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J Blangero

Publications and source records attributed to J Blangero.

At least 91 records · Page 5Linked to original sources
PubMed1999

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.

Age Factors↗
PubMed1999

Smoking behavior is under the influence of a major quantitative trait locus on human chromosome 5q.

Despite some evidence that smoking behavior is influenced by both genetic and environmental factors, efforts at identifying specific genes that influence smoking are extremely limited. Using information on smoking behavior history for 973 individuals distributed across 105 COGA families and a multipoint variance components method, we tested for linkage between smoking behavior (as defined by number of cigarette packs per day for 1 year) and chromosomal locations across the genome using information from 296 markers. We found strong evidence (lod = 3.2) for linkage of smoking behavior to a genetic location on chromosome 5q (D5S1354). Weaker evidence was found for linkage of smoking behavior to genetic locations on chromosomes 4 (between markers D4S244 and D4S2393), 15 (D15S642), and 17 (GATA193). Oligogenic linkage analyses suggest the putative locus on chromosome 5q is the primary determinant of genetic variation in smoking. Although the magnitude of the lod score is compelling, the large gap between the markers D5S1456 and D5S1354 (which is also at the end of chromosome 5q) in the COGA data set reduces the enthusiasm for this putative quantitative trait locus. However, typing of additional markers in this region may provide greater support for the localization of a susceptibility locus at this chromosomal location, which is not far from the DRD1 (D1 dopamine receptor gene) locus.

Chromosomes, Human, Pair 5↗
PubMed1999

An empirical test of the significance of an observed quantitative trait locus effect that preserves additive genetic variation.

We propose a constrained permutation test that assesses the significance of an observed quantitative trait locus effect against a background of genetic and environmental variation. Permutations of phenotypes are not selected at random, but rather are chosen in a manner that attempts to maintain the additive genetic variability in phenotypes. Such a constraint maintains the nonindependence among observations under the null hypothesis of no linkage. The empirical distribution of the lod scores calculated using permuted phenotypes is compared to that obtained using phenotypes simulated from the assumed underlying multivariate normal model. We make comparisons of univariate analyses for both a quantitative phenotype that appears consistent with a multivariate normal model and a quantitative phenotype containing pronounced outliers. An example of a bivariate analysis is also presented.

Alcoholism↗
PubMed1999

Effects of genotype x sex interaction on linkage analysis of visual event-related evoked potentials.

Autosomal genes contributing to variation in many complex traits are influenced by male or female physiological "environments." Accounting for such genotype-by-sex (G x S) interactions has been shown to be important in quantitative genetic, segregation, and linkage analyses of a number of sexually dimorphic traits. In analyses of data simulated for GAW10, we showed that incorporating sex-specific variance components into a variance components-based linkage method increased the power to detect linkage in a trait that exhibited G x S interaction. The goals of this study of data from the Collaborative Study on the Genetics of Alcoholism (COGA) were to screen the event-related brain potential (ERP) data from COGA participants for G x S interaction, and then to conduct variance components linkage analysis of ERP phenotypes showing evidence of G x S interaction using models incorporating sex-specific variance components. Significant G x S interaction was found in four ERP phenotypes: N100 measured at occipital leads 1 and 2, and P300 measured at occipital leads 1 and 2. In linkage analyses of these traits, the most significant lod score found was that between N100 occipital lead 1 amplitude and marker D7S490. The peak lod score at the D7S490 locus was 2.45 without sex-specific variance components, and 3.25 with sex-specific marker and residual polygenic components.

Alcoholism↗
PubMed1999

Asymptotic power of likelihood-ratio tests for detecting quantitative trait loci using the COGA data.

We investigated the asymptotic power of the likelihood-ratio test for detecting linkage to a quantitative trait locus (QTL) using the data set from the Collaborative Study on the Genetics of Alcoholism (COGA). Assuming a total trait heritability of 50% as determined for the COGA Cz P300 phenotype, we determined the minimum QTL heritability required at each point in the genome to achieve 80% power to detect linkage with a lod of 3.0. We find that there are regions of the genome where it is not possible to detect a QTL of any effect with 80% power, and that the overall minimum detectable QTL heritability for the COGA data is 0.35-0.40.

Alcoholism↗
PubMed1999

Power of variance component linkage analysis to detect quantitative trait loci.

Expressions are derived for the sample size required to achieve a given power in variance component linkage analysis of a quantitative trait in unascertained samples. For simplicity an additive model, comprising effects due to a single QTL, residual additive genetic factors, and individual-specific random environmental variation, is considered. Equations are given relating sample size to trait heritability for sibpairs, sib trios, nuclear families having two and three sibs, and arbitrary relative pairs. The effects of nonzero residual additive genetic variance and parental information are discussed, and a scale relationship for sample sizes with sibships and nuclear families is derived. For larger sampling structures such as extended pedigrees the inheritance space is randomly sampled and the relevant equations are solved numerically. Comparative power curves are presented for sibships of size 2-4 and for an extended pedigree of 48 individuals. Simulation results for sibpairs confirm the validity of the theoretical results.

Analysis of Variance↗
PubMed1999

Serum leptin concentration, body composition, and gonadal hormones during puberty.

BACKGROUND: Recent evidence has suggested that leptin concentration is associated with gonadal hormone levels, and that changes in leptin concentration may trigger the onset of reproductive function in children. However, the concurrent changes in body composition during puberty make the independent associations between leptin and gonadal hormone concentrations in children difficult to resolve. METHODS: To investigate the nature of associations between leptin levels and pubertal maturation, serum concentrations of leptin, estradiol, and testosterone and body composition measures were examined in a sample of 152 healthy pre-pubertal, pubertal, and post-pubertal children. RESULTS: Leptin concentration was nearly three-fold higher in post-pubertal girls than in pre-pubertal girls, but was relatively similar in pre- and post-pubertal boys. Significant sex differences in leptin concentration existed in prepubertal, pubertal and post-pubertal children, and these remained significant after controlling for adiposity. After adjusting for total body fat, fat-free mass and age, testosterone concentration was negatively associated with leptin levels in pubertal boys, while estradiol concentration was positively associated with leptin level in pubertal girls. CONCLUSIONS: Girls have higher serum leptin concentration before, during, and after puberty than boys, even after accounting for the development of greater female adiposity. Although other factors may be involved, sexual dimorphism in leptin concentrations during puberty appears to be partly due to a stimulatory effect of estradiol on leptin concentration in females and a suppressive effect of testosterone on leptin concentration in males.

Adipose Tissue↗
PubMed1999

Linkage of type 2 diabetes mellitus and of age at onset to a genetic location on chromosome 10q in Mexican Americans.

Since little is known about chromosomal locations harboring type 2 diabetes-susceptibility genes, we conducted a genomewide scan for such genes in a Mexican American population. We used data from 27 low-income extended Mexican American pedigrees consisting of 440 individuals for whom genotypic data are available for 379 markers. We used a variance-components technique to conduct multipoint linkage analyses for two phenotypes: type 2 diabetes (a discrete trait) and age at onset of diabetes (a truncated quantitative trait). For the multipoint analyses, a subset of 295 markers was selected on the basis of optimal spacing and informativeness. We found significant evidence that a susceptibility locus near the marker D10S587 on chromosome 10q influences age at onset of diabetes (LOD score 3.75) and is also linked with type 2 diabetes itself (LOD score 2.88). This susceptibility locus explains 63.8%+/-9.9% (P=. 000016) of the total phenotypic variation in age at onset of diabetes and 65.7%+/-10.9% (P=.000135) of the total variation in liability to type 2 diabetes. Weaker evidence was found for linkage of diabetes and of age at onset to regions on chromosomes 3p, 4q, and 9p. In conclusion, our strongest evidence for linkage to both age at onset of diabetes and type 2 diabetes itself in the Mexican American population was for a region on chromosome 10q.

Age Factors↗
PubMed1999

Human pedigree-based quantitative-trait-locus mapping: localization of two genes influencing HDL-cholesterol metabolism.

Common disorders with genetic susceptibilities involve the action of multiple genes interacting with each other and with environmental factors, making it difficult to localize the specific genetic loci responsible. An important route to the disentangling of this complex inheritance is through the study of normal physiological variation in quantitative risk factors that may underlie liability to disease. We present an analysis of HDL-cholesterol (HDL-C), which is inversely correlated with risk of heart disease. A variety of HDL subphenotypes were analyzed, including HDL particle-size classes and the concentrations and proportions of esterified and unesterified HDL-C. Results of a complete genomic screen in large, randomly ascertained pedigrees implicated two loci, one on chromosome 8 and the other on chromosome 15, that influence a component of HDL-C-namely, unesterified HDL2a-C. Multivariate analyses of multiple HDL phenotypes and simultaneous multilocus analysis of the quantitative-trait loci identified permit further characterization of the genetic effects on HDL-C. These analyses suggest that the action of the chromosome 8 locus is specific to unesterified cholesterol levels, whereas the chromosome 15 locus appears to influence both HDL-C concentration and distribution of cholesterol among HDL particle sizes.

Adult↗
PubMed1999

Testing the robustness of the likelihood-ratio test in a variance-component quantitative-trait loci-mapping procedure.

Detection of linkage to genes for quantitative traits remains a challenging task. Recently, variance components (VC) techniques have emerged as among the more powerful of available methods. As often implemented, such techniques require assumptions about the phenotypic distribution. Usually, multivariate normality is assumed. However, several factors may lead to markedly nonnormal phenotypic data, including (a) the presence of a major gene (not necessarily linked to the markers under study), (b) some types of gene x environment interaction, (c) use of a dichotomous phenotype (i.e., affected vs. unaffected), (d) nonnormality of the population within-genotype (residual) distribution, and (e) selective (extreme) sampling. Using simulation, we have investigated, for sib-pair studies, the robustness of the likelihood-ratio test for a VC quantitative-trait locus-detection procedure to violations of normality that are due to these factors. Results showed (a) that some types of nonnormality, such as leptokurtosis, produced type I error rates in excess of the nominal, or alpha, levels whereas others did not; and (b) that the degree of type I error-rate inflation appears to be directly related to the residual sibling correlation. Potential solutions to this problem are discussed. Investigators contemplating use of this VC procedure are encouraged to provide evidence that their trait data are normally distributed, to employ a procedure that allows for nonnormal data, or to consider implementation of permutation tests.

Analysis of Variance↗
PubMed1999

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.

Chromosome Mapping↗
PubMed1999

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.

Alcohol Dehydrogenase↗
PubMed1999

A genome search identifies major quantitative trait loci on human chromosomes 3 and 4 that influence cholesterol concentrations in small LDL particles.

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 approximately 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.

Adolescent↗
PubMed1999

Pleiotropy and genotype by diet interaction in a baboon model for atherosclerosis: a multivariate quantitative genetic analysis of HDL subfractions in two dietary environments.

We investigated dietary effects on pleiotropic relationships among 3 HDL cholesterol (C) subfractions (HDL1-C, HDL2-C, and HDL3-C; levels quantified by gradient gel electrophoresis) for 942 pedigreed baboons (Papio hamadryas) who were fed a basal (Chow) diet and a high cholesterol, saturated fat (HCSF) challenge diet. Using multivariate maximum likelihood methods we estimated heritabilities for all 6 traits, genetic and environmental correlations (rhoG and rhoE) between them, and the additive genetic variance of each subfraction's response to the diets. On the Chow diet, genetic correlations between the 3 subfractions were significant, and we observed complete pleiotropy between HDL1-C and HDL3-C (rhoG=-0.81). On the HCSF diet, only the genetic correlation between HDL1-C and HDL3-C (rhoG=-0.61) was significant. Genetic correlations between individual subfractions on the Chow and HCSF diets did not differ significantly from 1.0, indicating that the same additive genes influenced each subfraction's levels regardless of diet. However, the additive genetic variance of response to the diets was highly significant for HDL1-C and HDL2-C, but not for HDL3-C. Similar sets of genes influence variation in the 3 HDL subfractions on the Chow diet, and the same set influences variation in each subfraction on the HCSF diet. However, the expression of genes influencing HDL1-C and HDL2-C is altered by the HCSF diet, disrupting the pleiotropy observed between the 3 subfractions on the Chow diet.

Animals↗
PubMed1999

Normal variation in leptin levels in associated with polymorphisms in the proopiomelanocortin gene, POMC.

We previously reported that our genome-scanning initiative had detected a highly significant linkage (log odds ratio = 4.95; P = 9 x 10(-7)) between a quantitative trait locus (QTL) on chromosome 2 and leptin levels in Mexican American families. We now have typed additional microsatellite markers in this region, increasing this log odds ratio score to 7.46 (P = 2 x 10(-9)). This region of chromosome 2 contains a strong positional candidate gene, POMC. The POMC gene codes for POMC, the prohormone from which alphaMSH, ACTH, and beta-endorphin are derived. Studies by others have shown that POMC-derived products are involved in the regulation of appetite and obesity. We have used polymorphisms in POMC to map its location within the 95% confidence interval of the peak for the linkage signal for the QTL. We also constructed POMC haplotypes using these polymorphisms and have found a significant association with normal variation in leptin levels (P = 0.001). We conclude that variation in POMC is associated with normal variation in serum leptin levels, providing further evidence that POMC may be the leptin QTL previously identified in Mexican American families.

Chromosome Mapping↗
PubMed1999

A quantitative trait locus influencing BMI maps to the region of the beta-3 adrenergic receptor.

The beta-3 adrenergic receptor (ADRB3) has been implicated as a regulator of energy expenditure, and a polymorphism in codon 64 of this gene (Trp64Arg) has been associated in some studies with obesity and insulin resistance. However, many studies have failed to detect an effect of this variant, and the importance of the Trp64Arg variant in human obesity remains controversial. We performed a quantitative linkage analysis of the ADRB3 and obesity, using 12 markers (including the intragenic Trp64Arg polymorphism) spanning a 57-cM region of chromosome 8. The study population consisted of 470 individuals from 10 large multigenerational families of Mexican-American ancestry residing in San Antonio, TX. In two-point analysis, logarithm of odds (LOD) scores >1.0 were observed for six markers surrounding ADRB3 in a 33-cM region spanned by markers D8S1477 and D8S1136. The multipoint LOD score was 3.21, occurring between markers D8S1121 and ADRB3, approximately 2-3 cM from ADRB3. Adjusting for the presence of the Arg64 allele or excluding from the analysis the 11 individuals homozygous for the Arg64 allele did not reduce the evidence for linkage. A genome scan was conducted at 10 cM map density to detect other loci influencing variation in BMI. Multipoint LOD scores >1.0 were observed in four other regions, including two on chromosome 17, one on chromosome 6q, and one on chromosome 2p. These data suggest that the ADRB3 should continue to be regarded as a strong candidate gene for obesity even though evidence for an effect of the Trp64Arg polymorphism could not be established. It is also possible that a gene closely linked to ADRB3 may influence susceptibility to obesity.

Adult↗