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(Arteriosclerosis, Thrombosis, and Vascular Biology. 1999;19:777-783.)
© 1999 American Heart Association, Inc.

Original Contributions

A Genome Search Identifies Major Quantitative Trait Loci on Human Chromosomes 3 and 4 That Influence Cholesterol Concentrations in Small LDL Particles

David L. Rainwater; Laura Almasy; John Blangero; Shelley A. Cole; John L. VandeBerg; Jean W. MacCluer; James E. Hixson

From the Department of Genetics, Southwest Foundation for Biomedical Research, San Antonio, Tex.

Correspondence to David L. Rainwater, PhD, Department of Genetics, Southwest Foundation for Biomedical Research, PO Box 760549, San Antonio, TX 78245-0549. E-mail david{at}darwin.sfbr.org

	Abstract

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Abstract—Small, dense LDL particles are associated with increased risk of cardiovascular disease. To identify the genes that influence LDL size variation, we performed a genome-wide screen for cholesterol concentrations in 4 LDL size fractions. Samples from 470 members of randomly ascertained families were typed for 331 microsatellite markers spaced at 15 cM intervals. Plasma LDLs were resolved by using nondenaturing gradient gel electrophoresis into 4 fraction sizes (LDL-1, 26.4 to 29.0 nm; LDL-2, 25.5 to 26.4 nm; LDL-3, 24.2 to 25.5 nm; and LDL-4, 21.0 to 24.2 nm) and cholesterol concentrations were estimated by staining with Sudan Black B. Linkage analyses used variance component methods that exploited all of the genotypic and phenotypic information in the large extended pedigrees. In multipoint linkage analyses with quantitative trait loci for the 4 fraction sizes, only LDL-3, a fraction containing small LDL particles, gave peak multipoint log₁₀ odds in favor of linkage (LOD) scores that exceeded 3.0, a nominal criterion for evidence of significant linkage. The highest LOD scores for LDL-3 were found on chromosomes 3 (LOD=4.1), 4 (LOD=4.1), and 6 (LOD=2.9). In oligogenic analyses, the 2-locus LOD score (for chromosomes 3 and 4) increased significantly (P=0.0012) to 6.1, but including the third locus on chromosome 6 did not significantly improve the LOD score (P=0.064). Thus, we have localized 2 major quantitative trait loci that influence variation in cholesterol concentrations of small LDL particles. The 2 quantitative trait loci on chromosomes 3 and 4 are located in regions that contain the genes for apoD and the large subunit of the microsomal triglyceride transfer protein, respectively.

Key Words: LDL size fractions • genome-wide screen • linkage analysis • Mexican Americans

	Introduction

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LDL cholesterol (LDL-C) concentrations are positively associated with risk of cardiovascular disease (CVD). In addition to total LDL concentrations, many studies indicate that a small, dense LDL particle phenotype is also a risk factor for CVD.¹ In fact, several recent prospective studies have shown that LDL particle size phenotype is predictive of subsequent measurements of CVD.^{2 3 4} However, these associations are not independent of other correlated traits, such as triglyceride concentration, which suggests small LDLs are a component of an atherogenic lipoprotein phenotype.⁵ Possible mechanisms for a direct role of small LDLs in atherogenesis include heightened susceptibility to oxidation⁶ and/or altered affinity for the LDL receptor.⁷

LDL size phenotype is heritable⁸ and most segregation analyses suggest the existence of a major locus for the trait.^{9 10 11 12 13} Attempts to identify the gene(s) that might influence LDL size phenotype have so far met with equivocal results. A quantitative trait locus (QTL) for the dichotomous trait for LDL size (pattern A or large buoyant LDLs versus pattern B or small dense LDLs) was found to be linked to the LDL receptor locus,¹⁴ but not the apoB locus,^{15 16} whereas QTLs for LDL peak particle size were reported to be linked to the genes for the LDL receptor, apoB, cholesteryl ester transfer protein, and manganese superoxide dismutase.^{17 18} The evidence for these linkages has not been strong, with only the linkage of the dichotomous trait QTL to the LDL receptor locus¹⁴ having a log₁₀ odds in favor of linkage (LOD) score that exceeded 3, a nominal criterion for significant evidence of linkage. It should be noted that these previous studies did not survey the entire genome but were limited to small subsets of candidate genes.

Both the dichotomous and continuous traits studied above are based on LDL particle size distributions rather than concentrations. However, if small dense LDLs are directly atherogenic, it is likely that variation in their plasma concentrations will be more directly relevant to CVD risk than the size distribution phenotype. In this study, we have derived an LDL phenotype based on measurements of cholesterol concentrations in 4 LDL size fractions, using samples from a population of Mexican Americans participating in the San Antonio Family Heart Study.¹⁹ We performed a systematic survey of the entire genome to search for QTLs that may influence cholesterol concentration in LDL size fractions. These studies have yielded strong evidence that QTLs for cholesterol concentrations in small LDLs (LDL-3) are linked to 2 loci occurring on different chromosomes.

	Methods

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Subjects and Samples
Samples were drawn from the San Antonio Family Heart Study, a study of risk factors for CVD in Mexican Americans living in and around San Antonio, Texas.¹⁹ General lipoprotein and anthropometric characteristics of this population have been given elsewhere.¹⁹ Probands were selected randomly without regard to disease, and the study included 205 male and 265 female members of 10 extended pedigrees ranging in size from 35 to 71 individuals. Ages in this population ranged from 16 to 94 years for males and 16 to 92 years for females (mean values, 39.4±1.23 and 38.2±1.0 years, respectively). At a clinic visit, subjects provided information about sex, age, medications, and lifestyle variables, and a fasting blood sample. Diabetes was diagnosed by WHO criteria²⁰ as described previously.¹⁹ Plasma was isolated by low-speed centrifugation and the white cells in the buffy coat were harvested for DNA extraction. Samples were stored at -70°C until assayed. Procedures were approved by the Institutional Review Board of the University of Texas Health Science Center at San Antonio and subjects gave written informed consent.

Measurement of LDL Phenotypes
Cholesterol and triglyceride concentrations were determined enzymatically by using kits from Boehringer-Mannheim Diagnostics and Stanbio, respectively. Quality control procedures followed published guidelines.²¹ ApoB-containing lipoproteins in plasma were precipitated with dextran sulfate-Mg²⁺ before measuring HDL cholesterol (HDL-C) levels.²² LDL-C levels (in mmol/L) were estimated by the formula, (plasma cholesterol)-[HDL-C+(0.42 · triglycerides)]. This formula has been validated for triglyceride concentrations of <8.0 mmol/L^{23 24} ; samples with higher values were excluded. It should be noted that this formula was designed to remove the cholesterol contribution of VLDLs, not Lp(a) and IDLs. Consequently, the estimates of cholesterol in LDL size fractions may be somewhat inflated and contain additional variation due to these non-LDL lipoproteins.

LDLs were resolved on the basis of size by using nondenaturing gradient gel electrophoresis.²⁵ In brief, electrophoresis of plasma lipoproteins was for 3000 V · h and lipoprotein cholesterol was stained by use of Sudan Black B.²⁶ Distributions of stain among LDLs were measured with an LKB-Ultroscan LX laser densitometer and evaluated by using software we developed.^{25 27} Fractional absorbance was calculated for 4 LDL size fractions²⁸ (LDL-1, 26.4 to 29.0 nm; LDL-2, 25.5 to 26.4 nm; LDL-3, 24.2 to 25.5 nm; and LDL-4, 21.0 to 24.2 nm) and cholesterol concentration in each size fraction was estimated as the product of LDL-C and fractional absorbance. Repeatabilities for the fractions were 0.845, 0.748, 0.760, and 0.443 for LDL-1, LDL-2, LDL-3, and LDL-4, respectively (calculated from different aliquots of each sample run on different occasions, n=1410 samples in the entire study). The low repeatability for LDL-4 is probably because only 5% of LDL-C is found in this fraction. To reduce the contribution of day-to-day assay variation in the measurements, we used the sample average of all accepted values (at least 2 independent runs of each sample) for the analyses.

Genotypes
DNA was prepared from lymphocytes and used for PCR with fluorescently labeled primers from the MapPairs version 6 Linkage Screening Set (Research Genetics). Three hundred highly polymorphic microsatellite markers spaced at 15 cM were typed for each family member. PCR reactions (total volume, 5 µL) contained 100 ng DNA, 5 pmol of each fluorescently labeled primer, 0.2 U Taq polymerase, 0.25 mmol/L dNTPs, 1.5 mmol/L MgCl₂, and additional buffer components. PCR conditions included initial denaturation at 95°C for 5 minutes; 30 cycles of denaturation (94°C for 30 seconds), annealing (57°C for 30 seconds), and elongation (72°C for 30 seconds); and a final 10-minute elongation period at 72°C. PCR reactions were performed separately, and aliquots were pooled according to the multiplexed panels of MapPairs version 6 for typing with an automated DNA sequencer (Applied Biosystems Model 377 with GENESCAN and GENOTYPER programs). In addition, polymorphic markers in several known genes were included in the map as listed elsewhere.²⁹ The dinucleotide repeat polymorphism in intron 10 of the microsomal triglyceride transfer protein (MTP) gene was typed according to Austin et al.¹⁸

Statistical Methods
The level of a trait, y, for individual i (y_i) was modeled as a simple linear function as follows:

where µ is the mean of the trait in males and ß_j is the regression coefficient for covariate j. Covariates (Xij) were estimated simultaneously with the genetic effect. Covariants (Xij) effects included sex, sex-specific age and age-squared, smoking, diabetic status, use of diabetic medications, use of lipid-altering medications, and for females, postmenopausal status and use of exogenous sex hormones (ie, oral contraceptives or estrogen replacement therapy). The remaining parameters in the above formula, q_i, g_i, and e_i, represent the random deviations from µ for individual i that are attributable to a QTL in the chromosomal region being tested, residual additive genetic effects, and unmeasured environmental effects, respectively. The effects of q_i, g_i, and e_i are assumed to be uncorrelated with one another and normally distributed with mean zero and variances ²_q, ²_g, and ²_e. The likelihood of the phenotypes of the family members is assumed to follow a multivariate normal distribution with phenotypic covariance matrix , where is a function of the coefficient of relationship between individuals, the pattern of alleles that relatives share identical by descent at a specific chromosomal location, and the additive genetic, QTL, and environmental variances. The covariance among family members is modeled as follows:

where is a matrix of kinship coefficients defining the degree of relatedness for all pairs of relatives, is a matrix of identity by descent sharing at the specific chromosomal location, and I is an identity matrix. For oligogenic analyses, in which multiple QTL effects are simultaneously estimated, this model is extended to accommodate multiple ²_q, terms, each structured by its own identity by descent matrix.³⁰ Multipoint estimation of the matrices at 2 cM intervals throughout the genome was performed as described.³¹

The above-mentioned variance component method does not use a classic penetrance function and therefore falls under the class of semiparametric linkage methods. Instead of estimating the QTL allele frequencies and genotype-specific means or displacements as is done in parametric analyses, the variance component method models genetic effects by using a composite parameter, the QTL-specific heritability. Although a likelihood function based on multivariate normality is assumed, both parameter estimation and statistical inference have been shown to be robust to this assumption.^{30 31} The multilocus formulation uses an additive model and has been described elsewhere.^{30 31}

By using the computer package SOLAR,³¹ maximum likelihood methods were used to simultaneously estimate mean and variance values as well as the effects of covariates, specific QTLs, and residual additive genetic factors. Probability values for the heritabilities were obtained by likelihood ratio tests where the likelihood of a model in which the heritability is estimated is compared with the likelihood of a model in which the heritability is constrained to zero. Twice the difference in the log_e likelihoods is asymptotically distributed as a 1/2:1/2 mixture of a ² variable with 1 degree of freedom and a point mass at zero.³² A similar procedure is used to test for linkage. A model in which ²_q, is estimated is compared with a model in which it is constrained to zero. The difference in the log₁₀ likelihoods of these 2 models is equivalent to the classic LOD score of linkage analysis.

For oligogenic models in which multiple QTL effects are jointly estimated, the likelihood ratio test statistic has a more complex asymptotic distribution that continues to be a mixture of ² distributions.³² Although we report the joint oligogenic linkage results as LOD scores, it should be noted that they are not equivalent to the classic LOD score because the oligogenic likelihood ratio test has degrees of freedom that depend on the number of loci being modeled. Therefore, with the 2- and 3-locus LOD scores, we also give the associated probability value.

	Results

Top Abstract Introduction Methods Results Discussion References

 
Measurement of Cholesterol in LDL Size Fractions
We measured cholesterol concentrations in LDL size fractions in plasma samples from 470 Mexican Americans. LDL-C absorbance profiles from densitometric scans of gradient gels were fractionated into 4 subclasses as described in Methods. Several previous studies have reported a linear response of Sudan Black B staining to the amount of cholesterol in HDL and its subfractions,^{33 34 35} although there appeared to be a consistent difference in chromogenicities of LDLs and HDLs (ie, amount of stain bound per unit of cholesterol).³⁵ To test for differences in chromogenicities among LDLs, we chose pairs of samples with different LDL size distribution patterns and compared cholesterol concentrations in 4 LDL size fractions analyzed separately for each sample versus concentrations analyzed after mixing them together in a third lane. If chromogenicities are truly different among the LDL size classes, we would expect that particles in the mixed sample would have different absorbances compared with the expected absorbances derived from the 2 samples analyzed separately. Figure 1 shows the results of such comparisons for 11 pairs of samples. The cholesterol concentrations in size fractions from the samples run separately (ie, expected) showed a strong linear relationship with those observed in the mixture (r²=0.991), and the slope (0.93±0.01) and y intercept (0.4±0.1 nmol) were close to 1 and 0, respectively, which would be the expectation if all LDLs have identical chromogenicities. These results demonstrate that chromogenicities were similar not only for LDL particles of different sizes, but also for particles from different samples that likely differ in lipid composition.

View larger version (16K): [in this window] [in a new window]   Figure 1. Plot of expected cholesterol values versus observed cholesterol values for 4 LDL size fractions in each of 11 mixtures of 2 plasma samples with different LDL phenotypes. Observed values were generated as described in Methods, and the expected values for the mixture were derived by summing the observed values for the 2 samples run individually.

Heritability of Cholesterol Concentrations in LDL Size Fractions
Table 1 presents results of quantitative genetic analyses for cholesterol concentrations in each of the 4 LDL size fractions and total LDL-C in the Mexican American family members. These analyses indicated significant covariate effects for age, diabetes, use of lipid-lowering medications, and smoking for 1 or more of the LDL size fractions. Although not significant for each of the traits, the same set of covariates was included in each model for the sake of consistency. There were significant heritabilities for concentrations of each of the LDL size fractions, suggesting the existence of 1 or more genes that exert significant effects on variation in each of the 4 fractions. Total LDL-C showed a higher heritability (h²=0.541) than did LDL-C levels associated with any of the LDL size fractions.

View this table: [in this window] [in a new window]   Table 1. Mean Values, Covariate Effects, and Heritabilities From Quantitative Genetic Analyses of the Four LDL Size Fractions1

Genomic Screen for QTLs Influencing Cholesterol Concentration in LDL Size Fractions
We determined genotypes for 331 random microsatellite markers and known gene polymorphisms spaced at 15 cM intervals for 470 family members. We tested for linkage of the markers with QTLs for cholesterol concentrations in the 4 LDL size classes, using variance component methods for multipoint linkage analysis. Table 2 gives the highest multipoint LOD scores for each chromosome. The highest LOD score for the largest size fraction, LDL-1, was found on chromosome 19 at 38 cM (LOD=2.26). The highest LOD score for LDL-2 was also found on chromosome 19, but at 62 cM (LOD=1.86). The only LOD scores in the genome scan that exceeded 3, the nominal criterion for significant evidence of linkage, were found for an LDL-3 size fraction QTL. The highest LOD scores for LDL-3 were found on chromosome 3 at 244 cM (LOD=4.11) and on chromosome 4 at 126 cM (LOD=4.11). In addition, we found a LOD score of 2.92 on chromosome 6 at 162 cM. Figure 2 plots the multipoint LOD scores, computed at 2 cM intervals, for LDL-3 on chromosomes 3, 4, and 6. For LDL-4, we found no LOD scores that exceeded 1.6, and for total LDL-C, no LOD score in this survey exceeded 1.3.

View this table: [in this window] [in a new window]   Table 2. Peak Multipoint LOD Score on Each Chromosome for 4 LDL Size Fraction Traits and Total LDL-C

View larger version (15K): [in this window] [in a new window]   Figure 2. Plot of multipoint LOD scores (calculated at 2 cM intervals) for linkage of QTLs for LDL-3 concentrations to loci on chromosomes 3, 4, and 6. Distances from the p-terminus (given in centimorgans [cM]) are based on data from this study. However, locus order and interlocus distances are consistent with published values [Genome Database (http://gdbwww.gdb.org) and Genetic Location Database (http://cedar.genetics.soton.ac. uk/public-html)]. In addition to the microsatellite marker designations, the following several known genes were included in the map: GLUT2, glucose transporter 2; FABP2, fatty acid binding protein 2; LPA, apo(a) (GA polymorphism in the LPA promotor); PLG, plasminogen. Also indicated is the map location of the gene for the large subunit of MTP.

Oligogenic Linkage Analyses
We performed oligogenic linkage analyses in a step-wise fashion to test whether the QTLs we identified in the genome screen comprise a true multiple locus system to influence variation in LDL-3. The first step of the oligogenic analysis was to repeat the multipoint linkage analysis across the genome for LDL-3 size fraction QTLs, fixing the location of the major QTL on chromosome 4 (which had the highest LOD score in the initial screen). The highest LOD score from this conditional genome screen (ie, conditional on the chromosome 4 linkage) was found on chromosome 3, identifying the other major QTL that was found in the initial screen. Likelihood comparisons showed that a model that included independent effects of both QTLs was significantly better than a model that included only 1 QTL (P=0.0012). The joint 2-locus LOD score for the 2 QTLs on chromosomes 3 and 4 was 6.1 (P=2.56x10^-7). The next step of the oligogenic analysis was to simultaneously fix the locations of the 2 major QTLs on chromosomes 3 and 4 and to search for a third QTL. This conditional genome screen identified the same QTL on chromosome 6 that was found in the initial screen and the joint 3-locus LOD score was 6.5 (P=2.50x10^-7). However, likelihood comparisons showed that a model that included effects of the chromosome 6 QTL was not significantly better than a model that included only the QTLs on chromosomes 3 and 4 (P=0.064). By using this relatively simple model for linkage analysis, we found no support for the chromosome 6 QTL and thus it may have arisen by chance alone (ie, a false-positive).

The 2-locus model indicated that the QTLs on chromosomes 3 and 4 explained a substantial proportion of the total variation in LDL-3 cholesterol concentrations (22.8% and 23.0%, respectively); the residual genetic variance was not significantly different from 0. Together, the 2 QTLs (45.8%) and the covariates (5.8%) explained more than half the total trait variance.

	Discussion

Top Abstract Introduction Methods Results Discussion References

 
The present study was undertaken to identify genes that influence LDL size properties. Although LDL size phenotype has been shown in many studies to be an important CVD risk factor, little is known about the genes that influence LDL size properties. In this study, we used gradient gel electrophoresis to measure the amount of cholesterol in each of 4 different LDL size fractions, and performed mixing experiments to show that LDL particles that differed in size and composition have similar chromogenicities for cholesterol staining (Figure 1). For each of the 4 LDL size fractions, we found significant heritabilities, which ranged from 22% to 37% (Table 1), suggesting the existence of 1 or more genes that influence each of the 4 fractions. These measures of LDL size fractions were also significantly influenced by several covariates, including age and sex. The LDL size fractions are related metabolically³⁶ and they are significantly intercorrelated phenotypically (r² values for the 6 possible pairwise combinations ranged from 0.016 to 0.332 for 322 unrelated subjects in the San Antonio Family Heart Study, results not shown). Although not fully independent, the relatively modest correlations provide evidence for independent sources of variation in each of the LDL size fractions.

The simple additive variance component model that we used in the analysis ignores much of the complexity that is likely to underlie lipoprotein variation.^{37 38} Although our model can incorporate epistasis and genotypexenvironment interactions, we prefer to examine first-order effects initially. Including interactions in the variance component model increases the likelihood of type I errors, whereas ignoring true genetic interactions or misspecifying the mean effects model (ie, the covariate regression model) only reduces linkage power, by diminishing the genetic signal-to-noise ratio (ie, increases type II error). Because the variance component model is robust to misspecification errors, it is unlikely the 2 QTLs detected in this study resulted from type I error. However, these results must be confirmed in other studies.

We determined genotypes for microsatellite markers spaced at 15 cM intervals and performed a genome-wide search for genes that influence cholesterol concentration in LDL size fractions in these Mexican American families. In contrast to previous studies of LDL size distributions that used either sibling pair–based or penetrance model–based linkage analysis,^{14 15 16 17 18} we used variance component methods that do not require specification of a model of inheritance but still exploit all of the genetic and phenotypic information in the large extended pedigrees in this study. Multipoint linkage analysis identified several QTLs that influence LDL size fractions (Table 2).

We identified QTLs for the larger subclasses, LDL-1 and LDL-2, in the Mexican American families. The most likely QTL for LDL-1 (LOD=2.26) was located on chromosome 19 at 38 cM and for LDL-2 (LOD=1.86) was located on chromosome 19 at 62 cM (Table 2). Although neither of these QTLs reached LOD scores of 3.0, the LDL receptor locus (LDLR) maps at 22 cM on chromosome 19, and may represent a positional candidate gene at least for the LDL-1 size fraction. The identification of this QTL for LDL-1 in Mexican American families supports previous studies that identified linkage of LDLR with a QTL for LDL size distributions in other populations.^{14 17} It is noteworthy that we found no strong evidence for QTLs for total LDL-C concentrations in this study (the highest LOD score was 1.28 on chromosome 12). This observation highlights the value of measuring the cholesterol concentrations of individual LDL size fractions.

The strongest evidence for linkage (ie, LOD scores >3.0) was found for a QTL for cholesterol concentrations in LDL-3, a fraction containing small LDL particles. Two QTLs for LDL-3 were found on chromosome 3 at 244 cM (LOD=4.11) and chromosome 4 at 126 cM (LOD=4.11) (Figure 2). We also identified a QTL for LDL-3 on chromosome 6 with a LOD score only slightly smaller than 3.0 (LOD=2.92). We do not yet know the identity of the genes that are responsible for the QTLs. Of course, many genes will map to any region identified by linkage analysis (some known and many not yet known). For example, it has been estimated that each centimorgan of the human genome may contain an average of as many as 25 genes.³⁹ Nevertheless, it may prove fruitful to identify potential candidates for the QTLs we have detected. These positional candidate genes represent hypotheses to be tested in future studies.

One gene occurring in the approximate vicinity of the major LDL-3 QTL on chromosome 3 is the locus encoding apoD (APOD), which is located at 3q27-qter. The role of apoD in lipid metabolism is poorly understood,^{40 41 42 43} but several studies have reported a polymorphism in the apoD locus to be associated with diabetes and obesity.^{44 45}

The other major QTL for LDL-3 is located in a region of chromosome 4 that contains the gene for the large subunit of MTP (microsomal triglyceride transfer protein). MTP is a heterodimeric protein in the endoplasmic reticulum that is required for assembly of VLDL in the liver and chylomicrons in the intestine. Mutations in the MTP gene abolish production of apoB-containing lipoproteins, resulting in the rare recessive genetic disorder abetalipoproteinemia.^{46 47} Austin et al¹⁸ recently reported evidence for linkage of the MTP gene with a QTL for plasma triglyceride levels (P=0.03), but not for LDL peak particle diameters (P=0.4) in a study of dizygotic female twins. In addition, Karpe et al⁴⁸ recently reported that a polymorphism in the MTP 5' flanking region (T/G at position -493) is significantly associated with LDL-C concentrations in Swedish males. To determine the location of MTP relative to the LDL-3 QTL on chromosome 4, we typed a dinucleotide repeat polymorphism in intron 10 of the MTP gene⁴⁹ in samples from the San Antonio Family Heart Study. The MTP gene mapped between the markers D4S1647 and D4S2623 on chromosome 4 at 118 cM (Figure 2), 8 cM from the position on chromosome 4 with the highest multipoint LOD score for the LDL-3 QTL. Thus, the MTP gene remains a strong positional candidate gene for association studies to test the effects of MTP genetic variation on LDL size fractions.

The QTL linkage signal on chromosome 6 did not significantly improve the oligogenic LOD score in this study. However, we cannot exclude the existence of a QTL in this region. The putative QTL, mapping to 162 cM, is near several known genes, including LPA [encodes the apo(a) protein] at 154 cM (Figure 2C) and SOD2 (encodes manganese superoxide dismutase) at 6q25.2.⁵⁰ Thus, our results may support those of Rotter et al¹⁷ who previously reported linkage of a QTL for LDL peak particle size to SOD2.¹⁷

These results suggest that at least 2 major QTLs influence concentration of cholesterol in small LDLs in this population. Oligogenic analyses indicated that incorporating a third locus in the model did not significantly improve the joint LOD score. Our results strongly support the hypothesis that concentrations of small LDLs are largely determined by a few major genes, rather than by a large number of loci that exert only small additive effects. In addition, these findings are consistent with earlier reports that used complex segregation analyses to detect effects of major genes on LDL size. Although we have suggested positional candidate genes in the regions of chromosomes 3 and 4 that contain the QTLs, the definitive identification of the genes that are truly responsible for these linkages awaits future studies in this and other populations.

	Acknowledgments

 
This work was supported by PO1 HL45522 from the NIH. The authors gratefully acknowledge Perry H. Moore, Jr, Mahmood Poushesh, Wendy R. Shelledy, and Jane F. VandeBerg for their assistance with LDL phenotyping, Shifra Birnbaum, Marjorie Britten, Teresa Cantu, Roy Garcia, and Patricia Powers for their help with genotyping, and James M. Bridges and Thomas D. Dyer for their help with data management and genetic analyses.

Received June 23, 1998; accepted October 21, 1998.

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