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Evidence for type 2 diabetes related quantitative trait locus on chromosome 6q: Joint analysis of Finnish and Mexican American families

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Evidence for type 2 diabetes related quantitative trait locus on chromosome 6q: Joint analysis of Finnish and Mexican American families

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Content EVIDENCE FOR TYPE 2 DIABETES RELATED QUANTITATIVE
TRAIT LOCUS ON CHROMOSOME 6q: JOINT ANALYSIS OF
FINNISH AND MEXICAN AMERICAN FAMILIES
Copyright 2006
by
Mita Kuchimanchi
A Thesis Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(BIOSTATISTICS)
May 2006
Mita Kuchimanchi
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UMI Number: 1437833
Copyright 2006 by
Kuchimanchi, Mita
All rights reserved.
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DEDICATION
I would like to dedicate this thesis to my loving husband Dr. Kamesh Kuchimanchi,
whose support and encouragements were my source of inspiration in writing this
thesis.
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ACKNOWLEDGEMENTS
I would like to extend my sincere gratitude and thanks to Dr. Richard Watanabe for
serving as the chairperson of my thesis committee and for his help, support, and
guidance throughout my thesis work. I would also like to thank Dr. Kimberly
Siegmund, and Dr. David Conti for serving on my thesis committee and their
valuable insights regarding my thesis. I would like to acknowledge the Finland-
United States Investigation of Type 2 diabetes Genetics group, and San Antonio
Family Diabetes Study group for providing the datasets.
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TABLE OF CONTENTS
DEDICATION ii
ACKNOWLEDGEMENTS iii
LIST OF TABLES v
LIST OF FIGURES vi
ABSTRACT vii
INTRODUCTION 1
METHODS 8
Study Subjects, Clinics and Examinations 8
Statistical Analysis 9
RESULTS 13
DISCUSSION 25
CONCLUSIONS 30
BIBLIOGRAPHY 31
iv
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LIST OF TABLES
Table 1: Phenotypes of Subjects by Individual Groups 13
Table 2: Phenotypes of Subjects in Combined Groups 14
Table 3: Family Structure of the Individual Study Groups 15
Table 4: Summary Results From the QTL Linkage Analysis on Chromosome
6q Using Fasting Serum Insulin as the Trait of Interest 18
v
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LIST OF FIGURES
Figure 1: Maximum LOD Score Plot for Fasting Insulin Individual Data
Sets
Figure 2: Maximum LOD Score Plots for Fasting Insulin in Combined Data
Sets
Figure 3: Maximum LOD Score Plots for Fasting Insulin Adjusted for Age
and Gender in Combined Data Sets
Figure 4: Maximum LOD Score Plots for Fasting Insulin Adjusted for Age,
Gender, and BMI in Combined Data Sets
Figure 5: Maximum LOD Score Plots for Fasting Insulin After Adjusting
for Different Covariates in Combined Data Sets of FUSION 1,2
& SAFADS
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ABSTRACT
The goal of this study was to determine evidence for a type 2 diabetes susceptibility
gene on chromosome 6q by examining linkage to fasting serum insulin level. A
variance components-based quantitative trait locus linkage analysis was performed
on the combined datasets of Finnish and Mexican American families. After
adjusting for age, gender and study population, the strongest evidence for linkage to
fasting insulin was observed at 143 cM with a LOD of 2.69 for the combined data set
of Mexican American and Finnish families. BMI was found to decrease the linkage
signal to a LOD of 0.7 at 143 cM. Thus, this study suggests the presence of a type 2
diabetes susceptibility gene on chromosome 6q, which warrants further fine mapping
of this region.
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INTRODUCTION
Type 2 diabetes mellitus is a chronic disease that accounts for about 90% of all
diabetes cases. Recently a worldwide pandemic of type 2 diabetes, obesity, and
associated cardiovascular complications has been seen in both developing and
developed nations. According to the International Diabetes Federation, currently
190 million people worldwide have type 2 diabetes (Zimmet 2005), with
approximately 6% of the U.S. population being affected. Left untreated, secondary
complications associated with the disease include retinopathy (Howard-Williams et
al. 1984), neuropathy (Ellenberg 1982), nephropathy (Chukwuma 1995) and
cardiovascular implications (Tuck 1988). Thus prevention and control of type 2
diabetes are key in reducing morbidity and mortality and saving billions of dollars in
healthcare costs and lost productivity.
Type 2 diabetes is characterized by insulin resistance, (3-cell dysfunction, and obesity
(DeFronzo 1988; Bergman 1989) in adults mostly above 40 years of age. Insulin
resistance may be attributed to increase in non-esterified fatty acids, inflammatory
cytokines, adipokines and mitochondrial dysfunction (Stumvoll et al. 2005). On the
other hand (3-cell dysfunction may be attributed to glucotoxicity, lipotoxicity, and
amyloid formation (Stumvoll et al. 2005).
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Heritability studies have been performed to elucidate the relative contributions of
environmental and genetic factors on the development of Type 2 diabetes mellitus.
Twin studies have shown a higher concordance rate for Type 2 diabetes of about
34% and higher among monozygotic twins, which is approximately two times higher
than that observed in dizygotic twins (Barnett et al. 1981; Newman et al. 1987; Lo et
al. 1991; Kaprio et al. 1992). For example, the National Heart, Lung, and Blood
Institute twin study observed a 58% concordance for the disease among monozygotic
twins versus 17% among dizygotic twins in Caucasians (Newman et al. 1987). In
addition, family studies indicate an elevated risk of 3 to 4 times for first-degree
relatives of type 2 diabetes affected individuals, compared to the general population
(Rich 1990). These studies thus indicate a strong evidence for the involvement of
genetic factors in the etiology of Type 2 diabetes.
Type 2 diabetes is known to be a complex disease involving probably multiple
genes, and their interactions with each other, and with environmental factors. For
such complex diseases, it is difficult to localize the specific genetic loci involved.
Like many complex diseases that are known to be associated with quantitative risk
factors, or even clinically diagnosed through quantitative clinical measures, type 2
diabetes is diagnosed on quantitative levels of glucose. One solution to the problem
of studying the complex inheritance of disease susceptibility genes is to study the
normal physiological variations in these quantitative risk factors underlying the
disease liability. The identification of type 2 diabetes causing genes that contribute
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to such quantitative risk factors is the primary objective of quantitative trait locus
(QTL) linkage analysis.
The aim of quantitative trait linkage analysis is to identify loci that influence
quantitative traits. Some of the quantitative traits which have been studied for their
association with type 2 diabetes are those which 1) determine glucose tolerance, such
as insulin sensitivity (SI), glucose effectiveness (SG), insulin secretion assessed as
the acute insulin response to glucose (AIR), and the disposition index (DI) which is a
measure of insulin resistance-corrected beta-cell function, and 2) traits associated
with beta-cell function such as fasting insulin, fasting C-peptide, and 2-hour insulin
(Watanabe et al. 2000). Watanabe et al (Watanabe et al. 1999) studied whether
individual quantitative traits that determine glucose tolerance exhibit familiality in
Finnish families with at least one type 2 diabetes affected sibling pair. By using
variance components analysis they determined heritability for each trait and
concluded that there is strong evidence for modest heritability of type 2 diabetes-
related quantitative traits in unaffected spouses and offspring of Finnish affected
sibling pairs. Thus, scientists worldwide are trying to decipher the genetic
component of the disease using three main approaches such as positional cloning,
genome scanning and candidate gene approach.
Since the exact cause of type 2 diabetes is difficult to decipher, the study of
intermediate quantitative traits such as insulin resistance, body mass index, insulin
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secretory response, and fasting and 2hour glucose and insulin level can provide
some clue as to whether these traits are some how involved with (Ghosh and Schork
1996) type 2 diabetes causing genes. Only a few genes such as calpainlO
(CAPN10), peroxisome proliferator-activated receptor-gamma (PPARG), and ATP-
sensitive potassium channel, Kir6.2 (KCNJ11) (Barroso et al. 2003) have-shown
significant associations with Type 2 diabetes.
Different ethnic groups have been found to have different prevalence rates of type 2
diabetes, indicating that race might be an important factor in the disease etiology.
For example, the prevalence of type 2 diabetes among Finnish population of age 45-
64 years is about 4% (Tuomilehto et al. 1991, Bennett et al. 1992, Kenny et al. 1995,
Valle et al. 1997). Among the different ethnic groups in the United States, Mexican
Americans are found to have higher incident and prevalent cases of type 2 diabetes
than non-Hispanic whites. Moreover, non-diabetic Mexican Americans have higher
insulin levels and are more insulin resistant than non-diabetic non-Hispanic whites
(Haffner et al. 1990). Hence Mexican Americans are considered a population at
high risk for Type 2 diabetes (Stem and Haffner 1990).
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In order to map and positionally clone genes predisposed to type 2 diabetes and
intermediate quantitative traits in Finnish subjects, an international collaboration of
Finland and the United States led to the start of the FUSION study (Finland-United
States Investigation of NIDDM Genetics). The Finnish population was chosen for
the genetic study due to a number of reasons such as a small group of founders,
limited immigration, geographical, cultural, and genetic isolation, a comprehensive
population and medical database, and a well educated population supportive of
medical research (Ghosh et al. 2000).
The FUSION study reported evidence for a diabetes susceptibility locus on
chromosome 6q in two different sets of samples. In the first part of the FUSION
study namely FUSION 1, a genome wide scan in 495 affected Finnish sib pair
families showed a maximum LOD score (MLS) of 0.61 at 112.5 cM on chromosome
6 (Ghosh et al. 2000). In the second part of the FUSION study namely FUSION 2, a
genome wide scan in 242 affected Finnish sib pair families showed the strongest
linkage results at chromosome 6 with a MLS of 2.3 at 95 cM (Silander et al. 2004).
Stronger evidence for linkage to chromosome 6 was observed during analysis in the
combined set of 495 FUSION 1 and 242 FUSION 2 affected sib pair families
(Silander et al. 2004).
The San Antonio Family Diabetes study (SAFADS) looked into the underlying
genetic components of insulin resistance and hyperinsulinemia, which are both
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strongly correlated to obesity and type 2 diabetes. This study involved a genome
wide scan using a multipoint variance components approach to identify susceptibility
loci influencing fasting specific insulin (FSI) concentrations in nondiabetic Mexican
Americans (32 families), and details of this study are stated elsewhere (Duggirala et
al. 2001). Compared to direct insulin resistance measures such as the euglycemic
clamp and the intravenous glucose-tolerance test, the fasting insulin concentration is
considered as a simple non-invasive surrogate measure of insulin resistance (Haffner
et al. 1987; Haffner et al. 1996). The SAFADS study found significant evidence for
a major locus influencing FSI concentrations on chromosome 6q with a MLS of 4.1
at 150 cM (Duggirala et al. 2001).
Studies involving other genome wide scans have also reported linkage signals related
to type 2 diabetes on chromosome 6q. A genome wide scan involving affected
sibling pairs in African Americans affected with type 2 diabetes with impaired renal
function showed suggestive evidence for linkage at 6q24-q27 (163.5 cM, LOD 2.26)
(Sale et al. 2004). Another genome wide scan involving 257 pedigrees and 385
affected sibpairs in the Chinese population also showed strong evidence for linkage
to type 2 diabetes and impaired glucose homeostasis to chromosome 6q21-q23
(128.93 cM, MLS 6.23) (Xiang et al. 2004). In another study (Lindsay et al. 2001)
involving genome-wide linkage analysis assessing parent-of-origin effects in the
inheritance of type 2 diabetes and BMI in Pima Indians, analyses of sibling pairs
who were affected by diabetes and younger than 25 years of age showed an increase
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of sharing of maternally derived alleles on chromosome 6 (LOD(derived from
mother) = 3.0). Thus different studies have suggested that type 2 diabetes
susceptibility loci or loci influencing insulin concentrations may be located on
chromosome 6.
The FUSION 1 and 2 studies found evidence of linkage in Finnish population over a
70 cM region extending from 70-140 cM on chromosome 6 by applying affection
status linkage, ordered subset linkage (Silander et al. 2004), and fasting serum
insulin based quantitative trait linkage analysis (Watanabe et. al 2000). Duggirala et
al reported a QTL MLS of 4.1 based on fasting specific insulin in this same region
around 150 cM in Mexican Americans. Thus a common region on chromosome 6
between 70-150 cM in two different ethnic groups show putative Type 2 diabetes
related genes on chromosome 6 using fasting insulin as the trait of interest. The
objective of the present study is thus to perform quantitative trait linkage analysis on
the combined datasets of FUSION and SAFADS study. The aim is to examine
whether there is an improvement in the linkage signal on chromosome 6, related to
Type 2 diabetes mellitus, based on fasting insulin concentration, since the combined
datasets will provide a larger sample size, with greater power to detect relevant
linkage signals.
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METHODS
Study Subjects, Clinics and Examinations
A detailed description of the FUSION study has been provided elsewhere (Valle et
al. 1998; Silander et al. 2004). In the first phase of subject recruitment (FUSION 1),
index cases were ascertained on the basis of the National Hospital Discharge
Registry in Finland, and diagnosis of type 2 diabetes was based on the World Health
Organization (1985) criteria. Selection was based on age of diagnosis of type 2
diabetes between 35-60 yrs, at least one living affected sibling, at least one parent
unaffected, and no history of insulin dependant diabetes mellitus (IDDM) in first-
degree relatives. Families recruited for the FUSION study were classified as nuclear
(where all affected siblings and any living parents were studied) or extended (where
in addition to nuclear family members, a non-diabetic spouse and at least two
offspring of an affected sibling were studied).
The FUSION 1 genome scan included 408 microsatellite markers with an average
density of 8 cM (Ghosh et al. 2000).
The subjects for FUSION 2 project were ascertained in a similar manner to those in
FUSION 1, except that no spouse or offspring were recruited. The FUSION 2
genome scan included 392 microsatellite markers with an average marker spacing of
9 cM (Silander et al. 2004).
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Detailed description of the San Antonio family diabetes study (SAFADS) is given
elsewhere (Duggirala et al. 2001). Briefly, the SAFADS included 32 low-income
Mexican American extended families. Selection of families was based on type 2
diabetes probands. Fasting plasma glucose, serum immunoreactive insulin, fasting
specific Insulin, triglyceride,and leptin levels were measured in the study subjects.
The SAFADS genome scan was carried out using 301 microsatellite markers with an
average marker spacing of 10-15 cM (Duggirala et al. 2000).
Statistical Analysis
The rationale of using quantitative trait for detecting linkage signal is based on the
fact that qualitative diseases like type 2 diabetes may be related to quantitative risk
factors such as fasting insulin concentration. The quantitative trait locus linkage
analysis is based originally on Fisher’s polygenic model. The variance component
model for quantitative traits is a = a m g + a p g + < y e , where a is the total trait
variance, crm g 2 is the variance due to a major gene, crp g 2 is the variance due to additive
effects of polygenes, and a e 2 is the variance due to environmental factors unique to
the individual. In the present study we use the variance components approach for
QTL linkage analysis. The covariance between any two individuals i and j under the
■y -y
variance component model can be defined as Cov (Xi, Xj) =IBDjj C T m g + 2 < |> jj a a +
5,j < y e 2, where IBD,j is the estimated proportion of genes shared identical by descent
(IBD) by individual i and j at a given locus, ^ is probability that genes selected from
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the same locus from individuals i & j are IBD, a a 2 is the additive genetic variance
component and 8ij is =1 if i=j and 0 if i# j,. Assuming that the major gene acts
additively, we then use a likelihood ratio test (LRT) to test the hypothesis of no
linkage, where Ho: cra , m g 2 = 0, and HA : cia, m g 2 >0. This LRT being a one sided test is
asymptotically distributed as a 50: 50 mixture of y2 on 1 degree of freedom, and a
point mass at 0 (Self & Liang 1987). Along the different points of interest in the
genome, the presence of major gene was evaluated using the likelihood ratio statistic.
In this study we used a univariate multipoint linkage analysis based on maximum-
likelihood methods (Amos 1994; Almasy and Blangero 1998), assuming that our
phenotype of interest (in this case fasting insulin concentration) is univariately
normally distributed with mean p and variance Q, where Q is the covariance
between trait values of two individuals i and j of the pedigree. The mean p depends
on measured genetic and environmental factors, whereas the unmeasured polygenes
and shared environmental factors are included in the variance components. In order
to obtain an approximate univariate normal distribution for our trait of interest, we
used power transformations of 0.25 for fasting insulin concentration in all analysis,.
This power transformation was obtained by Box Cox power transformation (Box and
Cox 1964) using SAS® 9.1 (Cary, North Carolina). Standard regression diagnostic
tools provided by SAS® 9.1, such as evaluating the mean, median, skewness,
kurtosis, normality tests evaluating and residuals etc. were used to examine the
normality assumption.
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In both FUSION and SAFADS studies, we looked only at the markers on the
chromosome 6q. Markers on chromosome 6q from both these studies were placed
on a unified marker map based on the Marshfield maps (Broman et al. 1998). The
unified marker map including FUSION 1, FUSION 2 and SAFADS consist of 67
markers on chromosome 6q. Out of these 67 markers, 64 markers were found on the
Marshfield site. Three markers (D6S1045, D6S481, and F13A1) were not found on
the Marshfield site. For these three markers, marker positions were determined by
linear interpolation using their relative position with respect to the flanking markers.
The family structure of each of the three studies were analyzed individually using
Pedstats (Wigginton and Abecasis 2005).
(Si
Variance components QTL linkage analysis was performed using SOLAR
(Southwest Foundation for Biomedical Research, San Antonio, Texas) version 2.1.4.
Multipoint IBD (MIBD) were calculated for datasets (1) FUSION 1 and SAFADS;
(2) FUSION 2 and SAFADS; (3) FUSION 1, FUSION 2, and SAFADS; (4)
FUSION 1; (5) FUSION 2 and (6) SAFADS. Subsequently, multipoint linkage
analysis was performed for the six datasets individually using the variance
component algorithm. Mean components model adjusted for covariates such as age,
gender and BMI. For each dataset (combined and individual) three models were run,
(1) no adjustment for covariates, (2) adjusting for covariates age and gender, (3)
adjusting for covariates age, gender and BMI. The model which showed the
maximum linkage signal was further analyzed by incorporating an additional
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dichotomized variable called population (0 for Finnish, 1 for Mexican Americans)
to evaluate if study population had any confounding effect on the linkage signal.
Evidence for linkage was concluded if the maximum LOD score was > 3.0. P-values
for LOD scores were calculated based on large sample theory, where Z = square root
(2 ln(10) * LOD) was approximated by a standard normal variable with the null
hypothesis of no linkage (Kong and Cox 1997).
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RESULTS
Table 1 summarizes the characteristics of our subjects stratified by subgroups. The
FUSION 1 dataset consists of 2001 subjects from 547 families. The FUSION 2
dataset consists of 838 subjects from 264 families. The SAFADS dataset consists of
654 subjects from 32 families. The combined dataset of FUSION 1 and SAFADS
dataset consists of 2655 subjects from 579 families. The combined dataset of
FUSION 2 and SAFADS dataset consists of 1492 subjects from 296 families. The
combined dataset of FUSION 1, FUSION 2 and SAFADS dataset consists of 3493
subjects from 843 families.
Table 1: Phenotypes of Subjects by Individual Groups
Dataset FI F2 SAFADS
Sample size 2001 838 654
Sex (Male-.Female) 941:1060 399:439 319:335
Age (years) 56.5 ± 15.3 63.6 ±9.3 43.3 ± 17.3
BMI (kg/m2 ) 28.6 ±5.0 29.0 ± 4.6 29.4 ± 6.6
Fasting serum insulin (pM) 95.9 ±71.1 100.2 ± 74.3 84.9 ± 67.3
Data represented as mean ± standard deviation
The SAFADS subjects were slightly younger with a mean age of 43.3 ± 17.3 years,
followed by the FUSION 1 with a mean age of .5± 15.3 years, and the FUSION 2
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subjects with a mean age of 63.6 ± 9.3 years. The male to female ratio of the
FUSION 1, FUSION 2, and SAFADS subjects are 941:1060,399:439, and 319:335
respectively, which is approximately the same distribution of males to females in the
three datasets. The mean BMI of the FUSION 1, FUSION 2, and SAFADS subjects
are also approximately the same with a mean of 28.6 ± 5 kg/m2, 29.0 ± 4.6 kg/m2,
and 29.4 ± 6.6 kg/m2 respectively for the three groups. Fasting serum insulin
concentration of the SAFADS group was relatively lower with a mean of 84.9 ±
67.3 pM, followed by the FUSION 1 with a mean of 95.9 ±71.1 pM, followed by
the FUSION 2 set with a mean of 100.2 ± 74.3 pM.
Table 2: Phenotypes of Subjects in Combined Groups
Dataset FI and SAFADS F2 and SAFADS
FI, F2 and
SAFADS
N 2655 1492 3493
Sex (Male : Female) 1260 :1395 718 : 774 1659: 1834
Age (years) 54.1 ± 16.5 56.6 ± 15.9 56.5 ± 15.5
BMI (kg/m2 ) 28.8 ±5.2 29.1 ±5.3 28.8 ±5.1
Fasting serum insulin
(pM)
94.4 ±70.7 95.1 ±72.4 95.6 ±71.5
Data represented as mean ± standard deviation
1 4
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Table 2 summarizes the demographics of the combined datasets. For the combined
datasets of FUSION 1 and SAFADS, FUSION 2 and SAFADS, and FUSION 1,
FUSION 2, and SAFADS, the age was approximately the same in all three sets with
a mean of are 54.1 ± 16.5 years, 56.6 ± 15.9 years, and 56.5 ± 15.5 years
respectively. For the combined dataset of (1) FUSION 1 and SAFADS,
(2) FUSION 2 and SAFADS, and (3) FUSION 1, FUSION 2, and SAFADS, the
male to female ratios are 1260:1395, 718:774, and 1659:1834 respectively, which are
similar for all the sets. For the same three groups as above, the fasting serum insulin
concentrations are 94.4 ± 70.7 pM, 95.1 ± 72.4 pM, and 95.6 ±71.5 pM,
respectively.
Table 3: Family Structure of the Individual Study Groups
Dataset
FI F2 SAFADS
Families 545 265 27
Sibships 1358 984 632
Half sibships 16 17 39
Average sibship size 2.49 3.71 23.41
Table 3 summarizes the family structure of the study subjects in the three individual
studies. FUSION 1 had the largest number of families, followed by FUSION 2, and
SAFADS. Though SAFADS had the least number of families, it had the highest
average sibship per family. Hence, SAFADS families were all extended families.
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On the other hand, FUSION 1 and FUSION 2 were both nuclear families with an
average sibship size of approximately 2.5 to 3.7.
Table 4 summarizes results from the quantitative trait linkage analysis using
individual and combined datasets. Using combined datasets of FUSION 1, 2, and
SAFADS, the strongest evidence of linkage observed was a MLS value of 2.69 at
143 cM adjusted for age, gender, and study population. This is a change of
approximately 5%, compared to the value obtained when age and gender were
included as covariates (MLS of 2.56 at 143 cM). When no covariates were used, the
change in MLS (2.42 at 143 cM) was less than 10% than where both age and gender
were included. Hence, age, gender, or study population did not appear to have any
confounding effects on the linkage signal. However, after including BMI in the age
and gender adjusted model, the linkage signal reduced by 69% (MLS of 0.70 at 143
cM) indicating that BMI was a very important covariate in this linkage analysis.
Though a maximum LOD score of 0.79 was observed at chromosomal position of
191 cM, it is known that occasionally at the extremes of the chromosomal map, false
positive peaks may be observed in quantitative trait locus linkage analysis.
Therefore, the next highest LOD score of 0.698 at chromosomal location of 143 cM
has been reported as the actual linkage peak.
Using combined datasets of FUSION 1 and SAFADS, the strongest evidence of
linkage observed after adjusting for age and gender was a MLS of 2.49 at 143 cM.
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The MLS was almost same as above when no covariates were used. Again, after
adjusting for BMI, the linkage signal is reduced by 72% (MLS of 0.698 at 143 cM)
compared to using both age and gender as covariates.
The combined datasets of FUSION 2, and SAFADS showed the strongest evidence
of linkage with a MLS of 1.14 at 143 cM after adjusting for age and gender. This is
similar to the result obtained from analyses that did not include any covariates. After
adjusting for BMI in addition to age and gender, the linkage signal has reduced by
75% in this dataset.
In addition to the combined datasets, the individual datasets were also analyzed using
QTL linkage analysis. This analysis was done to give an insight as to the relative
contributions of linkage signals from the individual datasets. The individual dataset
of FUSION 1 showed the strongest evidence for linkage with a MLS of 2.15 at
144 cM after adjusting for age and gender. The MLS obtained without any covariate
was approximately the same as above. The strongest evidence for linkage when
adjusted for BMI was a MLS value of 2.42. This slight increase may be due to the
fact that BMI may be related to the disease in Finnish population, and hence
adjusting for it increased the linkage signal slightly.
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Table 4: Summary Results From the QTL Linkage Analysis on Chromosome 6q Using Fasting Serum Insulin as the Trait
of Interest
Dataset Covariates MaxLOD Position (cM) 1 LOD support (cM) p value
Fusionl, Fusion2, SAFDS None 2.42 143 139 147 0.000859
Fusionl, Fusion2, SAFDS Age, gender 2.56 143 139 148 0.0006
Fusionl, Fusion2, SAFDS Age, gender, population 2.69 143 136 163 0.000435
Fusionl, Fusion2, SAFDS Age, gender, BMI 0.70 143 135 191 0.056454
Fusionl, SAFDS None 2.40 143 138 148 0.000896
Fusionl, SAFDS Age, gender 2.49 143 139 150 0.000714
Fusionl, SAFDS Age, gender, BMt 0.70 143 135 191 0.073154
Fusion2, SAFDS None 1.12 9 0 181 0.023275
Fusion2, SAFDS Age, gender 1.14 143 137 165 0.022186
Fusion2, SAFDS Age, gender, BMI 0.27 170 0 185 0.261481
Fusionl None 2.04 144 137 149 0.002199
Fusionl Age, gender 2.15 144 132 149 0.001643
Fusionl Age, gender, BMI 2.42 140 133 146 0.000855
Fusion2 None 0.94 96 93 98 0.037306
SAFDS None 1.47 143 138 166 0.00925
SAFDS Age, gender 1.51 143 138 165 0.008425
SAFDS Age, gender, BMI 0.21 165 0 185 0.324082
0 0
The individual dataset of FUSION 2 that did not include any covariates showed the
strongest evidence of linkage with a MLS of 0.94 at 96 cM. For the FUSION 2
dataset, the linkage analysis failed to converge when adjusted for the covariates such
as age, gender and BMI. Convergence failure may be due to (1) violation of the
normality assumptions for the trait, (2) presence of outliers, (3) very weak linkage
signal in the dataset, and (4) presence of local maxima or minima in the data. Since
normality assumption for the FUSION 2 data was not violated, nor were there any
significant outliers, the possible reasons for the convergence failure may be
attributed to the inherent weak linkage signal in the FUSION 2 dataset, or due to
local maxima or minima.
The individual dataset of SAFADS showed the strongest evidence of linkage with a
MLS of 1.51 at 143 cM after adjusting for age and gender. This value (4.1 at
150 cM) is different to that reported by Duggirala et al. 2001. The reported location
of 150 cM is within thel LOD support of 138 and 165 cM obtained, and therefore is
within the same range of location. The discrepancy in MLS value may be due to the
fact that Duggirala et al used a subset of 310 individuals from the largest 27 out of 32
families for their study. This study on the other hand included all 654 subjects of the
27 families. After adjusting for BMI including age and gender, the linkage signal
was reduced by 86%. Region near marker D6S403 has been suggested as a putative
susceptibility locus for measures of obesity /adiposity and triglyceride levels in
19
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Mexican Americans (Duggirala et al. 2001). It is likely that overlapping of genes
related to type 2 diabetes and obesity caused a reduction in the linkage signal up on
inclusion of BMI as one of the covariates.
Figure 1 shows the MLS curves for unadjusted fasting insulin trait in the
FUSION 1,2, and SAFADS datasets analyzed separately. The strongest evidence of
linkage is seen in the FUSION 1 dataset with a MLS of 2.04 at 144 cM, followed by
SAFADS with a MLS of 1.47 at 143 cM, and a MLS of 0.94 at 96 cM.
Figure 1: Maximum LOD Score Plot for Fasting Insulin Individual
Data Sets
3
Fusion 2
Fusion 1
SAFADS
2
1
0
200 250 0 50 100 150
Map Position (cM)
20
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Figure 2 shows the MLS curves for unadjusted fasting insulin trait in the
FUSION 1,2, and SAFADS datasets analyzed jointly. The strongest evidence of
linkage is seen in the combined FUSION 1, 2, and SAFADS dataset with a MLS of
2.41 at 143 cM, followed by combined dataset of Fusion 1 and SAFADS with a MLS
of 2.4 at 143 cM, and a MLS of 1.12 at 9 cM for the combined Fusion2 and
SAFADS dataset. The MLS curves for the joint dataset FUSION 1,2 and SAFADS,
and the joint dataset of FUSION 1 and SAFADS are almost overlapping, indicating
minimal contribution from the FUSION 2 dataset.
Figure 2: Maximum LOD Score Plots for Fasting Insulin in
Combined Data Sets
3.0
Fusion 2 & SAFADS
Fusion 1 & SAFADS
Fusion 1,2 & SAFADS
2.5
2.0
i .•'v
0.5
0.0
50 100 150 200 250 0
Map Position (cM)
21
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Figure 3 shows the MLS curves for age and gender adjusted fasting insulin trait in
the FUSION 1,2, and SAFADS datasets analyzed jointly. Strongest evidence of
linkage is seen in the combined FUSION 1, 2, and SAFADS dataset with a MLS of
2.56 at 143 cM, followed by combined dataset of FUSION 1 and SAFADS with a
MLS of 2.49 at 143 cM, and a MLS of 1.14 at 143 cM for the combined Fusion2 and
SAFADS dataset. The MLS curves for the joint dataset FUSION 1, 2 and SAFADS,
and the joint dataset .of FUSION 1 and SAFADS super-imposable, indicating
minimal contribution from the FUSION 2 dataset.
Figure 3: Maximum LOD Score Plots for Fasting Insulin Adjusted
for Age and Gender in Combined Data Sets
3.0
2.5
Fusion 1,2 & SAFADS
Fusion 1 & SAFADS
Fusion 2 & SAFADS
2.0
£
8
c/5
8
s
3
E
'I
s
0.5
0.0
100 200 250 0 50 150
Map Position (cM)
22
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The MLS curves for age, gender, and BMI adjusted fasting insulin trait in the
FUSION 1,2, and SAFADS datasets analyzed jointly are shown in Figure 4.
For the combined FUSION 1, 2, and SAFADS dataset, the strongest evidence of
linkage is seen with a MLS of 0.79 at 191 cM, followed by combined dataset of
Fusionl and SAFADS with a MLS of 0.7 at 143 cM, and a MLS of 0.27 at 170 cM
for the combined Fusion2 and SAFADS dataset. The MLS curves are again almost
overlapping for the joint dataset FUSON 1, 2 and SAFADS, and the joint dataset .of
FUSION 1 and SAFADS, with little contribution from the FUSION 2 dataset.
Figure 4: Maximum LOD Score Plots for Fasting Insulin Adjusted
for Age, Gender, and BMI in Combined Data Sets
o
o
t/5
Q
O
h J
E
3
E
1.0
Fusion 2 & FAS ADS
Fusion 1 & SAFADS
Fusion 1, 2 & SAFADS 0.8
0.6
0.4
0.2
0.0
250 150 200 0 50 100
Map Position (cM)
23
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Figure 5 shows the MLS curves for fasting insulin trait in the FUSION 1, 2, and
SAFADS datasets analyzed jointly with and without using any covariates (age,
gender, population and BMI). The strongest evidence of linkage was seen with a
MLS of 2.69 at 143 cM after adjusting for age, gender and study population,
followed by a MLS of 2.56 at 143 cM, after adjusting for age and gender. The
lowest MLS of 0.70 at 143 cM was observed after adjusting for age, gender and
BMI.
Figure 5: Maximum LOD Score Plots for Fasting Insulin After
Adjusting for Different Covariates in Combined Data Sets of
FUSION1,2 & SAFADS
No Covariates
Age, Gender
Age, Gender, Population
Age, Gender, BMI
E
0
0 50 100 150 200 250
Map Position (cM)
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
DISCUSSION
Both the FUSION and SAFADS study show evidence of linkage on a common
region on chromosome 6 using fasting insulin concentration as the quantitative trait.
Thus joint analyses using the combined datasets from both these studies were
performed in this study. The aim was to detect any improved linkage signal on
chromosome 6 related to Type 2 diabetes.
In all the three combined datasets, the MLS was observed at position 143 cM (1
LOD support 139-148 cM) near marker D6S403 on chromosome 6. The MLS
reported for the SAFADS study at 150 cM (Duggirala et al. 2001) is 2 cM away from
our 1 LOD support, with the closest marker being D6S403, using fasting specific
insulin as the trait of interest. Despite the position of the quantitative trait locus
being slightly different, in both the studies their location is near the marker D6S403.
Further, a bivariate linkage analysis in the SAFADS study suggested the region near
D6S403 as the putative susceptibility locus, which appears to affect measures of
insulin concentration and insulin resistance, as well as other insulin-resistance
syndrome related traits such as various measures of obesity /adiposity and
triglyceride levels.
Other than the same location on chromosome 6, the joint analysis of the three
combined datasets showed approximately the same MLS using both age and gender
25
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as covariates, and without using any covariate. Hence age and gender did not have
any confounding effect on the QTL linkage analysis. The model adjusted for study
population in addition to age and gender as covariates for the three combined dataset
showed the highest MLS among all the analyses. Study population was not found to
be confounding the linkage signal. Moreover in all three joint analyses, BMI was
found to be a very important covariate, the inclusion of which dropped the linkage
signal by more than 60%. It has been reported that the total amount of fat in the
body and its distribution decreases insulin sensitivity, especially intra-abdominal fat,
as manifested by the waist-to-hip ratio. The intra abdominal fat accumulation has
been found to be related to high lipolytic rate of this adipose tissue, which in turn
results an increase in the portal and peripheral free fatty acid (FFA) concentration,
leading ultimately to the development of insulin resistance (Defronzo et al, 1997).
The region near marker D6S403 has been suggested as the putative susceptibility
locus for various measures of obesity /adiposity and triglyceride levels (Duggirala et
al. 2001). In general, obesity has been attributed as one of the major factors that
contribute to the development of insulin resistance, which again is related to Type 2
diabetes (Defronzo et al, 1997). Obesity has been shown to have a major negative
effect on insulin sensitivity. Hence it is quite possible that multiple genes in the
same region on chromosome 6 are linked not only to Type 2 diabetes, but also to
adiposity. Thus on adjusting for BMI, the effect of such genes on Type 2 diabetes is
also reduced, leading to the drastic loss in linkage signal. In future a bivariate
26
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linkage analysis using BMI and fasting serum insulin as two quantitative traits may
give us further insight into the disease susceptibility locus.
In this study, the linkage signal on Chromosome 6 related to Type 2 diabetes with
fasting insulin concentration as the disease trait improved most in the combined
dataset using FUSION 1, 2 and SAFADS study subjects, followed by combined
dataset of FUSION 1 and SAFADS (age and gender adjusted MLS of 2.49 at
position 143 near marker D6S403). The combined dataset with FUSION 2 and
SAFADS subjects did not show much improved linkage signal. Among the three
combined datasets, the result of FUSION 1 and SAFADS is very interesting since it
indicated a large decrease in linkage signal after adjusting for age, gender and BMI,
even though the FUSION 1 set (which has a bigger sample size) individually showed
a slight improvement in linkage signal. It is apparent from this that despite a bigger
sample size, the combined dataset showed a reduction in linkage signal possibly due
to genetic heterogeneity among the two ethnic groups, and the evidence of linkage
getting diluted. The linkage analyses from the individual datasets gave some insight
as to this linkage results from the joint analysis. The highest linkage signal detected
is from FUSION 1 dataset, followed by SAFADS, and then FUSION 2 dataset. The
difference in linkage signal between FUSION 1 and FUSION 2 dataset though
surprising, is not totally unexpected. This is because of the fact that though both the
datasets include Finnish subjects who were recruited using the same criteria, certain
subtle differences in the two study populations have been reported (Silander et al.
27
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2004). The FUSION 2 case subjects appear to be slightly less severely affected
than their FUSION 1 counterparts. Moreover, FUSION 1 subjects were older at the
age of diagnosis, and had lower fasting glucose values. The FUSION 2 sample size
was also smaller than FUSION 1, thus giving slightly less power than FUSION 1. In
addition, random variability between the two groups of study subjects may also
contribute to these differences in linkage signal. The relatively low linkage signal
related to fasting insulin concentration in FUSION 2 subjects may therefore be
responsible for the low linkage signal in the FUSION 2 and SAFADS dataset
combined.
The initial evidences from individual studies (Ghosh et al. 2000, Silander al. 2004)
using the Finnish families and the Mexican American families (Duggirala et al.
2001) suggested modest evidence of linkage on chromosome 6. However, in our
analysis, the joint dataset of Mexican American and Finnish families did not show
much increased linkage signal, despite a much bigger sample size, and thus increased
power. The possible reason behind this might be genetic heterogeneity in the
etiology of Type 2 diabetes causing genes in different ethnic groups. It is possible
that even on the same chromosomal region, different genes are responsible for the
disease in different races, thus diluting the evidence of linkage in the combined
datasets.
28
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It is evident from this study that Type 2 diabetes related quantitative trait namely
fasting insulin concentration susceptibility locus is found near marker D6S403 on
chromosome 6. Future studies to fine map this region of interest on chromosome 6
may provide further insight into the involvement of type 2 diabetes susceptibility
genes. Additionally, variance component due to shared environments common to
sibs in the combined data set may also be modeled by breaking up the pedigree
members into subsets of individuals sharing the same environment. This will help us
measure the shared sibship environment, and further refine the linkage signal due to
the major chromosomal locus related to fasting insulin concentration. Similar
studies to look at chromosome 6 in other ethnic groups will also provide us
additional susceptibility loci in this chromosomal region. Therefore these studies
will help us in identifying additional disease related genes, and also give a better
understanding of disease etiology, and treatment.
29
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CONCLUSIONS
Quantitative trait locus linkage analysis with fasting insulin concentration as the
disease related trait was performed using microsatellite markers on chromosome 6 on
combined datasets of FUSION 1, 2 and SAFADS study. After adjusting for age,
gender, and study population, the linkage signal improved the most in the dataset
including FUSION 1, 2 and SAFADS, followed by FUSION 1 and SAFADS
combined dataset at map position of 143 cM near marker D6S403. Relatively, the
least improvement in linkage signal was observed in the combined dataset of
FUSION 2 and SAFADS study. BMI was found to be a very important covariate in
the combined datasets that reduced the linkage signal. Future study to fine map this
region will provide additional information regarding Type 2 diabetes susceptibility
genes.
30
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Asset Metadata

Creator Kuchimanchi, Mita (author)

Core Title Evidence for type 2 diabetes related quantitative trait locus on chromosome 6q: Joint analysis of Finnish and Mexican American families

School Graduate School

Degree Master of Science

Degree Program Biostatistics

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

Tag biology, biostatistics,Health Sciences, Epidemiology,OAI-PMH Harvest,sociology, ethnic and racial studies

Language English

Contributor Digitized by ProQuest (provenance)

Advisor Watanabe, Richard M. (committee chair), Conti, David (committee member), Siegmund, Kimberly (committee member)

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c16-52928

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