Phenotypic and genetic analyses of a short measure of psychosis-proneness in a large-scale Australian twin study

Previous genetic analyses of psychosis proneness have been limited by their small sample size. For the purposes of large-scale screening, a 12-item questionnaire was developed through a two-stage process of reduction from the full Chapman and Chapman scales. 3685 individuals (including 1438 complete twin pairs) aged 18–25 years and enrolled in the volunteer Australian Twin Registry returned a mail questionnaire which included this psychosis proneness scale and the Eysenck Personality Questionnaire. Despite the brevity of the questionnaire, item and factor analysis identified four unambiguous and essentially uncorrelated scales. There were (1) Perceptual Aberration – Magical Ideation; (2) Hypomania – Impulsivity/Nonconformity; (3) Social Anhedonia and (4) Physical Anhedonia. Model-fitting analyses showed additive genetic and specific environmental factors were sufficient for three of the four scales, with the Social Anhedonia scale requiring also a parameter for genetic dominance. There was no evidence for the previously hypothesised sex differences in the genetic determination of psychosis-proneness. The potential value of multivariate genetic analysis to examine the relationship between these four scales and dimensions of personality is discussed. The growing body of longitudinal evidence on psychosis-proneness suggests the value of incorporating this brief measure into developmental twin studies.

Genetic covariance among measures of information processing speed, working memory, and IQ

The genetic relationship between lower (information processing speed), intermediate (working memory), and higher levels (complex cognitive processes as indexed by IQ) of mental ability was studied in a classical twin design comprising 166 monozygotic and 190 dizygotic twin pairs. Processing speed was measured by a choice reaction time (RT) task (2-, 4-, and 8-choice), working memory by a visual-spatial delayed response task, and IQ by the Multidimensional Aptitude Battery. Multivariate analysis, adjusted for test-retest reliability, showed the presence of a genetic factor influencing all variables and a genetic factor influencing 4- and 8-choice RTs, working memory, and IQ. There were also genetic factors specific to 8-choice RT, working memory, and IQ. The results confirmed a strong relationship between choice RT and IQ (phenotypic correlations: -0.31 to -0.53 in females, -0.32 to -0.56 in males; genotypic correlations: -0.45 to -0.70) and a weaker but significant association between working memory and IQ (phenotypic: 0.26 in females, 0.13 in males; genotypic: 0.34). A significant part of the genetic variance (43%) in IQ was not related to either choice RT or delayed response performance, and may represent higher order cognitive processes.

Trait physiology and crop modelling as a framework to link phenotypic complexity to underlying genetic systems

New tools derived from advances in molecular biology have not been widely adopted in plant breeding for complex traits because of the inability to connect information at gene level to the phenotype in a manner that is useful for selection. In this study, we explored whether physiological dissection and integrative modelling of complex traits could link phenotype complexity to underlying genetic systems in a way that enhanced the power of molecular breeding strategies. A crop and breeding system simulation study on sorghum, which involved variation in 4 key adaptive traits-phenology, osmotic adjustment, transpiration efficiency, stay-green-and a broad range of production environments in north-eastern Australia, was used. The full matrix of simulated phenotypes, which consisted of 547 location-season combinations and 4235 genotypic expression states, was analysed for genetic and environmental effects. The analysis was conducted in stages assuming gradually increased understanding of gene-to-phenotype relationships, which would arise from physiological dissection and modelling. It was found that environmental characterisation and physiological knowledge helped to explain and unravel gene and environment context dependencies in the data. Based on the analyses of gene effects, a range of marker-assisted selection breeding strategies was simulated. It was shown that the inclusion of knowledge resulting from trait physiology and modelling generated an enhanced rate of yield advance over cycles of selection. This occurred because the knowledge associated with component trait physiology and extrapolation to the target population of environments by modelling removed confounding effects associated with environment and gene context dependencies for the markers used. Developing and implementing this gene-to-phenotype capability in crop improvement requires enhanced attention to phenotyping, ecophysiological modelling, and validation studies to test the stability of candidate genetic regions.

The genetic basis of academic achievement on the Queensland Core Skills Test and its shared genetic variance with IQ

First, this study examined genetic and environmental sources of variation in performance on a standardised test of academic achievement, the Queensland Core Skills Test (QCST) (Queensland Studies Authority, 2003a). Second, it assessed the genetic correlation among the QCST score and Verbal and Performance IQ measures using the Multidimensional Aptitude Battery (MAB), [Jackson, D. N. (1984) Multidimensional Aptitude Battery manual. Port Huron, MI:Research Psychologist Press, Inc.]. Participants were 256 monozygotic twin pairs and 326 dizygotic twin pairs aged from 15 to 18 years (mean 17 years +/- 0.4 [SD]) when achievement tested, and from 15 to 22 years (mean 16 years +/- 0.4 [SD]) when IQ tested. Univariate analysis indicated a heritability for the QCST of 0.72. Adjustment to this estimate due to truncate selection (downward adjustment) and positive phenotypic assortative mating (upward adjustment) suggested a heritability of 0.76 The phenotypic (0.81) and genetic (0.91) correlations between the QCST and Verbal IQ (VIQ) were significantly stronger than the phenotypic (0.57) and genetic (0.64) correlations between the QCST and Performance IQ (PIQ). The findings suggest that individual variation in QCST performance is largely due to genetic factors and that common environmental effects may be substantially accounted for by phenotypic assortative mating. Covariance between academic achievement on the QCST and psychometric IQ (particularly VIQ) is to a large extent due to common genetic influences.

Trait physiology and crop modelling to link phenotypic complexity to underlying genetic systems

New tools derived from advances in molecular biology have not been widely adopted in plant breeding because of the inability to connect information at gene level to the phenotype in a manner that is useful for selection. We explore whether a crop growth and development modelling framework can link phenotype complexity to underlying genetic systems in a way that strengthens molecular breeding strategies. We use gene-to-phenotype simulation studies on sorghum to consider the value to marker-assisted selection of intrinsically stable QTLs that might be generated by physiological dissection of complex traits. The consequences on grain yield of genetic variation in four key adaptive traits – phenology, osmotic adjustment, transpiration efficiency, and staygreen – were simulated for a diverse set of environments by placing the known extent of genetic variation in the context of the physiological determinants framework of a crop growth and development model. It was assumed that the three to five genes associated with each trait, had two alleles per locus acting in an additive manner. The effects on average simulated yield, generated by differing combinations of positive alleles for the traits incorporated, varied with environment type. The full matrix of simulated phenotypes, which consisted of 547 location-season combinations and 4235 genotypic expression states, was analysed for genetic and environmental effects. The analysis was conducted in stages with gradually increased understanding of gene-to-phenotype relationships, which would arise from physiological dissection and modelling. It was found that environmental characterisation and physiological knowledge helped to explain and unravel gene and environment context dependencies. We simulated a marker-assisted selection (MAS) breeding strategy based on the analyses of gene effects. When marker scores were allocated based on the contribution of gene effects to yield in a single environment, there was a wide divergence in rate of yield gain over all environments with breeding cycle depending on the environment chosen for the QTL analysis. It was suggested that knowledge resulting from trait physiology and modelling would overcome this dependency by identifying stable QTLs. The improved predictive power would increase the utility of the QTLs in MAS. Developing and implementing this gene-to-phenotype capability in crop improvement requires enhanced attention to phenotyping, ecophysiological modelling, and validation studies to test the stability of candidate QTLs.

Some vocabulary and grammar for the analysis of multi-environment trials, as applied to the analysis of FPB and PPB trials

For the improvement of genetic material suitable for on farm use under low-input conditions, participatory and formal plant breeding strategies are frequently presented as competing options. A common frame of reference to phrase mechanisms and purposes related to breeding strategies will facilitate clearer descriptions of similarities and differences between participatory plant breeding and formal plant breeding. In this paper an attempt is made to develop such a common framework by means of a statistically inspired language that acknowledges the importance of both on farm trials and research centre trials as sources of information for on farm genetic improvement. Key concepts are the genetic correlation between environments, and the heterogeneity of phenotypic and genetic variance over environments. Classic selection response theory is taken as the starting point for the comparison of selection trials (on farm and research centre) with respect to the expected genetic improvement in a target environment (low-input farms). The variance-covariance parameters that form the input for selection response comparisons traditionally come from a mixed model fit to multi-environment trial data. In this paper we propose a recently developed class of mixed models, namely multiplicative mixed models, also called factor-analytic models, for modelling genetic variances and covariances (correlations). Mixed multiplicative models allow genetic variances and covariances to be dependent on quantitative descriptors of the environment, and confer a high flexibility in the choice of variance-covariance structure, without requiring the estimation of a prohibitively high number of parameters. As a result detailed considerations regarding selection response comparisons are facilitated. ne statistical machinery involved is illustrated on an example data set consisting of barley trials from the International Center for Agricultural Research in the Dry Areas (ICARDA). Analysis of the example data showed that participatory plant breeding and formal plant breeding are better interpreted as providing complementary rather than competing information.

Canopy carbon and oxygen isotope composition of 9-year-old hoop pine families in relation to seedling carbon isotope composition, growth, field growth performance, and canopy nitrogen concentration

Carbon isotope composition (delta C-13), oxygen isotope composition (delta O-18), and nitrogen concentration (N-mass) of branchlet tissue at two canopy positions were assessed for glasshouse seedlings and 9-year-old hoop pine (Araucaria cunninghamii Ait. ex D. Don) trees from 22 open-pollinated families grown in 5 blocks of a progeny test at a water-limited and nitrogen-deficient site in southeastern Queensland, Australia. Significant variations in canopy delta C-13, delta O-18, and N-mass existed among the 9-year-old hoop pine families, with a heritability estimate of 0.72 for branchlet delta C-13 from the upper inner canopy position. There was significant variation in canopy delta C-13 of glasshouse seedlings between canopy positions and among the families, with a heritability estimate of 0.66. The canopy delta C-13 was positively related to canopy N-mass only for the upper outer crown in the field (R = 0.62, p < 0.001). Phenotypic correlations existed between tree height and canopy delta C-13 (R = 0.37-0.41, p < 0.001). Strong correlations were found between family canopy delta C-13 at this site and those at a wetter site and between field canopy delta C-13 and glasshouse seedling delta C-13. The mechanisms of the variation in canopy delta C-13 are discussed in relation to canopy photosynthetic capacity as reflected in the N-mass and stomatal conductance as indexed by canopy delta O-18.

Genetic sources of covariation among P3(00) and online performance variables in a delayed-response working memory task

Genetic and environmental sources of covariation among the P3(00) and online performance elicited in a delayed-response working memory task, and psychometric IQ assessed by the multidimensional aptitude battery, were examined in an adolescent twin sample. An association between frontal P3 latency and task performance (phenotypic r = -0.33; genotypic r = -0.49) was indicated, with genes (i.e. twin status) accounting for a large part of the covariation ( > 70%). In contrast, genes influencing P3 amplitude mediated only a small part (2%) of the total genetic variation in task performance. While task performance mediated 15% of the total genetic variation in IQ (phenotypic r = 0.22; genotypic r = 0.39) there was no association between P3 latency and IQ or P3 amplitude with IQ. The findings provide some insight into the inter-relationships among psychophysiological, performance and psychometric measures of cognitive ability, and provide support for a levels-of-processing genetic model of cognition where genes act on specific sub-components of cognitive processes.

Genetic Covariation between Serum {gamma}-Glutamyltransferase Activity and Cardiovascular Risk Factors

Background: Several studies have shown that variation in serum gamma-glutamyltransferase (GGT) in the population is associated with risk of death or development of cardiovascular disease, type 2 diabetes, stroke, or hypertension. This association is only partly explained by associations between GGT and recognized risk factors. Our aim was to estimate the relative importance of genetic and environmental sources of variation in GGT as well as genetic and environmental sources of covariation between GGT and other liver enzymes and markers of cardiovascular risk in adult twin pairs. Methods: We recruited 1134 men and 2241 women through the Australian Twin Registry. Data were collected through mailed questionnaires, telephone interviews, and by analysis of blood samples. Sources of variation in GGT, alanine aminotransferase (ALT), and aspartate aminotransferase (AST) and of covariation between GGT and cardiovascular risk factors were assessed by maximum-likelihood model-fitting. Results: Serum GGT, ALT, and AST were affected by additive genetic and nonshared environmental factors, with heritabilities estimated at 0.52, 0.48, and 0.32, respectively. One-half of the genetic variance in GGT was shared with ALT, AST, or both. There were highly significant correlations between GGT and body mass index; serum lipids, lipoproteins, glucose, and insulin; and blood pressure. These correlations were more attributable to genes that affect both GGT and known cardiovascular risk factors than to environmental factors. Conclusions: Variation in serum enzymes that reflect liver function showed significant genetic effects, and there was evidence that both genetic and environmental factors that affect these enzymes can also affect cardiovascular risk. (C) 2002 American Association for Clinical Chemistry.

Fitting genetic models to twin data with binary and ordered categorical responses: A comparison of structural equation modelling and Bayesian hierarchical models

We compare Bayesian methodology utilizing free-ware BUGS (Bayesian Inference Using Gibbs Sampling) with the traditional structural equation modelling approach based on another free-ware package, Mx. Dichotomous and ordinal (three category) twin data were simulated according to different additive genetic and common environment models for phenotypic variation. Practical issues are discussed in using Gibbs sampling as implemented by BUGS to fit subject-specific Bayesian generalized linear models, where the components of variation may be estimated directly. The simulation study (based on 2000 twin pairs) indicated that there is a consistent advantage in using the Bayesian method to detect a correct model under certain specifications of additive genetics and common environmental effects. For binary data, both methods had difficulty in detecting the correct model when the additive genetic effect was low (between 10 and 20%) or of moderate range (between 20 and 40%). Furthermore, neither method could adequately detect a correct model that included a modest common environmental effect (20%) even when the additive genetic effect was large (50%). Power was significantly improved with ordinal data for most scenarios, except for the case of low heritability under a true ACE model. We illustrate and compare both methods using data from 1239 twin pairs over the age of 50 years, who were registered with the Australian National Health and Medical Research Council Twin Registry (ATR) and presented symptoms associated with osteoarthritis occurring in joints of the hand.

Multivariate genetic analysis of academic skills of the Queensland Core Skills Test and IQ highlight the importance of genetic g

This study examined the genetic and environmental relationships among 5 academic achievement skills of a standardized test of academic achievement, the Queensland Core Skills Test (QCST; Queensland Studies Authority, 2003a). QCST participants included 182 monozygotic pairs and 208 dizygotic pairs (mean 17 years +/- 0.4 standard deviation). IQ data were included in the analysis to correct for ascertainment bias. A genetic general factor explained virtually all genetic variance in the component academic skills scores, and accounted for 32% to 73% of their phenotypic variances. It also explained 56% and 42% of variation in Verbal IQ and Performance IQ respectively, suggesting that this factor is genetic g. Modest specific genetic effects were evident for achievement in mathematical problem solving and written expression. A single common factor adequately explained common environmental effects, which were also modest, and possibly due to assortative mating. The results suggest that general academic ability, derived from genetic influences and to a lesser extent common environmental influences, is the primary source of variation in component skills of the QCST.