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Gregory Carey

Publications and source records attributed to Gregory Carey.

6 recordsLinked to original sources
PubMed2005

The Washington University Twin Study of alcoholism.

Genetic contributions to the liability to develop alcoholism in males of Northern and Western European ancestry are well-established. However, questions remain concerning the role of genetic variation in the etiology of alcoholism among non-white populations, among women, and the possibility of etiological heterogeneity in subtypes of alcoholism. The answers to these questions are needed to help define phenotypes for molecular genetic studies searching for QTLs for alcoholism. Twins from 295 pairs were consecutively ascertained at inpatient and outpatient psychiatric and alcohol treatment facilities in St. Louis, MO in 1981-1986. Probands and willing cotwins were evaluated by structured psychiatric interviews, psychometric assessment, and lifetime treatment records. One hundred fifty-four probands met criteria for alcohol abuse/dependence (AAD), including twins from 45 MZ, 50 same-sex DZ, and 59 opposite-sex pairs. Twin-pair resemblance was evaluated for AAD and alcohol dependence (AD), as well as for subsets defined by gender, patterns of comorbidity, ethnic background, and clinical features. Among males, heritability of AAD and AD was substantial, with little evidence for common environmental contributions to family resemblance. Pair resemblance among females was also substantial, but similar for MZ and DZ pairs, yielding near-zero heritability estimates. However, based on these sample sizes, the sex differences were not statistically significant. The results confirm prior studies of strong genetic influences on alcoholism in males, but suggest lower genetic influence in females. Power to test other sources of heterogeneity was limited, but the results suggest no evidence for higher heritability for male early onset alcoholism or for alcoholism with comorbid antisocial personality.

Adolescent↗
PubMed2005

Cholesky problems.

Behavioral geneticists commonly parameterize a genetic or environmental covariance matrix as the product of a lower diagonal matrix postmultiplied by its transpose-a technique commonly referred to as "fitting a Cholesky." Here, simulations demonstrate that this procedure is sometimes valid, but at other times: (1) may not produce fit statistics that are distributed as a chi2; or (2) if the distribution of the fit statistic is chi2, then the degrees of freedom (df) are not always the difference between the number of parameters in the general model less the number of parameters in a constrained model. It is hypothesized that the problem is related to the fact that the Cholesky parameterization requires that the covariance matrix formed by the product be either positive definite or singular. Even though a population covariance matrix may be positive definite, the combination of sampling error and the derived--as opposed to directly observed--nature of genetic and environmental matrices allow matrices that are negative (semi) definite. When this occurs, fitting a Cholesky constrains the numerical area of search and compromises the maximum likelihood theory currently used in behavioral genetics. Until the reasons for this phenomenon are understood and satisfactory solutions are developed, those who fit Cholesky matrices face the burden of demonstrating the validity of their fit statistics and the df for model comparisons. An interim remedy is proposed--fit an unconstrained model and a Cholesky model, and if the two differ, then report the difference in fit statistics and parameter estimates. Cholesky problems are a matter of degree, not of kind. Thus, some Cholesky solutions will differ trivially from the unconstrained solutions, and the importance of the problems must be assessed by how often the two lead to different substantive interpretation of the results. If followed, the proposed interim remedy will develop a body of empirical data to assess the extent to which Cholesky problems are important substantive issues versus statistical curiosities.

Algorithms↗
PubMed2005

The intraclass covariance matrix.

Introduced by C.R. Rao in 1945, the intraclass covariance matrix has seen little use in behavioral genetic research, despite the fact that it was developed to deal with family data. Here, I reintroduce this matrix, and outline its estimation and basic properties for data sets on pairs of relatives. The intraclass covariance matrix is appropriate whenever the research design or mathematical model treats the ordering of the members of a pair as random. Because the matrix has only one estimate of a population variance and covariance, both the observed matrix and the residual matrix from a fitted model are easy to inspect visually; there is no need to mentally average homologous statistics. Fitting a model to the intraclass matrix also gives the same log likelihood, likelihood-ratio (LR) chi2, and parameter estimates as fitting that model to the raw data. A major advantage of the intraclass matrix is that only two factors influence the LR chi2--the sampling error in estimating population parameters and the discrepancy between the model and the observed statistics. The more frequently used interclass covariance matrix adds a third factor to the chi2--sampling error of homologous statistics. Because of this, the degrees of freedom for fitting models to an intraclass matrix differ from fitting that model to an interclass matrix. Future research is needed to establish differences in power-if any--between the interclass and the intraclass matrix.

Algorithms↗