Genotype-by-management interactions for grain yield and grain protein concentration of wheat

The magnitude of genotype-by-management (G x M) interactions for grain yield and grain protein concentration was examined in a multi-environment trial (MET) involving a diverse set of 272 advanced breeding lines from the Queensland wheat breeding program. The MET was structured as a series of management-regimes imposed at 3 sites for 2 years. The management-regimes were generated at each site-year as separate trials in which planting time, N fertiliser application rate, cropping history, and irrigation were manipulated. irrigation was used to simulate different rainfall regimes. From the combined analysis of variance, the G x M interaction variance components were found to be the largest source of G x E interaction variation for both grain yield (0.117 +/- 0.005 t(2) ha(-2); 49% of total G x E 0.238 +/- 0.028 t(2) ha(-2)) and grain protein concentration (0.445 +/- 0.020%(2); 82% of total G x E 0.546 +/- 0.057%(2)), and in both cases this source of variation was larger than the genotypic variance component (grain yield 0.068 +/- 0.014 t(2) ha(-2) and grain protein 0.203 +/- 0.026%(2)). The genotypic correlation between the traits varied considerably with management-regime, ranging from -0.98 to -0.31, with an estimate of 0.0 for one trial. Pattern analysis identified advanced breeding lines with improved grain yield and grain protein concentration relative to the cultivars Hartog, Sunco and Meteor. It is likely that a large component of the previously documented G x E interactions for grain yield of wheat in the northern grains region are in part a result of G x M interactions. The implications of the strong influence of G x M interactions for the conduct of wheat breeding METs in the northern region are discussed. (C) 2001 Elsevier Science B.V. All rights reserved.

Gene-Environment Interaction Effects on Behavioral Variation and Risk of Complex Disorders: The Example of Alcoholism and Other Psychiatric Disorders

There have been few replicated examples of genotype x environment interaction effects on behavioral variation or risk of psychiatric disorder. We review some of the factors that have made detection of genotype x environment interaction effects difficult, and show how genotype x shared environment interaction (GxSE) effects are commonly confounded with genetic parameters in data from twin pairs reared together. Historic data on twin pairs reared apart can in principle be used to estimate such GxSE effects, but have rarely been used for this purpose. We illustrate this using previously published data from the Swedish Adoption Twin Study of Aging (SATSA), which suggest that GxSE effects could account for as much as 25% of the total variance in risk of becoming a regular smoker. Since few separated twin pairs will be available for study in the future, we also consider methods for modifying variance components linkage analysis to allow for environmental interactions with linked loci.

Factors affecting cane yield and commercial cane sugar in the Tully district

The Tully Sugar Mill has collected information about sugarcane supplied for crushing from every block in the mill district from 1970 to 1999. Data from 1988 to 1999 were analysed to understand the extent of the variation in cane yield per hectare and commercial cane sugar in the Tully mill area. The key factors influencing the variation in cane yield and commercial cane sugar in this commercial environment were identified and the variance components computed using a restricted maximum likelihood methodology. Cane yield was predominantly influenced by the year in which it was harvested, the month when the crop was ratooned (month of harvest in the previous year) and the farm of origin. These variables were relatively more important than variety, age of crop or crop class (plant crop, first ratoon through to fourth or older ratoons) and fallowing practice (fallow or ploughout-replant). The month-of-ratooning effect was relatively stable from year-to-year. Commercial cane sugar was influenced by the year of harvest, the month of harvest and their interaction, in that the influence of the month of harvest varied from year to year. Variety and farm differences were also significant but accounted for a much lower portion of the variation in commercial cane sugar. An empirical model was constructed from the key factors that influenced commercial cane sugar and cane yield to quantify their combined influence on sugar yield (t/ha). This may be used to assist mill personnel to predict their activities more accurately, for example to calculate the impact of a late finish to the current harvest season on the following year's crop.

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.

Sex differences in heritability of BMI: A comparative study of results from twin studies in eight countries

Body mass index (BMI), a simple anthropometric measure, is the most frequently used measure of adiposity and has been instrumental in documenting the worldwide increase in the prevalence of obesity witnessed during the last decades. Although this increase in overweight and obesity is thought to be mainly due to environmental changes, i.e., sedentary lifestyles and high caloric diets, consistent evidence from twin studies demonstrates high heritability and the importance of genetic differences for normal variation in BMI. We analysed self-reported data on BMI from approximately 37,000 complete twin pairs (including opposite sex pairs) aged 20-29 and 30-39 from eight different twin registries participating in the GenomEUtwin project. Quantitative genetic analyses were conducted and sex differences were explored. Variation in BMI was greater for women than for men, and in both sexes was primarily explained by additive genetic variance in all countries. Sex differences in the variance components were consistently significant. Results from analyses of opposite sex pairs also showed evidence of sex-specific genetic effects suggesting there may be some differences between men and women in the genetic factors that influence variation in BMI. These results encourage the continued search for genes of importance to the body composition and the development of obesity. Furthermore, they suggest that strategies to identify predisposing genes may benefit from taking into account potential sex specific effects.

Genetic variation in foliar carbon isotope composition in relation to tree growth and foliar nitrogen concentration in clones of the F-1 hybrid between slash pine and Caribbean pine

The objectives of this study were: (1) to quantify the genetic variation in foliar carbon isotope composition (delta(13)C) of 122 clones of ca. 4-year-old F-1 hybrids between slash pine (Pinus elliottii Engelm var. elliottii) and Caribbean pine (Pinus caribaea var. hondurensis Barr.,et Golf.) grown at two field experimental sites with different water and nitrogen availability in southeast Queensland, Australia, in relation to tree growth and foliar nitrogen concentration (N-mass); and (2) to assess the potential of using delta(13)C measurements, in the foliage materials collected from the clone hedges at nursery and the 4-year-old tree canopies in the field, as an indirect index of tree water use efficiency for selecting elite F-1 hybrid pine clones with improved tree growth. There were significant differences in foliar delta(13)C between the nursery hedges and the 4-year-old tree canopies in the field, between the summer and winter seasons, between the two experimental sites, and between the upper outer and lower outer canopy positions sampled. This indicates that delta(13)C measurements in the foliage materials are significantly influenced by the sampling techniques and environmental conditions. Significant differences in foliar delta(13)C, at the upper outer canopy in both field experiments in summer and winter, were detected between the clones, and between the female parents of the clones. Clone means of tree height at age ca. 3 years were positively related to those of the upper outer canopy delta(13)C at both experimental sites in winter, but only for the wetter site in summer. There were positive, linear relationships between clone means of canopy delta(13)C and those of canopy N-mass, indicating that canopy photosynthetic capacity might be an important factor regulating the clonal variation in canopy delta(13)C. Significant correlations were found between clone means of canopy delta(13)C at both experimental sites in summer and winter, and between those at the upper outer and lower outer canopy positions. Mean clone delta(13)C for the nursery hedges was only positively related to mean clone stem diameter at 1.3 m height at age 3 years on the wetter site. The clone by site interaction for foliar delta(13)C at the upper outer canopy was significant only in summer. Overall, the relatively high genetic variance components for foliar delta(13)C and significant, positive correlations between clone means of foliar delta(13)C and tree growth have highlighted the potential of using foliar delta(13)C measurements for assisting in selection of the elite F-1 hybrid pine clones with improved tree growth. (C) 2002 Elsevier Science B.V. All rights reserved.

Algebraic structure for the crossing of balanced and stair nested designs

Stair nesting allows us to work with fewer observations than the most usual form of nesting, the balanced nesting. In the case of stair nesting the amount of information for the different factors is more evenly distributed. This new design leads to greater economy, because we can work with fewer observations. In this work we present the algebraic structure of the cross of balanced nested and stair nested designs, using binary operations on commutative Jordan algebras. This new cross requires fewer observations than the usual cross balanced nested designs and it is easy to carry out inference.

Crossing balanced and stair nested desings

Balanced nesting is the most usual form of nesting and originates, when used singly or with crossing of such sub-models, orthogonal models. In balanced nesting we are forced to divide repeatedly the plots and we have few degrees of freedom for the first levels. If we apply stair nesting we will have plots all of the same size rendering the designs easier to apply. The stair nested designs are a valid alternative for the balanced nested designs because we can work with fewer observations, the amount of information for the different factors is more evenly distributed and we obtain good results. The inference for models with balanced nesting is already well studied. For models with stair nesting it is easy to carry out inference because it is very similar to that for balanced nesting. Furthermore stair nested designs being unbalanced have an orthogonal structure. Other alternative to the balanced nesting is the staggered nesting that is the most popular unbalanced nested design which also has the advantage of requiring fewer observations. However staggered nested designs are not orthogonal, unlike the stair nested designs. In this work we start with the algebraic structure of the balanced, the stair and the staggered nested designs and we finish with the structure of the cross between balanced and stair nested designs.

Orthogonal models, structure crossing, nesting and inference

We intend to study the algebraic structure of the simple orthogonal models to use them, through binary operations as building blocks in the construction of more complex orthogonal models. We start by presenting some matrix results considering Commutative Jordan Algebras of symmetric matrices, CJAs. Next, we use these results to study the algebraic structure of orthogonal models, obtained by crossing and nesting simpler ones. Then, we study the normal models with OBS, which can also be orthogonal models. We intend to study normal models with OBS (Orthogonal Block Structure), NOBS (Normal Orthogonal Block Structure), obtaining condition for having complete and suffcient statistics, having UMVUE, is unbiased estimators with minimal covariance matrices whatever the variance components. Lastly, see ([Pereira et al. (2014)]), we study the algebraic structure of orthogonal models, mixed models whose variance covariance matrices are all positive semi definite, linear combinations of known orthogonal pairwise orthogonal projection matrices, OPOPM, and whose least square estimators, LSE, of estimable vectors are best linear unbiased estimator, BLUE, whatever the variance components, so they are uniformly BLUE, UBLUE. From the results of the algebraic structure we will get explicit expressions for the LSE of these models.

On estimation and identifiability issues of sex-linked inheritance with a case study of pigmentation in Swiss barn owl (Tyto alba)

Genetic evaluation using animal models or pedigree-based models generally assume only autosomal inheritance. Bayesian animal models provide a flexible framework for genetic evaluation, and we show how the model readily can accommodate situations where the trait of interest is influenced by both autosomal and sex-linked inheritance. This allows for simultaneous calculation of autosomal and sex-chromosomal additive genetic effects. Inferences were performed using integrated nested Laplace approximations (INLA), a nonsampling-based Bayesian inference methodology. We provide a detailed description of how to calculate the inverse of the X- or Z-chromosomal additive genetic relationship matrix, needed for inference. The case study of eumelanic spot diameter in a Swiss barn owl (Tyto alba) population shows that this trait is substantially influenced by variation in genes on the Z-chromosome (sigma(2)(z) = 0.2719 and sigma(2)(a) = 0.4405). Further, a simulation study for this study system shows that the animal model accounting for both autosomal and sex-chromosome-linked inheritance is identifiable, that is, the two effects can be distinguished, and provides accurate inference on the variance components.

Total hemoglobin mass--a new parameter to detect blood doping?

PURPOSE: All kinds of blood manipulations aim to increase the total hemoglobin mass (tHb-mass). To establish tHb-mass as an effective screening parameter for detecting blood doping, the knowledge of its normal variation over time is necessary. The aim of the present study, therefore, was to determine the intraindividual variance of tHb-mass in elite athletes during a training year emphasizing off, training, and race seasons at sea level. METHODS: tHb-mass and hemoglobin concentration ([Hb]) were determined in 24 endurance athletes five times during a year and were compared with a control group (n = 6). An analysis of covariance was used to test the effects of training phases, age, gender, competition level, body mass, and training volume. Three error models, based on 1) a total percentage error of measurement, 2) the combination of a typical percentage error (TE) of analytical origin with an absolute SD of biological origin, and 3) between-subject and within-subject variance components as obtained by an analysis of variance, were tested. RESULTS: In addition to the expected influence of performance status, the main results were that the effects of training volume (P = 0.20) and training phases (P = 0.81) on tHb-mass were not significant. We found that within-subject variations mainly have an analytical origin (TE approximately 1.4%) and a very small SD (7.5 g) of biological origin. CONCLUSION: tHb-mass shows very low individual oscillations during a training year (<6%), and these oscillations are below the expected changes in tHb-mass due to Herythropoetin (EPO) application or blood infusion (approximately 10%). The high stability of tHb-mass over a period of 1 year suggests that it should be included in an athlete's biological passport and analyzed by recently developed probabilistic inference techniques that define subject-based reference ranges.

Maternal and paternal contributions to pathogen resistance dependent on development stage in a whitefish (Salmonidae)

It is often assumed that maternal and paternal contributions to offspring phenotype change over the lifetime of an individual. However, studies on parental effects typically suffer from the problems that heritabilities and maternal environmental effects are difficult to separate, and that both may depend on environmental factors and developmental stage. In order to experimentally disentangle maternal from paternal contributions and the likely effects of developmental stage from ecological effects, we sampled a natural population of the whitefish Coregonus palaea, used gametes for full-factorial in vitro fertilizations, raised over 10000 of the resulting offspring singly at controlled conditions, and exposed them at different points during embryonic development to one of two strains of Pseudomonas fluorescens that differed in their virulence characteristics (only one caused mortality, while both delayed hatching and reduced growth). Vulnerability to infection increased markedly over embryo development. This change coincided with a distinct shift in the importance of maternal to additive genetic effects on survival. Timing of exposure also affected the variance components for hatching time and larval length, but in a less consistent direction than the variance components for mortality. No significant genetic variation was found for any reaction norms across time points of exposure, indicating a uniformity among genotypes in how susceptibility changed over development. Phenotypes were also typically correlated across time points, which could constrain the evolution of the reaction norms. Our experiment demonstrates that the relative maternal and paternal contributions to susceptibility to an infection, and hence the evolutionary potential to respond to pathogen-induced selection, depends not only on the kind of pathogenic stress but also on the timing of the challenge.

Genetic association tests: a method for the joint analysis of family and case-control data.

With the trend in molecular epidemiology towards both genome-wide association studies and complex modelling, the need for large sample sizes to detect small effects and to allow for the estimation of many parameters within a model continues to increase. Unfortunately, most methods of association analysis have been restricted to either a family-based or a case-control design, resulting in the lack of synthesis of data from multiple studies. Transmission disequilibrium-type methods for detecting linkage disequilibrium from family data were developed as an effective way of preventing the detection of association due to population stratification. Because these methods condition on parental genotype, however, they have precluded the joint analysis of family and case-control data, although methods for case-control data may not protect against population stratification and do not allow for familial correlations. We present here an extension of a family-based association analysis method for continuous traits that will simultaneously test for, and if necessary control for, population stratification. We further extend this method to analyse binary traits (and therefore family and case-control data together) and accurately to estimate genetic effects in the population, even when using an ascertained family sample. Finally, we present the power of this binary extension for both family-only and joint family and case-control data, and demonstrate the accuracy of the association parameter and variance components in an ascertained family sample.

Genome-wide association analysis identifies variants associated with nonalcoholic fatty liver disease that have distinct effects on metabolic traits.

Nonalcoholic fatty liver disease (NAFLD) clusters in families, but the only known common genetic variants influencing risk are near PNPLA3. We sought to identify additional genetic variants influencing NAFLD using genome-wide association (GWA) analysis of computed tomography (CT) measured hepatic steatosis, a non-invasive measure of NAFLD, in large population based samples. Using variance components methods, we show that CT hepatic steatosis is heritable (∼26%-27%) in family-based Amish, Family Heart, and Framingham Heart Studies (n = 880 to 3,070). By carrying out a fixed-effects meta-analysis of genome-wide association (GWA) results between CT hepatic steatosis and ∼2.4 million imputed or genotyped SNPs in 7,176 individuals from the Old Order Amish, Age, Gene/Environment Susceptibility-Reykjavik study (AGES), Family Heart, and Framingham Heart Studies, we identify variants associated at genome-wide significant levels (p<5×10(-8)) in or near PNPLA3, NCAN, and PPP1R3B. We genotype these and 42 other top CT hepatic steatosis-associated SNPs in 592 subjects with biopsy-proven NAFLD from the NASH Clinical Research Network (NASH CRN). In comparisons with 1,405 healthy controls from the Myocardial Genetics Consortium (MIGen), we observe significant associations with histologic NAFLD at variants in or near NCAN, GCKR, LYPLAL1, and PNPLA3, but not PPP1R3B. Variants at these five loci exhibit distinct patterns of association with serum lipids, as well as glycemic and anthropometric traits. We identify common genetic variants influencing CT-assessed steatosis and risk of NAFLD. Hepatic steatosis associated variants are not uniformly associated with NASH/fibrosis or result in abnormalities in serum lipids or glycemic and anthropometric traits, suggesting genetic heterogeneity in the pathways influencing these traits.

A genomewide linkage study on suicidality in major depressive disorder confirms evidence for linkage to 2p12.

Suicidal behavior is commonly associated with depression. Twin studies indicate that both suicidality and major depressive disorder (MDD) are heritable. However, epidemiological evidence suggests that the inheritance of suicidality is likely to be independent of the underlying psychiatric disorder, implying a distinct genetic contribution to suicidality. We conducted a genomewide linkage search aiming to detect genomic loci that may harbor susceptibility genes contributing to risk for suicidality in recurrent MDD. Affected sibling pair (ASP) variance components analysis was performed using the Depression Network cohort of 971 ASPs. The quantitative trait measuring suicidality as a broad phenotype, encompassing ideation and suicide attempts, was established from Schedules for Clinical Assessment in Neuropsychiatry interview items. We examined 1,060 genotyped microsatellite markers with an average spacing of 3.3 cM. Empirical thresholds for linkage evidence were set by whole-genome simulations (LOD = 2.71 for genomewide significance, 1.71 for suggestive linkage). No genomewide significant findings were found. Marker D3S1234 on 3p14 achieved suggestive linkage and yielded a maximum LOD of 1.853 (P = 0.0017), loci 9p24.3 and 18q22-q23 achieved LOD scores >1.5. We found some support for linkage to 2p12 (LOD = 1.2, P = 0.0087) which was previously implicated in linkage studies of suicidality. Our follow-up meta-analysis of five studies showed strong linkage to this region (P = 2 × 10(-6) ). In conclusion, this study analyzed suicidality as a continuous trait in MDD. We found modest evidence for linkage on 3p14. Our meta-analysis supports previous evidence of linkage to suicidality on 2p12. Some candidate genes in these regions may plausibly be implicated in suicidality.