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Vol. 10, Issue 10, 1568-1578, October 2000
LETTER
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ABSTRACT |
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 TOP
 ABSTRACT
 INTRODUCTION
RESULTS
DISCUSSION
METHODS
REFERENCES
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Obesity, a major risk factor for type II diabetes, is becoming more prevalent in Western populations consuming high calorie diets while expending less energy both at the workplace and at home. Most human obesity, and probably most type II diabetes as well, reflects polygenic rather than monogenic inheritance. We have genetically dissected a polygenic mouse model of obesity-driven type II diabetes by outcrossing the obese, diabetes-prone, NZO (New Zealand Obese)/HlLt strain to the relatively lean NON (Nonobese Nondiabetic)/Lt strain, and then reciprocally backcrossing obese F1 mice to the lean NON/Lt parental strain. A continuous distribution of body weights was observed in a population of 203 first backcross males. The 22% of first backcross males developing overt diabetes showed highest peripubertal weight gains and earliest development of hyperinsulinemia. We report a complex diabetes-predisposing ("diabesity") QTL (Quantitative Trait Loci) on chromosome 1 contributing significant main effects to increases in body weight, plasma insulin, and plasma glucose. NZO contributed QTL with significant main effects on adiposity parameters on chromosomes 12 and 5. A NON QTL on chromosome 14 interacted epistatically with the NZO obesity QTL on chromosome 12 to increase adiposity. Although the main effect of the diabetogenic QTL on chromosome 1 was on rapid growth rather than adiposity, it interacted epistatically with the obesity QTL on chromosome 12 to increase plasma glucose levels. Additional complex epistatic interactions eliciting significant increases in body weight and/or plasma glucose were found between the NZO-contributed QTL on chromosome 1 and other NZO-contributed QTL on chromosomes 15 and 17, as well as with an NON-contributed QTL on chromosome 2. We further show that certain of these intergenic interactions are predicated on, or enhanced by, the maternal postparturitional environment. We show by cross-fostering experiments that the maternal environmental influence in part is because of the presence of early obesity-inducing factors in the milk of obese F1 dams. We also discuss a strategy for using recombinant congenic strains to separate and reassemble interacting QTL for future study.
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INTRODUCTION |
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TOP
ABSTRACT
INTRODUCTION
RESULTS
DISCUSSION
METHODS
REFERENCES
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Simpson's paradox (Yule 1903
; Simpson 1951) is a
statistical phenomenon in which marginal effects, e.g., effects
associated with a single genetic locus, can be masked, enhanced, or
even reversed in the presence of interactions that are not detected and
accounted for. The implications of Simpson's paradox for the study of
complex diseases such as type II (non-insulin dependent) diabetes are
that, in some cases, it may not be possible to predict a phenotype from
a given genotype if the interactions among many components of the
system cannot be fully characterized (Clark 2000). It is becoming
increasingly clear that understanding a complex multifactorial disease
such as type II diabetes requires methodology to identify interactions
between multiple susceptibility genes expressing within certain
environmental limits. To date, genome-wide scans of relatively isolated
human populations with a high frequency of obesity-associated type II
diabetes have uncovered only a few susceptibility genes with major
effects (Hanis et al. 1996; Mahtani et al. 1996; Stern et al. 1996).
Statistical methods to uncover intergenic interactions recently have
been applied to genome-wide scan data that established the presence of
NIDDM1, a major locus on human chromosome 2, and an unlinked
susceptibility locus on chromosome 15 (Cox et al. 1999). Similar
analysis of intergenic interaction uncovered another human
susceptibility QTL (Quantitative Trait Loci) on chromosome 2 that
interacted with NIDDM2, a diabetes locus on human chromosome
12, in a relatively isolated Finnish population (Mahtani et al. 1996).
The genome-wide scan analysis for main effects had indicated only weak
evidence for linkage of this QTL (F. Collins, pers. comm.).
The ability to control both genotype and environment in inbred populations of rodents greatly simplifies analysis of these complex gene-gene and gene-environment interactions. When compared with human populations, the genetics of an inbred line cross are relatively simple. Allelic variation is restricted to a small or moderate number of loci segregating at most two alleles and IBD (Identity by Descent) status can be inferred unambiguously from marker data. The ability to produce large populations of mice in a controlled environment provides an optimal situation for the statistical detection of genetic effects. Even with these advantages, we show in this report that type II diabetes in mice presents a challenge for statistical analysis because of interactions among multiple genetic and environmental factors.
Visceral obesity, a phenotype with high heritability, is the major risk
factor for type II diabetes development in humans (Bjorntorp 1993). In
mice, diabesity genes are defined as obesity-predisposing genes capable
of interacting deleteriously with other susceptibility loci, as well as
with environmental factors, to elicit a state of impaired glucose
tolerance (IGT) and insulin resistance sufficiently severe to
precipitate development of overt type II diabetes (Leiter and Herberg
1997). We previously have identified a collection of QTL from two
unrelated inbred strains, the relatively lean NON (Nonobese
Nondiabetic)/Lt strain and the markedly obese NZO (New Zealand
Obese)/HlLt strain. NZO mice represent a model of polygenic,
juvenile-onset obesity (Proietto and Larkins 1993). Obese NZO/HlLt mice
of both genders show IGT, as well as insulin and leptin resistance
(Halaas et al. 1997). However, only approximately half of NZO/HlLt
males transit from IGT to overt diabetes by 24 weeks of age. NON males
do not develop marked obesity, but show IGT, low insulin secretory
responses (Leiter and Herberg 1997), and a more normal level of
circulating leptin (data not shown). Crossing these two mouse strains
showed that both parental genomes contributed to increase diabetes
frequency to nearly 100% in F1 males (Leiter et al. 1998).
Intercrossing these F1 hybrids to produce a segregating F2 generation
permitted mapping of QTL from both strains contributing to diabetes
subphenotypes (PG [Plasma Glucose] and PI [Plasma Insulin]).
However, we could not identify diabesity genes because all F2 males
became obese (Leiter et al. 1998). In the present report, we show that
backcrossing the obese F1 hybrids to the NON/Lt parental strain allowed
identification of epistatic diabesity QTL from NZO/HlLt. Furthermore,
we show that penetrance of these QTL is postnatally regulated by
factors in milk from obese dams.
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RESULTS |
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TOP
ABSTRACT
INTRODUCTION
RESULTS
DISCUSSION
METHODS
REFERENCES
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Segregation of Diabesity in BC1 Males as a Threshold Phenomenon
In contrast to our previous inability to segregate the obesity
phenotype in an F2 population, the reciprocal first backcross (BC1) of
obese F1 hybrids of both genders to the relatively lean NON/Lt strain
resulted in a continuous distribution of BW (Body Weight) between the
two parental extremes (Fig. 1A). Of the 203 BC1 males that were monitored for type II diabetes development, 38%
had an abnormal PG level >225 mg/dL before termination at 24 weeks
of age. Diabetes (conservatively defined as chronic PG 
300 mg/dL)
was diagnosed in 22% of the cross, with an additional 16% classified
as borderline diabetics because their PG oscillated between 225 and 300 mg/dL. Analysis of the phenotypic data showed that a BW threshold must
be crossed for the mice to become at risk for diabetes (Fig. 1B). The
threshold was progressive; at the 16-week time point, all mice that
were to develop diabetes weighed >40 g, and by the 24-week time
point, nearly all of the overtly diabetic males weighed in excess of 50 g. Crossing the threshold, however, was not a guarantee of diabetes,
because fully 41% of those higher than the 24-week BW threshold
remained normoglycemic. Hyperinsulinemia more extreme than observed in
the NZO/HlLt parental males was the additional phenotype distinguishing
obese BC1 males that eventually developed diabetes versus those that
did not. NON/Lt parental males have a PI in the low normal range (1-2
ng/mL), whereas PI values in NZO/HlLt males are moderately elevated,
ranging between 3 and 6 ng/mL. Of the BC1 males higher than the BW
threshold at 24 weeks, those with an extremely elevated PI level
(>24 ng/mL) had a mean PG in the diabetic range. Those mice showing
hyperinsulinemia of intermediate severity (6-24 ng/mL) or had a normal
insulin level (<6 ng/mL) maintained a mean PG in the normal range
(Fig. 1C). Early hyperinsulinemia was also a predictor of eventual
diabetes. Those mice already showing elevated PI level at 16 weeks (the earliest time point taken) also showed the highest rate of weight gain
and the highest percentage of diabetes compared with mice developing
later hyperinsulinemia at 20 weeks, 24 weeks, or not at all (Fig. 1D).
Presumably, the early development of hyperinsulinemia contributed to an
earlier development of insulin resistance that became progressively
more severe as the males aged. In fact, the phenotypic cluster of
highest BW gain during the peripubertal period and earliest development
of marked hyperinsulinemia accounted for most of the diabetics in the
cross. Continuous distribution of multiple phenotypes (BW, logPG, PI,
BMI [Body Mass Index], AI [adiposity index], individual fat depot
weights, and serum leptin) in these BC1 males permitted genome-wide
scan for QTL with main effects to elucidate the genetic determinants of
disease.
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Dominant NZO/HlLt QTL Contributing toward Attainment of Diabetogenic Thresholds
Genome-wide scan identified a region on proximal chromosome 1 that
met the criterion for a diabesity QTL in affecting not only BW at 8, 16, and 24 weeks, but also in contributing significantly to the
diabetes subphenotypes of increased PG at 20 and 24 weeks and early
hyperinsulinemia at 16 weeks (Table 1).
This NZO-derived QTL spans a large segment of chromosome 1 (delimited
by the D1Mit411-D1Mit123-D1Mit76 interval,
~14 cM) with different markers within this span affecting different
phenotypes in an age-dependent fashion. Hence, it is probable that
there may be more than one gene contributing to diabesity-related
traits in this region (Fig. 2A).
Interestingly, although the chromosome 1 effect gave a significant QTL
for BMI, and suggestive linkage for three of the four fat pads weighed (inguinal, gonadal, and mesenteric; data not shown), it did not contribute significantly to AI or to serum leptin levels. This suggests
that the primary effect of this QTL was on early growth parameters
distinct from adipogenesis. The percentage of the variance contributed
by this QTL was only 16% for BW and only 8% and 9% for PG and PI,
respectively, clearly indicating that additional contributions were
required for diabesity development. Indeed, our genome-wide scan also
indicated significant evidence for a NZO-derived QTL on chromosome 15 affecting PG, a NZO-derived QTL on chromosome 12 affecting AI, BMI, and
leptin, and a NZO-derived QTL on chromosome 5 affecting AI and leptin.
Additionally, suggestive linkage by NZO-derived QTLs was indicated on
chromosome 18 affecting AI and BMI, a locus on chromosome 3 affecting
PI level, a locus on chromosome 13 affecting BMI, a second separate
locus on chromosome 15 contributing to BW, BMI, and leptin, and a third
separate locus on chromosome 15 contributing to early BW. Suggestive
linkage from a homozygous NON-derived QTL was found on chromosome 6 contributing to BW and PI (Table 1). Many of these loci gave
significant or suggestive QTL for one or more of the four fat pads
weighed, with an additional locus, D4Mit166, that gave a
significant LOD score for only gonadal fat pad weight and for no other
parameter measured (data not shown). The complexity of the multiple QTL
on chromosome 15 is depicted in Figure 2B and Table 1 by the
demonstration of variable effects on diabesity subphenotypes over time.
The most proximal locus, marked by D15Mit13, was associated
with early BW increases, whereas the next distal locus, marked by
D15Mit26 (20 cM away), was associated with later BW increase.
The most distal locus, marked by D15Mit159 (another 20 cM
away), was associated significantly with elevated PG and
suggestively with BW.
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Maternal Lactational Environment as an Interactive Factor Regulating Penetrance of Diabesity QTL
Maternal environment also contributed to the attainment of the
BW-related diabetogenic threshold. Because (NZO × NON)F1 and (NON × NZO)F1 males and females were mated to NON/Lt males and females to
generate the BC1 progeny, roughly half of the males had an obese F1
mother whereas the rest had a lean NON mother. The frequency of
diabetes development was greater than twice as high in progeny from
obese F1 mothers (32%) than from lean NON/Lt mothers (14%). This
maternal influence significantly increased mean BW at 4 weeks of age,
the earliest time point measured, and the increases persisted to the
termination point at 24 weeks, suggesting that factors in milk from
obese dams fundamentally altered early growth parameters. In addition,
mean PG, PI, BMI, AI, and leptin levels were significantly higher in
males with an obese F1 mother (Table 2).
Contributions of X- and Y-linked genes could be excluded because
reciprocal F1 hybrid combinations were used to backcross both males and
females to NON mice. Although neither NZO/HlLt nor F1 virgin females
develop overt diabetes, they do show IGT when challenged with a bolus
of glucose administered intraperitoneally. Glucose tolerance tests
performed on a pair of pregnant F1 females confirmed presence of IGT,
but not fasting hyperglycemia (data not shown). That the early
postnatal lactational environment was an essential covariable for
diabesity gene-gene interactions to achieve diabetogenic thresholds
was shown by cross-fostering experiments (Fig.
3). Litters of newborn F1 pups (in which
the genetic variance is fixed) were randomized and fostered onto either lactating obese F1 or nonobese NON females. Postnatal day 18 BW of F1
pups suckled by obese F1 dams averaged 3.7 g more than F1 pups fostered
onto lean NON lactating females (14.4 ± 0.4 vs. 10.7 ± 0.2 g).
Similarly, NON pups fostered on F1 dams were 2.4 g larger than NON pups
fostered on lactating NON dams (11.6 ± 0.2 g vs. 9.1 ± 0.3 g).
Preliminary analysis indicated the lipid content of milk from obese F1
dams to be almost twice as high as that of lean NON females (40% vs.
22% v/v) and to contain four times the amount of leptin (4.3 ng/mL vs.
1.0 ng/mL). Thus, although we cannot assess the contribution of
maternal obesity during gestation, it is clear that milk from obese F1
dams contains increased levels of diabesity-promoting factors compared
with milk from lean NON/Lt dams.
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Intergenic and Gene-Maternal Environment Interactions
When considered individually, the diabesity QTL on chromosome 1 and
the maternal effect both make significant contributions to the
diabesity phenotypes. However, a general linear model analysis (ANOVA)
suggested that these effects were not strictly additive. A genome-wide
scan was performed to search for pairwise interactions among all pairs
of loci and/or the maternal environment. Four interacting pairs were
identified (Table 3 and Fig.
4), all contingent on maternal environment.
These included the QTLs with significant or suggestive main effects
already noted on chromosomes 1, 6, 12, and 15 as well as additional QTL
on chromosomes 2, 14, and 17 (NON-derived modifier at
D2Mit182, NON-derived modifier at D14Mit212, and
NZO-derived modifier at D17Mit240, respectively). Note that
both alleles of the QTL marked by the D15Mit26 polymorphism interact with separate loci to enhance separate phenotypes (Fig. 4B,C).
Thus, heterozygosity for the NZO allele at D15Mit26 interacts epistatically with the NZO allele on chromosome 1 marked by
D1Mit46 to increase the 8-week BW phenotype (Fig. 4B), whereas
NON homozygosity at D15Mit26 interacts epistatically with
D2Mi182 on chromosome 2 to elevate the 20-week PG phenotype
(Fig. 4C). A second all-pairs genome-wide scan was conducted to search
for three-way interactions. The relatively small sample size of ~200
mice and the magnitude of the multiple testing implicit in a three-way
scan reduce the power to detect three-way interactions. Thus, we
restricted our search to three-way interactions between marker pairs
and maternal environment. This search identified four significant
interacting pairs (Table 4 and Fig.
5), each of which includes a chromosome 1 locus in combination with loci from chromosome 2, 12, or 17 (Fig.
5A-D). All diabetogenic alleles were contributed by the NZO/HlLt
genome with the exception of the chromosome 2 contribution (D2Mit109, interestingly, a second separate locus 44 cM distal to D2Mit182 cited above), which represented a homozygous
contribution from NON/Lt. The many interactions between NZO and
NON-derived loci help explain the synergism of the two parental genomes
that leads to nearly 100% diabetes in the F1 males. In addition to the
significant interaction effects shown in Tables 3 and 4, several
interactions achieved near-significant levels in our analysis (Table 4
and Fig. 5E-G). These involved the maternal environment interacting
with the QTL on chromosomes 1 and 15 and with pairs of loci (12 × 15, 15 × 17) separate from the chromosome 1 diabesity complex.
Although the main effects of some of these loci individually were on
obesity phenotypes, as pairs they become diabesity loci by interacting
to elevate PG.
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DISCUSSION |
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TOP
ABSTRACT
INTRODUCTION
RESULTS
DISCUSSION
METHODS
REFERENCES
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We have identified a major NZO/HlLt-contributed diabesity QTL
(probably comprising two or more separable genes) on chromosome 1 affecting early rapid somatic growth, development of early insulin resistance, and maturity-onset diabetes. Another QTL contributing significantly to hyperglycemia also appears to be complex. We previously identified non-insulin-dependent diabetes (Nidd)
QTL in a segregating F2 population of males generated from reciprocal F1 hybrids between NZO/HlLt and NON/Lt (Leiter et al. 1998). Because polygenic obesity was present in all segregants, the Nidd QTL identified were associated with the diabetes subphenotypes of PG and/or
PI but were actually negatively correlated with long-term weight gain.
Two of these segregated as dominant contributions from the NON/Lt
genome and were provisionally designated Nidd1 (chromosome 4)
and Nidd3 (chromosome 18). Another, a recessive QTL derived
from the NZO/HlLt genome, was designated Nidd2 (chromosome 11). More recently, others have used Nidd nomenclature for QTL identified in various mouse models of either polygenic obesity, IGT, or
both (Hirayama et al. 1999; Ueda et al. 1999). However, note that the
phenotype described in these later reports was not nonfasting
hyperglycemia, the benchmark clinical phenotype defining diabetes in
humans, but rather, obesity and IGT. For the new QTL identified in the
present analysis, we have chosen not to use the provisional
Nidd nomenclature because of the complexity of the gene-gene
and gene-environment interactions required for diabetogenesis. For
example, the NZO/HlLt-contributed QTL on chromosome 12 identified by
genome scan exerted significant main effects on obesity subphenotypes (AI, leptin, BMI), but not PG or PI, suggesting that it should be
designated as an obesity QTL, or Obq (Taylor and Phillips
1996), but not necessarily a Nidd QTL. However, interaction
analysis showed that chromosome 12 QTL interacted epistatically with
the chromosome 1 QTL complex to elevate PG, and that detection of this
interaction was absolutely contingent on an obese F1 dam providing the
postparturitional environment. A recent congenic analysis of the
contribution of a mouse adiposity QTL required feeding a very high fat
diet (York et al. 1999). Our model system of juvenile-onset obesity and
maturity-onset type II diabetes in mice has provided an excellent
illustration of how Simpson's paradox relates to a complex disease
wherein gene-environment interactions are essential factors in
development of a clinical phenotype. The system of interacting genes,
like the composition of breast milk, is quite complex but certain
regularities are apparent that suggest testable hypotheses. There
appear to be separate QTL controlling early somatic growth and
subsequent fat deposition that, in combination, will drive obesity and
additional QTL that will drive subsequent diabetes onset through
exacerbation of insulin resistance (reflected by extreme
hyperinsulinemia). The chromosome 1 locus and the maternal environment
are the major factors that drive early weight gain. Certain of these
QTL may control components of the growth hormone/insulin-like growth
factor system, because elevations in serum IGF-1 is the earliest
endocrine anomaly we have observed in NZO/HlLt mice (Flurkey et al.
1998). The chromosome 1 locus contributes to transition from juvenile obesity and IGT into overt diabetes, presumably by catalyzing development of severe hyperinsulinemia whereas the maternal
environmental contribution, entailing factors in the milk, enhances
penetrance of the combined genetic susceptibilities. The activation of
these major effects depends on the presence of one or more modifier loci. Among the modifiers, the NZO/HlLt-derived chromosome 17 locus,
marked by D17Mit61, has the largest effect for BW and PG when
combined with the QTL on chromosomes 1 and 15. A second chromosome 17 interaction with an NON/Lt-derived locus on chromosome 6 affecting BW
also appears to be a major contributor. This interaction is with a
distal locus, marked by D17Mit240, suggesting that multiple genes on chromosome 17 may be contributing to diabetes development. Of
course, these interactions are not occurring on their own, but
simultaneously with the other pairwise and three-way interactions. Unfortunately, the sample size is not large enough to look for four-way
interactions, but ANOVA analysis suggests that these three-way
interactions marked by the maternal environment, chromosome 1 and a
second locus (chromosome 12, 15, or 17) are dependent on a third
diabetogenic allele also being present from that group. Thus, it is
quite likely that a congenic strain with just a single locus will not
be diabetes prone, especially in view of our experience and that of
others, and that a QTL on a given chromosome entails contributions from
multiple loci (Legare et al. 2000). This certainly has been true for
fine mapping of the genes predisposing the NOD/Lt strain to
insulin-dependent diabetes (Podolin et al. 1998; Serreze et al. 1998).
Under these circumstances, a recombinant congenic strain (made by
inbreeding a second backcross to NON/Lt) would be required to fix
numbers of interactive NZO/HlLt and NON/Lt QTL necessary to exceed a
diabetogenic threshold. Indeed, we have completed preliminary analysis
of a matched pair of incipient recombinant congenic strains. Both
(sub)strains derived from a common stem mating and thus share the
chromosome 1 diabesity QTL from NZO/HlLt as well as numerous other
known NZO and NON diabesity-associated QTL. They differ only by a
recombination event that allowed one line to be selected for a
truncated segment of the NZO-derived diabesity region on chromosome 15 marked by D15Mit159. The substrain carrying NON/Lt genome at
this marker is diabesity resistant, whereas the substrain with NZO
genome spanning this marker develops diabetes. Although it is premature
to select candidate genes on chromosome 1, the finding of a diabesity
requirement for NZO genome at D15Mit159 marker suggests two
interesting candidates, peroxisome proliferator activated receptor
alpha (Ppara) and carnitine palmitoyltransferase (Cpt1b). Either of these two genes may be defective in their
ability to interact with loci controlling fatty acid oxidation on
chromosome 1. Such a potential interaction is suggested by the finding
of diabetogenic accelerants in the milk of obese F1 dams, which appears to be lipid enriched. The biochemical properties of milk are quite complex and involve a plethora of nutrients and bioactive factors, many
of which become active on digestion. Milk contains vitamins, minerals,
sugars, and fats along with enzymes, growth hormones, and immune cells
to provide the infant/pup with complete nourishment until weaning (Kunz
et al. 1999). Although milk constituents have received considerable
attention as potential triggers for autoimmune (type 1)
insulin-dependent diabetes (Norris and Pietropaolo 1999), our results
in an animal model clearly indicate the potential for milk factors from
obese mothers to serve as early triggers for type II diabetes
development. The mice in this study model for juvenile obesity in
humans. Pima Indians represent a human population in which juvenile
obesity is prevalent, and in which mothers are frequently obese.
Studies analyzing the obesity status of the Pima mother have shown that
children born either to obese females or to underweight females, but
not normal weight females, are at higher risk to develop type II
diabetes (Pratley 1998). Although a recent study in normal weight human
females indicates that breast feeding is highly beneficial when
compared with feeding cow's milk-based infant formulas in preventing
juvenile obesity (von Kries et al. 1999), the study did not evaluate
whether breast milk from overweight females might be quantitatively
different. Gestational obesity in the mother has been correlated with
increased obesity in offspring (Levin and Govek 1998). The present
study in mice raises the clear possibility that certain genetic
combinations might create an excess or dearth of one or more nutrients
and/or factors in breast milk that would have a profound effect on
metabolically "imprinting" the postnate for later obesity and its sequelae.
Recognizing the extent and nature of the genetic complexity that
underlies the many phenotypes of interest will be a key step in
unraveling the complexity of type II diabetes in humans. It is becoming
increasingly apparent that many phenotypes of biomedical importance in
humans are influenced by multiple gene-gene and gene-environment
interactions. Thus, it is essential that we begin to see the whole of a
complex phenotype as more than the sum of independent, additive
effects. Identification of disease modifying QTL deriving from both
sides of a pedigree is becoming increasingly more common (Ranheim et
al. 1997; Kido et al. 2000; Kuida and Beier 2000). New statistical
approaches for the detection and characterization of interactions are
needed. Mouse models of human disease can reduce the complexity of
multiple genetic and environmental interactions that confound the
detection of genes in human populations. As such, they are essential
tools for the eventual discovery of the relevant genes.
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METHODS |
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TOP
ABSTRACT
INTRODUCTION
RESULTS
DISCUSSION
METHODS
REFERENCES
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Mice and Phenotype Characterization
NZO/HlLt males and females were mated to NON/Lt males and females to generate reciprocal F1 hybrids that then were backcrossed to NON/Lt males and females. Mice were fed NIH-31 (4% fat) grain and housed in double pen Plexiglass boxes with free access to food and water. All mice shared the same mouseroom with controlled temperature and humidity and a 12-h light/dark cycle. Two hundred three male progeny were aged to 24 weeks with BW (in grams) measured every week, PG (mg/dL) measured every 4 weeks (glucose analyzer; Beckman Instruments), and PI (ng/mL) measured at weeks 16, 20, and 24 by rat insulin radioimmune assay kit (Linco). On termination at 24 weeks, four fat pads (inguinal, retroperitoneal, gonadal, and mesenteric) were removed and weighed to calculate total fat (sum of four fat pad weights taken), AI (weight of four fat pads [g]/body weight [g] × 100], and BMI (body weight [g]/body length [cm2]).
Genotyping
DNA was initially isolated from 5-mm tail clips and later from frozen tissue (liver or kidney). A genome scan was conducted by polymerase chain reaction using 83 microsatellite markers under conditions recommended by the supplier (Research Genetics) using various cyclers (MJ Research and Perkin-Elmer), and the products were separated on 4% Metaphor/LE (3:1) agarose gels (BMA). Markers were distributed ~20 cM apart with higher concentrations of markers in areas around suggestive QTL linkage.
Statistical Analysis
We performed three genome scans: first to look for QTL with main
effects; second to look for pairwise interactions, including interaction with maternal environment; and third to look for three-way interactions involving pairs of loci interacting with maternal environment. Genome scans for main effects were performed by one-way ANOVA of the phenotype across the genotype classes at each marker locus. Significance of the ANOVA F statistic (with 1 and 201 degrees of freedom) was assessed by permutation analysis (Churchill and Doerge 1994). Based on 1000 permutations, the 0.05 critical value was
estimated to be 11.76, corresponding to a LOD (Logarithm of Odds) score
of 2.6. Pairwise interactions were assessed by a two-stage procedure.
In the first stage, significant marker pairs among all possible pairs
were identified by computing the overall F statistic in a
two-way ANOVA. This statistic compares the likelihood under the full
model (with two main effects and an interaction) with the likelihood
under a null model of no genetic effects. It has 3 and 199 d.f.
Significance of marker pairs was assessed by permutation analysis of
the overall F statistic to account for multiple testing across
markers and traits. Marker pairs with an overall F statistic
exceeding 8.3 were deemed to be significant at a genome-wide 0.05 level. Once a significant marker pair was identified, a second stage
test to assess the significance of the interaction term was conducted.
An F statistic (with 1 and 199 d.f.) is used to compare the
full model likelihood to the likelihood of a model with two main
effects but no interaction term. A nominal significance level is
appropriate here because the marker pairs already have been screened
for genome-wide significance. We chose a conservative 0.005 level
(F > 8.06) for this test to ensure that only the most
significant interactions would be selected. Maternal environment was
included as a "marker" in this genome scan, which allowed us to
detect pairwise interaction of a marker locus with the maternal
environment. For the three-way interaction search, which is analogous
to the pairwise search, maternal environment is included in the model,
and we then search through all marker pairs. The initial screen is
based on an overall F statistic (with 6 and 195 d.f.) that
compares the full model (with three main effects, three pairwise
interactions, and a three-way interaction) with a null model that
includes only the maternal effect. Genome-wide significance was
determined by permutation analysis of the overall F
statistics. The genome-wide 0.05 critical value was found to be 7.7. A
second F test (with 1 and 195 d.f.) that compares the full
model to a model with no three-way interaction was performed at a
nominal 0.005 level (F > 8.06) and was used to identify
those triplets (two markers plus maternal environment) that show
significant three-way interactions. All linkages detected by genome
scans were calculated by marker regression using MATLAB software
(Mathworks, Inc.). Source code and analysis scripts are available at
http://jax.org/research/churchill. MapManager QTb28ppc
(www.mcbio.med.buffalo.edu/mapmgr.html) (Manley and Olson 1999) was
used to identify QTL during data acquisition and for generating the
graphics in Figure 2. ANOVA graphics in Figures 4 and 5 were generated
by Stat View (Abacus Concepts).
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ACKNOWLEDGMENTS |
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We thank Jenn Kintner and Bruce Regimbal for excellent technical assistance. This research was supported by National Institutes of Health-National Center for Research Resources 88911. Institutional shared services were supported by National Cancer Institute Center Support Grant CA34196.
The publication costs of this article were defrayed in part by payment of page charges. This article must therefore be hereby marked "advertisement" in accordance with 18 USC section 1734 solely to indicate this fact.
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FOOTNOTES |
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1 Corresponding author.
E-MAIL ehl{at}jax.org; FAX (207) 288-6079.
Article and publication are at www.genome.org/cgi/doi/10.1101/gr.147000.
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ABSTRACT
INTRODUCTION
RESULTS
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2-microglobulin.
J. Immunol.
160:
1472-1478Received May 4, 2000; accepted in revised form August 11, 2000.
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