Biomedical subjects

J Blangero

Publications and source records attributed to J Blangero.

At least 37 records · Page 2Linked to original sources

PubMed2003

Novel family-based approaches to genetic risk in thrombosis.

The genetic basis of thrombosis is complex, involving multiple genes and environmental factors. The field of common complex disease genetics has progressed enormously over the past 10 years with the development of powerful new molecular and analytical strategies that enable localization and identification of the causative genetic variants. During the course of these advances, a major paradigmatic change has been taking place that focuses on the genetic analysis of measurable quantitative traits that are correlated with disease risk vs. the previous emphasis on the analysis of the much less informative dichotomous disease trait. Because of their closer proximity to direct gene action, disease-related quantitative phenotypes represent our best chance to identify the underlying quantitative trait loci (QTLs) that influence disease susceptibility. This approach works best when data can be collected on extended families. Unfortunately, family-based designs are still relatively rare in thrombosis/hemostasis studies. In this review, we detail the reasons why the field would benefit from a more vigorous pursuit of modern family-based genetic studies.

Alleles↗

PubMed2003

Obesity and diabetes gene discovery approaches.

New treatments are currently required for the common metabolic diseases obesity and type 2 diabetes. The identification of physiological and biochemical factors that underlie the metabolic disturbances observed in obesity and type 2 diabetes is a key step in developing better therapeutic outcomes. The discovery of new genes and pathways involved in the pathogenesis of these diseases is critical to this process, however identification of genes that contribute to the risk of developing these diseases represents a significant challenge as obesity and type 2 diabetes are complex diseases with many genetic and environmental causes. A number of diverse approaches have been used to discover and validate potential new targets for obesity and diabetes. To date, DNA-based approaches using candidate gene and genome-wide linkage analysis have had limited success in identifying genomic regions or genes involved in the development of these diseases. Recent advances in the ability to evaluate linkage analysis data from large family pedigrees using variance components based linkage analysis show great promise in robustly identifying genomic regions associated with the development of obesity and diabetes. RNA-based technologies such as cDNA microarrays have identified many genes differentially expressed in tissues of healthy and diseased subjects. Using a combined approach, we are endeavouring to focus attention on differentially expressed genes located in chromosomal regions previously linked with obesity and/or diabetes. Using this strategy, we have identified Beacon as a potential new target for obesity and diabetes.

Amino Acid Sequence↗

PubMed2002

Additive host genetic factors influence fecal egg excretion rates during Schistosoma mansoni infection in a rural area in Brazil.

This study quantifies the influence of shared household and kinship on egg counts during Schistosoma mansoni infection in a sample from rural Brazil. Detailed genealogic information allowed assignment of 597 individuals to 6 multihousehold pedigrees residing in 145 households. A variance component method was used to partition egg counts into shared household, additive genetic, and individual-specific environmental effects. Host additive genetic effects consistently accounted for a large proportion of the variation in egg counts: 43% in an unadjusted model and 40% in model adjusted for covariates. In a model that examined the confounding of shared household with kinship, additive genetic effects still accounted for 27% of the variation in egg counts and shared household only 12%. The consistently important role for host additive genetic factors on the variation in egg counts points to new ways of modeling and understanding the mechanisms that contribute to trait variation during infection with S. mansoni.

Animals↗

PubMed2001

Large upward bias in estimation of locus-specific effects from genomewide scans.

The primary goal of a genomewide scan is to estimate the genomic locations of genes influencing a trait of interest. It is sometimes said that a secondary goal is to estimate the phenotypic effects of each identified locus. Here, it is shown that these two objectives cannot be met reliably by use of a single data set of a currently realistic size. Simulation and analytical results, based on variance-components linkage analysis as an example, demonstrate that estimates of locus-specific effect size at genomewide LOD score peaks tend to be grossly inflated and can even be virtually independent of the true effect size, even for studies on large samples when the true effect size is small. However, the bias diminishes asymptotically. The explanation for the bias is that the LOD score is a function of the locus-specific effect-size estimate, such that there is a high correlation between the observed statistical significance and the effect-size estimate. When the LOD score is maximized over the many pointwise tests being conducted throughout the genome, the locus-specific effect-size estimate is therefore effectively maximized as well. We argue that attempts at bias correction give unsatisfactory results, and that pointwise estimation in an independent data set may be the only way of obtaining reliable estimates of locus-specific effect-and then only if one does not condition on statistical significance being obtained. We further show that the same factors causing this bias are responsible for frequent failures to replicate initial claims of linkage or association for complex traits, even when the initial localization is, in fact, correct. The findings of this study have wide-ranging implications, as they apply to all statistical methods of gene localization. It is hoped that, by keeping this bias in mind, we will more realistically interpret and extrapolate from the results of genomewide scans.

Bias↗

PubMed2001

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.

Adult↗

PubMed2001

A quantitative trait locus influencing estrogen levels maps to a region homologous to human chromosome 20.

Estrogen, a steroid hormone, regulates reproduction and has been implicated in several diseases. We performed a genome-wide scan using multipoint linkage analysis implemented in a general pedigree-based variance component approach to identify genes with measurable effects on variation in estrogen levels in baboons. A microsatellite polymorphism, D20S171, located on human chromosome 20q13.11, showed strong evidence of linkage with a LOD score of 3.06 (P = 0.00009). This region contains several potential candidate genes including melanocortin 3 receptor (MC3R), cytochrome P-450 subfamily XXIV (CYP24), and breast carcinoma amplified sequence (BCAS1). This is the first evidence of a quantitative trait locus with a significant effect on estrogen.

Aging↗

PubMed2001

Endophenotypes as quantitative risk factors for psychiatric disease: rationale and study design.

Genetic influences on psychiatric disorders are well established. However, localization of the genes responsible for these effects has proved extremely difficult. One emerging strategy that may circumvent some of these difficulties is the use of quantitative risk factors, or endophenotypes, which are correlated with disease and may be closer to underlying genetic liability and to gene action than are diagnostic phenotypes. Genetic studies of quantitative endophenotypes require different sampling and analysis strategies than studies of disease state. The rationale for using quantitative risk factors as indicators of disease liability and the optimal study design for localizing genes influencing such risk factors are discussed.

Genetic Linkage↗

PubMed2001

Heritability of plasma amyloid beta in typical late-onset Alzheimer's disease pedigrees.

Plasma amyloid beta42 peptide (Abeta42) levels are significantly elevated in all genetic forms of early-onset Alzheimer's disease caused by familial Alzheimer's disease mutations or Down's syndrome. Moreover, recent studies have determined that both plasma Abeta42 and Abeta40 levels are significantly elevated in late-onset Alzheimer's disease (LOAD) patients, their cognitively normal first-degree relatives, and members of typical LOAD families when compared to appropriate controls. To determine the magnitude of the genetic component affecting plasma Abeta levels, we estimated the heritability of plasma Abeta42 and Abeta40 in 15 extended, multigenerational LOAD pedigrees, using a variance components method. Heritability estimates as high as 73 and 54% were found for plasma Abeta42 and Abeta40 levels, respectively. Inclusion of the ApoE epsilon4 dosage as a covariate was not found to have a significant effect on the heritability of these traits. These results suggest that genetic determinants other than ApoE account for a very substantial percentage of the phenotypic variance in plasma Abeta levels. The high heritability and the significant elevation of these traits in LOAD pedigrees suggest that at least some of the genetic determinants of plasma Abeta levels may lead to elevated Abeta and LOAD in these families. Thus, we suggest that plasma Abeta levels are quantitative traits that may be excellent surrogate markers for use in linkage analysis to identify loci that are important in typical LOAD.

Adult↗

PubMed2001

The effect of pedigree complexity on quantitative trait linkage analysis.

Due to the computational difficulties of performing linkage analysis on large complex pedigrees, most investigators resort to simplifying such pedigrees by some ad hoc strategy. In this paper, we suggest an analytical method to compare the power of various pedigree simplification schemes by using the asymptotic distribution of the likelihood-ratio statistic. We applied the method to the large Hutterine pedigree. Our results indicate that the breaking and reduction of inbreeding loops can greatly diminish the power to localize quantitative trait loci. We also present an efficient Monte Carlo method for estimating identity-by-descent allele sharing in large complex pedigrees. This method is used to facilitate a linkage analysis of serum IgE levels in the Hutterites without simplifying the pedigree.

Adult↗

PubMed2001

Evolution of the simulated data problem.

The simulated data problem was designed via an interactive process by the Simulation Problem Organizing Committee and the selected data simulators. Based on discussions at the previous Genetic Analysis Workshop, many of the features of previous simulation problems, such as a complex disease, genome scan, and replication, were retained and in addition, a population genetics model was used to generate the simulated genes. We describe the process that was used to structure the problem and summarize the discussions about many of the scientific issues that were considered.

Chromosome Mapping↗

PubMed2001

GAW12: simulated genome scan, sequence, and family data for a common disease.

The Genetic Analysis Workshop (GAW) 12 simulated data involves a common disease defined by imposing a threshold on a quantitative liability distribution. Associated with the disease are five quantitative risk factors, a quantitative environmental exposure, and a dichotomous environmental variable. Age at disease onset and household membership were also simulated. Genotype data, including 2,855 microsatellites on 22 autosomes, were simulated for 1,497 individuals in 23 families. Phenotype data and sequence data for seven candidate genes were provided for 1,000 of these individuals who were "living" and available for study. Data were simulated for 50 replicate samples in each of two populations, a general population and a population isolate formed from a small group of founders.

Adolescent↗

PubMed2001

Using step-wise linear regression to detect "functional" sequence variants: application to simulated data.

Step-wise linear regression was used to detect the "functional" sequence variant in gene 6 responsible for phenotypic variation in traits Q1 and Q2. Prior to analysis, single-nucleotide polymorphisms (SNPs) that were in complete or near complete linkage disequilibrium were binned. In total, we identified 11 separate alleles (or allelic bins). Analyses were performed on all 50 replicates. The "functional" allele variant in gene 6 (at position 5782) accounted for 24% of the variation in Q1 and 11% of the variation in Q2. We detected a significant association between this SNP and Q1 in 90% of the replicates (i.e., in 45 of 50 replicates) and between this SNP and Q2 in 78% of the replicates. Although significant associations were also observed with some nonfunctional SNPs, our results nevertheless suggest that simple step-wise regression may play a useful role in analyzing sequence data. Some additional extensions to this approach are suggested.

Adult↗

PubMed2001

Replication of linkage to quantitative trait loci: variation in location and magnitude of the lod score.

Replication of linkage signals from independent samples is considered an important step toward verifying the significance of linkage signals in studies of complex traits. The purpose of this empirical investigation was to examine the variability in the precision of localizing a quantitative trait locus (QTL) by analyzing multiple replicates of a simulated data set with the use of variance components-based methods. Specifically, we evaluated across replicates the variation in both the magnitude and the location of the peak lod scores. We analyzed QTLs whose effects accounted for 10-37% of the phenotypic variance in the quantitative traits. Our analyses revealed that the precision of QTL localization was directly related to the magnitude of the QTL effect. For a QTL with effect accounting for > 20% of total phenotypic variation, > 90% of the linkage peaks fall within 10 cM from the true gene location. We found no evidence that, for a given magnitude of the lod score, the presence of interaction influenced the precision of QTL localization.

Chromosome Mapping↗

PubMed2001

Power to localize the major gene for disease liability is increased after accounting for the effects of related quantitative phenotypes.

We used the simulated general population data for Genetic Analysis Workshop 12 to test whether power to localize the major gene for liability to disease is increased after accounting for the effects of correlated quantitative phenotypes. We performed the multipoint variance-component linkage analyses for the discrete trait twice: first analysis with age, sex, and EF1 as covariates, and the second analysis with age, sex, EF1, and Q1-Q5 as covariates. Major locus heritability (h2q) (0.80 +/- 0.06) and lod score (6.4) averaged over the number of replicates used are significantly higher in the second analysis compared with the first analysis (h2q = 0.39 +/- 0.12, lod = 2.6). Thus, in the present analysis, power to detect linkage and localize the genes for liability to disease appears to be increased after accounting for the effects of five quantitative traits that are correlated with the liability.

Chromosome Mapping↗

PubMed2001

Does accounting for mitochondrial genetic variation improve the fit of genetic models?

We describe a simple variance component model for estimating the effect of mitochondrial DNA (mtDNA) inheritance on quantitative trait variation. The model is applied to quantitative trait Q5 in the simulated general population data from Genetic Analysis Workshop (GAW) 12. Although the mitochondrial effect on Q5 is small (5.3%) and the power of the method to detect the effect is correspondingly low, analysis over the available population replicates demonstrates that the effect of maternal relatedness can be detected and estimated accurately.

Analysis of Variance↗

PubMed2001

Linkage analysis of quantitative traits in randomly ascertained pedigrees: comparison of penetrance-based and variance component analysis.

Penetrance-based linkage analysis and variance component linkage analysis are two methods that are widely used to localize genes influencing quantitative traits. Using computer programs PAP and SOLAR as representative software implementations, we have conducted an empirical comparison of both methods' power to map quantitative trait loci in extended, randomly ascertained pedigrees, using simulated data. Two-point linkage analyses were conducted on several quantitative traits of different genetic and environmental etiology using both programs, and the lod scores were compared. The two methods appear to have similar power when the underlying quantitative trait locus is diallelic, with one or the other method being slightly more powerful depending on the characteristics of the quantitative trait and the quantitative trait locus. In the case of a multiallelic quantitative trait locus, however, the variance component approach has much greater power. These findings suggest that one should give careful thought to the likely allelic architecture of the quantitative trait to be analyzed when choosing between these two analytical approaches. It may be the case in general that linkage methods which explicitly or implicitly rely on the assumption of a diallelic trait locus fare poorly when this assumption is incorrect.

Chromosome Mapping↗

PubMed2001

The utility of Bayesian model averaging for detecting known oligogenic effects.

In this paper we examine the utility of the Bayesian model averaging approach proposed by Blangero et al. [Genet Epidemiol 17(Suppl. 1):S67-72, 1999] for the detection of known oligogenic effects on quantitative trait 1 (Q1) in the simulated Genetic Analysis Workshop 12 data. The results were consistent with the underlying genetic model: two major QTLs were identified on chromosomes 19 and 2, with heritabilities of 17% and 20%, respectively, located near the position of major genes 1 and 2.

Analysis of Variance↗

PubMed2001

Distribution of lod scores in oligogenic linkage analysis.

In variance component oligogenic linkage analysis it can happen that the residual additive genetic variance bounds to zero when estimating the effect of the ith quantitative trait locus. Using quantitative trait Q1 from the Genetic Analysis Workshop 12 simulated general population data, we compare the observed lod scores from oligogenic linkage analysis with the empirical lod score distribution under a null model of no linkage. We find that zero residual additive genetic variance in the null model alters the usual distribution of the likelihood-ratio statistic.

Chromosome Mapping↗