Genetic Epidemiology 12:475488 (1995)

Major Gene With Sex-Specific Effects Influences Fat Mass in Mexican Americans

Anthony G. Comuzzie, John Blangero, Michael C. Mahaney, Braxton D. Mitchell, James E. Hixson, Paul B. Samollow, Michael P. Stern, and Jean W. MacCluer

Department of Genetics, Southwest Foundation for Biomedical Research (A.G.C., J.B., M.C.M., B.D.M., J.E.H., PB.S., J.WM.), and Division of Clinical EpjdemjologH Department of Medicine, University of Texas Health Science Center (M. PS.), San Antonio

Increased adiposity has repeatedly been identified as a major risk factor for a variety of chronic diseases. However, the question still remains whether the amount of adipose tissue itself is genetically mediated. To address this question, a segregation analysis, using maximum likelihood techniques as implemented in the computer program Pedigree Analysis Package (PAP), was performed on fat mass (kilograms of body fat) in a large sample of extended Mexican American families residing in San Antonio, TX. The only model not rejected was a Mendelian mixed model for fat mass, incorporating genotype X sex interaction. In males the major gene accounted for 37% of the total variance compared with 43% in females. In both sexes homozygous recessive individuals have a fat mass more than double that of individuals of the other two genotypes. It was possible to reject linkage of the anonymous major gene for fat mass with several candidate loci for obesity. However, tentative evidence of linkage was detected with markers on both chromosomes 2 and 11, thereby providing hypotheses for future testing. 0 1995 Wiley-Liss, Inc.

Key words: segregation analysis, linkage analysis, body composition, Mexican American, genotype x sex interaction

INTRODUCTION

A major contribution of epidemiological research over the last several decades has been the identification of risk factors for a variety of chronic diseases. However,

Received for publication December 16,1994; revision accepted April 21, 1995.

Address reprint requests to Dr. Anthony G. Comuzzie, Department of Genetics, Southwest Foundation for Biomedical Research, P.O. Box 28147, San Antonio, TX 78228-0147.

0 1995 Wiley-Liss, Inc.

476 Comuzzie et al.

relatively little research has been directed toward assessing the genetic component of their development and expression, and virtually none of this work has focused on ethnic minorities, particularly Mexican Americans. A thorough understanding of the mode of inheritance of risk factors can help provide much needed insight into the complex etiology and variation in expression of chronic diseases, and such knowledge could significantly increase the potential for early intervention and more effective treatment [Ford and DeStefano, 1991; Webber et al., 19911.

Risk for several chronic diseases (e.g., coronary heart disease [CHD] and non- insulin dependent diabetes mellitus [NIDDM]) has been found to increase with in- creasing levels of obesity [e.g., Ohlson et al., 1985; Stem and Haffner, 1986; Reichley et al., 1987; Guo et al., 19941. The question that has now engendered much interest is to what extent is the accumulation of excess fat itself a genetically mediated phe- nomenon [Zonta et al., 1987; Hastedt et al., 1989; Price et al., 1990; Province et al., 1990; Moll et al., 1991; Tiret et al., 1992; Gage et al., 1993; Rice et al., 19931.

A detailed understanding of obesity and other risk factors associated with CHD and NIDDM in the Mexican American population is important for a number of rea- sons. Mexican Americans of both sexes have a higher prevalence of NIDDM than non-Hispanic whites [Mueller et al., 1984; Gardner et al., 1984; Stern et al., 19841. However, the prevalence of CHD appears to be lower in Mexican American men compared to non-Hispanic white men, despite more adverse cardiovascular risk factor profiles of the former [Mitchell et al., 19901. This has led some researchers to suggest a possible genetic or behavioral factor which might be counteracting the CHD risk factors, at least among Mexican American males [Mitchell et al., 19901. It has also been shown, however, that as socioeconomic conditions and patterns of acculturation change in this population, there are concomitant changes in the pattern of expres- sion of these chronic diseases [Hazuda et al., 1988; Stern et al., 19841. Therefore, an intensive study of the genetic variation in the distribution and expression of risk fac- tors, such as body fat accumulation, in this population is warranted [Reichley et al., 1987; Webber et al., 19911. Here we present results of a complex segregation analy- sis demonstrating a major gene with sex-specific effects on fat mass as estimated by bioimpedance in a large sample of Mexican Americans in San Antonio, TX.

MATERIALS AND METHODS Population

This study uses data obtained from Mexican American families living in San Antonio, TX who are participants in the San Antonio Family Heart Study, a broader project examining the genetics of risk factors for atherosclerosis, NIDDM, and obe- sity. Ascertainment of families for this analysis was done without regard to preex- isting medical conditions. For this analysis data were available from 543 individuals (210 males; 333 females) ranging in age from 16 to 87 years, with an average age of 39.7 years. Of the 543 individuals, 15 are unrelated to any other individuals in the sample, while the remaining individuals are distributed among 26 pedigrees. The sizes of these pedigrees range from 3 to 71 individuals, with the majority containing at least 3 generations of relatives.

In comparison with non-Hispanic whites from the San Antonio area, this pop- ulation has been described as having a greater tendency to overweight [Hazuda et al.,

Major Gene Influences Fat Mass 477

19911. Using a definition of overwieght based on body mass index (BMI) from the second National Health and Nutrition Examination Survey (NHANES 11) (a BMI at age 40 years of >29.5 for males and 31.0 for females (Frisancho, 1990]), the prevalence of overwieght for this Mexican American population was approximately 55% for females and 51% for males vs. 27% and 38% among non-Hispanic white females and males, respectively [Hazuda et al., 19911. Comparisons with NHANES I1 skinfold standards for U.S. whites and blacks consistently place the estimated means for both sexes in the present sample between the 50th and 90th percentiles.

Data The phenotype studied in this analysis was fat mass (FM), expressed as kilo-

grams of fat. Estimates of FM were obtained by bioimpedance using a Valhala bioimpedance machine. Increasingly, bioimpedance is gaining attention as a rel- atively quick and simple method to collect body composition data in population studies [Baumgartner et al., 1990; Guo et al., 1987, 1994; Shephard, 19911 and was first used for such purposes by Nyboer [1981]. Simply stated, bioimpedance is a measure of the efficiency with which a low radiofrequency (500-800 pA) altemat- ing electrical current can be transmitted through the body. This approach is based on the fact that for any given electrical frequency, the impedance measured is di- rectly correlated with the distance between the electrodes (e.g., wrist and ankle) and inversely correlated with the cross-section of interposed lean tissue [Baumgartner et al., 1990; Shephard, 19911. Since fat has a lower water content than lean tis- sue, it is less effective in conducting an electrical current [Baumgartner et al., 1990; Shephard, 199 11. As a result, increasing bioimpedance correlates with increasing fat accumulation [Baumgartner et al., 1990; Shephard, 19911. For this analysis percent FM, taking into account the subject’s height, weight, and sex, was converted to kilo- grams by multiplying these percentages by total body weight. Bioimpedance has repeatedly been shown to represent a simple and reliable method for estimating fat mass in large population-based studies.

In addition to the phenotypic data needed for the segregation analysis, genotypic data were also available for use in subsequent linkage analysis. A total of 8 loci, all of which could be considered candidates for obesity and other chronic diseases (e.g., NIDDM and CHD) for which obesity is considered a risk factor, were utilized in this analysis (Table I). Of these 8 loci, 4 are involved in glucose metabolism (GCKZ, GLUT2, INS, and INSr), 3 are involved in fat and lipid metabolism (ApoB, FABP2, and LPL), and 1 is involved in growth (IGF-I). ApoB is considered a candidate locus based on the work of Pouliot et al. [1994].

The markers were typed by the polymerase chain reaction (PCR) using lym- phocyte DNA prepared from 20 ml blood samples as previously described [Hixson et al., 19891. PCR amplifications used 0.5 p g of lymphocyte DNA, 1.0 pmol/pl of each primer, 0.025 unit/pl Taq polymerase (Perkin-Elmer Cetus, Norwalk, CT), and buffer and nucleotide components described by the supplier of Tuq polymerase. The sequences of amplification primers and PCR temperatures for each marker are avail- able from the references in Table I.

Complex Segregation Analysis Complex segregation analysis [Elston and Stewart, 19711 was performed on FM

in an effort to obtain evidence of a major gene influencing this phenotype. Maximum

478 Comuzzie et al.

TABLE I. Candidate Loci for FM

Loci Marker Alleles Location Reference

Glucokinase GCKl 4 7P [Matsutani et al., 19921

Glucose transport GLUT2 7 3q26.2-q27 [Patel et al., 19931 Insulin (structural gene) INSPst I 2 1 lp15.5 [Hoban and Kelsey, 19911 Insulin (receptor gene) INSr repeat 1 6 19~13.3 [Hanis and Bertin, 19921

INSr repeat 2 5 191113.3 [Hanis and Bertin, 19921

(dinucleotide repeat)

(tetranucleotide repeat)

(dinucleotide repeat) Insulin-like growth factor I IGF-I 8 12q22-q23 [Weber and May, 19881 Fatty acid binding protein FABPZ 7 4q28-q3 1 [Edwards et al., 1991 J

Lipoprotein lipase LPL3 14 8p22 [Wood et al., 19911

Apolipoprotein B ApoB GZ9 5 2p24-p23 [Zuliani and Hobbs, 19901

(short tandem repeat)

LPLH/Hind 111 2 8p22 [Gotoda et al., 19921

(tetranucleotide repeat)

likelihood techniques, as implemented in our modification of the computer program Pedigree Analysis Package (PAP) [Hasstedt, 19891, were used to compare a series of models with restricted parameterizations against a more generalized model in which all parameters were estimated. In these analyses the model design allowed for major genotype X sex interaction effects as well as polygenotype X sex interaction [Konigs- berg et al., 1991; Blangero, 19931.

The five models utilized in these analyses were 1) a general transmission model (against which all of the other models were compared); 2) an environmental trans- mission model; 3) a mixed Mendelian model; 4) a polygenic model; and 5 ) a sporadic model. The general transmission model [Lalouel et al., 19831, which assumes three phenotypic distributions with common standard deviations (SD) and a polygenic con- tribution to the variation around the mean of each distribution, represents the most generalized model. In these analyses 19 parameters were estimated in the general model and included PA (the allelic frequency of the hypothesized major gene); three transmission probabilities (TAA, T A ~ , and T,,), which are associated with the trans- mission of allele A by each of the three genotypes (AA, Aa, and aa); male genotype- specific means ( ~ A A , p ~ ~ , ~ ~ ) ; the female displacements from the male genotypic means (AAA, A A ~ , Aaa); sex-specific age and age2 effects (Pdage, Psage2, &age, P9age2); effects of diabetic status and use of diabetic medication (p diabetes and p medication); sex-specific environmental and genetic SDs ( ~ 6 , a G 6 , U E ~ , t ~ G p ) ; and genetic correlation between the sexes (pG6, p ).

The four remaining models all represent restricted versions of the the general model and vary with respect to the parameters that were omitted or constrained to preset values. Both the sporadic and the polygenic models differ from the general transmission model by the omission of the parameters and TAA, TA,, and T ~ ~ . In the case of the sporadic model, the only parameters estimated are a male mean, a female displacement from the male mean, sex-specific age and age2 effects, diabetic status and diabetic medication effects, and sex-specific phenotypic SDs. The sporadic model allows for random environmental effects, but excludes any genetic effects. The poly-

Major Gene Influences Fat Mass 479

genic model is similar to the sporadic model with respect to the parameters estimated, but differs in that it partitions the phenotypic SD into sex-specific environmental and genetic components (i.e., (TJE, u ~ G , C T ~ E , a p ~ ) . As a result of this partitioning of the variance, this model allows for both polygenic and random environmental effects in the expression of the trait.

The mixed Mendelian and environmental models, unlike the sporadic and poly- genic models, contain the full parameterization of the general transmission model, but differ from it with respect to constraints placed on the transmission probabilities. The mixed Mendelian model allows for both a major gene and residual polygenic and random environmental components. In the mixed Mendelian model the transmission probabilities are constrained to their Mendelian expectations (TAA = 1.0, TAa = 0.5, T,, = O.O), while all the remaining parameters are estimated. In the case of the envi- ronmental model, all three transmission probabilities are constrained to equal the al- lelic frequency p A with all remaining parameters estimated. The environmental model provides a test of the assumption of Mendelian transmission.

Comparisons of the more restricted models against the general model were made using the In likelihood ratio test which produces a statistic calculated as

-2(ln likelihoodrestricted model - In likelihoodgeneral transmission model)

which is asymptotically distributed as x2 with degrees of freedom equal to the differ- ence in the number of parameters estimated in the two models being compared. Those models with a In likelihood significantly worse than that of the general transmission model (P 5 0.05) are rejected. Those models that do not have a significantly worse In likelihood score than the general transmission model are retained and considered a more parsimonious representation of the data, since they require fewer parameters to be estimated.

Hypotheses regarding the presence of major gene X sex (MG X S) and poly- gene X sex (PG X S) interactions were tested using the strategy developed by Blangero [1993]. MG X S interaction was tested by comparing a model in which genotype-specific sex effects (AAA, AA,, Aaa) were estimated against one in which these effects were forced to be equal (AAA = = A,,). A finding of significant MG X S interaction is evidence that major genic expression differs between the sexes. The test for PG X S interaction requires two distinct constraints. In the ab- sence of PG X S interaction, the residual genetic variances are equal in males and females (a& = a:*) and the genetic correlation between the trait’s expression in males and females is one ( p ~ ( d , o ) = 1) [Blangero, 19931. If either of these two con- straints can be statistically rejected, there is evidence for residual PG X S interaction. For completeness, we also tested the null hypothesis that the two sexes exhibit sim- ilar residual environmental variances (a& = a&). Rejection of this constraint is evidence for environment X sex (E X S) interaction and indicates the presence of differential environmental lability between sexes.

Linkage Analysis After a major gene for a quantitative trait is detected by complex segregation

analysis, the next step is to establish its chromosomal location by linkage analysis, using marker data and the parameter values of the best Mendelian model for the

480 Comuzzie et al.

major locus. In this approach, all parameters except the recombination fraction (0) are fixed at their maximum likelihood estimates, and In likelihoods are obtained for a grid of 0 values ranging from 0.0 (complete linkage) to 0.50 (no linkage). Statistical support for linkage is obtained using a In likelihood ratio test, calculated as

-2[ln likelihood (0 = 0.50) - In likelihood (@)I

where 6i is a recombination fraction less than 0.50. This test statistic is asymptot- ically distributed as a x2 with one degree of freedom. Values of the test statistic significantly greater than zero (P 5 0.05) are taken as evidence for linkage of the major locus and the marker, while values of the test statistic significantly less than zero are taken as evidence of no linkage. To follow convention, test results are given as LOD scores with a LOD score of 0.83 corresponding to the critical 0.05 a level. Since our linkage tests involved candidate loci, with prior support for influencing obesity risk, and the inheritance of FM is complex (not strictly monogenic), we have opted not to use the traditional LOD 2 3 criterion to test for linkage because it is based on an inappropriate prior probability for the current case. Additionally, the use of a model for the transmission of FM which was dictated by the data provides much greater prior probability for detecting linkage.

RESULTS

By complex segregation analysis, allowing for sex-specific genotypic effects and including diabetic status and use of diabetic medications as covariates, we found evidence of a major gene influencing FM. Table I1 shows the In likelihood scores and x2 values for the five models for which parameter values were estimated for FM. The mixed Mendelian model was the only one that was not significantly worse than the general model, thus providing evidence for a major gene effect for FM. Furthermore, because the mixed Mendelian model assuming a simple dominant effect at the major gene locus (i.e., AA = Aa) was found not be have a significantly worse In likelihood value than the codominant model (x2 = 0.972; P > O.lO), this dominant model was accepted as being more parsimonious. Additionally, general models were estimated using both parameterizations (i.e., codominant and dominant patterns) and the same result obtained each time. The In likelihood scores given in Table I1 are for dominant mixed Mendelian, environmental, and general models.

The In likelihood for the dominant mixed Mendelian model, which allowed for sex-specific genotypic effects, was statistically compared ( x 2 ) to the In likelihood

TABLE 11. Likelihood Ratio Tests of Competing Models From a Complex Segregation Analysis of FM in Mexican Americans

Model In likelihood X 2 Degrees of freedom P

General - 2457.6 1 - - -

Environmental -2461.86 8.50 3 0.037 Mixed Mendelian -2460.32 5.42 3 0.144 Polygenic -2500.49 92.39 4 <0.001 Sporadic -25 17.05 125.50 6 <0.001

Major Gene Influences Fat Mass 481

TABLE 111. Likelihood Ratio Test for Competing Dominant Mixed Mendelian Models From a Complex Segregation Analysis of FM in Mexican Americans

Model In likelihood XZ Degrees of freedom P

- - - Sex-specific effects -2460.32 AAAAa = &a -2463.1 1 5.58 1 <0.025 uGd = uG4 -2462.87 5.10 1 <0.025 UEd = C E P -2460.41 0.18 1 >0.50

scores for three dominant mixed Mendelian models not allowing for such effects: 1) AAAAa = Aaa; 2) UGJ = a ~ p ; and 3 ) ( T E ~ and PEP (Table 111). These compar- isons demonstrated that constraining the female displacements from the male geno- typic means to be equal (AAAAa = A,,) and constraining the male and female genetic SDs to be equal ( U G ~ = a ~ q ) produce In likelihood significantly worse ( P 5 0.05) than those for the model in which these values are estimated, thereby confirming the existence of a sex-specific genotypic effect [Comuzzie et al., 1993; Towne et al., 19921 (Table 111). However, when the genetic correlation ( p ~ ) between the sexes was estimated in the dominant mixed Mendelian model it quickly bounded at its upper limit of 1.0, implying that the same genes affect the expression of this trait in both males and females. Additionally, age X genotype as well as age X sex X genotype effects were tested and found not to be significant.

The In likelihood for the dominant mixed Mendelian model with the male and female environmental SDs constrained to be equal ( A E J = U E ~ ) was not significantly worse than the dominant mixed Mendelian model where these parameters were al- lowed to float (x2 = 0.18; P > 0.50) (Table 111). The model with the male and female environmental SDs constrained to be equal has one less parameter estimated than the model in which these parameters are both estimated, and is, therefore, considered to be more parsimonious. Thus, there is no evidence for a sex-specific environmental effect on FM.

Table IV provides the parameter estimates and their associated standard errors (SEs) for both the mixed Mendelian and general models for the analysis of FM. In the case of the transmission probabilities, the T values estimated in the general model strongly correspond to those predicted under Mendelian segregation, with TAA,

TA,, and T,, having estimated values of 0.92, 0.50, and 0.00, respectively (Table IV). From the parameter estimates obtained for the mixed Mendelain model, it appears that for both male and females, a recessive allele (a) at this major gene locus leads to homozygous individuals (aa) with nearly twice as much fat as individuals with the other two genotypes (AA, Aa) (Table IV). In this sample homozygous recessive males (aa), with age standardized to 40 years, have an estimated average of 44.8 kg of fat, while homozygous recessive females (aa), also standardized to 40 years of age, on average have 63.3 kg of fat. This is in comparison to individuals with the other two genotypes (AA, Aa), who have mean values of 19.8 and 28.7 kg of fat for males and females, respectively. Based on an estimated frequency of 0.25 for the recessive allele (1 - PA) in the mixed Mendelian model (Table IV), approximately 6.25% of this population are expected to be homozygous recessive (aa) for the gene responsible for this increased accumulation of fat. Figure 1 shows the distributions

482 Comuzzie et al.

TABLE IV. Parameter Estimates and SEs for the Mixed Mendelian and General Models From Complex Segregation Analysis of FM (kg of Fat) in Mexican Americans

Model

Parameter Mixed Mendelian General

PA 0.754 Z 0.033 0.817 rt 0.047 TAA 1 .oa 0.919 t 0.043 TAa O S a 0.500 ? 0.094

PAAAaS 19.762 ? 0.837 19.751 t 0.816 44.829 2 2.246 44.833 -f 2.173 Paad

AAAAaP 8.883 ? 1.097 8.904 t 1.066 b a a ? 18.470? 4.156 18.441 t 3.883 Kage 8) 0.217 t 0.039 0.220 t 0.039 Kage 9 ) 0.279 ? 0.038 0.279 ? 0.037

P(age2 9) -0.008 t 0.002 -0.009 ? 0.002 P(diabetes) 2.006 ? 1.639 2.345 rt 1.652 P (medication) -0.533 t 2.085 -0.829 t 2.056 UE6 6.586 t 0.751 6.418 t 0.715 U E P 6.075 t 0.971 5.948 t 0.973 UG6 4.159 ? 1.122 4.349 t 1.088 U E P 7.477 t 0.987 7.553 ? 0.978

aValues constrained in model. bParameter estimated at the boundary.

Taa O.oa 0.0b

P(age2 8 -0.002 t 0.002 -0.002 t 0.002

of the three genotypes superimposed on the population distribution for FM for both males and females.

Table V details the decomposition of the total phenotypic variance into that due to the major gene (for both its additive and dominance components), the residual polygenic component, and the random environmental component for both males and females. With respect to the variance due to the major gene, females have a vari- ance (68.276) nearly twice that of males (35.724), although the relative proportions

MALES

1 0.20

FEMALES r 020

0 20 40 60 80 100

Adbsted Fat Mass (Ka)

Fig. 1. Plots showing the sex-specific distributions for the three genotypes for a major gene for FM.

Major Gene Influences Fat Mass 483

TABLE V. Phenotypic Variance in FM (X Sex) Accounted for by Genetic and Environmental ComDonents*

Component Males Females

Major gene Additive 14.106 (0.15) 26.960 (0.17) Dominance 21.618 (0.22) 41.316 (0.26)

Total 35.724 (0.37) 68.276 (0.43) Polygenic 17.297 (0.18) 55.906 (0.35) Environmental 43.375 (0.45) 36.906 (0.23)

*Relative proportion of the variance explained is given in parentheses.

are approximately equal (Table V). In the case of the residual polygenic component, females have a variance more than twice that of males: 55.906 vs. 17.297 respec- tively. As a result, a much larger portion of the female phenotypic variance (78%) is due to genetic iactor (i.e., major gene and polygenic components) than is seen for males (55%).

We next tested for linkage of this anonymous major gene influencing FM with any of 8 candidate loci. Tight linkage was excluded for all but 2 of the markers, since they all displayed increasingly negative LOD scores for values of 8 5 0.45. Those markers for which no evidence of linkage was detected included IGF-I, GCKl, CLUT2, INST, FABP2, and LPL. Tentative evidence for loose linkage was detected with both ApoB (LOD score of 1.1 at 8 = 0.15) and the insulin structural gene (ZNS) (LOD score of 0.87 at 8 = 0.10) (Table VI, Fig. 2).

DISCUSSION

The detection of a major gene influencing FM is consistent with results of Rice et al. [1993], in which this phenotype was measured by hydrostatic weighing as op- posed to bioimpedance. The similarity in our findings to those of Rice et al. [I9931 is significant for several reasons. The fact that we were able to detect a major gene influencing FM in a sample that is ethnically distinct from that used by Rice et al. [1993] suggests that these findings are not entirely population-specific. Such an in- terpretation is further supported by the fact that our estimate for the frequency of the recessive allele (pa = 0.25) is very similar to that reported by Rice et al. [1993] (pa = 0.30). However, there is one difference between our study and that of Rice et al. [ 19931 which deserves further discussion.

Perhaps the most striking difference between the results of this study and that of Rice et al. [1993] lies in the detection of a marked genotype X sex interaction in the present study. In fact, no evidence of a major gene for FM could be detected without allowing for such a genotype X sex interaction effect. The existence of such an interaction for FM should not be altogether unexpected since in a previous quan- titative genetic study, we [Comuzzie et al., 19931 demonstrated the existence of genotype X sex interactions for a variety of other measures of body fat accumulation and distribution, findings which are also confirmed by other researchers [Shimokata et al., 1989; Borecki et al., 19931. However, what is of interest here is that a pro- nounced genotype X sex interaction is seen not only in the polygenic component,

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Major Gene Influences Fat Mass 485

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Fig. 2. LOD plots from linkage analyses of the major gene for FM and the ApoB and INS loci.

but in the major gene component as well. As can be seen in Table V, females dis- play an absolute variance roughly twice that of males for the genetic components. Thus females possess a greater degree of genetic variance with respect to FM. Such a conclusion is interesting given the generally pervasive assumption that the vari- ance in anthropometric characters, such as measures of body fat accumulation and topography, primarily reflects variation in environmental factors (e.g., diet and activ- ity patterns) both within and between the sexes [e.g., Brauer, 1982; Eveleth, 1975; Stini, 19851. From this work we have clearly shown that while environmental factors may be a major contributor to the variance seen for FM in males, the female variance appears to be influenced primarily by genetic factors.

With respect to linkage, we were able to strongly reject 6 of the candidate loci: GCKl, GLUT2, INSr, FABP2, LPL, and IGF-I. Failure to find linkage between this anonymous major gene for FM and loci associated with both glucose and lipid metabolism is of particular interest since products of these loci impact or interact with adipose tissue formation and utilization. Additionally, failure to detect linkage with the IGF-I marker is interesting given the role of this hormone in affecting the overall size of the organism and its apparent pleiotropic effects on BMI (Comuzzie et al., submitted).

There was, however, evidence for possible linkage of the major gene for FM with two of the markers examined. The first of these is INS located at llp15.5, and certainly a likely candidate locus for obesity. The In likelihood ratio test yields a x2 value significant at P = 0.046 (corresponding to a LOD score of 0.865 at 0 = 0.10) (Table VI). Additional evidence for possible linkage for the FM gene to this chro- mosomal region comes from the presence of the homolog of the rodent obesity gene (Tubby) in this same region (1 lp15). The second marker showing evidence of possi- ble linkage with his major gene is ApoB located at 2p24-p23. In this case the maxi- mum LOD score obtained is 1.095 (0 = 0.15) (Table VI), and yields a x 2 value for a likelihood ratio significant at P = 0.025. Pouliot et al. [1994] have reported an as- sociation of ApoB with measures of visceral/abdominal obesity, thereby qualifying this as a candidate loci for obesity. While the direct mechanism of ApoB effect on obesity is unclear, it is flanked on both sides by 2 loci either of which could be di- rectly involved: TPO and Steroid a-5-Reductase. The argument for TPO involvement

486 Comuzzie et al.

is particularly straightforward given the effects of thyroid hormones on metabolic function. Given that these two markers, each on different chromosomes, show tenta- tive evidence of linkage, several scenarios can be posited to explain these findings: 1) one of these linkages may represent a false positive; 2) both linkages may repre- sent false positives; and 3) both may be correct, suggesting a 2-loci model for this major gene for FM. While no definitive conclusions can be drawn concerning linkage of this major gene for FM with ApoB and INS, these results do provide considerable information for refining the focus of future linkage analyses.

CONCLUSIONS

Work by Shirnokata et al. [ 19891 on body fat topography and by Borecki et al. [ 19931 on BMI has shown that sex and age can have a major effect on the expression of body mass-related traits. Similarly, we found a pronounced sex-specific genotypic effect on the expression of FM derived from bioimpedance. Specifically, by using a model allowing for such interaction, we were able to detect a major gene which leads to homozygous recessive individuals having twice as much fat as individuals with either of the other two genotypes. The detection of a major gene for FM in this population of Mexican Americans not only provides support for the previous findings of Rice et al. [1993], but extends those results by demonstrating that genotype X sex interactions can have a significant role in the expression of a major gene for FM. The demonstration of a major gene influencing the excess accumulation of fat, along with preliminary evidence suggesting linkage with markers on either chromosome 2 or 11, offers new information for studies of the chronic diseases for which increased adiposity is a known risk factor.

ACKNOWLEDGMENTS

This research was supported by NIH program project grant HL45522 and by NIH grants GM15803 and DK44297.

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