Genetic variability in Larus argentatus and Sterna hirundo

Blood serum and egg-white protein samples from individuals representing seven colonies of Larusargentatus, and four colonies of Sterna hirundo were electrophoretically analysed to determine levels of genetic variability and to assess the utility of polymorphic loci as genetic markers. Variability occurred at five co-dominant autosomal loci. S. hirundo protein polymorphism occurred at the Est-5 and the Oest-l loci, while nineteen loci were monomorphic. L. argentatus samples were monomorphic at seventeen loci and polymorphic at the Ldh-A and the Alb loci. Intergeneric differences existed at the Oalb and the Ldh-A loci. Although LDH-A100 from both species possessed identical electrophoretLc mobilities, the intergeneric differences were expressed as a difference in enzyme the'ITIlostabilities. Geographical distribution of alleles and genetic divergence estimates suggest ~ hirundo population panmixis,at least at the sampled locations. The h argentatus gene pool appears relatively heterogeneous with a discreet Atlantic Coast population and a Great Lakes demic population. These observed population structures may be maintained by the relative amount of gene flow occurring within and among populations. Mass ringing data coupled to reproductive success information and analysis of dispersal trends appear to validate this assumption. Similar results may be generated by either selection or both small organism and low locus sample sizes. To clarify these results and to detect the major factor(s) affecting the surveyed portions of the genome, larger sample sizes in conjunction with precise eco-demographic data are required.

Disease-Gene Association Using Genetic Programming

As a result of mutation in genes, which is a simple change in our DNA, we will have undesirable phenotypes which are known as genetic diseases or disorders. These small changes, which happen frequently, can have extreme results. Understanding and identifying these changes and associating these mutated genes with genetic diseases can play an important role in our health, by making us able to find better diagnosis and therapeutic strategies for these genetic diseases. As a result of years of experiments, there is a vast amount of data regarding human genome and different genetic diseases that they still need to be processed properly to extract useful information. This work is an effort to analyze some useful datasets and to apply different techniques to associate genes with genetic diseases. Two genetic diseases were studied here: Parkinson’s disease and breast cancer. Using genetic programming, we analyzed the complex network around known disease genes of the aforementioned diseases, and based on that we generated a ranking for genes, based on their relevance to these diseases. In order to generate these rankings, centrality measures of all nodes in the complex network surrounding the known disease genes of the given genetic disease were calculated. Using genetic programming, all the nodes were assigned scores based on the similarity of their centrality measures to those of the known disease genes. Obtained results showed that this method is successful at finding these patterns in centrality measures and the highly ranked genes are worthy as good candidate disease genes for being studied. Using standard benchmark tests, we tested our approach against ENDEAVOUR and CIPHER - two well known disease gene ranking frameworks - and we obtained comparable results.