Case-only study of interactions between genetic polymorphisms of GSTM1, P1, T1 and Z1 and smoking in Parkinson's disease

Current opinion contends that complex interactions between genetic and environmental factors play a role in the etiology of Parkinson's disease (PD). Cigarette smoking is thought to reduce risk of PD, and emerging evidence suggests that genetic factors may modulate smoking's effect. We used a case-only design, an approach not previously used to study gene-environment interactions in PD, specifically to study interactions between glutathione-S-transferase (GST) gene polymorphisms and smoking in relation to PD. Four-hundred PD cases (age at onset: 60.0 +/- 10.7 years) were genotyped for common polymorphisms in GSTM1, PI, T1 and Z1 using well-established methods. Smoking exposure data were collected in face-to-face interviews. The independence of the studied GST genotypes and smoking exposure was confirmed by studying 402 healthy, aged individuals. No differences were observed in the distributions of GSTM1, T1 or Z1 polymorphisms between ever-smoked and never-smoked PD cases using logistic regression (all P > 0.43). However, GSTP1 *C haplotypes were over-represented among PD cases who ever smoked (odds ratio for interaction (ORi) = 2.00 (95% Cl: 1.11-3.60, P = 0.03)). Analysis revealed that ORi between smoking and the GSTP1-114Val carrier status increased with increasing smoking dose (P = 0.02 for trend). These data suggest that one or more GSTP1 polymorphisms may interact with cigarette smoking to influence the risk for PD. (C) 2004 Elsevier Ireland Ltd. All rights reserved.

Analysis of line x environment interactions for yield in navy beans. 2. Pattern analysis of lines and environment within years

Seven years of multi-environment yield trials of navy bean (Phaseolus vulgaris L.) grown in Queensland were examined. As is common with plant breeding evaluation trials, test entries and locations varied between years. Grain yield data were analysed for each year using cluster and ordination analyses (pattern analyses). These methods facilitate descriptions of genotype performance across environments and the discrimination among genotypes provided by the environments. The observed trends for genotypic yield performance across environments were partly consistent with agronomic and disease reactions at specific environments and also partly explainable by breeding and selection history. In some cases, similarities in discrimination among environments were related to geographic proximity, in others management practices, and in others similarities occurred between geographically widely separated environments which differed in management practices. One location was identified as having atypical line discrimination. The analysis indicated that the number of test locations was below requirements for adequate representation of line x environment interaction. The pattern analyses methods used were an effective aid in describing the patterns in data for each year and illustrated the variations in adaptive patterns from year to year. The study has implications for assessing the number and location of test sites for plant breeding multi-environment trials, and for the understanding of genetic traits contributing to line x environment interactions.

Challenges to environmental toxicology and epidemiology: where do we stand and which way do we go?

Modern toxicology investigates a wide array of both old and new health hazards. Priority setting is needed to select agents for research from the plethora of exposure circumstances. The changing societies and a growing fraction of the aged have to be taken into consideration. A precise exposure assessment is of importance for risk estimation and regulation. Toxicology contributes to the exploration of pathomechanisms to specify the exposure metrics for risk estimation. Combined effects of co-existing agents are not yet sufficiently understood. Animal experiments allow a separate administration of agents which can not be disentangled by epidemiological means, but their value is limited for low exposure levels in many of today's settings. As an experimental science, toxicology has to keep pace with the rapidly growing knowledge about the language of the genome and the changing paradigms in cancer development. During the pioneer era of assembling a working draft of the human genome, toxicogenomics has been developed. Gene and pathway complexity have to be considered when investigating gene-environment interactions. For a best conduct of studies, modem toxicology needs a close liaison with many other disciplines like epidemiology and bioinformatics. (C) 2004 Elsevier Ireland Ltd. All rights reserved.

Measurement and management of genotype-environment interaction (GxE) for the improvement of rainfed lowland rice yield in Cambodia

The magnitude and nature of genotype-by-environment interactions (G×E) for grain yield (GY) and days to flower (DTF) in Cambodia were examined using a random population of 34 genotypes taken from the Cambodian rice improvement program. These genotypes were evaluated in multi-environment trials (MET) conducted across three years (2000 to 2002) and eight locations in the rainfed lowlands. The G×E interaction was partitioned into components attributed to genotype-by-location (G×L), genotype-by-year (G×Y) and genotype-by-location-by-year (G×L×Y) interactions. The G×L×Y interaction was the largest component of variance for GY. The G×L interaction was also significant and comparable in size to the genotypic component (G). The G×Y interaction was small and non significant. A major factor contributing to the large G×L×Y interactions for GY was the genotypic variation for DTF in combination with environmental variation for the timing and intensity of drought. Some of the interactions for GY associated with timing of plant development and exposure to drought were repeatable across the environments enabling the identification of three-target populations of environments (TPE) for consideration in the breeding program. Four genotypes were selected for wide adaptation in the rainfed lowlands in Cambodia.

Application of DNA parentage analyses for determining relative growth rates of Penaeus japonicus families reared in commercial ponds

The ability to track large numbers of individuals and families is a key determinant of the power and precision of breeding programs, including the capacity to quantify interactions between genotypes and their environment. Until recently, most family based selective breeding programs for shrimp, and other highly fecund aquaculture species, have been restricted by the number of animals that can be physically tagged and individually selected. Advances in the development of molecular markers, such as microsatellite loci, are now providing the means to track large numbers of individuals and families in commercial production systems. In this study microsatellites, coupled with DNA parentage analyses, were used to determine the relative performance of 22 families of R japonicus reared in commercial production ponds. In the experimental design 6000 post-larvae from each of 22 families, whose maternal parents had been genotyped at 8 microsatellite loci, were stocked into each of four I ha ponds. After 6 months the ponds were harvested and a total of 6000 individuals were randomly weighed from each pond. Mean wet weight of the shrimp from one pond was significantly lower than that of the other three ponds demonstrating a possible pond effect on growth rate. The representation of families in the top 10% of each pond's weight distribution was then determined by randomly genotyping up to 300 individuals from this upper weight class. Parentage analyses based on individual genotypic data demonstrated that some families were over-represented in the top 10% in all ponds, while others were under-represented due to slower growth rates. The results also revealed some weak, but significant, male genotype x environment (G x E) interactions in the expression of shrimp growth for some families. This indicates that G x E effects may need to be factored into future R japonicus selective breeding programs. This study demonstrated the utility of DNA parentage analyses for tracking individual family performance in communally stocked shrimp pond populations and, its application to examining G x E effects on trait expression under commercial culture conditions. Crown Copyright (c) 2005 Published by Elsevier B.V. All rights reserved.

Improving drought tolerance in rainfed lowland rice: An example from Thailand

A large portion of the world's poor farm in rainfed systems where the water supply is unpredictable and droughts are common. In Thailand there are approximately 6.2 million ha of rain fed lowland rice, which account for 67% of the country's total rice-growing area. This rice system is often characterised by too much and too little water in the same season. Farmers' estimates of their annual losses to drought are as high as 45% in the upper parts of the toposequence. In contrast to irrigated rice systems, gains from crop improvement of rainfed rice have been modest, in part because there has been little effort to breed and select for drought tolerance for the target rainfed environments. The crop improvement strategy being used in Thailand considers three mechanisms that influence yield in the drought prone targets: yield potential as an important mechanism for mild drought (where yield loss is less than 50%), drought escape (appropriate phenology) and drought tolerance traits of leaf water potential, sterility, flower delay and drought response index for more severe drought conditions. Genotypes are exposed to managed drought environments for selection of drought tolerant genotypes. A marker assisted selection (MAS) scheme has been developed and applied for selection of progenies in the backcrossing program. The plant breeding program uses rapid generation advance techniques that enable early yield testing in the target population of environments (TPE) through inter-station (multi-location yield testing) and on-farm trials. A farmer participatory approach has been used to identify the TPE for the breeding program. Four terrace paddy levels have been identified, upper (drought), middle (drought prone to favorable) and lower (flooded). This paper reports the change in the breeding program for the drought prone tainted lowland rice environments of North and Northeast Thailand by incorporating our knowledge on adaptation and on response of rice to drought. (c) 2005 Elsevier B.V. All rights reserved.

Pattern analysis on grain yield performance of Chinese and CIMMYT spring wheat cultivars sown in China and CIMMYT

Understanding the relationships among testing environments is essential for better targeting cultivars to production environments. To identify patterns of cultivar, environment, cultivar-by-environment interactions, and opportunities for indirect selection for grain yield, a set of 25 spring wheat cultivars from China and the International Maize and Wheat Improvement Center (CIMMYT) was evaluated in nine environments in China and four management environments at CIMMYT in Cd. Obregon, Mexico, during two wheat seasons. Genetic background and original environment were the main factors influencing grain yield performance of the cultivars. Baviacora M 92, Xinchun 2 and Xinchun 6 showed relatively more stable and higher grain yields, whereas highly photoperiod sensitive cultivars Xinkehan 9, Kefeng 6 and Longmai 19 proved consistently inferior across environments, except in Harbin and Keshan, the two high latitude environments. Longmai 26, also from high latitude environments in the northeastern Heilongjiang province, was however probably not as photoperiodicly sensitive as other cultivars; from that region, and produced much higher grain yield and expressed a broader adaptation. None of the environments reported major diseases. Pattern analyses revealed that photoperiod response and planting option on beds were the two main factors underlying the observed interactions for grain yield. The production environment of planting on the flat in Mexico grouped together with Huhhot and Urumqi in both wheat seasons, indicating an indirect response to selection for grain yield in this CIMMYT managed environment could benefit the two Chinese environments. Both the environment of planting on the flat with Chinese Hejin and Yongning, and the three CIMMYT enviromnents planting on raised beds with Chinese Yongning grouped together only in one season, showing that repeatability may not be stable in this case.

Pattern analysis on protein properties of Chinese and CIMMYT spring wheat cultivars sown in China and CIMMYT

Improvement of processing quality is a very important objective for Chinese wheat breeding programs. Twenty-five CIMMYT and Chinese spring wheat cultivars were grown at four managed conditions by CIMMYT in Cd. Obregon, Sonora, Mexico and in nine environments in China, over two successive wheat seasons from 2000 to 2002. These trials were used to identify patterns of cultivar, environment and cultivar x environment interactions, and to determine opportunities for indirect selection for protein content and the protein-quality related parameter, SDS sedimentation (SDSS) value. The cultivar Inqalab 91 showed low levels of interaction with environments in the 2000-01 crop cycle for protein content, and expressed intermediate levels for both protein content and SDSS value, across most of the environments in both years. Longmai 26 had consistently high protein content and SDSS value across environments in both years, indicating that it is possible to breed cultivars expressing high yields with good protein properties. Cluster analyses revealed that cultivars grouped differently for protein content and SDSS value. Besides photoperiod, water availability appeared to influence the ranking of cultivars for protein content and SDSS value. Temperature and soil type may underlie the observed interactions for protein content, while temperature may also be a factor associated with interactions for SDSS value. The full irrigation managed environment in Mexico, with the cultivars sown on raised beds two months later than optimum and exposing them to late heat, clustered together with the Chinese environments Huhhot, Yongning, and Hejin in the 2000-01 season for SDSS value. This indicates that there is an opportunity to exploit indirect responses to selection in the CIMMYT management environments for SDSS value with relevance for China's spring wheat regions. However, there seemed little chance for positive indirect selection in CIMMYT's managed environments for China in regard to protein content, as environments clustered distinctly. Pattern analyses permitted a sensible and useful summary for this multi environment experiment, helping in understanding natural relationships and variations in cultivar performance among the various environment groups, and assisting in the structuring of environments.

The branching gene RAMOSUS1 mediates interactions among two novel signals and auxin in pea

In Pisum sativium, the RAMOSUS genes RMS1, RMS2, and RMS5 regulate shoot branching via physiologically defined mobile signals. RMS1 is most likely a carotenoid cleavage enzyme and acts with RMS5 to control levels of an as yet unidentified mobile branching inhibitor required for auxin inhibition of branching. Our work provides molecular, genetic, and physiological evidence that RMS1 plays a central role in a shoot-to-root-to-shoot feedback system that regulates shoot branching in pea. Indole-3-acetic acid (IAA) positively regulates RMS1 transcript level, a potentially important mechanism for regulation of shoot branching by IAA. In addition, RMS1 transcript levels are dramatically elevated in rms3, rms4, and rms5 plants, which do not contain elevated IAA levels. This degree of upregulation of RMS1 expression cannot be achieved in wild-type plants by exogenous IAA application. Grafting studies indicate that an IAA-independent mobile feedback signal contributes to the elevated RMS1 transcript levels in rms4 plants. Therefore, the long-distance signaling network controlling branching in pea involves IAA, the RMS1 inhibitor, and an IAA-independent feedback signal. Consistent with physiological studies that predict an interaction between RMS2 and RMS1, rms2 mutations appear to disrupt this IAA-independent regulation of RMS1 expression.

GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease

Background The identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease. Results We show that GPNN has high power to detect even relatively small genetic effects (2–3% heritability) in simulated data models involving two and three locus interactions. The limits of detection were reached under conditions with very small heritability (

GPNN: Power studies and applications of a neural network method for detecting gene-gene interactions in studies of human disease

Background: The identification and characterization of genes that influence the risk of common, complex multifactorial disease primarily through interactions with other genes and environmental factors remains a statistical and computational challenge in genetic epidemiology. We have previously introduced a genetic programming optimized neural network (GPNN) as a method for optimizing the architecture of a neural network to improve the identification of gene combinations associated with disease risk. The goal of this study was to evaluate the power of GPNN for identifying high-order gene-gene interactions. We were also interested in applying GPNN to a real data analysis in Parkinson's disease. Results: We show that GPNN has high power to detect even relatively small genetic effects (2-3% heritability) in simulated data models involving two and three locus interactions. The limits of detection were reached under conditions with very small heritability (