RAPD profiling in detecting genetic variation in endemic Coelonema (Brassicaceae) of Qinghai-Tibet Plateau of China

Random amplified polymorphic DNA ( RAPD) markers were used to measure genetic diversity of Coelonema draboides ( Brassicaceae), a genus endemic to the Qilian Mountains of the Qinghai-Tibet Plateau. We sampled 90 individuals in 30 populations of Coelonema draboides from Datong and Huzhu counties of Qinghai Province in P. R. China. A total of 186 amplified bands were scored from the 14 RAPD primers, with a mean of 13.3 amplified bands per primer, and 87% ( 161 bands) polymorphic bands (PPB) was found. Analysis of molecular variance (AMOVA) shows that a large proportion of genetic variation (84.2%) resides among individuals within populations, while only 15.8% resides among populations. The species shows higher genetic diversity between individuals than other endemic and endangered plants. The RAPDs provide a useful tool for assessing genetic diversity of rare, endemic species and for resolving relationships among populations. The results show that the genetic diversity of this species is high, possibly allowing it to adapt more easily to environmental variations. The main factor responsible for the high level of differentiation within populations and the low level of diversity among populations is probably the outcrossing and long-lived nature of this species. Some long-distance dispersal, even among far separated populations, is also a crucial determinant for the pattern of genetic variation in the species. This distributive pattern of genetic variation of C. draboides populations provides important baseline data for conservation and collection strategies for the species. It is suggested that only populations in different habitats should be studied and protected, not all populations, so as to retain as much genetic diversity as possible.

Switching Between Discrete and Continuous Process Models to Predict Molecular Genetic Activity

Two kinds of process models have been used in programs that reason about change: Discrete and continuous models. We describe the design and implementation of a qualitative simulator, PEPTIDE, which uses both kinds of process models to predict the behavior of molecular energetic systems. The program uses a discrete process model to simulate both situations involving abrupt changes in quantities and the actions of small numbers of molecules. It uses a continuous process model to predict gradual changes in quantities. A novel technique, called aggregation, allows the simulator to switch between theses models through the recognition and summary of cycles. The flexibility of PEPTIDE's aggregator allows the program to detect cycles within cycles and predict the behavior of complex situations.

Girolando breed genetic improvement program - Sire summary - Progeny test results - July/2015.

The Girolando breed progeny test was established in 1997, as a result of the partnership between Girolando and Embrapa Dairy Cattle. In 2007, the Programa de Melhoramento Genético da Raça Girolando? PMGG (Genetic Improvement Program of the Girolando Breed) was implemented. Besides interacting with previously existing initiatives of the Girolando Breeders Association, such as the genealogical register service, the progeny test and the dairy control service, the PMGG launched the Linear Evaluation System (SLAG). The main objectives of the PMGG comprises identification of genetically superior individuals, the technically-oriented multiplication of genetics, the evaluation of economic traits and the promotion of sustainable dairy activities. The program have yielded impressive results. The Girolando breed semen sales increases faster than any other breed in Brazil.

Genes and the ageing muscle: a review on genetic association studies

Western populations are living longer. Ageing decline in muscle mass and strength (i.e. sarcopenia) is becoming a growing public health problem, as it contributes to the decreased capacity for independent living. It is thus important to determine those genetic factors that interact with ageing and thus modulate functional capacity and skeletal muscle phenotypes in older people. It would be also clinically relevant to identify 'unfavourable' genotypes associated with accelerated sarcopenia. In this review, we summarized published information on the potential associations between some genetic polymorphisms and muscle phenotypes in older people. A special emphasis was placed on those candidate polymorphisms that have been more extensively studied, i.e. angiotensin-converting enzyme (ACE) gene I/D, α-actinin-3 (ACTN3) R577X, and myostatin (MSTN) K153R, among others. Although previous heritability studies have indicated that there is an important genetic contribution to individual variability in muscle phenotypes among old people, published data on specific gene variants are controversial. The ACTN3 R577X polymorphism could influence muscle function in old women, yet there is controversy with regards to which allele (R or X) might play a 'favourable' role. Though more research is needed, up-to-date MSTN genotype is possibly the strongest candidate to explain variance among muscle phenotypes in the elderly. Future studies should take into account the association between muscle phenotypes in this population and complex gene-gene and gene-environment interactions.

Genetic predisposition to acute respiratory distress syndrome in patients with severe sepsis

The objective of this study was to analyze the association between candidate gene polymorphisms and susceptibility to acute respiratory distress syndrome (ARDS) in patients with severe sepsis. Patients older than 18 years admitted to the intensive care un

On Genetic Programming and Knowledge Discovery in Transcriptome

Rowland, J. J. (2004) On Genetic Programming and Knowledge Discovery in Transcriptome Data. Proc. IEEE Congress on Evolutionary Computation, Portland, Oregon. pp 158-165. ISBN 0-7803-8515-2

Adaptive denoising in spectral analysis by genetic programming

Rowland, J.J. and Taylor, J. (2002). Adaptive denoising in spectral analysis by genetic programming. Proc. IEEE Congress on Evolutionary Computation (part of WCCI), May 2002. pp 133-138. ISBN 0-7803-7281-6

Taking advantage of an old concept, "illegitimate transcription", for a proposed novel method of genetic diagnosis of McArdle disease

McArdle disease is a metabolic disorder caused by pathogenic mutations in the PYGM gene. Timely diagnosis can sometimes be difficult with direct genomic analysis, which requires additional studies of cDNA from muscle transcripts. Although the "nonsense-mediated mRNA decay" (NMD) eliminates tissue-specific aberrant transcripts, there is some residual transcription of tissue-specific genes in virtually all cells, such as peripheral blood mononuclear cells (PBMCs).We studied a subset of the main types of PYGM mutations (deletions, missense, nonsense, silent, or splicing mutations) in cDNA from easily accessible cells (PBMCs) in 12 McArdle patients.Analysis of cDNA from PBMCs allowed detection of all mutations. Importantly, the effects of mutations with unknown pathogenicity (silent and splicing mutations) were characterized in PBMCs. Because the NMD mechanism does not seem to operate in nonspecific cells, PBMCs were more suitable than muscle biopsies for detecting the pathogenicity of some PYGM mutations, notably the silent mutation c.645G>A (p.K215=), whose effect in the splicing of intron 6 was unnoticed in previous muscle transcriptomic studies.We propose considering the use of PBMCs for detecting mutations that are thought to cause McArdle disease, particularly for studying their actual pathogenicity.

Soil differentiation using fingerprint Fourier transform infrared spectroscopy, chemometrics and genetic algorithm-based feature selection

Elliott, G. N., Worgan, H., Broadhurst, D. I., Draper, J. H., Scullion, J. (2007). Soil differentiation using fingerprint Fourier transform infrared spectroscopy, chemometrics and genetic algorithm-based feature selection. Soil Biology & Biochemistry, 39 (11), 2888-2896. Sponsorship: BBSRC / NERC RAE2008

A genetic algorithm based economic dispatch (GAED) with environmental constraint optimisation

The role of renewable energy in power systems is becoming more significant due to the increasing cost of fossil fuels and climate change concerns. However, the inclusion of Renewable Energy Generators (REG), such as wind power, has created additional problems for power system operators due to the variability and lower predictability of output of most REGs, with the Economic Dispatch (ED) problem being particularly difficult to resolve. In previous papers we had reported on the inclusion of wind power in the ED calculations. The simulation had been performed using a system model with wind power as an intermittent source, and the results of the simulation have been compared to that of the Direct Search Method (DSM) for similar cases. In this paper we report on our continuing investigations into using Genetic Algorithms (GA) for ED for an independent power system with a significant amount of wind energy in its generator portfolio. The results demonstrate, in line with previous reports in the literature, the effectiveness of GA when measured against a benchmark technique such as DSM.

Genetic correlates of longevity and selected age-related phenotypes: a genome-wide association study in the Framingham Study

BACKGROUND: Family studies and heritability estimates provide evidence for a genetic contribution to variation in the human life span. METHODS:We conducted a genome wide association study (Affymetrix 100K SNP GeneChip) for longevity-related traits in a community-based sample. We report on 5 longevity and aging traits in up to 1345 Framingham Study participants from 330 families. Multivariable-adjusted residuals were computed using appropriate models (Cox proportional hazards, logistic, or linear regression) and the residuals from these models were used to test for association with qualifying SNPs (70, 987 autosomal SNPs with genotypic call rate [greater than or equal to]80%, minor allele frequency [greater than or equal to]10%, Hardy-Weinberg test p [greater than or equal to] 0.001).RESULTS:In family-based association test (FBAT) models, 8 SNPs in two regions approximately 500 kb apart on chromosome 1 (physical positions 73,091,610 and 73, 527,652) were associated with age at death (p-value < 10-5). The two sets of SNPs were in high linkage disequilibrium (minimum r2 = 0.58). The top 30 SNPs for generalized estimating equation (GEE) tests of association with age at death included rs10507486 (p = 0.0001) and rs4943794 (p = 0.0002), SNPs intronic to FOXO1A, a gene implicated in lifespan extension in animal models. FBAT models identified 7 SNPs and GEE models identified 9 SNPs associated with both age at death and morbidity-free survival at age 65 including rs2374983 near PON1. In the analysis of selected candidate genes, SNP associations (FBAT or GEE p-value < 0.01) were identified for age at death in or near the following genes: FOXO1A, GAPDH, KL, LEPR, PON1, PSEN1, SOD2, and WRN. Top ranked SNP associations in the GEE model for age at natural menopause included rs6910534 (p = 0.00003) near FOXO3a and rs3751591 (p = 0.00006) in CYP19A1. Results of all longevity phenotype-genotype associations for all autosomal SNPs are web posted at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007. CONCLUSION: Longevity and aging traits are associated with SNPs on the Affymetrix 100K GeneChip. None of the associations achieved genome-wide significance. These data generate hypotheses and serve as a resource for replication as more genes and biologic pathways are proposed as contributing to longevity and healthy aging.

Exponential sensitivity of noise-driven switching in genetic networks

Cells are known to utilize biochemical noise to probabilistically switch between distinct gene expression states. We demonstrate that such noise-driven switching is dominated by tails of probability distributions and is therefore exponentially sensitive to changes in physiological parameters such as transcription and translation rates. However, provided mRNA lifetimes are short, switching can still be accurately simulated using protein-only models of gene expression. Exponential sensitivity limits the robustness of noise-driven switching, suggesting cells may use other mechanisms in order to switch reliably.

How Good are Genetic Algorithms at Finding Large Cliques: An Experimental Study

This paper investigates the power of genetic algorithms at solving the MAX-CLIQUE problem. We measure the performance of a standard genetic algorithm on an elementary set of problem instances consisting of embedded cliques in random graphs. We indicate the need for improvement, and introduce a new genetic algorithm, the multi-phase annealed GA, which exhibits superior performance on the same problem set. As we scale up the problem size and test on \hard" benchmark instances, we notice a degraded performance in the algorithm caused by premature convergence to local minima. To alleviate this problem, a sequence of modi cations are implemented ranging from changes in input representation to systematic local search. The most recent version, called union GA, incorporates the features of union cross-over, greedy replacement, and diversity enhancement. It shows a marked speed-up in the number of iterations required to find a given solution, as well as some improvement in the clique size found. We discuss issues related to the SIMD implementation of the genetic algorithms on a Thinking Machines CM-5, which was necessitated by the intrinsically high time complexity (O(n3)) of the serial algorithm for computing one iteration. Our preliminary conclusions are: (1) a genetic algorithm needs to be heavily customized to work "well" for the clique problem; (2) a GA is computationally very expensive, and its use is only recommended if it is known to find larger cliques than other algorithms; (3) although our customization e ort is bringing forth continued improvements, there is no clear evidence, at this time, that a GA will have better success in circumventing local minima.

A Lower-Bound Result on the Power of a Genetic Algorithm

This paper presents a lower-bound result on the computational power of a genetic algorithm in the context of combinatorial optimization. We describe a new genetic algorithm, the merged genetic algorithm, and prove that for the class of monotonic functions, the algorithm finds the optimal solution, and does so with an exponential convergence rate. The analysis pertains to the ideal behavior of the algorithm where the main task reduces to showing convergence of probability distributions over the search space of combinatorial structures to the optimal one. We take exponential convergence to be indicative of efficient solvability for the sample-bounded algorithm, although a sampling theory is needed to better relate the limit behavior to actual behavior. The paper concludes with a discussion of some immediate problems that lie ahead.