary heart disease and macular

degeneration (3, 4, 6). Although, it has been difficult to uncover the functional

significance of discovered SNPs, GWAS remain a way to discover genetic risk factors

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for complex diseases. Most GWAS have been performed on European populations.

There is a need to conduct GWAS on different ethnic populations to discover novel risk

factors, especially if we are to apply these discoveries on other populations.

Previously, we performed GWAS on two ethnic populations of ancient descent,

the Circassians and the Chechans. Both the Circassians and the Chechans, are the

largest indigenous nationalities of North Caucasus (7, 8, 9). The Circassian and

Chechan populations are descendants of a single ancient origin with later divisions

along linguistic and geographic borders (8, 10). Previous analysis of classical genetic

markers such as blood groups and serum proteins have shown statistical significant

genetic diversity in the Caucasus (7, 11). The genetic diversity has been confirmed by

mitochondrial DNA and Y chromosome analysis (8, 11). Under military pressure

groups of Circassians and Chechans immigrated to Jordan 140 years ago. Circassians

and Chechans in Jordan are endogamous and have managed to keep their separate sense

of identity and ethnicity (12). The Circassian and Chechan communities represent

unique populations that are genetically distinct from the Arab population.

A recent study has shown that the prevalence of impaired fasting glycemia and

diabetes was 18.5% and 9.6%, respectively, for Circassians, and 14.6% and 10.1%,

respectively, for Chechans. In both ethnicities, the prevalence of impaired fasting

glycemia and diabetes was significantly higher in men, older age groups, married,

subjects of lower educational level, past smokers, and subjects with obesity. Moreover,

low high density lipoprotein (HDL) cholesterol was the most common abnormality

observed in the Circassian and Chechan populations in Jordan (13).

A following study, using the same Circassian and Chechan persons with

diabetes compared them to normal participants from both ethnicities. They found that

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intake of all macronutrients and most of micronutrients (vitamin B12, folic acid, vitamin

C, iron, selenium, and zinc) did not differ between participants with normal blood

glucose and those with impaired fasting glucose or diabetes in both Circassian and

Chechan populations (13,14).

In view of high incidence of T2D, we performed GWAS to search for T2D

disease genes. Our GWAS studies demonstrated that multiple variants underlie T2D in

the Chechen and Circassian populations in Jordan, some of which are shared with other

ethnic groups. No single SNP met genome-wide significance criteria at 5x10-8

level, in

either populations (in publication). We propose in this study to further analyse the

GWAS datasets for the Circassian and Chechan populations in order to determine

associations between the GWAS data and nutrition intake of these populations in

relation to diabetes. Macronutrients intake and dietary behavior, which is related to

many factors such as culture, economics, social and psychologic and health beliefs,

varies among individuals. This variance can be partly related to genetic components.

The heritability is reported to range from 8% to 70% (1). The genetics behind

macronutrient intake can shed light on the biology of dietary behavior. There have been

GWAS that have identified chromosomal regions associates with macronutrient intake

(2-5).

The present study sheds more light on multiple genetic factors that underlie T2D in

association with macro/micronutrient intake in Chechen and Circassian populations in

Jordan. Some of these factors may be shared with other ethnic groups.

Materials and Method

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Subjects: The sample consisted of 34 Circassians and 34 Chechans. Inclusion criteria

included: diagnosis with T2D and receiving medical treatment for it, pure ethnicity

(Circassian, Chechen) in the last 3 generations, living in Jordan, age >30 years old, age

of diagnosis >18 years old, and time since diagnosis and commencement of treatment

>6 months.

GWAS Data: GWAS data produced from two previous research projects funded by

King Hussein institute for biotechnology and cancer (KHCIBC) (Chechan population)

and the Higher council for science and technology (HCST) (Circassian population).

High-throughput, genome-wide SNP genotyping was conducted using the Infinium II

OMNI-Express BeadChip technology (Illumina), at the Center for Applied Genomics

(CAG) at the Children’s Hospital of Philadelphia (CHOP), USA, according to

manufacturer’s standard protocol. Both SNP genotype data and intensity data including

information of Log R Ratio (LRR) and B Allele Frequency (BAF) was extracted from

GenomeStudio project files.

Biochemical data: Biochemical data produced from two previous research projects

funded by KHCIBC (Chechan population) and HCST (Circassian-population) included

Lipid parameters (Total Cholesterol, HDL, low density lipoprotein (LDL) and

triacylglycerol (TG)), and glucose were analyzed for all samples using Enzymatic

assays. The following definitions and cutoff points were used in the study according to

the (ADA) guidelines ( 2014): Glycemic control was considered satisfactory (good) if

HbA1c levels were <7.0%, and unsatisfactory (poor) if HbA1c levels were ≥7.0%. An

elevated LDL level was defined as ≥100 mg/dL, a low HDL level was defined as <40

mg/dL in males and <50 mg/dL in females, an elevated cholesterol level was defined as

≥200 mg/dL, and an elevated triglycerides level was defined as ≥150 mg/dL. Patients

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were considered dyslipidemic if they had at least one of the previously mentioned lipid

abnormalities.

Macro/micronutrient intake data: Macro/micronutrient data produced from two

previous research projects funded by KHCIBC (Chechan population) and HCST

(Circassian population). 24-hour recall was collected from participants to estimate mean

nutrient intake. The individual was asked to recall information on portion sizes, cooking

methods, recipe of dishes, condiments, and beverages for the past 24-hour. All details

about estimating portion sizes were explained by trained research assistants. The intakes

of total calories, macro- and micronutrients as well as other dietary substances were

calculated using ESHA Food Processor (Version 7.9, 1987–2002: Salem, OR). Foods

not listed in ESHA were entered item by item after getting its components and recipe

from the participant.

SNPs analysis: We also performed a manual search of the NCBI database for all genes

related to the list of the annotated nominally significant SNPs (P<0.05). We did not find

databased-evidence discussing the exact SNP. Therefore, we used a multi-step search

technique, which starts by identifying the SNP, determining its location and the related

gene. Then, we searched NIH database for studies that discussed the related gene and if

it is correlated with T2D. After that, we described the full name of the gene, its function

(if known) and the major disease associations based on the NIH database.

Statistical Analysis

All statistical tests for association were carried out using the software package plink

(http://pngu.mgh.harvard.edu/~purcell/plink/index.shtml). Principal components

analysis was carried out using Eigenstrat 3.0

(http://genepath.med.harvard.edu/~reich/Software.htm ).The single-marker analysis for

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the genome-wide data was carried out for different traits using linear regression on

allele counts with the first 10 principle components as covariates. P values and effect

sizes (log odds ratios) with the corresponding 95% confidence intervals were calculated

for the association analysis.

A Manhattan plot is a scatter plot used to display a large number of data-points such as

genome-wide association studies (GWAS). In GWAS Manhattan plots, genomic

coordinates are displayed along the X-axis, with the negative logarithm of the

association P-value for each single nucleotide polymorphism (SNP) displayed on the Y-

axis. Each dot on the Manhattan plot signifies a SNP. Because the strongest

associations have the smallest P-values, their negative logarithms will be the greatest.

A Q–Q plot is a quantile-quantile probability plot where graphical method is used for

comparing two probability distributions by plotting their quantiles against each other.

First, the set of intervals for the quantiles is chosen. A point (x, y) on the plot

corresponds to one of the quantiles of the second distribution (y-coordinate) plotted

against the same quantile of the first distribution (x-coordinate). Thus the line is a

parametric curve with the parameter which is the number of the interval for the quantile.

If the two distributions being compared are similar, the points in the Q–Q plot will

approximately lie on the line y = x. In a case-control comparison of genotype markers,

if the cases are enriched for specific markers, they will deviate away from the control

data at a certain significance level. If there is no difference, then y = x.

For calculation of genomic inflation factor also known as lambda(λ) in genome-wide

association studies (GWAS), λ is defined as the median of the resulting chi-squared test

statistics divided by the expected median of the chi-squared distribution. The median of

a chi-squared distribution with one degree of freedom is 0.4549364. A λ value can be

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calculated from z-scores, chi-square statistics, or p-values, depending on the output

from the association analysis. The algorithm we used is automated in the software

PLINK, which provides accurate measures on the population inflation. In our instance

GIF was 1.00, indicating there was no evidence of population stratification causing

inflation.

Quality control

After filtering out SNPs with minor allele frequency <0.05, missing rate > 0.05, Hardy-

Weinberg Equilibrium Test P value < 0.0001, we ended up with ~588K SNPs for

association analysis. The top hit SNP rs16871944 has reached genome wide

significance level (p=3.35E-08).

An association being detected means that its effect size should be large enough at the

given sample size. For example, as we show in our result section, we found the

estimated effect size for SNP rs16871944 is beta=1071, with 95% confidence interval of

[177, 1419]. Given that N=103, minor allele frequency = 0.27 (rs16871944), and

response CALs SD=500, we can obtain its power as 0.85 when beta=1071. We now

make a power vs effect size plot with effect size beta ranging from 177 to 1419. The

tool website for GPC used is R package “GeneticsDesign” (Figure 6).

Results

We analyzed a total of 36 traits including the macro/micronutrient intake and

biochemical data with valid values in association with the GWAS data for 34 persons

with diabetes Chechans and 34 persons with diabetes Circassians (Table 1 -list of

traits). For each trait that showed SNPs with P<0.001 we plotted a Manhattan plot and

a QQ plot as described in the methods section.

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Only one trait, Calorie intake (CALs) (genomic inflation factor 1.00), showed SNPs

with P<0.001 in the Chechan population (Figure 1.a -QQ-plot; Figure 1.b -Manhattan

plot). However, none of these SNPs has been reported in the literature to be related to

diabetes. We further analyzed these SNPs and found a number of them associated with

fatty liver disease. In Table 2 for each trait we show all the SNPS that were statistically

significant and their related genes and diseases.

Four traits showed potential significant signals in the Circassian population. The

first trait is carbohydrate intake (CARB) (genomic inflation factor 1) and the second

trait is calorie intake (CAL) (genomic inflation factor 1.06736) (Figure 2.a -QQ-plot

and b Manhattan plot, and Figure 3.a -QQ-plot and b Manhattan plot, respectively).

Both showed SNPs with P<0.001 on Histone deacetylase 9 (HDAC9) gene which is

reported to be relevant to diabetes. Other SNPs on other genomic regions that are

related to CAL but not to T2D were associated with fatty liver disease Table 2.

The third trait that showed SNPs that were statistically significant was Vitamin

B2 (genomic inflation factor 1.09848) (Figure 4.a -QQ-plot and b. Manhattan plot). The

SNPs were located on a second locus of potential interest on chromosome 11. This

locus is not on any gene (intergenic) and the nearby genes are LOC101928989 and

teneurin transmembrane protein 4 (TENM4). Other SNPs in other genomic regions that

are associated with vitamin B2 trait but not with T2D were correlated with the

occurrence of other diseases such as ovarian and cervical cancers Table 2.

The fourth trait that showed SNPs that were statistically significant was caffeine

intake. Although caffeine intake (CAFF) (genomic inflation factor 1.03214) (Figure 5.a

-QQ-plot and b. Manhattan plot) showed significant SNPs with P<0.001, none of them

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were related to T2D. These SNPs were of significant interest, because they were

associated with Huntington disease and lateral temporal epilepsy Table 2.

Discussion

In the present study, we analyzed a total of 36 traits including macro/micronutrient

intake and biochemical data in Chechen and Circassian populations. Among these traits,

only CALs intake was associated with significant SNPs in Chechans, however, none of

these SNPs was related to T2D. On the other hand, CALs, CARB, Vitamin B2 and

CAFF intake were associated with significant SNPs in Circassians, and all these traits

except CAFF were related to T2D. In addition, our research revealed 7 SNPs that were

associated with macro/micronutrient intake but unrelated to diabetes, and found that

they were associated with other diseases.

We found that SNPs correlated with CALs and CARB intake in Circassians

were carried on HDAC9 gene. This gene is reported to be relevant to T2D. It is highly

expressed in insulin producing β-cells of pancreas (15, 16), and its product acts

epigenetically to prevent the differentiation of these cells during embryogenesis and

after birth (16). A previous study showed that one SNP carried on this gene was

significantly associated with high BMI, T2D and hyperlipidemia, and as a result it

significantly modified the susceptibility to coronary artery disease and increased the

severity to coronary atherosclerosis (17). In our study, SNPs identified on HDAC9 gene

in T2D patients might contribute to the occurrence/and or severity of the disease by

changing the expression of the gene, and consequently changing the epigenetic

regulation of β-cells differentiation during development.

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We also showed that Vitamin B2 intake was associated with SNPs located on an

intergenic region carried on chromosome 11. This region exists near LOC101928989

and TENM4 genes. The former is a hypothetical gene with still uncharacterized

function, while the later encodes for Teneurin-4, a member of type II transmembrane

glycoprotein family which includes Teneurin 1-4 (18). TENM4 is expressed in the

central nervous system and in the mesenchymal tissues including cartilage and muscles

(19). Despite its association with several diseases, no relevance was reported in

literature between TENM4 and T2D. Nevertheless, chromosome 11, where the

intergenic region is located, was shown to be related to T2D.

Chromosome 11 carries six genes that are implicated in insulin secretion [ATP

binding cassette subfamily C member 8 (ABCC8, also known as SUR1), potassium

voltage-gated channel subfamily J member 11 (KCNJ11, also known as Kir6.2),

uncoupling protein 2 (UCP2) and melatonin receptor 1B (MTNR1B)], signaling

[exostosin glycosyltransferase 2 (EXT2)] and resistance [uncoupling protein 3 (UCP3)]

(20). SNPs on genes related to secretion (21, 22, 23. 24) or signaling (25, 26) of insulin

were associated with T2D. Moreover, variants on UCP3 gene were correlated with

insulin resistance causing T2D (27). The ethnic background is also associated with

certain SNPs that might have a role in the occurrence of T2D (20). Some of them are

carried on chromosome 11. For example, certain SNPs on UCP2-UCP3 gene cluster

were correlated with T2D in Caucasian Americans (28). Other variants near Fanconi

anemia complementation group F (FANCF) gene were associated with young-onset of

T2D in Pima Indian Americans (29). Furthermore, variants in an intergenic region of

chromosome 11p12 might contribute to the occurrence of T2D in Finnish population

(30). Although chromosome 11 carries SNPs specific for certain ethnicity, it also carries

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certain variants on genes such as potassium voltage-gated channel subfamily Q member

1 (KCNQ1) (31; 32), KCNJ11 (22; 33), ABCC8 (22), UCP2 (34; 35; 36) and ArfGAP

with RhoGAP domain, ankyrin repeat and PH domain 1 (ARAP1, also known as

CENTD2) (37; 33) that were reported to be related to T2D in wide range ethnic groups.

Taken together, the SNPs we identified on chromosome 11 might have a role in down-

regulating the insulin pathway or increasing the insulin resistance. Further studies are

required to uncover the underlying mechanisms of different polymorphic alleles and to

explore whether these SNPs are specific for Circassians or prevalent in other ethnicities.

We explored the significant variants that were associated with

macro/micronutrient intake but unrelated to diabetes, and found that they were

associated with other diseases. One SNP (rs2143571) was related to CALs intake. It was

carried on the sorting and assembly machinery component 50 homolog (SAMM50) gene

and associated with susceptibility to nonalcoholic fatty liver disease (NAFLD) (38).

Four SNPs were related to Vitamin B2 intake. Two of them were carried on DNA

methyltransferase 3 alpha (DNMT3A) (rs11887120) and deoxyuridine triphosphate

(DUT) (rs3784621) genes (39; 40). The former was correlated with the occurrence of

ovarian cancer (39), while the later was correlated with cervical cancer associated with

human papillomavirus persistence (40). The third SNP (rs1393350) was located on

tyrosinase (TYR) gene and used as a predictor of the eye color (41,42). The last SNP

(rs12904216) was carried on solute carrier family 12 member 1 (SLC12A1) gene and

was associated with developing thiazolidine-related edema in patients with T2D (43).

Although this SNP was unrelated to T2D in our study, the difference in ethnicity

between our study (Circassians) and the previous one (Taiwanese) may explain this

inconsistency. Finally, two SNPs were related to CAFF intake. One of them

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(rs2276881) is carried on huntingtin (HTT) gene and associated with Huntington’s

disease (44), while the other (rs7099034) was located at glioma inactivated 1 (LGI1)

gene and might have a role in the occurrence of lateral temporal epilepsy (45).

Our study has its limitations since it is based on a select cohort of T2D individuals, and

as a result, they may not be generalizable. Moreover, the small size of the cohort may

have prevented us from detecting more modest effects. Although we observed

consistent patterns of association, many of these associations were of nominal

significance. Nevertheless, there have been GWAS studies that have been successful

with subject numbers that are comparable to ours (46, 47). Therefore, replication in an

independent cohort should be conducted to support or refute the obtained results.

However, obtaining a second cohort from these populations is extremely difficult as

these are the first ever samples collected. In addition, nutrients intake estimation was

based on 24-hour recall which depends on memory, and as a consequence recall bias is

expected.

In summary, our study showed new SNPs are associated with T2D, and that they play a

role in nutrient-specific choice and dietary preference. Although the associations

reported here are significant, the effects are small in magnitude. Given the complexity

of the investigated traits, it is likely that other as yet unidentified SNPs and copy

number variants, contribute to macro/micronutrient intake. Future studies should focus

on conducting the same experiments in non-European cohorts to determine whether or

not the identified SNPs are associated with macro/micronutrient intake in other

ethnicities.

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Funding:

This research was funded by the Hashemite University.

Conflict of interest: None

Author contribution: RD designed research project and wrote the manuscript; HH and

ZW contributed to the design of the research project and performed the data analysis

and contributed to the writing; SS, RT, SS, HB and AA contributed to data analysis and

writing of the manuscript

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Figure 1 a QQ-plot of Chechan for train CALs (calorie intake)

Figure 1 b Manhattan Plot of Chechan for trait CALs (calorie intake). SNPs are sorted

by chromosomal location along X axis against their –log10(P-value) shown on Y axis.

The horizontal line is for the suggested significance level p=10-5

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Figure 2 a QQ plot of Circassians for trait CARB (Carbohydrate intake).

Figure 2 b Manhattan Plot of Circassian for trait CARB (Carbohydrate intake). SNPs

are sorted by chromosomal location along X axis against their –log10(P-value) shown

on Y axis. The horizontal line is for the suggested significance level p=10-5

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Figure 3 a QQ plot for trait CALs (Calorie intake) in Circassian population

Figure 3 b Manhattan Plot of Circassian for trait CALs (Calorie intake). SNPs are sorted

by chromosomal location along X axis against their –log10(P-value) shown on Y axis.

The horizontal line is for the suggested significance level p=10-5

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Figure 4 a QQ Plot of Circassian for trait Vitamin B2

Figure 4 b Manhattan Plot of Circassian for trait Vitamin B2. SNPs are sorted by

chromosomal location along X axis against their –log10(P-value) shown on Y axis. The

horizontal line is for the suggested significance level p=10-5

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Figure 5 a QQ Plot of Circassian for trait CAFF

Figure 5 b Manhattan Plot of Circassian for trait CAFF. SNPs are sorted by

chromosomal location along X axis against their –log10(P-value) shown on Y axis. The

horizontal line is for the suggested significance level p=10-5

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Figure 6 A power vs effect size plot with effect size beta ranging from 177 to 1419. The

tool website for GPC used is R package “GeneticsDesign”

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Number Trait ABBREVIATION

1 Protein PROT

2 Carbohydrate CARB

3 Fiber FIBER

4 Lactose LACT

5 Fat FAT

6 Monounsaturated fatty acid MONO

7 Saturated fatty acid SAT

8 Polyunsaturated fatty acid POLY

9 Trans fatty acid TFA

10 Cholesterol CHOL

11 Vitamin A (international unit) AIU

12 Vitamin B1 B1

13 Vitamin B2 Vitamin B2

14 Vitamin B3 B3

15 Vitamin B12 B12

16 Vitamin B6 B6

17 Biotin BIOT

18 Vitamin C VITC

19 Vitamin D DIU

20 Vitamin E EIU

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21 Folic acid FOLAC

22 Vitamin K VITK

23 Pantothenic acid PANTHOTHE

24 Calorie CALC

25 Iron IRON

26 Selenium SELNIUM

27 Caffeine CAFF

28 Omega-3 OMEGA3

29 Omega-6 OMEGA6

30 Zinc ZINC

31 Glucose GLUCOSE

32 Glycosylated hemoglobin HBAIC

33 Cholesterol CHOL

34 High-Density lipoprotein HDL

35 Low-Density lipoprotein LDL

36 Triglyceride TRIG

Table 1 Macro/micronutrient intake and biochemical traits for Chechan and Circassian

populations

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SNP Gene Locus

ID

Chr

omo

som

e #

Biochemi

cal factor

Disease Functional

_class

P-value References

rs21

435

71

SAM

M50

25813 22 CALS Genetic

Variants

in the

SAMM5

0 Gene

Create

Suscepti

bility to

Nonalco

holic

Fatty

Liver

Disease

in a

Chinese

Han

Populati

on

Intron-

variant

0.0000

6646

( Chen et al.

2015)

rs11

887

DNM

T3A

1788 2 B2MG Ovarian

CA

intron-

variant

0.0000

02139

(Kelemen et al.

2008)

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120

rs13

933

50

TYR 7299 11 B2MG Predictio

n of eye

color in

the

Slovenia

n

populatio

n using

the

IrisPlex

SNPs.

Intron-

variant

0.0000

4833

(

Kasteli

c and

Drobni

c 2012;

Kasteli

c et al.

2013)

rs37

846

21

DUT 1854 15 B2MG risk for

HPV

persisten

ce and

progressi

on to

cervical

cancer.

Intron-

variant

0.0000

3019

(Wang et al.

2010)

rs12

904

SLC1

2A1

10537

08066

15 B2MG Genetic

predispo

intron-

variant,upstr

0.0000

6383

(Chang et al.

2011)

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Table 2 List of macro/micronutrient associated loci associated with other diseases from

both Circassian and Chechan populations

216 557 sition

and

nongenet

ic risk

factors

of

thiazolidi

nedione-

related

edema in

patients

with type

2

diabetes.

eam-

variant-2KB

rs22

768

81

HTT 3064 4 CAFF Huntingt

on’s

disease

reference,sy

nonymous-

codon

0.0000

1753

(Drouet et al.

2014)

rs70

990

34

LGI1 10537

84379

211

10 CAFF Lateral

temporal

epilepsy

intron-

variant

0.0000

001995

( Fanciulli et al.

2012)

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Abbreviation list

CAG: Center for Applied Genomics

GWAS: Genome Wide Association Studies

HDL: high density lipoprotein

HSDP: the Heart and Capillary Disease Prevention directorate

KHCIBC: King Hussein Cancer Institute for Biotechnology and Cancer

LDL: low density lipoprotein

SNPs: Single Nucleotide Polymorphisms

T2D: Type two diabetes

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Highlights

Title: Association of genetic variants with macro/micronutrient intake in Circassian and

Chechan populations in relation to diabetes”

Type two Diabetes (T2D) is a complex disease including environmental and

genetic factors. Food intake plays a role in the manifestation of T2D.

Macronutrient and micronutrient intake varies among individuals. This variance

can be partly related to genetic components.

This is the first study to study the association between GWAS and food intake in

diabetic patients in the Circassian and Chechan population in Jordan.

We were able to show that within the Circassian population, traits including

carbohydrate, calorie and vitamin B2 intake that were found to correlate with the

occurrence of T2D were also shown to be associated with single nucleotide

polymorphisms (SNPs) located on certain genomic regions.

The association between macronutrient intake and diabetes development can

shed light on causative variants for the pathogenesis of T2D.

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