Understanding responses to predictive genetic testing: A grounded theory approach

Abstract

In view of the absence of data concerning the understanding and experience of families in which one or more members have undergone predictive genetic testing, a pilot study using a qualitative methodology was conducted with members of families at risk for the late-onset genetic disease, familial adenomatous polyposis (FAP). Semistructured interviews were conducted to elicit illness representations, with responses tape-recorded and analysed using a grounded theory approach. Several themes emerged. The most striking was that when genetic testing indicated an extremely low risk of developing the disease, there was a desire to continue regular bowel screening, even though it was experienced as extremely aversive. Possible explanations draw upon both other themes of the interviews, and psychological models. The role of “functional pessimism” and “uncertain wellness” in maintaining a high threat from the disease, and the role of reinforcement and the nature of tests in providing bowel screening with a high value are discussed.     

Approaching archetypes: reconsidering innateness

The question of innateness has hounded Jungian psychology since Jung originally postulated the archetype as an a priori structure within the psyche. During his life and after his death he was continually accused of Lamarckianism and criticized for his theory that the archetypes existed as prior structures. More recently, with the advent of genetic research and the human genome project, the idea that psychological structures can be innate has come under even harsher criticism even within Jungian thought. There appears to be a growing consensus that Jung's idea of innate psychological structures was misguided, and that perhaps the archetype-as-such should be abandoned for more developmental and 'emergent' theories of the psyche. The purpose of this essay is to question this conclusion, and introduce some literature on psychological innateness that appears relevant to this discussion.     

A genetic variant (COMT) coding dopaminergic activity predicts personality traits in healthy elderly

Association studies between the NEO five factor personality inventory and COMT rs4680 have focused on young adults and the results have been inconsistent. However, personality and cortical changes with age may put older adults in a more sensitive range for detecting a relationship. The present study examined associations of COMT rs4680 and personality in older adults.Genetic association analyses were carried out between the NEO and the targeted COMT rs4680 in a large, well-characterized sample of healthy, cognitively normal older adults (N = 616, mean age = 69.26 years).Three significant associations were found: participants with GG genotype showed lower mean scores on Neuroticism (p = 0.039) and higher scores on Agreeableness (p = 0.020) and Conscientiousness (p = 0.006) than participants with AA or AG genotypes.These results suggest that older adults with higher COMT enzymatic activity (GG), therefore lower dopamine level, have lower Neuroticism scores, and higher Agreeableness and Conscientiousness scores. This is consistent with a recent model of phasic and tonic dopamine release suggesting that even though GG genotype is associated with lower tonic dopamine release, the phasic release of dopamine might be optimal for a more adaptive personality profile.     

Classification of dog barks: a machine learning approach

In this study we analyzed the possible context-specific and individual-specific features of dog barks using a new machine-learning algorithm. A pool containing more than 6,000 barks, which were recorded in six different communicative situations was used as the sound sample. The algorithm's task was to learn which acoustic features of the barks, which were recorded in different contexts and from different individuals, could be distinguished from another. The program conducted this task by analyzing barks emitted in previously identified contexts by identified dogs. After the best feature set had been obtained (with which the highest identification rate was achieved), the efficiency of the algorithm was tested in a classification task in which unknown barks were analyzed. The recognition rates we found were highly above chance level: the algorithm could categorize the barks according to their recorded situation with an efficiency of 43% and with an efficiency of 52% of the barking individuals. These findings suggest that dog barks have context-specific and individual-specific acoustic features. In our opinion, this machine learning method may provide an efficient tool for analyzing acoustic data in various behavioral studies.     

IRT模型参数估计的新方法——MCMC算法

本研究主要探讨MCMC算法在IRT模型参数估计中的实现及其估计精度.通过模拟多种实验条件(人少题少、人题适中、人多题多、被试数及其参数固定情况下项目数变化、项目数及其参数固定情况下人数变化),考察两参数和叁参数Logistic模型的MCMC算法对其参数估计的精度,并与国际通用测量程序-Bilog程序(E-M算法)进行比较研究.模拟实验研究表明,上述各种实验条件下,MCMC算法均可用于IRT模型参数估计,且其估计的精度均较Bilog程序(E-M算法)高,值得推广.     

缺失数据处理的期望-极大化算法与马尔可夫蒙特卡洛方法

缺失数据普遍存在于心理学研究中, 影响着统计推断。极大似然估计(MLE)与基于贝叶斯的多重借补(MI)是处理缺失数据的两类重要方法。期望-极大化算法(EM)是寻求MLE的一种强有力的方法。马尔可夫蒙特卡洛方法(MCMC)可以相对简易地实现MI, 而且可以适用于复杂情况下的缺失数据处理。结合研究的需要讨论了实现这两类方法的适用软件。     

Temporalising Tableaux

As a remedy for the bad computational behaviour of first-order temporal logic (FOTL), it has recently been proposed to restrict the application of temporal operators to formulas with at most one free variable thereby obtaining so-called monodic fragments of FOTL. In this paper, we are concerned with constructing tableau algorithms for monodic fragments based on decidable fragments of first-order logic like the two-variable fragment or the guarded fragment. We present a general framework that shows how existing decision procedures for first-order fragments can be used for constructing a tableau algorithm for the corresponding monodic fragment of FOTL. Some example instantiations of the framework are presented.     

An EM algorithm for fitting two-level structural equation models

Maximum likelihood is an important approach to analysis of two-level structural equation models. Different algorithms for this purpose have been available in the literature. In this paper, we present a new formulation of two-level structural equation models and develop an EM algorithm for fitting this formulation. This new formulation covers a variety of two-level structural equation models. As a result, the proposed EM algorithm is widely applicable in practice. A practical example illustrates the performance of the EM algorithm and the maximum likelihood statistic.We are thankful to the reviewers for their constructive comments that have led to significant improvement on the first version of this paper. Special thanks are due to the reviewer who suggested a comparison with the LISREL program in the saturated means model, and provided its setup and output. This work was supported by National Institute on Drug Abuse grants DA01070, DA00017, and a UNH 2002 Summer Faculty Fellowship.     

Leading Voices and the Power of One: 2002 Presidential Address to the National Society of Genetic Counselors

Presented as the annual education conference of the National Society of Genetic Counselors in Phoenix, Arizona on November 11, 2002.     

A marginal maximum likelihood method for the vector threshold model to analyze dichotomous choice data

Abstract: At least two types of models, the vector model and the unfolding model can be used for the analysis of dichotomous choice data taken from, for example, the pick any/ n method. The previous vector threshold models have a difficulty with estimation of the nuisance parameters such as the individual vectors and thresholds. This paper proposes a new probabilistic vector threshold model, where, unlike the former vector models, the angle that defines an individual vector is a random variable, and where the marginal maximum likelihood estimation method using the expectation-maximization algorithm is adopted to avoid incidental parameters. The paper also attempts to discuss which of the two models is more appropriate to account for dichotomous choice data. Two sets of dichotomous choice data are analyzed by the model.