Jonathan Corbett

Longitudinal and Age Trends of Metabolic Syndrome and Its Risk Factors: The Family Heart Study

BackgroundWe report longitudinal changes in the metabolic syndrome (MetS) in 2,458 participants from 480 families in the Family Heart Study. Participants were examined between 1994–96 (FHS-T1) and 2002–03 (FHS-T2), about 7.4 years apart. Additionally, the impact of medication on estimates of MetS prevalence, and associations of MetS with prevalent coronary heart disease (CHD) and type 2 diabetes (T2D) were studied.MethodsThree definitions for MetS prevalence were considered. One represented the original (o) National Cholesterol Education Program (NCEP) MetS criteria. Two others considered the confounding of medications effects, respectively (m) lipid medications constituted a categorical diagnostic criterion for lipids variables, and (c) lipids and blood pressure variables were corrected with average clinical trials medications effects. Logistic regression of MetS on CHD and T2D, as well as the trend analysis of MetS by age, were performed.ResultsMetS increased from 17.1% in FHS-T1(o) to 28.8% in FHS-T2(o); from 19.7% in FHS-T1(m) to 42.5% in FHS-T2(m); and from 18.4% in FHS-T1(c) to 33.6% in FHS-T2(c). While we observed adverse changes in all risk factors, the greatest increase was for waist circumference (25%). The percentages of MetS were about 2 to almost 3 times higher in ages 50 years and older than in younger ages. The odds of having prevalent CHD were about 2.5 times higher in the subjects classified with MetS than without.ConclusionMetS percentages increased noticeably longitudinally and cross-sectionally with older age. These conclusions were reached with and without considering medication use, but correcting risk factors for medications use affects the MetS prevalence estimates. As found in other studies, MetS was associated with increased odds for prevalent CHD.

A sex-adjusted and age-adjusted genome screen for nested alcohol dependence diagnoses.

Alcohol dependence is a complex disorder with a substantial genetic contribution to susceptibility. The Collaborative Study on the Genetics of Alcoholism is a multi-site study whose purpose is to detect, localize, and characterize genes contributing to this susceptibility. Previous linkage analyses of the trait of alcohol dependence in Collaborative Study on the Genetics of Alcoholism have used affected sib-pair methods with a dichotomous phenotype definition. In contrast, the analysis in this paper uses a sex-adjusted and age-adjusted multiple threshold liability model. The use of such a model, in that it includes unaffected as well as as affected subjects and in that it utilizes the differential severity of a diagnosis scale, should heuristically be more powerful than a straight affected sib-pair analysis. Three regions of interest are found on chromosome 1 (lod 5.17), chromosome 4 (lod 3.46), and chromosome 8 (lod 4.31). The region on chromosome 1 near the marker D1S532 is in the region previously reported as linked to alcohol dependence and correlated phenotypes in this dataset. The region on chromosome 4 near the alcohol dehydrogenase gene cluster has been reported to be linked to alcohol dependence in other studies, as well as to the alcohol consumption phenotype ‘Maximum Number of Drinks in a 24-Hour Period’ in this dataset. The region on chromosome 8 near the marker D8S1988 is homologous to a section of rat chromosome 5 to which an alcohol consumption phenotype has been linked.

Power Loss for Linkage Analysis due to the Dichotomization of Trichotomous Phenotypes

Objectives: Some traits, while naturally polychotomous, are routinely dichotomized for genetic analysis. Dichotomization, intuitively, leads to a loss of power to detect linkage, as some phenotypic variability is discarded. This paper examines this power loss in the context of a trichotomous trait. Methods: To examine this power loss, we performed a simulation study where a trichotomous trait was simulated in a sample of 1,000 sib-pairs under various genetic models. The study was replicated 1,000 times. Linkage analysis using a variance components method, as implemented in Mx, was then performed on the trichotomous trait and compared with that on a dichotomized version of the trait. Results: A comparison of the power and false positive rates of the analyses shows that power to detect linkage was increased by up to 22 percentage points simply by examining the trait as a trichotomy instead of a dichotomy. Under all models examined, the trichotomous analysis outperformed the dichotomous version. Conclusions: Comparable levels of false positive rates under both methods confirm that this power gain comes solely from the information lost upon dichotomization. Thus, dichotomizing tri- or poly-chotomous traits can lead to crippling power loss, especially in the case of many loci of small effect.

8 The lod score method

The lod score method originated in a seminal article by Newton Morton in 1955. The method is broadly concerned with issues of power and the posterior probability of linkage, ensuring that a reported linkage has a high probability of being a true linkage. In addition, the method is sequential, so that pedigrees or lod curves may be combined from published reports to pool data for analysis. This approach has been remarkably successful for 50 years in identifying disease genes for Mendelian disorders. After discussing these issues, we consider the situation for complex disorders, where the maximum lod score (MLS) statistic shares some of the advantages of the traditional lod score approach but is limited by unknown power and the lack of sharing of the primary data needed to optimally combine analytic results. We may still learn from the lod score method as we explore new methods in molecular biology and genetic analysis to utilize the complete human DNA sequence and the cataloging of all human genes.

Model-based methods for linkage analysis.

The logarithm of an odds ratio (LOD) score method originated in a seminal article by Newton Morton in 1955. The method is broadly concerned with issues of power and the posterior probability of linkage, ensuring that a reported linkage has a high probability of being a true linkage. In addition, the method is sequential so that pedigrees or LOD curves may be combined from published reports to pool data for analysis. This approach has been remarkably successful for 50 years in identifying disease genes for Mendelian disorders. After discussing these issues, we consider the situation for complex disorders where the maximum LOD score statistic shares some of the advantages of the traditional LOD score approach, but is limited by unknown power and the lack of sharing of the primary data needed to optimally combine analytic results. We may still learn from the LOD score method as we explore new methods in molecular biology and genetic analysis to utilize the complete human DNA sequence and the cataloging of all human genes.

Use of a random coefficient regression (RCR) model to estimate growth parameters

We used a random coefficient regression (RCR) model to estimate growth parameters for the time series of observed serum glucose levels in the Replicate 1 of the Genetic Analysis Workshop 13 simulated data. For comparison, a two time-point interval was also selected and the slope between these two observations was calculated. This process yielded four phenotypes: the RCR growth phenotype, a two time-point slope phenotype, and Time 1 and Time 2 serum glucose level phenotypes. These four phenotypes were used for linkage analyses on simulated chromosomes 5, 7, 9, and 21, those chromosomes that contained loci affecting the growth course for serum glucose levels. The linkage analysis of the RCR-derived phenotype showed overwhelming evidence for linkage at one locus (LOD 65.78 on chromosome 5), while showing elevated but nonsignificant LOD scores for two other loci (LOD 1.25 on chromosome 7, LOD 1.10 on chromosome 9), and no evidence of linkage for the final locus. The two time-point slope phenotype showed evidence for linkage at one locus (LOD 4.16 on chromosome 5) but no evidence for linkage at any of the other loci. A parallel cross-sectional approach, using as input phenotypes the endpoints of the two-point slope phenotype, gave strong linkage results for the major locus on chromosome 5 (maximal LOD scores of 17.90 and 27.24 for Time 1 and Time 2, respectively) while showing elevated but nonsignificant linkage results on chromosome 7 (maximal LOD scores of 1.71 and 1.48) and no evidence for linkage at the two remaining loci. The RCR growth parameter showed more power to detect linkage to the major locus than either the cross-sectional or two-point slope approach, but the cross-sectional approach gave a higher maximal LOD score for one of the minor loci.

Analysis of deformational transformations with spatio-temporal continuous wavelet transforms

This paper deals with the estimation of deformational parameters in discrete spatio-temporal signals. The parameters of concern correspond to time-varying scales. As such they can be the coefficients of either a Taylor expansion of the scale or a given deformational transformation. At first sight there are just a few deformational transformations that provide continuous wavelet transforms. The approach presented in this paper associates deformational transformations to motion transformations taking place in higher dimensional spaces and projected on the sensor plane. Then finding continuous wavelet transforms becomes much easier since numerous continuous wavelet transforms have already been defined for motion analysis. It is also known that spatio-temporal continuous wavelet transforms provide minimum-mean-squared-error estimates of motion parameters. Any deformational transformation of features embedded in a spatio-temporal signal may always be related to the projection on the sensor plane of the motion of a rigid object taking place in a higher dimensional space. This reasoning applies conversely. The associated rigid motion may be actual or virtual may take place either on a flat space or on a curved space immersed in higher dimensions. Continuous wavelet transforms for the estimation of deformational parameters may be then deduced from those already existing in motion analysis.

A clustering approach for localizing disease susceptibility loci.

Latent class (LCA) and cluster analysis (CLA) were utilized to identify trait loci for the Genetic Analysis Workshop 12 simulated disease. These techniques create non‐overlapping subsets of concordant and discordant affected relative pairs based upon identity‐by‐descent (IBD) allele sharing at sequences of markers. Subgroups with a large proportion of affected pairs are used to identify markers in proximity to disease susceptibility loci. Both methods are model‐free and make use of information from affected and unaffected subjects. In analyses performed without knowledge of the true disease model, LCA and CLA identified regions containing five of the seven trait loci.