Univariate and bivariate analyses of cholesterol and triglyceride levels in pedigrees

Genomics and metabolomics of muscular mass in a community-based sample of UK females

The contribution of specific molecular-genetic factors to muscle mass variation and sarcopenia remains largely unknown. To identify endogenous molecules and specific genetic factors associated with appendicular lean mass (APLM) in the general population, cross-sectional data from the TwinsUK Adult Twin Registry were used. Non-targeted mass spec-based metabolomic profiling was performed on plasma of 3953 females (mostly dizygotic and monozygotic twins). APLM was measured using dual-energy X-ray absorptiometry (DXA) and genotyping was genome-wide (GWAS). Specific metabolites were used as intermediate phenotypes in the identification of single-nucleotide polymorphisms associated with APLM using GWAS. In all, 162 metabolites were found significantly correlated with APLM, and explained 17.4% of its variation. However, the top three of them (unidentified substance X12063, urate, and mannose) explained 11.1% (P ≤ 9.25 × 10(-26)) so each was subjected to GWAS. Each metabolite showed highly significant (P ≤ 9.28 × 10(-46)) associations with genetic variants in the corresponding genomic regions. Mendelian randomization using these SNPs found no evidence for a direct causal effect of these metabolites on APLM. However, using a new software platform for bivariate analysis we showed that shared genetic factors contribute significantly (P ≤ 4.31 × 10(-43)) to variance in both the metabolites and APLM--independent of the effect of the associated SNPs. There are several metabolites, having a clear pattern of genetic inheritance, which are highly significantly associated with APLM and may provide a cheap and readily accessible biomarker of muscle mass. However, the mechanism by which the genetic factor influences muscle mass remains to be discovered.

Genome-wide genetic and transcriptomic investigation of variation in antibody response to dietary antigens

Increased immunoglobulin G (IgG) response to dietary antigens can be associated with gastrointestinal dysfunction and autoimmunity. The underlying processes contributing to these adverse reactions remain largely unknown, and it is likely that genetic factors play a role. Here, we estimate heritability and attempt to localize genetic factors influencing IgG antibody levels against food-derived antigens using an integrative genomics approach. IgG antibody levels were determined by ELISA in >1,300 Mexican Americans for the following food antigens: wheat gliadin; bovine casein; and two forms of bovine serum albumin (BSA-a and BSA-b). Pedigree-based variance components methods were used to estimate additive genetic heritability (h(2) ), perform genome-wide association analyses, and identify transcriptional signatures (based on 19,858 transcripts from peripheral blood lymphocytes). Heritability estimates were significant for all traits (0.15-0.53), and shared environment (based on shared residency among study participants) was significant for casein (0.09) and BSA-a (0.33). Genome-wide significant evidence of association was obtained only for antibody to gliadin (P = 8.57 × 10(-8) ), mapping to the human leukocyte antigen II region, with HLA-DRA and BTNL2 as the best candidate genes. Lack of association of known celiac disease risk alleles HLA-DQ2.5 and -DQ8 with antigliadin antibodies in the studied population suggests a separate genetic etiology. Significant transcriptional signatures were found for all IgG levels except BSA-b. These results demonstrate that individual genetic differences contribute to food antigen antibody measures in this population. Further investigations may elucidate the underlying immunological processes involved.

Heritability in the genome-wide association era

Heritability, the fraction of phenotypic variation explained by genetic variation, has been estimated for many phenotypes in a range of populations, organisms, and time points. The recent development of efficient genotyping and sequencing technology has led researchers to attempt to identify the genetic variants responsible for the genetic component of phenotype directly via GWAS. The gap between the phenotypic variance explained by GWAS results and those estimated from classical heritability methods has been termed the "missing heritability problem". In this work, we examine modern methods for estimating heritability, which use the genotype and sequence data directly. We discuss them in the context of classical heritability methods, the missing heritability problem, and describe their implications for understanding the genetic architecture of complex phenotypes.

Genetic analysis of motor milestones attainment in early childhood

The age of attainment for four motor developmental traits, such as turning over, sitting up without support, pulling up to a standing position and walking without support, was examined in 822 children, including 626 siblings from families with 2 to 6 children, 68 pairs of dizygotic twins and 30 pairs of monozygotic twins. Correlation analysis, carried out separately for each type of sibship, showed the highest pairwise correlations in monozygotic twins and the lowest correlation in non-twin siblings for all motor milestones. Variance component analysis was used to decompose the different independent components forming the variation of the studied trait, such as genetic effect, common twin environment, common sib environment and residual factors. The results revealed that the major proportion of the total variance after adjustment for gestation age for the attainment of each motor skill, except pulling up to standing position, is explained by the common twin environment (50.5 to 66.6%), whilst a moderate proportion is explained by additive genetic factors (22.2 to 33.5%). Gestational age was found to be an important predictor of appearance of all motor milestones, affecting delay of 4.5 to 8.6 days for the attainment of the motor abilities for each week of earlier gestation. The age of attainment of the standing position was affected only by shared sibs environment (33.3% of the total variance) and showed no influence of either genetic or common twin environment. Phenotypic between trait correlations were high and significant for all studied traits (range between 0.40 and 0.67,P< 0.01 in all instances). Genetic cross correlations, however, were not easily interpreted and did not show clear variance trends among the different groups of children.

Genetic factors influence serological measures of common infections

BACKGROUND/AIMS

Antibodies against infectious pathogens provide information on past or present exposure to infectious agents. While host genetic factors are known to affect the immune response, the influence of genetic factors on antibody levels to common infectious agents is largely unknown. Here we test whether antibody levels for 13 common infections are significantly heritable.

METHODS

IgG antibodies to Chlamydophila pneumoniae, Helicobacter pylori, Toxoplasma gondii, adenovirus 36 (Ad36), hepatitis A virus, influenza A and B, cytomegalovirus, Epstein-Barr virus, herpes simplex virus (HSV)-1 and -2, human herpesvirus-6, and varicella zoster virus were determined for 1,227 Mexican Americans. Both quantitative and dichotomous (seropositive/seronegative) traits were analyzed. Influences of genetic and shared environmental factors were estimated using variance components pedigree analysis, and sharing of underlying genetic factors among traits was investigated using bivariate analyses.

RESULTS

Serological phenotypes were significantly heritable for most pathogens (h(2) = 0.17-0.39), except for Ad36 and HSV-2. Shared environment was significant for several pathogens (c(2) = 0.10-0.32). The underlying genetic etiology appears to be largely different for most pathogens.

CONCLUSIONS

Our results demonstrate, for the first time for many of these pathogens, that individual genetic differences of the human host contribute substantially to antibody levels to many common infectious agents, providing impetus for the identification of underlying genetic variants, which may be of clinical importance.

Genetic and household determinants of predisposition to human hookworm infection in a Brazilian community

BACKGROUND

Predisposition to heavy or light human hookworm infection is consistently reported in treatment-reinfection studies. A significant role for host genetics in determining hookworm infection intensity has also been shown, but the relationship between host genetics and predisposition has not been investigated.

METHODS

A treatment-reinfection study was conducted among 1302 individuals in Brazil. Bivariate variance components analysis was used to estimate heritability for pretreatment and reinfection intensity and to estimate the contribution of genetic and household correlations between phenotypes to the overall phenotypic correlation (ie, predisposition).

RESULTS

Heritability for hookworm egg count was 17% before treatment and 25% after reinfection. Predisposition to heavy or light hookworm infection was observed, with a phenotypic correlation of 0.34 between pretreatment and reinfection intensity. This correlation was reduced to 0.23 after including household and environmental covariates. Genetic and household correlations were 0.41 and 1, respectively, and explained 88% of the adjusted phenotypic correlation.

CONCLUSIONS

Predisposition to human hookworm infection in this area results from a combination of host genetics and consistent differences in exposure, with the latter explained by household and environmental factors. Unmeasured individual-specific differences in exposure did not contribute to predisposition.

Hypercholesterolemia in Thai primary school children: relation to maternal and nutritional factors

BACKGROUND

There is evidence to show that atherosclerosis can occur in young children and that elevated total cholesterol and low density lipoprotein cholesterol concentrations are risk factors for atherosclerosis. The aim of the present cross-sectional study was to investigate the influence of maternal and nutritional factors on blood cholesterol in primary school children.

METHODS

One hundred and ninety-five population-based mother-child pairs (obese child-overweight mother pairs, n = 60; obese child-normal-weight mother pairs, n = 48; wasted child-overweight mother pairs, n = 37; normal-weight child-normal-weight mother pairs, n = 50), were enrolled in the study. Various anthropometric parameters were measured and serum lipids of subjects were further determined. Biological data and children's eating behavior were obtained from the mothers through interviews.

RESULTS

Hypercholesterolemia was found in 64.6-65% of obese children, 24.3% of wasted children and in 56% of the normal-weight children; whereas the proportion of children in all groups who had normal blood cholesterol levels was in the lower range. Multivariate logistic regression indicated that mother's serum cholesterol (odds ratio [OR], 2.41; 95% confidence interval [CI]: 1.12-4.78), child obesity defined by weight-for-height Z-score > +2SD (OR, 2.56; 95%CI: 1.33-4.98), and child's energy intake >/=75th percentile (OR, 2.59; 95%CI: 1.01-6.66) were the significant factors associated with hypercholesterolemia in children.

CONCLUSION

Hypercholesterolemia in school children is associated with familial factor, bodyweight and nutrient intake. Elevated blood cholesterol was also found in some of the normal-weight and wasted children. Effective family-based intervention programs are urgently needed to modify risk factors predisposing to coronary heart disease.

Coronary Artery Calcification Progression Is Heritable

Background— Coronary artery calcification (CAC), a marker of coronary artery atherosclerosis, can be measured accurately and noninvasively with the use of electron beam computed tomography. Serial measures of CAC quantify progression of calcified coronary artery plaque. Little is known about the role of genetic factors in progression of CAC quantity.

Methods and Results— We quantified the relative contributions of measured risk factors and unmeasured genes to CAC progression measured by 2 electron beam computed tomography examinations an average of 7.3 years apart in 877 asymptomatic white adults (46% men) from 625 families in a community-based sample. After adjustment for baseline risk factors and CAC quantity, the estimated heritability of CAC progression was 0.40 ( P <0.001). Baseline risk factors and CAC quantity explained 64% of the variation in CAC progression. Thus, genetic factors explained 14% of the variation [(100−64)×(0.40)] in CAC progression. After adjustment for risk factors, the estimated genetic correlation (pleiotropy) between baseline CAC quantity and CAC progression was 0.80 and was significantly different than 0 ( P <0.001) and 1 ( P =0.037). The environmental correlation between baseline CAC quantity and CAC progression was 0.42 and was significantly different than 0 ( P =0.006).

Conclusions— Evidence was found that many but not all genetic factors influencing baseline CAC quantity also influence CAC progression. The identification of common and unique genetic influences on these traits will provide important insights into the genetic architecture of coronary artery atherosclerosis.

Variance decomposition of apolipoproteins and lipids in Danish twins

OBJECTIVE

Twin studies are used extensively to decompose the variance of a trait, mainly to estimate the heritability of the trait. A second purpose of such studies is to estimate to what extent the non-genetic variance is shared or specific to individuals. To a lesser extent the twin studies have been used in bivariate or multivariate analysis to elucidate common genetic factors to two or more traits.

METHODS AND RESULTS

In the present study the variances of traits related to lipid metabolism is decomposed in a relatively large Danish twin population, including bivariate analysis to detect possible common genetic factors of the traits.

CONCLUSIONS

The heritabilities of apolipoprotein B and E, cholesterol, LDL, and high density lipoprotein (HDL) were significant in the general population, although gender-specific levels and significance were detected. Heritabilities of apolipoprotein A1, triglycerides, and very low density protein (VLDL) were only significant when the population was stratified according to gender.

Heritability of circulating growth factors involved in the angiogenesis in healthy human population

The present study examined the extent of genetic and environmental influences on the populational variation of circulating growth factors (VEGF, EGF) involved in angiogenesis in healthy and ethnically homogeneous Caucasian families. The plasma levels of each of the studied biochemical indices were determined by enzyme-linked immunoassay in 478 healthy individuals aged 18-75 years. Quantitative genetic analysis showed that the VEGF and EGF variation was appreciably attributable to genetic effects, with heritability estimates of 79.9% and 48.4%, respectively. Yet, common environmental factors, shared by members of the same household, also played a significant role (P < 0.01) and explained between 20.1% and 32.6% of the variation. The present study additionally examined the covariations between these molecules and either transforming growth factor-beta 1 (TGF-beta 1) or tissue inhibitors of matrix metalloproteinases 1 (TIMP-1), likewise relevant for angiogenesis. Bivariate analysis revealed significant phenotypic correlations (P < 0.002) between all pairs of variables, thus indicating the possible existence of common genetic and environmental factors. The analysis suggested that the pleiotropic genetic effects were consistently the primary (or even the sole) source of correlation between all pairs of studied molecules. The results of our study affirm the existence of specific and common genetic pathways that commonly determine the greater part of the circulating variation of these molecules.

Multivariate variance-components analysis of longitudinal blood pressure measurements from the Framingham Heart Study

Multivariate variance-components analysis provides several advantages over univariate analysis when studying correlated traits. It can test for pleiotropy or (in the longitudinal context) gene × age interaction. It can also have more power than univariate analyses to detect a quantitative trait locus influencing several traits. We apply multivariate variance components to longitudinal systolic blood pressure data from the Framingham Heart Study. We find evidence for a polygenic influence on blood pressure (heritabilities at different ages range from 27% to 38%). Tests based on a factor-analytic parameterization of the polygenic variance find significant (p < 2 × 10^-3) evidence that different genes affect blood pressure at different ages. Still, estimates for the proportion of polygenic variance due to shared genes ran as high as 85% for some trait pairs. Univariate and multivariate linkage analyses replicate previous linkage results on chromosome 17 (maximum LOD scores of 2.2 and 2.4, respectively). In this study, multivariate analysis provides no increase in power; this is likely due to the strong positive correlation in systolic blood pressure measured at different ages.

A major locus for fasting insulin concentrations and insulin resistance on chromosome 6q with strong pleiotropic effects on obesity-related phenotypes in nondiabetic Mexican Americans

Insulin resistance and hyperinsulinemia are strong correlates of obesity and type 2 diabetes, but little is known about their genetic determinants. Using data on nondiabetics from Mexican American families and a multipoint linkage approach, we scanned the genome and identified a major locus near marker D6S403 for fasting "true" insulin levels (LOD score 4.1, empirical P<.0001), which do not crossreact with insulin precursors. Insulin resistance, as assessed by the homeostasis model using fasting glucose and specific insulin (FSI) values, was also strongly linked (LOD score 3.5, empirical P<.0001) with this region. Two other regions across the genome were found to be suggestively linked to FSI: a location on chromosome 2q, near marker D2S141, and another location on chromosome 6q, near marker D6S264. Since several insulin-resistance syndrome (IRS)-related phenotypes were mapped independently to the regions on chromosome 6q, we conducted bivariate multipoint linkage analyses to map the correlated IRS phenotypes. These analyses implicated the same chromosomal region near marker D6S403 (6q22-q23) as harboring a major gene with strong pleiotropic effects on obesity and on lipid measures, including leptin concentrations (e.g., LOD(eq) for traits-specific insulin and leptin was 4.7). A positional candidate gene for insulin resistance in this chromosomal region is the plasma cell-membrane glycoprotein PC-1 (6q22-q23). The genetic location on chromosome 6q, near marker D6S264 (6q25.2-q26), was also identified by the bivariate analysis as exerting significant pleiotropic influences on IRS-related phenotypes (e.g., LOD(eq) for traits-specific insulin and leptin was 4.1). This chromosomal region harbors positional candidate genes, such as the insulin-like growth factor 2 receptor (IGF2R, 6q26) and acetyl-CoA acetyltransferase 2 (ACAT2, 6q25.3-q26). In sum, we found substantial evidence for susceptibility loci on chromosome 6q that influence insulin concentrations and other IRS-related phenotypes in Mexican Americans.

Comparison of variance components and sibpair-based approaches to quantitative trait linkage analysis in unselected samples

We compared the statistical performance of sibpair-based and variance components approaches to multipoint linkage analysis of a quantitative trait in unselected samples. As a benchmark dataset, we used the simulated family data from Genetic Analysis Workshop 10 [Goldin et al., 1997], and each method was used to screen all 200 replications of the GAW10 genome for evidence of linkage to quantitative trait Q1. The sibpair and variance components methods were each applied to datasets comprising single-sibpairs and complete sibships, and for further comparison we also applied the variance components method to the nuclear family and extended pedigree datasets. For each analysis, the unbiasedness and efficiency of parameter estimation, the power to detect linkage, and the Type I error rate were estimated empirically. Sibpair and variance components methods exhibited comparable performance in terms of the unbiasedness of the estimate of QTL location and the Type I error rate. Within the single-sibpair and sibship sampling units, the variance components approach gave consistently superior power and efficiency of parameter estimation. Within each method, the statistical performance was improved by the use of the larger and more informative sampling units.

Correcting for ascertainment bias in the COGA data set

The effects of several corrections for ascertainment bias on a linkage result in the COGA data are examined. A correction that models the complete ascertainment protocol markedly reduces the evidence for linkage. Different partial corrections increase the linkage signal and recover good estimates of the population trait prevalence, but do not represent adequately the complexity of the COGA ascertainment scheme. Evidently the present size of the COGA data set cannot support a complete correction for ascertainment bias, but an effective partial correction can and should be implemented.

Genetic analysis of personality traits and alcoholism using a mixed discrete continuous trait variance component model

Bivariate analyses can improve power to detect linkage. This paper describes one application of a bivariate variance component method for estimating joint likelihoods of a continuous and a discrete trait. This method is applied to the Collaborative Study on the Genetics of Alcoholism data set to investigate the relationship between personality traits derived from the tridimensional personality questionnaire (TPQ) and alcoholism. The results indicate that the novelty-seeking subscale of the TPQ and alcoholism share a strong and significant genetic correlation (rho G = 0.83) and modest environmental correlation (rho E = 0.31). When both traits are considered jointly in a multipoint linkage model compared with the alcoholism trait alone, there is an improvement in the ability to detect and localize a quantitative trait locus on chromosome 4.

Joint multipoint linkage analysis of multivariate qualitative and quantitative traits. II. Alcoholism and event-related potentials

The availability of robust quantitative biological markers that are correlated with qualitative psychiatric phenotypes can potentially improve the power of linkage methods to detect quantitative-trait loci influencing psychiatric disorders. We apply a variance-component method for joint multipoint linkage analysis of multivariate discrete and continuous traits to the extended pedigree data from the Collaborative Study on the Genetics of Alcoholism, in a bivariate analysis of qualitative alcoholism phenotypes and quantitative event-related potentials. Joint consideration of the DSM-IV diagnosis of alcoholism and the amplitude of the P300 component of the Cz event-related potential significantly increases the evidence for linkage of these traits to a chromosome 4 region near the class I alcohol dehydrogenase locus ADH3. A likelihood-ratio test for complete pleiotropy is significant, suggesting that the same quantitative-trait locus influences both risk of alcoholism and the amplitude of the P300 component.

Joint multipoint linkage analysis of multivariate qualitative and quantitative traits. I. Likelihood formulation and simulation results

We describe a variance-components method for multipoint linkage analysis that allows joint consideration of a discrete trait and a correlated continuous biological marker (e.g., a disease precursor or associated risk factor) in pedigrees of arbitrary size and complexity. The continuous trait is assumed to be multivariate normally distributed within pedigrees, and the discrete trait is modeled by a threshold process acting on an underlying multivariate normal liability distribution. The liability is allowed to be correlated with the quantitative trait, and the liability and quantitative phenotype may each include covariate effects. Bivariate discrete-continuous observations will be common, but the method easily accommodates qualitative and quantitative phenotypes that are themselves multivariate. Formal likelihood-based tests are described for coincident linkage (i.e., linkage of the traits to distinct quantitative-trait loci [QTLs] that happen to be linked) and pleiotropy (i.e., the same QTL influences both discrete-trait status and the correlated continuous phenotype). The properties of the method are demonstrated by use of simulated data from Genetic Analysis Workshop 10. In a companion paper, the method is applied to data from the Collaborative Study on the Genetics of Alcoholism, in a bivariate linkage analysis of alcoholism diagnoses and P300 amplitude of event-related brain potentials.

Tel-Aviv-Heidelberg Three Generation Offspring Study: Genetic and environmental sources of variation and covariation among plasma lipids, lipoproteins, and apoliproteins

Multivariate genetic analysis, implemented in the statistical package for pedigree analysis, FISHER, was carried out on a large sample of Israeli pedigrees to evaluate heritability and genetic correlations among an array of plasma lipids: total cholesterol (TCHL), triglycerides (TRIG), HDL-C and HDL2-C, HDL3-C, LDL-C and HDL-C%, apolipoproteins A and B (APO-A1 and APO-B), lipoprotein LP(a) and fibrinogen (FIBR). Multiple regression analysis showed that although sex, age, smoking and other study environmental factors, have a significant contribution to the variation of each plasma lipid, they exert little effect on covariation in lipids. Genes, however, are important factors of the variation and covariation in lipids. Thus, variance component analysis showed that the genetic component of the study variables, adjusted on age, sex and environmental factors, ranged between 31% for logarithm-transformed TRIG and 77% for plasma concentrations of LP(a). Coefficients of multiple genetic determination of the genetic variation of each variable attributable to all of the other variables, ranged from low values (<30%) for TRIG, LP(a) and FIBR, to moderate (64%) for HDL2-C. The genetic variation of each of the remaining variables was completely explained by variation in other lipids. The results of a factor analyses of phenotypic, genetic and environmental correlation matrices were similar and clearly identified several clusters of variables. The first included APO-A1, HDL-C, HDL2-C and HDL3-C, and the second-APO-B, LDL-C and THCL. Further analysis showed that it was probable the genetic component of variation of HDL3-C plasma concentration that fully depended on APO-A1, while those of LDL-C and TCHL fully depended on APO-B. The degree of correlation between TRIG, LP(a), FIBR and other variables, if any, was considerably lower. Am. J. Hum. Biol. 9:357-370, 1997. © 1997 Wiley-Liss, Inc.

Mixture distributions in human genetics research

The use of mixture distributions in genetics research dates back to at least the late 1800s when Karl Pearson applied them in an analysis of crab morphometry. Pearson's use of normal mixture distributions to model the mixing of different species of crab (or 'families' of crab as he referred to them) within a defined geographic area motivated further use of mixture distributions in genetics research settings, and ultimately led to their development and recognition as intuitive modelling devices for the effects of underlying genes on quantitative phenotypic (i.e. trait) expression. In addition, mixture distributions are now used routinely to model or accommodate the genetic heterogeneity thought to underlie many human diseases. Specific applications of mixture distribution models in contemporary human genetics research are, in fact, too numerous to count. Despite this long, consistent and arguably illustrious history of use, little mention of mixture distributions in genetics research is made in many recent reviews on mixture models. This review attempts to rectify this by providing insight into the role that mixture distributions play in contemporary human genetics research. Tables providing examples from the literature that describe applications of mixture models in human genetics research are offered as a way of acquainting the interested reader with relevant studies. In addition, some of the more problematic aspects of the use of mixture models in genetics research are outlined and addressed.

Variance components for statistical genetics: applications in medical research to characteristics related to human diseases and health

RA Fisher introduced variance components in 1918. He synthesized Mendelian inheritance with Darwin's theory of evolution by showing that the genetic variance of a continuous trait could be decomposed into additive and non-additive components. The model can be extended to include environmental factors, interactions, covariation, and non-random mating. Identifiability depends critically on design. Methods of analysis include modelling the mean squares from a fixed effects analysis of variance, and covariance structure modelling, which can be extended to multivariate traits and has been used to study ordinal traits by reference to postulated, unmeasured, latent 'liabilities'. These methods operate on dependent observations within independent groups of the same size and structure, and therefore require balanced designs ('regular' pedigrees). A multivariate normal model handles data in its generic form, utilizes data efficiently from all members of pedigrees of unequal size or varying structure, accommodates individuals missing at random, and allows flexible modelling with tests of distributional assumptions and fit. Most analytical methods use least squares or maximum likelihood under normal theory. Robust methods, scale transformation, ascertainment, path diagrams and correlational path models (popular in behavioural genetics through addressing nonrandom mating and social interactions), 'heritability', and the contribution and limitations of statistical modelling to the 'nature-nurture' debate, are discussed.

The role of XbaI polymorphism of the apolipoprotein B gene in determining levels and covariability of lipid and lipoprotein variables in a sample of Israeli offspring with family history of myocardial infarction

We have determined the frequency of DNA polymorphism of the gene for human apolipoprotein B detected with XbaI in 525 Israeli offspring whose parents experienced a myocardial infarction. The relative frequencies of the X1 (8.6 kb) and X2 (5.0 kb) alleles were 0.67 and 0.33, respectively, with no significant differences between males and females and across the different origin groups. Significant variation in sex, age and body mass adjusted plasma levels of cholesterol (P = 0.02), LDL-C (P = 0.02) and apo B (P = 0.03) were associated with the XbaI polymorphism. An interaction with age was demonstrated. For young individuals a simple codominant association of the XbaI site with cholesterol and LDL-C was evident and the differences between the two homozygote groups ranged between 22 and 25 mg/dl. For individuals above age 25 these differences were about 12 mg/dl with no significant difference between the X1X2 and the X2X2 genotype groups. In our study sample the apo B XbaI polymorphism accounted for 1% of the variability of plasma cholesterol, LDL-C and apo B levels. The XbaI polymorphism also had an effect on the associations among lipid and lipoprotein variables. In conclusion, we have demonstrated an association of the apo B XbaI polymorphism with the metabolism of the apo B-containing lipoprotein particles in a sample of Israeli offspring with a family history of myocardial infarction.

Lipid data from NHLBI veteran twins: interpreting genetic analyses when model assumptions fail

Analyses were performed on lipid data from the NHLBI Veteran Twin Study. The analyses focused on longitudinal multivariate models, describing how the genetic effects on lipids vary over time. Our pedigree-based model selection approach allows simultaneous estimation of both covariance structure parameters and regression parameters. The analyses reveal strong correlations between additive genetic effects over time, implying that genetic effects on lipids are somewhat constant throughout the life span represented within this sample. Both univariate preliminary analyses and robust fitting applied to the longitudinal models indicate that several assumptions underlying the twin analyses are violated. Although variance component and correlation parameter estimates are not much changed by robust fitting analyses, questions remain about the behavior of parameter estimates in multivariate genetic models under departures from model assumptions.

Multivariate genetic analysis of high density lipoprotein particles

The aim of this study was to investigate the effect of genetic factors on three components of plasma high density lipoproteins, HDL-cholesterol (HDL-C), apolipoprotein A-I (apo A-I) and lipoprotein particle Lp A-I (Lp A-I), which contains apo A-I but not apo A-II. These analyses were carried out on 106 nuclear families with one or more children (407 subjects) who volunteered for health screening at the Center for Preventive Medicine, Vandoeuvre, France. After adjustment by stepwise multiple linear regression analysis for age, gender, weight, height, ponderosity, alcohol consumption, smoking habits, and hormonal treatment in females, a multifactorial model (considering the effect of polygenes, individual, specific, environmental and common household factors) was fitted to each variable separately. The hypothesis of no common household effects was accepted for each of the traits. The contribution of genetic factors to inter-individual variance was larger than the contribution of environmental factors for apo A-I (h2 = 0.81) and Lp A-I (h2 = 0.63) but not for HDL-C (h2 = 0.44). Bivariate analyses were carried out by parameterizing covariance components between traits. The genetic correlations were always significantly different from zero. They were estimated to be 0.73 between HDL-C and apo A-I, 0.40 between HDL-C and Lp A-I, 0.51 between apo A-I and Lp A-I. These results suggest that HDL-C, apo A-I and Lp A-I are only in part affected by the same genes and that the measurement of lipids as well as the apo A-I and Lp A-I gives complementary and different information on the metabolic and genetic aspects.

Genetic and environmental determinants of serum lipids and lipoproteins in French Canadian families

The contribution of genetic and environmental factors in serum triglycerides (TG), total cholesterol (CHOL), low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C), and HDL-C/CHOL ratio were studied in 1630 subjects from 375 families of French descent by using a path analysis procedure. Familial correlations were computed in several pairs of biological relatives and relatives by adoption after adjustment for age and gender effects and after further adjustment for physical fitness, level of habitual physical activity, total body fat and fat distribution, diet, smoking, and alcohol consumption. The model of path analysis allowed the separation of transmissible variance (t2) into genetic (h2) and cultural (b2) components of inheritance. Under the most parsimonious solution and after adjustment for age, gender, and concomitants, the transmissible variance was entirely accounted for by genetic factors (t2 = h2), with h2 estimates of 0.52, 0.55, 0.60, and 0.63 for TG, CHOL, LDL-C, HDL-C, and HDL-C/CHOL, respectively. These estimates were similar to those obtained after adjustment for age and gender effects only. The contribution of nontransmissible environmental factors ranged from 0.48 for TG to 0.37 for HDL-C/CHOL ratio. These results suggest that both genetic and environmental factors contribute to the variation in blood lipids and lipoproteins in this population and that nongenetic influences are not associated with cultural factors transmitted across generations.

Estimation of genetic model parameters: variables correlated with a quantitative phenotype exhibiting major locus inheritance

A major locus that is detected through its effect on one phenotype (a primary trait) may also affect other quantitative phenotypes or qualitative disease endpoints (secondary traits). The pattern of effects of the major locus on a set of primary and secondary traits suggests candidate defects for the mutant allele. The effects are directly estimable when "measured genotypes" or a tightly linked marker allow unambiguous assignment of major locus genotypes. When genotypes assignments are ambiguous for a major locus detected through its effect on a quantitative primary trait, we propose estimators using genotypic probabilities. Making certain reasonable assumptions, we demonstrate asymptotic unbiasedness of these genotypic probability estimators of the genotypic means and variances for either the quantitative primary or secondary traits, of the covariances between quantitative primary and secondary traits, and of prevalences for the secondary qualitative traits. An important application of genotypic probability estimators is to define an effect of a major locus that cannot be detected upon analysis of the variable; for example, major locus effects may be defined for hypertension or blood pressure as secondary traits, but not detected as primary traits.

The use of measured genotype information in the analysis of quantitative phenotypes in man. II. The role of the apolipoprotein E polymorphism in determining levels, variability, and covariability of cholesterol, betalipoprotein, and triglycerides in a sample of unrelated individuals

Recent advances in molecular biology provide measures of genotypes at loci involved in lipid metabolism. Genotypes for apolipoprotein E (apo E) and quantitative levels of total plasma cholesterol, betalipoprotein, and triglycerides were measured in a sample of 223 unrelated individuals from Nancy, France. The frequencies of the epsilon 2, epsilon 3, and epsilon 4 alleles are 0.13, 0.74, and 0.13, respectively, in this sample. Significant differences among apo E genotypes were detected for these lipoprotein phenotypes. The average effect of the epsilon 2 allele was to reduce total plasma cholesterol and betalipoprotein levels by 0.52 mmol/L and 0.98, respectively, while the epsilon 4 allele raised these levels by 0.26 mmol/L and 0.61, respectively. Apo E genotype specific correlations suggest that this locus also has an effect on the coordinated metabolism between cholesterol and triglycerides. We infer that approximately 17% of the genetic variability in total plasma cholesterol may be attributable to this apo E polymorphism. No other single locus has been identified with such a large contribution to cardiovascular disease risk factors in the general population.