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clinicalepigeneticsjournal.com

Encompassing the broad spectrum of epigenetics research from basic research to innovations in therapeutic treatments, Clinical Epigenetics is a top tier, open ...

amb-express.com

amb express is a peer-reviewed open access journal focusing on the innovative field of industrial microbiology. combines research in applied and industrial ...

bsd-journal.com

Biology of Sex Differences is a unique scientific journal that incorporates basic and clinical research focusing on sex differences in physiology, behavior, ...

cancerci.com

Cancer Cell International publishes articles on all aspects of cancer cell biology originating from work using laboratory experimentation. The journal ...

ccforum.com

Critical Care, the top open access journal in critical care medicine, advances critically ill patient care with high-quality, evidence-based ...

gsejournal.org

Background Genetic variation for environmental sensitivity indicates that animals are genetically different in their response to environmental factors. Environmental factors are either identifiable (e.g. temperature) and called macro-environmental or unknown and called micro-environmental. The objectives of this study were to develop a statistical method to estimate genetic parameters for macro- and micro-environmental sensitivities simultaneously, to investigate bias and precision of resulting estimates of genetic parameters and to develop and evaluate use of Akaike’s information criterion using h-likelihood to select the best fitting model. Methods We assumed that genetic variation in macro- and micro-environmental sensitivities is expressed as genetic variance in the slope of a linear reaction norm and environmental variance, respectively. A reaction norm model to estimate genetic variance for macro-environmental sensitivity was combined with a structural model for residual variance to estimate genetic variance for micro-environmental sensitivity using a double hierarchical generalized linear model in ASReml. Akaike’s information criterion was constructed as model selection criterion using approximated h-likelihood. Populations of sires with large half-sib offspring groups were simulated to investigate bias and precision of estimated genetic parameters. Results Designs with 100 sires, each with at least 100 offspring, are required to have standard deviations of estimated variances lower than 50% of the true value. When the number of offspring increased, standard deviations of estimates across replicates decreased substantially, especially for genetic variances of macro- and micro-environmental sensitivities. Standard deviations of estimated genetic correlations across replicates were quite large (between 0.1 and 0.4), especially when sires had few offspring. Practically, no bias was observed for estimates of any of the parameters. Using Akaike’s information crite

chiromt.com

Chiropractic & Manual Therapies aims to improve patient care of musculoskeletal conditions by publishing basic science, clinical and health services research ...