Skip to main content
Download PDF
  • Research article
  • Open access
  • Published:

Consistency of genetic inheritance mode and heritability patterns of triglyceride vs. high density lipoprotein cholesterol ratio in two Taiwanese family samples

BMC Genetics volume 4, Article number: 7 (2003) Cite this article

Abstract

Background

Triglyceride/HDL cholesterol ratio (TG/HDL-C) is considered as a risk factor for cardiovascular events. Genetic components were important in controlling the variation in western countries. But the mode of inheritance and family aggregation patterns were still unknown among Asian-Pacific countries. This study, based on families recruited from community and hospital, is aimed to investigate the mode of inheritance, heritability and shared environmental factors in controlling TG/HDL-C.

Results

Two populations, one from community-based families (n = 988, 894 parent-offspring and 453 sibling pairs) and the other from hospital-based families (n = 1313, 76 parent-offspring and 52 sibling pairs) were sampled. The population in hospital-based families had higher mean age values than community-based families (54.7 vs. 34.0). Logarithmic transformed TG/ HDL-C values, after adjusted by age, gender and body mass index, were for genetic analyses. Significant parent-offspring and sibling correlations were also found in both samples. The parent-offspring correlation coefficient was higher in the hospital-based families than in the community-based families. Genetic heritability was higher in community-based families (0.338 ± 0.114, p = 0.002), but the common shared environmental factor was higher in hospital-based families (0.203 ± 0.042, p < 0.001). Commingling analyses showed that more than one-component distribution models were the best-fit models to explain the variance in both populations. Complex segregation analysis by regressive models revealed that in both samples the best-fit model of TG/HDL-C was the model of environmental effects plus familial correlation, in which significant parent-offspring and sibling correlations were demonstrated. Models of major gene effects were rejected in both samples.

Conclusion

Variations of TG/HDL-C in the normal ranges were likely to be influenced by multiple factors, including environmental and genetic components. Higher genetic factors were proved in younger community-based families than in older hospital-based families.

Background

Triglyceride and high density lipoprotein (HDL) cholesterol are important components in insulin resistance syndrome and are considered as risk factors for cardiovascular diseases [1–4]. Atherogenic dyslipidemia, including hypertriglyceridemia and low HDL cholesterol levels are the targets for primary prevention by lifestyle intervention in Adult Treat Panel III guidelines [5]. The ratio of triglyceride vs. HDL cholesterol (TG/HDL-C) can be considered a single phenotype trait for cardiovascular risk [6, 7]. TG/HDL-C is a simple, readily calculated measurement about atherogenic plasma lipoproteins, and related to lipoprotein particle sizes [8]. Shared gene and environmental factors were reported to influence HDL cholesterol and triglyceride levels simultaneously [9]. Also, pleiotrophic effects on low HDL cholesterol and high triglyceride were evident among families [7, 9, 10]. Extensive genomic scanning study on this combined trait was also reported to linkage with chromosome 7 [11].

Many factors were associated with HDL cholesterol and triglyceride levels, such as age, gender, lifestyle activities, and obesity. But the proportion of TG/HDL-C variances explained by known factors was small. Genetic factors was considered as a significant role in determining serum levels [12]. Even linkage results of TG/HDL-C were reported in Framingham families [11], there were inconsistent study results about the mode of inheritance of TG/HDL-C, especially for HDL cholesterol phenotype. The reasons of inconsistency were due to different ascertainment strategies and study population characteristics [13–21]. In brief, most studies on the mode of inheritance of HDL cholesterol demonstrated major gene effects. Amos et al. showed major gene effect existed in a multiple generation pedigree family [13]. Also, Borecki et al demonstrated that HDL cholesterol was controlled by the major gene effect [22]. The allele frequency of autonomic recessive gene in the Jerusalem families was 0.06 [23]. Coresh et al. [17] proved one autosomal dominant gene with allele frequency of 0.25. Mahaney et al. also demonstrated in Mexican American population, HDL cholesterol was controlled by a major dominant recessive gene [20]. In the other side, Cupples et al. cannot demonstrate the major gene effect in Berkeley families [18]. Moll et al. [24] and Prenger et al. [21] cannot define the major gene effect in HDL cholesterol levels among high risk probands of coronary heart disease. The controversial results about the mode of inheritance of HDL cholesterol implied that complex pathogenesis mechanisms existed. Furthermore, genetic factors controlling TG/HDL-C ratio were more complicated. Although several disorders of dyslipidemia were identified by candidate-gene studies and positional cloning experiments, there remain many problems still remain to be resolved.

Different mechanisms exist between special monogenic hyperlipidemia and general population. For example, more than 600 low density lipoprotein (LDL) receptor mutations underlie familial hypercholesterolemia type, but no single LDL receptor single nucleotide polymorphism has been associated with variation of plasma lipoproteins in the general population [12]. Also, it is still unclear whether common genetic variant can help to predict risk of CHD in general population. So we should define homogeneous and low-risk general population samples to elucidate the genetic roles in controlling TG/HDL-C. This study, based on two samples, one from rather young families in community-based population, another from older adult subjects undertaking health screening in hospital-based population, was designed to estimate the roles of genetic components and the mode of inheritance in TG/HDL-C among Taiwanese.

Results

Hospital-based family members had higher mean age values than community-based families (34.0 vs. 54.7, respectively, Table 1). Also, there were higher total cholesterol, triglyceride, HDL cholesterol, LDL cholesterol and lower TG/HDL-C values in hospital-based families than those in community based families, with smaller standard deviation values (Table 1). High values of skewness and kurtosis were found in both of triglyceride and TG/HDL-C ratio, and the logarithm transformed TG/HDL-C had near zero values of skewness and kurtosis, indicating compatible with normal distribution. We adjusted logarithm transformed TG/HDL-C in linear model by age, gender and body mass index, and residual values were for further genetic analyses. The explained variability of TG/HDL-C by gender, age, interaction of age and gender, and body mass index were 24.1% in the community-based sample and 19.4% in hospital-based one. Age had significant effects only among younger community families, but not among older hospital-based families. Body mass index had significant positive effects on TG/HDL-C in both samples (Table 2).

Table 1 Various lipid profiles in the two study population samples
Full size table
Table 2 Estimated parameter, standard error and explained variability in the linear regression models for logarithm transformed TG/HDL ratio values in two samples.
Full size table

The commingling distributions of two populations were compatible with more than one-component normal distribution (Table 3). In younger community-based families, the best-fit model of explaining TG/HDL-C variance was the 2-component model, whose component means, variances, and proportions were (-0.256, 0.754), (0.609, 1.389), and (74.7%, 25.3%), respectively. The χ2 for comparing the 2-component with 1-component distribution was 6.46 (degree of freedom [df] = 3, p = 0.091). For hospital-based families, the best-fit model was the 3-component model (χ2 = 3.27, df = 3, p = 0.353, compared with 4-component model). We found in older hospital-based families, there were wider range of mean estimates and lower proportions of high mean components than the younger community-based families.

Table 3 Commingling analysis of adjusted log TG/HDL ratio in two population samples.
Full size table

We found that all correlation coefficient values in hospital-based relative pairs were higher than those in community, and the parent-offspring coefficient in hospital was significantly higher than one in community(0.420 vs. 0.215, P = 0.046) (Table 4). The range of parent-offspring and sibling correlations were 0.22 to 0.42, highest in hospital-based families. Also, in the hospital families, the spouse correlation was 0.142, which implied possible assortive mating or common shared household factors in older hospital-based families. But in the community-based sample, the spouse correlation was only 0.044, without significant difference from zero. To explore the possible effects on examination dates, we re-arranged the dataset according to examination dates, and found all spouses in the hospital-based sample were recruited in the same day, and almost spouses (90%) in the community-based sample were recruited within two weeks. The spouse correlations in the remaining different exam date were similar as one in the same date (0.044), so the examination dates did not affect apparently spouse correlation values.

Table 4 Familial correlation coefficients in residual log TG/HDL values in the two populations, after adjusting with age and gender effects.
Full size table

The heritability and common shared household estimates were presented in Table 5. After incorporating common household effects in the model, the heritability was still significant in younger community-based population (0.338 ± 0.114, P = 0.002), but did not reach significant level in older hospital-based families (0.213 ± 0.164, P = 0.106). The estimated common shared household effect was significant only in the hospital-based families (0.203 ± 0.042, P < 0.001).

Table 5 Estimated heritability and common shared household effects of logarithm TG/HDL ratio in two study samples
Full size table

The estimated parameters and corresponding models by complex segregation analyses of TG/HDL-C in both populations were presented in Table 6. The results were consistent in both populations. Comparing with the general model, we found all p values were less than 0.05, and AIC values were lowest in the general models (1276.03 in community and 2645.11 in hospital). If excluding the general model, we found that in both populations the environmental plus familial correlation model had the lowest AIC values (1278.71 in community and 2651.51 in hospital). The major gene effects model had higher AIC values than the environmental model. So we concluded that the environmental model, compared with the major gene effects model, was the best-fit model in controlling TG/HDL-C. The estimated spouse correlation value was higher in hospital-based families than that in community (0.292 vs. 0.028), and the parent-offspring and sibling correlations were both high to 0.6.

Table 6 Logarithm triglyceride vs. HDL cholesterol ratio: Class D regressive models, conditional on proband phenotypes
Full size table

Discussion

This study clearly demonstrated that consistency of the mode of inheritance and genetic components of TG/HDL-C from two different populations in Taiwan. We found that the environmental model, compared with major gene effects model, was the more parsimonious one to explain the mode of inheritance of TG/HDL-C. There were relative importance of genetic heritability and shared household effects in both populations. In older hospital-based families, shared common household effects and high spouse correlation were found significantly, but in younger community-based families, genetic heritability seemed to play more significant roles in TG/HDL-C variations. Spouse correlations showed an environmental accent in pairs who spent probably longer time together.

There were rather healthy subjects in both samples. We have excluded cases with history of myocardial infarction and stroke, and extreme high levels of triglyceride in both samples. So, the results are particularly relevant for a population at low risk for atherosclerosis. Our findings were consistent with those of previous familial correlation and commingling studies which demonstrated that more than one component was needed to explain the distribution of HDL cholesterol levels [25]. The finding of multiple distributions is compatible with a major gene hypothesis; however, commingling may also arise through other causes. Thus, we must further apply complex segregation analysis to determine whether these major effects segregated in families according to Mendelian expectations.

In the complex segregation analyses results, we cannot define the major gene effects in controlling TG/HDL-C. The results were compatible with previous high risk population studies [18, 19]. The reason is following. First, age-differential gene effect might be responsible for TG/HDL-C variance in different populations. Second, the trait can be influenced by various environmental and lifestyle risk factors. For examples, physical activity and exercise have proved to effect on triglyceride and HDL cholesterol levels, and not included in the analyses [26, 27]. Third, there are gene-gene and gene environmental interaction that cannot be resolved by segregation analyses. Fortunately, even segregation analysis is assumed as one-locus model, the power of detecting major gene in multiple loci model is still significantly high [28, 29]. Finally, the characteristics of study population in Taiwanese, a low risk of coronary heart disease, might have different patterns in risk factor profiles [30]. Although one gene locus was identified to control TG/HDL-C trait in a genome-wide scan in Framingham study [11], their population seem to be younger than our hospital-based families. Thus, lack of a major gene in the population, as was the case in this study, may not exclude the possibility that there are major genes that will influence TG/HDL-C levels in general population.

Especially, the age distribution differed much in these both study populations. Older hospital-based families had higher lipid levels and lesser standard deviation of lipid levels, such as cholesterol, triglyceride and lipoproteins, than younger community-based families. The commingling results also implied the same evidences with wider means and components estimations in the hospital-based sample than the community-based one. Further, we test if the distributions of logarithm TG/HDL-C levels of two samples were different. We found the significance level of the Student t test statistics was 0.05, just rejecting the homogenous of two sample means. So we consider that there existed heterogeneity between two samples. The possible explanations of heterogeneity between two samples were following. The hospital-based participants were older than the community-based subjects and the lipid profiles were higher in hospital group. Poor lifestyle patterns, such as high fat diet and physical inactivity, might affect lipid levels greater in older than in younger persons. The genetic heritability was significant only in younger community-based families, but the shared household effects and spouse correlation were significant only in older hospital-based population. This finding implied that common household factors might play important roles in TG/HDL-C values in older age [31–34]. According, family-based intervention programs, such as dietary modification and exercise promotion, are important and suitable for dyslipidemia intervention [35, 36].

The estimates of component frequency in commingling analyses were different from the environmental factor allele frequencies. The possible explanation is due to different parameter assumptions in both analyses. We still can use the estimated parameters from commingling components as initial values for segregation analysis [37].

It remains a difficult task to dissect the genetic control of TG/HDL-C values despite of many candidate genes and genome-wide linkage analyses results [11, 38–42]. The reasons were due to quantitative estimation of heritability values vary due to inter-generational difference or age effects, with relatively low genetic heritability in older adult families. Also, the population heterogeneity and phenocopy are also difficult problems to resolve. Anyway, in this study based on two different populations, we consider the results can extend to general adult Taiwanese population and provide clues for proposing prevention strategy for dyslipidemia.

The limitation of this study was two-folds. First, there were a disproportional low number of sibling pairs in hospital-based families. We can still find comparably significant sibling correlations in both samples, and it might be due to significant genetic components in TG/HDL-C. Second, complex mathematic algorithms in segregation analyses made researchers difficult to explain the results [42]. To incorporate genotype markers would make the results more convincing.

Conclusions

Consistency in mode of inheritance of TG/HDL-C by environmental factors and age-differential genetic heritability were clearly defined in two Taiwanese populations.

Methods

Subjects

There were two samples recruited into the study. One is from community-based subjects. It started in 1997 and was designed to recruit adolescent probands from students in the only junior high school in the community. At first, a total of 1063 students (response rate 94.6%) agreed to participate in a general health check-up after informed consent was obtained. They underwent examinations for anthropometric measures, blood pressure, and lipid profiles. The selected youths were stratified into two groups on the basis of seven measures, including total cholesterol, triglyceride, LDL cholesterol, body mass index, systolic pressure, diastolic pressure, and HDL cholesterol. Probands were then selected through stratified random sampling (n = 368). After obtaining informed consent from probands' family members, the same measures were performed for each family member. Only first-degree relatives were included for this genetic study. Total 988 subjects were included.

Another population was from persons who undertook health-screening examination in one tertiary hospital. We collected the subjects from the family pairs, such as spouse, parent-offspring, and sibling relatives when they came to hospital at the same time for health examination purpose. Every subject filed out one questionnaire and received physical examination when he/she admitted into the hospital. Detailed medical history, smoking and drinking history were taken. Also, family history of parental medical diseases was also collected. Physical examination and 12-lead electrocardiogram were performed concurrently. We also excluded significant cardiovascular morbidity, such as coronary heart disease and stroke history in our study population. The subjects treated with cholesterol-lowering agents were also excluded from our analyses. From Aug 1998 to Sep, 1999, total 4,331 subjects completed the health examination. Total 1,313 subjects were included into this family study due to concomitant family member involved in the health examination.

Laboratory tests

Blood sample was collected in each participant at the fasting status at least 12 hours. Serum total cholesterol levels were measured using the CHOD-PAP method (Boehringer Mannheim, Germany). HDL-C was measured following precipitation of apolipoprotein B-containing lipoproteins with phosphotungstic acid and magnesium ions (Boehringer Mannheim, Germany) [43]. Triglyceride concentrations were measured by the GPO-DAOS method (Wako Co., Japan). All of the lipids mentioned above were measured using a Hitachi 7450 automated analyzer (Hitachi, Japan). LDL-C concentrations were calculated using the Friedewald formula [44]. All the measures of both samples were carried out in the single hospital. The coefficient of variation was 5%.

Statistical Analyses

All lipid profiles were described by their mean, variance, skewness and kurtosis. Because of skewed distribution of shifting to right in the triglyceride and TG/HDL-C values, natural logarithm transformation was performed to get normalized distribution. Linear regression model was used to get the residual lipid values, after adjusting with age, gender, interaction of age and gender, body mass index effects, and the residual values plus sample means were used for further genetic analyses. Also, we excluded the cases of serum triglyceride higher than 400 mg/dL to exclude extreme outliers in our study population.

Commingling analysis, familial correlation coefficients and heritability analyses

Commingling analysis of various lipid profiles was performed using the ADMIX program [45] to test whether the data were best described by one, two, or more component distributions. The parameters for each component's mean, variance, and proportion were estimated by the maximum likelihood method. Hypothesis testing for nested models was carried out with the likelihood ratio test. The correlations of various lipid profiles between spouse, parent-offspring, and siblings were estimated by using the FCOR program in SAGE [46]. We tested if the correlation coefficients were different from zero and tested if the two family coefficients between two samples by the methods of Snedecor and Cochran [47]. To estimating the shared environmental effects by inclusion of household effects, we used the variance component models to evaluate the heritability and common shared environmental factors, implemented in SOLAR software [48].

Complex Segregation Analysis

Segregation analyses of HDL-C and TG/HDL-C were conducted using regressive models as implemented in the REGC program in SAGE [46]. These models assume that variation of HDL-C and TG/HDL-C concentration among family members is the result of a major gene effect, with the residual variation reflecting both familial correlations and individual variation. The presence of a major gene is assessed by allowing two factors or alleles (A and B) at a single locus, resulting in three 'ousiotypes' (AA, AB, BB) in individuals. The means of HDL-C and TG/HDL-C for each ousiotype is denoted μAA, μAB, μBB, with one common variance of σ2. The frequencies of allele A and B are denoted qA and (1 - qA), respectively. The distribution of types in the population is assumed to be in Hardy-Weinberg equilibrium. Individuals of each type are assumed to transmit allele A to their offspring with transmission probabilities τAA, τAB and τBB, respectively. Residual familial resemblance not explained by this major locus is modeled by familial correlations. The correlation between spouses, parent and offspring, mother and offspring, father and offspring, and between siblings, are denoted ρMF, ρPO, ρMO, ρFO, and ρSS, respectively. For this study, we adopted class D regressive models, in which residual sib-sib correlations are equal among all sibs of common parentage and can be due to any cause. In the hospital-based family population, there were more numbers of spouse than parental-offspring pairs, so initially we tested if familial correlation numbers can be reduced from four (ρMF, ρPO, ρMO, and ρSS) to two (ρMF, ρPO = ρMO = ρFO = ρSS). If ρPO is held equal to ρSS, these models have been shown to be mathematically and numerically equivalent to the conventional mixed model of inheritance in nuclear families [49]. In community-based families, we still adopt 4-parameter familial correlation model. The analyses started with fitting a general model, in which all parameters were allowed to be estimated. Then we compared the general model with various submodels in which certain parameters were restricted to specific values. Under a Mendelian model, the transmission probabilities, i.e., τAA, τAB and τBB, were held equal to Mendelian expectations of 1, 0.5, and 0. A nontransmitted environmental effect model predicts that the probability that an individual has one ousiotype or another is independent of both the person's generation and the ousiotypes of his/her parents. For the environmental model in this study, each of the transmission probabilities is taken to be equal to the factor frequency, i.e., τAA = τAB = τBB = qA. Both the Mendelian and environmental models can allow for residual familial correlations. A pure polygenic model assumes no major gene effect, so gene frequency and transmission probabilities are all fixed to one. The fit of hierarchical models is compared with the likelihood ratio test, calculated as -2 of the difference between the ln likelihood of the models being compared. The likelihood ratio value follows a chi-square distribution, with degrees of freedom equal to the difference between the models in the number of parameters estimated. Among nonhierarchical models, the most parsimonious model is that with the lowest values of Akaike's information criterion (AIC=-2 ln likelihood +2 [number of estimated parameters]) [50].

References

  1. Austin MA, Hokanson JE, Edwards KL: Hypertriglyceridemia as a cardiovascular risk factor. Am J Cardiol. 1998, 81: 7B-12B. 10.1016/S0002-9149(98)00031-9.

    Article  CAS  PubMed  Google Scholar 

  2. Kaplan NM: The deadly quartet: upper-body adiposity, glucose intolerance, hypertriglyceridemia and hypertension. Arch Intern Med. 1989, 149: 1514-1520. 10.1001/archinte.149.7.1514.

    Article  CAS  PubMed  Google Scholar 

  3. Chien KL, Hsu HC, Su TC, Hwang YL, Chang WT, Sung FC, Lin RS, Lee YT: Components of Insulin Resistance Syndrome in a Community-based Population Assessed by Log-Linear Models. Journal of Formosan Medical Association. 2001, 100: 587-591.

    CAS  Google Scholar 

  4. Assmann G, Schulte H: Relation of high-density lipoprotein cholesterol and triglycerides to incidence of atheosclerotic coronary artery disease (the PROCAM experience). Am J Cardiol. 1992, 70: 733-737.

    Article  CAS  PubMed  Google Scholar 

  5. Executive summary of the third report of the National Cholesterol Education Program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult treatment panel III). J A M A. 2001, 285: 2486-2497.

  6. Assmann G, Schulte H, von Eckardstein A: Hypertriglyceridemia and elevated lipoprotein(a) are risk factors for major coronary events in middle-aged men. American Journal of Cardiology. 1996, 77: 1179-1184. 10.1016/S0002-9149(96)00159-2.

    Article  CAS  PubMed  Google Scholar 

  7. Austin MA, King MC, Vranizan KM, Krauss RM: Atherogenic lipoprotein phenotype. A proposed genetic marker for coronary heart disease risk. Circulation. 1990, 82: 495-506.

    Article  CAS  PubMed  Google Scholar 

  8. Dobiasova M, Frohlich J: The plasma parameter log (TG/HDL-C) as an atherogenic index: correlation with lipoprotein particle size and esterification rate in apoB-lipoprotein-depleted plasma (FER(HDL)). Clin Biochem. 2001, 34: 583-588. 10.1016/S0009-9120(01)00263-6.

    Article  CAS  PubMed  Google Scholar 

  9. Mahaney MC, Blangero J, Comuzzie AG, VandeBerg JL, Stern MP, MacCluer JW: Plasma HDL cholesterol, triglycerides, and adiposity. A quantitative genetic test of the conjoint trait hypothesis in the San Antonio Family Heart Study. Circulation. 1995, 92: 3240-3248.

    Article  CAS  PubMed  Google Scholar 

  10. Edwards KL, Mahaney MC, Motulsky AG, Austin MA: Pleiotropic genetic effects on LDL size, plasma triglyceride, and HDL cholesterol in families. Arterioscler Thromb Vasc Biol. 1999, 19: 2456-2464.

    Article  CAS  PubMed  Google Scholar 

  11. Shearman AM, Ordovas JM, Cupples LA, Schaefer EJ, Harmon MD, Shao Y, Keen JD, DeStefano AL, Joost O, Wilson PW: Evidence for a gene influencing the TG/HDL-C ratio on chromosome 7q32.3-qter: a genome-wide scan in the Framingham study. Human Molecular Genetics. 2000, 9: 1315-1320. 10.1093/hmg/9.9.1315.

    Article  CAS  PubMed  Google Scholar 

  12. Hegele RA: Monogenic dyslipidemias: window on determinants of plasma lipoprotein metabolism. Am J Hum Genet. 2001, 69: 1161-1177. 10.1086/324647.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  13. Amos CI, Elston RC, Srinivasan SR, Wilson AF, Cresanta JL, Ward LJ, Berenson GS: Linkage and segregation analyses of apolipoproteins A1 and B, and lipoprotein cholesterol levels in a large pedigree with excess coronary heart disease: the Bogalusa Heart Study. Genetic Epidemiol. 1987, 4: 115-128.

    Article  CAS  Google Scholar 

  14. Atwood LD, Kammerer CM, Mitchell BD: Exploring the HDL likelihood surface. Genet Epidemiol. 1993, 10: 641-645.

    Article  CAS  PubMed  Google Scholar 

  15. Borecki IB, Rao DC, Third JL, Laskarzewski PM, Glueck CJ: A major gene for primary hypoalphalipoproteinemia. Am J Hum Genet. 1986, 38: 373-381.

    PubMed Central  CAS  PubMed  Google Scholar 

  16. Bucher KD, Kaplan EB, Namboodiri KK, Glueck CJ, Laskarzewski P, Rifkind BM: Segregation analysis of low levels of high-density lipoprotein cholesterol in the collaborative Lipid Research Clinics Program Family Study. Am J Hum Genet. 1987, 40: 489-502.

    PubMed Central  CAS  PubMed  Google Scholar 

  17. Coresh J, Beaty TH, Prenger VL, Xu J, Kwiterovich PO: Segregation analysis of HDL3-C levels in families of patients undergoing coronary arteriography at an early age. Arteriosclerosis, Thrombosis & Vascular Biology. 1995, 15: 1307-1313.

    Article  CAS  Google Scholar 

  18. Cupples LA, Myers RH: Segregation analysis for high density lipoprotein in the Berkeley data. Genet Epidemiol. 1993, 10: 629-634.

    Article  CAS  PubMed  Google Scholar 

  19. Juo SHH, Beaty TH, Duffy DL, Coresh J, Kwiterovich PO: No common major gene for apolipoprotein A-I and HDL3-C levels:Evidence from bivariate segregation analysis. Genetic Epidemiology. 1999, 16 (1): 54-68. 10.1002/(SICI)1098-2272(1999)16:1<54::AID-GEPI5>3.3.CO;2-J.

    Article  CAS  PubMed  Google Scholar 

  20. Mahaney MC, Blangero J, Rainwater DL, Comuzzie AG, VandeBerg JL, Stern MP, Maccluer JW, Hixson JE: A major locus influencing plasma high-density lipoprotein cholesterol levels in the San Antonio Family Heart Study. Segregation and linkage analyses. Arteriosclerosis, Thrombosis & Vascular Biology. 1995, 15: 1730-1739.

    Article  CAS  Google Scholar 

  21. Prenger VL, Beaty TH, Kwiterovich PO: Genetic determination of high-density lipoprotein-cholesterol and apolipoprotein A-1 plasma levels in a family study of cardiac catheterization patients. Am J Hum Genet. 1992, 51: 1047-1057.

    PubMed Central  CAS  PubMed  Google Scholar 

  22. Borecki IB, Rao DC, Third JL, Laskarzewski PM, Glueck CJ: A major gene for primary hypoalphalipoproteinemia. Am J Hum Genet. 1986, 38: 373-381.

    PubMed Central  CAS  PubMed  Google Scholar 

  23. Friedlander Y, Kark JD, Cohen T, Eisenberg S, Stein Y: Admixture analysis of high density lipoprotein cholesterol distribution in a Jerusalem population sample. Clin Genet. 1983, 24: 117-127.

    Article  CAS  PubMed  Google Scholar 

  24. Moll PP, Sing CF, Williams RR, Mao SJ, Kottke BA: The genetic determination of plasma apolipoprotein A-I levels measured by radioimmunoassay: a study of high-risk pedigrees. Am J Hum Genet. 1986, 38: 361-372.

    PubMed Central  CAS  PubMed  Google Scholar 

  25. Friedlander Y, Kark JD, Cohen T, Stein Y: Heterogeneity in multifactorial inheritance of plasma lipids and liproteins in ethnically diverse families in Jerusalem. Clin Genet. 1986, 3: 95-112.

    CAS  Google Scholar 

  26. Forde OH, Thelle DS, Arnesen E, Mjos OD: Distribution of high density lipoprotein cholesterol according to relative body weight, cigarette smoking and leisure time physical activity. The Cardiovascular Disease Study in Finnmark 1977. Acta Medica Scandinavica. 1986, 219: 167-171.

    Article  CAS  PubMed  Google Scholar 

  27. Krauss RM, Deckelbaum RJ, Ernst N, Fisher E, Howard BV, Knopp RH, Kotchen T, Lichtenstein AH, McGill HC, Pearson TA: Dietary guidelines for healthy American adults. A statement for health professionals from the Nutrition Committee, American Heart Association. Circulation. 1996, 94: 1795-1800.

    Article  CAS  PubMed  Google Scholar 

  28. Dizier MH, Bonaiti-Pellie C, Clerget-Darpoux F: Conclusions of segregation analysis for family data generated under two-locus models. Am J Hum Genet. 1993, 53: 1338-1346.

    PubMed Central  CAS  PubMed  Google Scholar 

  29. Go RC, Elston RC, Kaplan EB: Efficiency and robustness of pedigree segregation analysis. Am J Hum Genet. 1978, 30: 28-37.

    PubMed Central  CAS  PubMed  Google Scholar 

  30. Lee YT, Lin RS, Sung FC, Yang CY, Chien KL, Chen WJ, Su TC, Hsu HC, Huang YC: Chin-Shan Community Cardiovascular Cohort in Taiwan: baseline data and five-year follow-up morbidity and mortality. J Clin Epidemiol. 2000, 53: 836-846.

    Google Scholar 

  31. Perusse L, Rice T, Despres JP, Bergeron J, Province MA, Gagnon J, Leon AS, Rao DC, Skinner JS, Wilmore JH: Familial resemblance of plasma lipids, lipoproteins and postheparin lipoprotein and hepatic lipases in the HERITAGE Family Study. Arteriosclerosis, Thrombosis & Vascular Biology. 1997, 17: 3263-3269.

    Article  CAS  Google Scholar 

  32. Namboodiri KK, Green PP, Kaplan EB, Tyroler HA, Morrison JA, Chase GA, Elston RC, Rifkind BM, Glueck CJ: Family aggregation of high density lipoprotein cholesterol. Collaborative lipid research clinics program family study. Arteriosclerosis. 1983, 3: 616-626.

    Article  CAS  PubMed  Google Scholar 

  33. Rice T, Province M, Perusse L, Bouchard C, Rao DC: Cross-trait familial resemblance for body fat and blood pressure: familial correlations in the Quebec Family Study. Am J Hum Genet. 1994, 55: 1019-1029.

    PubMed Central  CAS  PubMed  Google Scholar 

  34. Ward R: familial aggregation and genetic epidemiology of blood pressure. In Hypertension : pathophysiology, diagnosis and management. Edited by: Laragh JH, Brenner BM. 1990, 81-100.

    Google Scholar 

  35. Knutsen SF, Knutsen R: The Tromso Survey: the Family Intervention study – the effect of intervention on some coronary risk factors and dietary habits, a 6-year follow-up. Preventive Medicine. 1991, 20: 197-212.

    Article  CAS  PubMed  Google Scholar 

  36. Perry CL, Griffin G, Murray DM: Assessing needs for youth health promotion. Preventive Medicine. 1985, 14: 379-393.

    Article  CAS  PubMed  Google Scholar 

  37. Kwon JM, Boehnke M, Burns TL, Moll PP: Commingling and segregation analyses: comparison of results from a simulation study of a quantitative trait. Genetic Epidemiol. 1990, 7: 57-68.

    Article  CAS  Google Scholar 

  38. Akita H, Chiba H, Tsuji M, Hui SP, Takahashi Y, Matsuno K, Kobayashi K: Evaluation of G-to-A substitution in the apolipoprotein A-I gene promoter as a determinant of high-density lipoprotein cholesterol level in subjects with and without cholesteryl ester transfer protein deficiency. Hum Genet. 1995, 96: 521-526.

    Article  CAS  PubMed  Google Scholar 

  39. Barre DE, Guerra R, Verstraete R, Wang Z, Grundy SM, Cohen JC: Genetic analysis of a polymorphism in the human apolipoprotein A-I gene promoter: effect on plasma HDL-cholesterol levels. J Lipid Res. 1994, 35: 1292-1296.

    CAS  PubMed  Google Scholar 

  40. Hegele RA, Harris SB, Brunt JH, Young TK, Hanley AJ, Zinman B, Connelly PW: Absence of association between genetic variation in the LIPC gene promoter and plasma lipoproteins in three Canadian populations. Atherosclerosis. 1999, 146: 153-160. 10.1016/S0021-9150(99)00113-6.

    Article  CAS  PubMed  Google Scholar 

  41. Minnich A, DeLangavant G, Lavigne J, Roederer G, Lussier-Cacan S, Davignon J: G -> A substitution at position -75 of the apolipoprotein A-I gene promoter. Evidence against a direct effect on HDL cholesterol levels. Arteriosclerosis, Thrombosis & Vascular Biology. 1995, 15: 1740-1745.

    Article  CAS  Google Scholar 

  42. Jarvik GP: Complex segregation analyses: uses and limitations. Am J Hum Genet. 1998, 63: 942-946. 10.1086/302075.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  43. Lopes-Virella M, Stone P, Ellis S, Colwell JA: Cholesterol determination in high-density lipoproteins separated by three different methods. Clin Chem. 1977, 23: 382-

    Google Scholar 

  44. Friedewald WT, Levy RI, Fredrickson DS: Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972, 18: 499-502.

    CAS  PubMed  Google Scholar 

  45. Cloninger CR, von Knorring L, Oreland L: Pentametric distribution of platelet monoamine oxidase activity. Psychiatry Research. 1985, 15: 133-143. 10.1016/0165-1781(85)90049-6.

    Article  CAS  PubMed  Google Scholar 

  46. S.A.G.E : Statistical analysis for genetic epidemiology, Release 3.1. Computer program package available from the Department of Epidemiology and Biostatistics, Rammelkamp Center for Education and Research, MetroHelath Campus, Case Western Reserve University, Cleveland. 1997

    Google Scholar 

  47. Snedecor GW, Cochran WG: Statistical methods. 1980, Ames, Iowa, USA: The Iowa State University Press, 7

    Google Scholar 

  48. Almasy L, Blangero J: Multipoint quantitative-trait linkage analysis in general pedigrees. Am J Hum Genet. 1998, 62: 1198-1211. 10.1086/301844.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  49. Demenais FM, Bonney GE: Equivalence of the mixed and regressive models for genetic analysis. I. Continuous traits. Genetic Epidemiol. 1989, 6: 597-617.

    Article  CAS  Google Scholar 

  50. Akaike H: A new look at the statistical model identification. IEEE Transactions on Automatic Control. 1974, AC-19: 716-723.

    Article  Google Scholar 

Download references

Acknowledgements

The study was partly supported by the grants from National Taiwan University Hospital (NTUH 90S1538, 1500-38-1538) and National Science Council in Taiwan (NSC 89-2314-B-002-403). The authors wish to thank Miss Chien, Gen-Chu, the cardiologists and the health examination staff in National Taiwan University Hospital for their assistance.

Author information

Authors and Affiliations

  1. Graduate Institute of Preventive Medicine, School of Public Health, National Taiwan University, Taipei, TAIWAN

    Kuo-Liong Chien

  2. Department of Internal Medicine, National Taiwan University Hospital, Taipei, TAIWAN

    Hsiu-Ching Hsu, Ta-Chen Su, Chi-Yu Yang & Yuan-Teh Lee

Authors
  1. Kuo-Liong Chien
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hsiu-Ching Hsu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ta-Chen Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chi-Yu Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yuan-Teh Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yuan-Teh Lee.

Additional information

Authors' contributions

KLC carried out the data collection, statistical analyses, participated in the study design and processing data. YTL participated in the design of the study and supervised the ideas developing in hypothesis generation. TCS & CYY performed the data collection and participated in the field work. HCH performed the laboratory measurement in lipid levels and in charge of quality control. All authors read and approved the final manuscript.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chien, KL., Hsu, HC., Su, TC. et al. Consistency of genetic inheritance mode and heritability patterns of triglyceride vs. high density lipoprotein cholesterol ratio in two Taiwanese family samples. BMC Genet 4, 7 (2003). https://doi.org/10.1186/1471-2156-4-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/1471-2156-4-7

Keywords

BMC Genomic Data

ISSN: 2730-6844

Contact us