Segregation analysis of NIDDM in Caucasian families
Diabetologia
Segregation analysisof NIDDM in Caucasian families
J. I".E. C o o k 0
Shields 0
R. C. L. Page 0
J. C. L e v y 0
A. T. Hattersley 0
J. A. G. Shaw 0
H . A . W . Neil 0
J. S. Wainscoat 0
R. C. Turner 0
0 1Diabetes Research Laboratories, Radcliffe Infirmary , Oxford , UK 2CRC Genetic Epidemiology Research Group, Department of Child Health, University of Southampton, UK 3Department of Public Health and Primary Care, University of Oxford, UK 4Department of Haematology, John Radcliffe Hospital , Oxford , UK
Summary N o n - i n s u l i n - d e p e n d e n t diabetes mellitus (NIDDM) has a substantial genetic component, but the mode of inheritance and the molecular basis are unknown. We have undertaken segregation analysis of N I D D M after studying 247 subjects in 59 Caucasian nuclear pedigrees ascertained without regard to family history of the disorder. The analyses were performed using P O I N T E R and COMDS, which are computer programs which apply statistical models to the data. P O I N T E R analysis was performed defining the phenotype as a presence or absence of hyperglycaemia. A m o n g single locus hypotheses, the analyses rejected a recessive model and favoured a dominant model, but could not statistically show that this fitted better than a mixed model (a single locus against a polygenic background) or a polygenic model. COMDS analysis assumed a continuum of hyperglycaemia from normality to NIDDM, classified family members into a series of diathesis classes with
Non-insulin-dependent diabetes mellitus; genetic epidemiology; genetic linkage
9 Springer-Verlag 1994
Non-insulin-dependent diabetes mellitus (NIDDM)
is a c o m m o n metabolic disorder with considerable
morbidity and mortality. Despite evidence for a
substantial genetic component, the m o d e of inheritance
and the molecular basis of this inheritance remain
unAbbreviations: MODY, Maturity onset diabetes of the young;
IDDM, insulin-dependent diabetes mellitus; NIDDM,
non-insulin-dependent diabetes mellitus; FPG, fasting plasma
glucose, AIC, Akaike Information criterion.
increasing plasma glucose levels and compared the
distribution with that found by screening the normal
population. This analysis improved the likelihood of
a dominant single locus model and suggested a g e n e
frequency of 7.4 %. It raised the possibility of a
second locus, but cannot identify or exclude a polygenic
model. In conclusion, two types of segregation
analyses rejected a recessive model and favoured a
dominant model of inheritance, although they could not
statistically show that this fitted better than the
polygenic model. The results raised the possibility of a
c o m m o n dominant gene with incomplete p e n e
trance, but genetic analysis of N I D D M needs t o take
into account the likelihood of polygenic inheritance
with genetic heterogeneity. [Diabetologia (1994) 37:
1231-1240]
known. The identification of a gene (or genes)
contributing to susceptibility to N I D D M would have
profound implications upon its prevention and
management.
Barnett et al. [
1
] documented 91% concordance
for N I D D M in monozygotic twin pairs. Whilst this
study may have included some ascertainment bias,
the prospective study of unselected twins by
Newm a n et al. [
2
] provided supportive data. At the initial
examination 58% concordance for N I D D M was
found, and only 1 of !5 originally discordant twin
pairs remained discordant after 10 years. These
studies suggest a strong genetic predisposition to
NIDDM, but the lack of complete concordance and
variation in age of onset between twins suggest input
from environmental influences. No comprehensive
prospective studies comparing the development of
diabetes in monozygotic and dizygotic twins, or the
concordance rates in monozygotic twins reared
together and apart, have been reported.
Bimodality of glucose tolerance has been
demonstrated in three populations where diabetes is
comm o n [
3-5
], and bimodality has been taken to suggest
a single major gene influence. A n alternative
explanation may be that the rate of transition from
normality to disease in these populations is rapid, perhaps
due to the deleterious effects of hyperglycaemia.
Bimodality has not been described in the Caucasian
population [
7
] but has been reported in the first-degree
relatives of Caucasian N I D D M subjects [
8
].
Linkage analysis requires knowledge of genetic
parameters such as the m o d e of inheritance and the
gene frequency and the penetrance, and if these are
incorrectly specified the sensitivity is reduced.
Linkage analysis has been successfully applied to the
clinical subtype termed maturity-onset diabetes of the
young (MODY), which is characterised by the
presentation of diabetes in early adult life and by
pedigree structures suggestive of autosomal dominant
transmission [
9
]. The application of linkage analysis
to N I D D M has been more problematic because of
the lack of data concerning its mode of inheritance.
Segregation analysis is the statistical technique
used for the detection of the m o d e of inheritance of
familial disease. Although it would appear to be a
necessary prerequisite to more sophisticated genetic
studies, segregation analysis data has not been
previously reported for N I D D M . Early studies of the
familial prevalence of diabetes are difficult to interpret
because insulin and non-insulin dependent diabetes
were not discriminated [
10-14
]. Studies
documenting family history of the disease are likely to be only
partially informative because N I D D M m a y be
subclinical [
15
]. Studies in which complete nuclear families
have been tested were difficult to undertake because
of the late age of onset and increased mortality of
the disease. The major difficulty is that in most cases
one or both parents of a subject with N I D D M are
deceased, while their children are not yet old enough to
express the disease.
A recent study examined 20 consecutive nuclear
families selected without regard to family history of
the disorder, in which both parents of probands were
alive and available for study [
16
]. Seven probands
were found to have neither parent affected with
diabetes or impaired glucose tolerance, ten probands
had one affected parent (six with diabetes and four
with impaired glucose tolerance) and three had both
parents affected (one with concordant diabetes and
two with concordant impaired glucose tolerance).
These data did not support the assumption of
autosomal dominant inheritance with complete penetrance,
although the data set was too small for statistical
analysis.
This study describes the application of formal
segregation analysis to the nuclear families of a series of
59 Caucasian N I D D M probands who were
ascertained without regard to family history of the
disorder. The implications of our findings for the genetic
analysis of N I D D M are discussed.
Methods and materials
The protocol was approved by the Central Oxford Research
and Ethics Committee and informed consent obtained from
all subjects.
Nt~clear pedigrees. We investigated 697 Caucasian NIDDM
subjects attending routine diabetic clinicsin Oxfordshire
concerning the availability of a complete nuclear family for
testing. We ascertained 59 nuclear families without regard to
family history of the disorder. From 431 subjects 21 probands
with both parents alive were ascertained. An additional
38 nuclear pedigrees were identified by seeking elderly
probands with a living spouse and offspring aged older than
25 years. All probands were diagnosed after age 35 years, had
no history of ketosis and had been treated initially by diet or
oral hypoglycaemic agents.
The 21 probands with living parents were aged 44 + 6 years
(mean + SD), had a duration of diabetes of 6 + 5 years and
were of body mass index (BMI) 29.4 5.1 kg/m2. We studied
the 28 siblings and 42 parents of these probands. The 38
probands with a living spouse and offspring were aged
71 -+8 years, had a duration of diabetes 10 _+7 years and were
of BMI 28.5 +4.7kg/m2. We studied the 38 spouses and
82 offspring of these probands. None of the probands had
ketonuria greater than 1.5 mmol/1(Bayer Diagnostics,
Basingstoke, UK) at presentation, and all were treated by diet or
tablets for at least 3 months.
Plasma glucose samples were obtained from the probands,
their siblings and both parents after a 12-h overnight fast. Of
the 247 subjects 229 were then studied with a continuous
infusion of glucose test [
17
].This consisted of a continuous
intravenous infusion of 5 mg glucose 9kg ideal body weight, min-1for
60 min. Ideal body weight was taken from the Metropolitan
Life Insurance tables for a medium frame [
18
]. The achieved
plasma glucose is the mean of the 50-, 55- and 60-min
samples. Seventeen of the subjects declined glucose tolerance
testing, and only fasting plasma glucose was obtained. Plasma
glucose was measured with a hexokinase method using a Cobas
MIRA centrifugal analyser (Roche Diagnostica, Welwyn
Garden City,UK).
Liability classes. Liability classes were used to specify the
ageassociated risk of diabetes. The diabetic and the non-diabetic
nuclear family members were assigned to liability classes on
the basis of age: Class1, 20-29 years; Class 2, 30-39 years;
Class 3, 40-49years; Class4, 50-59years; Class5, 60-69
years; Class6, 70--79years; Class 7, ~>80 years. The
population frequencies of NIDDM for these seven classeswere
calculated from two Oxford community survey studies which
documented the prevalence of known and newly-diagnosed
NIDDM, respectively. The Oxford Community Diabetes
Study [
19
] documented the frequencies of known diabetes in
adult subjects at different ages.This study did not discriminate
between IDDM and NIDDM. As the prevalence of IDDM is
reported to be 0.3 % in adults [
20
], the frequencies in each lia,
bility class were lowered by 0.003. The population frequencies
of newly-diagnosed NIDDM were taken from the prevalence
J. T. E. Cook et al.: Segregation analysis of N I D D M in Caucasian families
II1
L.
._o.
O
D.
d
t
~
F
I
GG
i
G'G
Liability
I
G'G'
i
Fig.1. Calculating penetrances i.e. probability of affection
given genotype. From left to right, the three genotypes are
homozygote normal (GG), heterozygote (G'G) and
susceptible homozygote (G'G'). The size of the distributions is
determined by the frequency of the rare allele q, while the
displacement between the three genotypes is determined by d (degree
of dominance) and t (the displacement between 2
homozygotes of major gene). In this example, d, t and q are 0.5, 3.0
and 0.2, respectively, and the calculations are carried out for
the oldest class, where the population frequency of diabetes is
0.1146. A threshold is found, such that the overall probability
of affection is 0.1146. The penetrance for each genotype is
calculated as the ratio of the shaded to the total area of the
genotypic distribution, giving penetrances of 0.0002, 0.2349 and
0.9827 for each genotype. A similar calculation is carried out
for diathesis classes in the C O M D S analysis, replacing t with Bt
of fasting plasma glucose (FPG) greater than 7.8 mmol/1 in a
screening study of an Oxford population sample of
4201 subjects without known diabetes [
21
]. In addition to the
4006 subjects aged 25-60 years described by Neil et al. [
21
],
we included 195 subjects from the screening study who were
aged Over 60 years. The frequencies of known and
newly-diagnosed N I D D M were added together, and the final population
frequencies used were: Class l, 0.0008; Class2, 0.0042;
Class 3, 0.0089; Class 4, 0.0211; Class 5, 0.0283; Class 6, 0.0927
and Class 7, 0.1146.
Affection status. For analysis with P O I N T E R , affection was
defined as having hyperglycaemia defined as a fasting plasma
glucose, or an achieved plasma glucose level after a
continuous infusion of glucose test, more than two standard
deviations above the mean normal value for the subject's age and
obesity as determined in comparison with a population of
104 normal subjects (age range 21-76 years, ideal body weight
range 86 %-158 %). A comparison study of 30 subjects who
had both the glucose infusion test and a standard 75 g oral
glucose tolerance test showed that these criteria gave 89 %
sensitivity and 100 % specificity for World Health Organisation
( W H O ) defined impaired glucose tolerance [
22
]. N I D D M
was diagnosed according to the W H O criterion of fasting
plasma glucose greater than 7.8 mmol/1 [
23
]. As I G T and diabetes
form a continuum, and the W H O definition of N I D D M
related to the increased risk for microvascular disease and not to a
specific phenotype, the phenotype chosen for study has been
the presence of abnormal hyperglycaemia per se. The use of
age-adjusted criteria makes it unlikely that over-diagnosis in
old age would occur.
With COMDS, the non-diabetic family members were
assigned t o classes of increasing FPG, termed diathesis classes.
Increasing diathesis class can be used to imply increasing
probability of being genetically predisposed to the disease. The
population distribution of F P G among non-diabetic individuals
was taken from the Oxford population screening study of
4201 subjects [
21
]. The F P G was shown by linear regression to
associate with age. The residual F P G was therefore obtained
as [FPG - 3.91 - 0.013 (age)]. This transformation also fits the
age-dependence of F P G in a United States population survey
[
24
]. The age-corrected residual was subdivided into five
diathesis classes of increasing FPG, comprising 75 %, 10 %, 5 %,
5 % and 5 % of the population, respectively, using cut-off
points of 0.28, 0.49, 0.63 and 0.90 mmol/1 above the
agecorrected FPG. Non-diabetic individuals in the 59 families
were assigned to one of the five diathesis classes on the basis
of their F P G corrected for age.
Segregation analysis
Segregation analysis was performed with P O I N T E R [
25
] and
C O M D S [
26
], which are computer programs which apply
statistical models to the nuclear family data.
POINTER segregation analysis. P O I N T E R was used to fit
various single gene, polygenic and mixed models to the data. The
mixed model assumes that a continuous variable X results
from the independent contribution of a major locus, a
polygenic component and random environmental effects. The
following parameters are provided at maximum likelihood: d
degree of dominance, which ranges between 0 for a recessive
gene and 1 for a dominant; t - displacement between the two
homozygotes of the major gene; q - gene frequency of allele
leading to affection (Fig. 1); H - the heritability of the
polygenic component; Z - the ratio of adulthood to childhood
heritability. The displacement between homozygote normal and
the heterozygote is calculated as (Fig.l). In P O I N T E R , a
quantitative trait may be considered in addition to affection
status if it is normally distributed, an example being the
consideration of serum iron levels in the segregation analysis of
idiopathic haemochromatosis [
27
]. However, F P G is not normally
distributed, and a correction for skewness is unlikely to be
sufficient to make the distribution normal. Therefore, the
POINT E R analysis was restricted to a categorical analysis
considering affection status only. It was used because it can model a
multifactorial (polygenic) component. P O I N T E R
transmission parameters were not reported as recent analysis has
shown that their calculation is incorrect [
28
].
COMDS segregation analysis. In the general population and in
families, glucose tolerance and diabetes form a continuum. In
COMDS, the phenotype includes increasing degrees of
hyperglycaemia as a polychotomy with different diathesis classes.
This additional information may increase the power to
distinguish between hypotheses. Genotypic displacement is a
measure of the difference between two degrees of genetic
susceptibility (Fig. 1). Diathesis is introduced into the model through
the parameter B which scales the genotypic displacement
between diathesis classes between affected and normal (Fig.2).
When B = 0 there are no genotypic differences between
diathesis classes, and the analysis is equivalent to a categorical
dichotomy. When B is less than 1, the genotypic difference
between individuals drawn from the different diathesis classes is
less than the genotypic difference between an affected and
the highest diathesis class (Fig. 2). Holding B = 1 is equivalent
to assuming that the affected status is an additional
equally
Dlathesls classes
1. 2, 3. 4, 5
I
NIDDM
2,
spaced class at the top end of the distribution of diathesis
classes. This latter is equivalent to a simpler model, where there is
a single underlying distribution split into an ordered
polychotomy with affected as the highest class, and with diathesis
classes as lower categories. When B is more than 1, there is a
smaller genotypic difference between affected and the highest
diathesis class than between diathesis classes.
POINTER allows a mixed model, where disease is defined
as occurring above a certain threshold of a continuous
variable, which comprises the additive effects of a single locus,
polygenes and the environment. COMDS differs by having
single and two locus models, but no polygenic effect. Two
autosomal loci can be considered, each with a high-risk and a low-risk
allele, whose effects are additive on a scale of liability. For
convenience the loci are termed major and modifier, although the
NIDDM
I
NIDDIVI
-major locus need not necessarily have the greater influence.
The modifier locus has the parameters qm, din, trn, Bin" A
"pseudopolygenic" effect can be approximated in COMDS at
one or two loci by holding gene frequency and dominance of
0.5. This does not correspond exactly to the multifactorial
effect in POINTER; but it is a more parsimonious
representation of a residual familial effect, because it requires fewer
parameters than a full two-locus model.
Application of the models. COMDS and POINTER were used
to calculate the likelihood of the offsprings' phenotypes,
conditional on parental phenotypes. The nuclear families were
sampled through an affected proband. The likelihood was
corrected for ascertainment by conditioning on whether the family
was identified through a parent or offspring. The probability
of ascertaining an individual in the population was assumed to
be small, and taken to be 0.001. The likelihood is presented as
twice the natural log likelihood, plus a constant. The
significance of adding parameter(s) to a model may be evaluated by
taking the difference in -2 in(L) between the two models as a
chi-square with the number of degrees of freedom equal to
the number of extra parameters.
The Akaike Information Criteria (AIC) [
29, 30
] were
calculated from the POINTER analysis likelihood by adding twice
the degrees of freedom to the likelihood and comparing the
overall values. The lowest number represents the best model
fit. AIC was also calculated for COMDS except for B = 1 and
B = B m = I .
Results
The distribution o f affection in relatives'. O f t h e p r o
b a n d s w i t h living p a r e n t s , 7 h a d n e i t h e r p a r e n t
affected, 11 h a d o n e p a r e n t a f f e c t e d a n d 3 h a d b o t h p a r e n t s
a f f e c t e d . T h e p r o b a n d s w i t h a f f e c t e d p a r e n t s a n d
t h o s e w i t h u n a f f e c t e d p a r e n t s h a d similar a g e at
diagnosis (39 + 5, 42 + 5 y e a r s ) a n d o b e s i t y (30 _+3,
29 + 8 kg/m2). I n t h e 7 f a m i l i e s w i t h n e i t h e r p a r e n t
aff e c t e d , 30 % o f t h e siblings o f t h e p r o b a n d s w e r e
aff e c t e d . I n t h e 11 f a m i l i e s w i t h o n e a f f e c t e d p a r e n t ,
46 % o f t h e siblings w e r e a f f e c t e d . I n t h e 3 f a m i l i e s
w i t h b o t h p a r e n t s a f f e c t e d , e a c h o f t h e t h r e e siblings
a v a i l a b l e f o r t e s t i n g h a d i m p a i r e d g l u c o s e t o l e r a n c e .
O f t h e p r o b a n d s w i t h a living s p o u s e a n d offspring,
32 h a d a n o r m o g l y c a e m i c s p o u s e . I n t h e s e families,
26 % o f t h e o f f s p r i n g w e r e a f f e c t e d . Six o f t h e p r o
b a n d s h a d a n a f f e c t e d s p o u s e , a n d 65 % o f t h e
offs p r i n g in t h e s e f a m i l i e s w e r e a f f e c t e d ( T a b l e t ) . A
s u m m a r y o f t h e p e d i g r e e s t r u c t u r e s is p r o v i d e d
(Fig. 3).
Formal segregation analysis
P O I N T E R segregation analysis. L i k e l i h o o d r a t i o
tests w e r e c o n s t r u c t e d f o r t h e g e n e r a l m o d e l w h e r e
all r e l e v a n t p a r a m e t e r s w e r e e s t i m a t e d e x c e p t t h a t Z
was f i x e d to 1 ( T a b l e 2). T h e h y p o t h e s i s o f n o m a j o r
g e n e w a s r e j e c t e d (Z23 = 6.4, p < 0,05) a n d t h e h y p o
thesis o f n o m u t t i f a c t o r i a l c o m p o n e n t was r e j e c t e d
(Z21 = 0), h o w e v e r , b o t h c o u l d n o t b e d r o p p e d f r o m
:G;
the m o d e l simultaneously Q~24 = 28.1). A m o n g single
locus hypotheses, the analysis f a v o u r e d a dominant
model. Assuming there is a major gene, the recessive
m o d e l was rejected (/%21 = 5.5, p < 0.025) b u t a
cod o m i n a n t m o d e l was not rejected (Z2~= 0 . 4 - 0 = 0.4).
The families ascertained through an offspring and
those ascertained through a parent b o t h contributed
to the evidence for a single d o m i n a n t gene. U n d e r
the dominant gene model, there was no evidence of
h e t e r o g e n e i t y (holding d = 1, q = 0.013 estimating t,
)~21 = 0 . 5 ) . A mixed m o d e l of a single locus plus a
multifactorial effect could not b e fitted that was b e t t e r
than the dominant gene alone. The general m o d e l
converged to a single dominant locus with no
polygenic effect (i. e., d c o n v e r g e d to 1 and H to 0). For
comparison with the polygenic model, the dominant
m o d e l m a y be t a k e n to b e the mixed m o d e l with a
dominant major gene effect. While the polygenic
and multifactorial models had a s o m e w h a t lower
likelihood, they were not significantly different from the
mixed m o d e l (Z23= 6.4, p < 0.10). Thus, the analysis
suggested a single dominant locus but could not
statistically show that this fitted b e t t e r than a mixed
m o d e l or a polygenic model. There was no
significant evidence that childhood and adult heritability
differ; w h e n their ratio, Z, is allowed to depart form
1, Z21 = 6.4-4.8 = 1.6. U s i n g AIC, the autosomal
dominant m o d e l p r o v i d e d the best fit.
C O M D S analysis. W h e n the p a r a m e t e r B is held at 0,
C O M D S is equivalent to the categorical affection
status of P O I N T E R , with all non-diabetic patients being
included together. W h e n B and B m are held at 0, the
genetic displacement r e p r e s e n t e d by Bt and Brat m in
the m o d e l are also 0 (Fig. 1). With a single locus m o d e l
w h e n B = 0 (analysis of affection status only) the
likelihood of differences w e r e identical to the P O I N T E R
results (Table 3). The addition of the F P G diathesis
(estimating B) to allow for the continuum of F P G as
a p o l y c h o t o m y i m p r o v e d the likelihood of the
general (dominant) single locus m o d e l ( ) ~ = 239.4-105.2 =
134.2 = p < 0.001) (Table 3). The dominant m o d e l was
f a v o u r e d and b o t h the recessive m o d e l (X21=
123.0105.2 = 17.8, p<0.001) and the co-dominant m o d e l
were rejected (X21= 119.5-105.2 = 14.3) W h e n d was
estimated, it converged to 1. The addition of the
diathesis classes thus increased the p o w e r to distinguish
b e t w e e n models. The families ascertained through a
parent and those ascertained through an offspring
b o t h contributed to the evidence favouring a single
dominant gene. U n d e r a single, dominant gene m o d
el, there was no evidence for h e t e r o g e n e i t y (holding
d = 1, q = 0.075, B = 1, estimating t, X21 = 2.16).
W h e n a second locus was a d d e d to the m o d e l an
i m p r o v e d fit was obtained, but this was not
significant (X24= 105.2-100.0 = 5.2) (Table 4) and it was not
possible to ascertain w h e t h e r a dominant or
recessive second locus was present. Using A I C the same
conclusion was reached, with A I C equal to 111.2 for
a dominant single locus and 112.0 for the best 2 locus
model. This eight-parameter m o d e l is quite complex
for a relatively small data set, so models with a
second locus effect and fewer p a r a m e t e r s were also
considered to determine w h e t h e r a significant residual
genetic effect could b e modelled. A
dominant/pseudopolygenic model, with only one estimated second
locus p a r a m e t e r failed to provide an i m p r o v e m e n t in
fit ()~21 --~ 105.2-104.6 = 0.6). O t h e r simple models
tested also failed to provide a significant
improvement: those which did not simply reduce to the
dominant single locus m o d e l are p r e s e n t e d in Table 4.
Hypothesis
Sporadic
Single gene models
Autosomal dominant
Co-dominant
Autosomal recessive
General
Polygenic models
Polygenic
Multifactorial
Mixed models
General
(Recessive mixed)
POINTER is used to fit various single gene, polygenic and
mixed models to the data. Estimates of parameters such as
degree of dominance (d), gene frequency (q) are provided at
maximum likelihood; d, Degree of dominance of major locus;
t, displacement between 2 homozygotes of major gene; q,
gene frequency at major locus; H, childhood heritability; Z,
ratio of adult: childhood heritability; B, diathesis parameter at
major locus; din, dominance parameter of modifier locus; tin,
displacement or scale parameter of modifier locus; qm, gene
frequency at modifier locus; Bin, diathesis parameter at
moditier locus; -21n(L)+ C, minus twice log-likelihood plus
constant; Parenthesis indicates fixed parameter; AIC, Akaike's
information criterion
The susceptibility g e n e f r e q u e n c y for t h e d o m i
n a n t single locus m o d e l in P O I N T E R , c o n s i d e r i n g a
categorical a f f e c t i o n status alone, was 1.3 %
(Table 2). T h e p e n e t r a n c e factors are a f u n c t i o n o f the
p o p u l a t i o n f r e q u e n c y of d i a b e t e s in e a c h liability
class a n d the p a r a m e t e r s of the m o d e l a n d are s h o w n
in Table 5. W h e n the c o n t i n u u m o f glucose f r o m
norm a l to d i a b e t i c was m o d e l l e d w i t h C O M D S , the
susceptibility g e n e f r e q u e n c y was 7.4 % (Table 3) a n d
the p e n e t r a n c e factors are in Table 5. A s e c o n d
locus, if p r e s e n t , w o u l d r e d u c e the first d o m i n a n t
susceptibility g e n e f r e q u e n c y to 5.9 %, a n d t h e s e c o n d
locus m a y h a v e a g e n e f r e q u e n c y in t h e o r d e r of
4.3 % for a d o m i n a n t a n d 2 2 . 1 % for a recessive g e n e
effect. It m u s t be e m p h a s i s e d t h a t the m o d e l s are
b a s e d o n a m o d e r a t e l y - s i z e d d a t a set, a n d the g e n e
f r e q u e n c i e s a n d p e n e t r a n c e s are a p p r o x i m a t e .
Discussion
T h e s e g r e g a t i o n analysis of N I D D M in C a u c a s i a n
nuclear families r e j e c t e d b o t h no genetic c o m p o n e n t
a n d a recessive m o d e l . A d o m i n a n t single g e n e m o d
el gave the best fit to the data, b u t the analysis could
n o t statistically s h o w t h a t this fitted b e t t e r t h a n t h e
m i x e d m o d e l or the p o l y g e n i c m o d e l .
The families w e r e a s c e r t a i n e d w i t h o u t r e g a r d to
f a m i l y history, a n d t h o s e a s c e r t a i n e d t h r o u g h a
pare n t a n d t h o s e a s c e r t a i n e d t h r o u g h an offspring b o t h
c o n t r i b u t e d to t h e e v i d e n c e f a v o u r i n g a single
domin a n t gene. A h e t e r o g e n e i t y analysis f o u n d n o
difference b e t w e e n families a s c e r t a i n e d t h r o u g h a diabetic
p a r e n t a n d a s c e r t a i n e d t h r o u g h an a d u l t offspring.
H o w e v e r c e r t a i n biases m a y h a v e arisen f r o m the
n e e d to d e t e r m i n e a c c u r a t e l y the a f f e c t i o n status of
B
(0)
(0)
(0)
(0)
(0)
B
(0)
(0)
(0)
251.7
246.6
247.4
132.6
114.1
112.6
112.0
n/a
n/a
n/a
n/a
n/a
n/a
J. T.E. Cook et al.: Segregation analysis of NIDDM in Caucasian families
p r e s e n t e d m a k e s no correction for obesity, and in
the absence of a precise understanding of the
interaction b e t w e e n genetic factors, obesity and diabetes this
is appropriate [
32
]. If the obesity that predisposes an
individual to N I D D M has a genetic component, this
could b e r e g a r d e d as part of the genetic
predisposition to diabetes studied in the pedigrees. H o w e v e r , if
obesity were purely an environmental risk factor, a
correction for this precipitating factor w o u l d b e
desirable; failure to correct w o u l d not bias the genetic
models obtained, although it w o u l d reduce their
statistical p o w e r for detecting genetic determinants.
The effect of specific mortality due t o diabetes
would b e to reduce the gene frequency in the older
age groups, whereas the P O I N T E R and C O M D S
programs assume a constant gene frequency through
all age groups. The data of Panzram et al. [
33
]
indicate that the risk of death is only d o u b l e d in N I D D M
t h e r e f o r e this factor should not seriously bias the
analysis.
The C O M D S analysis considered models w h e r e
genetic factors influence the range of F P G levels
from the non-diabetic first degree relatives through
to diabetes. This is theoretically advantageous as
there is a continuous distribution of plasma glucose
values in the Caucasian population [
7
], and subjects
with impaired glucose tolerance have an increased
risk of progression to N I D D M [
34-37
]. The
likelih o o d of single locus models was significantly
imp r o v e d by considering the F P G diathesis by C O M D S
in addition to affection status using P O I N T E R or
C O M D S . With inclusion of the F P G diathesis, a
second major locus was suggested but not s u p p o r t e d
statistically, although a larger sample might reveal such
an effect. C O M D S was particularly applicable to
anastudy participants. Firstly, families m o t i v a t e d to
attend for testing m a y include an excess of those with
a positive family history. Secondly, the increased
mortality of N I D D M could bias towards the
ascertainm e n t of families with unaffected parents. Thirdly, the
p r o b a n d s with living parents had an early age of
onset of N I D D M and this has b e e n associated with a
higher incidence of diabetic parents in Caucasian
subjects [31]. This effect was not as apparent as in
the data of O ' R a h i l l y et al. [
31
] who r e p o r t e d 92 %
affection in the 23 available parents of 13 N I D D M
subjects who p r e s e n t e d in this age group. A n increased
chance of finding diabetes in the relatives was
possible in that study, as six p r o b a n d s w e r e ascertained
through an affected family member.
The effects of obesity and disease-specific
mortality are potential confounding factors in the
application of segregation analysis to N I D D M . The analysis
J.T.E. Cook et al.: Segregation analysisof NIDDM in Caucasian families
lysis of the trait of FPG, as it is difficult to transform it
to allow analysis by methods which assume normality
e. g., Y P O I N T developed by Lalouel et al. [
27
]. Either
m e t h o d could be used to investigate traits associated
with diabetes which can be treated as being normal,
such as obesity. The mixed model approach of
Y P O I N T provides a more rigorous methodology for
confirming or rejecting the hypothesis of single gene
inheritance. The two-locus modelling of COMDS is
most useful when segregation analysis is to be
extended to a linkage analysis, where linkage could be
detected to either of the two loci, with the other locus
acting as a surrogate for residual familial components.
Examples of other disorders to which COMDS
analysis has been applied include Graves' disease, where
normal subjects were classified into diathesis classes
of increasing thyroid autoantibody titre [
38
], and
schizophrenia, where auditory P300 latency has been
measured as a possible correlate of the genetic
predisposition to schizophrenia [
39
].
The segregation analysis data reject the autosomal
recessive hypothesis for the inheritance of N I D D M
in Caucasian families and one of the models,
COMDS, also rejected co-dominant inheritance.
This finding is consistent with the relatively low
reported prevalence of N I D D M in the offspring of
Caucasian conjugal diabetic parents, compared with
the 100 % expected with a recessive model [
40-43
].
However, the present data do not exclude the
possibility that a recessive gene or co-dominant gene may
play a crucial role in some pedigrees, in combination
with other genetic or environmental factors. A n
example of the potential interactions that might occur
is the report that some patients with extreme insulin
resistance are c o m p o u n d heterozygotes for different
mutant alleles that impair insulin receptor function
by different mechanisms [44]. The parents who were
heterozygous carriers demonstrated less severe
insulin resistance. In a similar manner, N I D D M in some
pedigrees may be due to combinations of mutations
in one or more genes. It is possible that N I D D M is
inherited in a dominant m a n n e r in some families, is
recessively inherited in others, and in other families is
polygenic, Segregation analysis is not a sensitive tool
for the detection of such heterogeneity.
A limiting factor in the analysis of genetic linkage
with N I D D M has been the requirement to specify a
genetic model. The present study indicates that a
dominant model is most applicable, but the
possibility of a polygenic c o m p o n e n t implies that linkage
analysis could model more than one locus; when a single
locus model is used, lower penetrance parameters
should be included [
45, 46
]. The best fit model with
COMDS gave a susceptibility gene frequency 7.4 %
and the penetrance factors derived in this study may
be applied to future linkage analysis. Nevertheless,
the likelihood of polygenic probably limits the
applicability of formal linkage analysis to classic N I D D M
pedigrees. In any case, the premature disease
mortality and late-onset of this disease means that suitable
pedigrees with one affected and one unaffected
parent are unusual [47]. Thus, linkage analysis is most
appropriate in pedigrees with specific monogenic
disorders such as M O D Y [
48, 49
] although it can be useful
in excluding a major dominant gene effects in a series
of N I D D M nuclear families [46]. If sufficient
numbers of pedigrees are being studied, the two-locus
model could be applied, although combined
segregation and linkage analysis of plasma glucose levels
and diabetes with candidate genes in a given data set
is an alternative approach [
38
].
Other robust methods of analysis are available,
but they are less statistically powerful than classic
linkage analysis. The affected sibling-pair approach
[
50, 51
] does not require assumptions about the
m o d e of inheritance, but the collection of a suitably
large number of N I D D M sibling-pairs with living
parents for identity by descent analysis is difficult
[47]. In the absence of parental information, the
statistical power of sibling-pair analyses is reduced, and
affected-pedigree-member analysis based on
identity-by-state comparisons requires a large number of
sibling-pairs to overcome the possibility of
heterogeneity between sibling-pairs.
Genetic heterogeneity has recently been
demonstrated within a large pedigree with NIDDM, with
some diabetic individuals not having the glucokinase
mutation found in other family members [
50
]. In
such cases, linkage in a pedigree may be missed
despite a significant role for the gene under
consideration. By analogy, in hypertensive patients, mutations
of the angiotensinogen gene account for genetic
linkage in only a subset of the population study [
51
]. In
the context of anticipated polygenicity and
heterogeneity, the direct search for mutations in candidate
genes in subjects with N I D D M is likely to prove a
valuable alternative approach. Mutations can be
detected using the polymerase chain reaction [
52
] with
electrophoresis for single-strand conformation
polymorphisms [
53
] or with heteroduplex scanning [
54
],
followed by direct D N A sequencing. These
techniques can be applied to individual patients or specific
cohorts chosen for their pathophysiological
characteristics (e. g., beta-cell dysfunction or insulin
insensitivity), and do not depend on the availability of large
pedigrees or on the m o d e of inheritance.
In conclusion, the segregation analysis of N I D D M
in Caucasian pedigrees favoured a dominant model
of inheritance, rejected a recessive model and
suggested a co-dominant model was unlikely. The
analysis was also in accord with a mixed model or a
polygenic model. The genetic analysis of N I D D M in
Caucasian subjects needs to deal with the potential
complexities of polygenicity and genetic heterogeneity.
Acknowledgements. We are grateful to the families for their
collaboration, to Ms. B.Barrow and Ms. N.Walravens for
their help with glucose tolerance tests, to Ms. M.Burnett, Ms.
R Sutton and Mr. D.Jelfs for their technical assistance. We
thank Professor N.E. Morton for advice. We are grateful to
the Alan and Babette Sainsbury Charitable Trust for financial
support. Dr. J. Cook was a Rhodes scholar.
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