Robust trend tests for genetic association in case-control studies using family data

We studied a trend test for genetic association between disease and the number of risk alleles using case-control data. When the data are sampled from families, this trend test can be adjusted to take into account the correlations among family members in complex pedigrees. However, the test depends on the scores based on the underlying genetic model and thus it may have substantial loss of power when the model is misspecified. Since the mode of inheritance will be unknown for complex diseases, we have developed two robust trend tests for case-control studies using family data. These robust tests have relatively good power for a class of possible genetic models. The trend tests and robust trend tests were applied to a dataset of Genetic Analysis Workshop 14 from the Collaborative Study on the Genetics of Alcoholism.     

Collecting and organizing systematic sets of protein data

Systems biology, particularly of mammalian cells, is data starved. However, technologies are now in place to obtain rich data, in a form suitable for model construction and validation, that describes the activities, states and locations of cell-signalling molecules. The key is to use several measurement technologies simultaneously and, recognizing each of their limits, to assemble a self-consistent compendium of systematic data.     

The genetic basis of mammalian neurulation

More than 80 mutant mouse genes disrupt neurulation and allow an in-depth analysis of the underlying developmental mechanisms. Although many of the genetic mutants have been studied in only rudimentary detail, several molecular pathways can already be identified as crucial for normal neurulation. These include the planar cell-polarity pathway, which is required for the initiation of neural tube closure, and the sonic hedgehog signalling pathway that regulates neural plate bending. Mutant mice also offer an opportunity to unravel the mechanisms by which folic acid prevents neural tube defects, and to develop new therapies for folate-resistant defects.     

Dog as a mammalian genetic model

Up to recently, studies on dog genetics were rather scare notwithstanding the enormous potential that the canine model can offer in the study of the genotype/phenotype relationship and the analysis of the causes of many genetic diseases, with simple or complex inheritance, that affect dogs but also the human population. This potentiality is essentially due to the natural history of dogs whose domestication from wolves dated back 15,000 years, at least. All modern dogs originated from a limited number of female wolves from Eastern Asia. By applying a combination of selections and strong inbreeding practices, humans have created over 350 breeds, each of them corresponding to a genetic isolate and altogether offering a unique panel of polymorphism never encountered in any other mammals. In this review we summarized what makes dogs an unavoidable model. Contrary to the classical models like the two yeasts, nematode, fish, fly, mouse, or rat mainly used to understand the function of genes, dog with the creation across the centuries of numerous breeds offers a unique opportunity to study the role of their alleles. We report recent data on the construction of genomic maps and on the sequencing program of the dog genome launched by the National Institute of Health (NIH). To take fully advantage of the canine model, we advocate for the systematic construction of a rich canine single nucleotide polymorphisms (SNP) ressource to perform linkage desiquilibrium studies of normal or pathological traits as well as to get insight into the genetic diversity of the canine species.     

Probabilistic graphical models for genetic association studies

Probabilistic graphical models have been widely recognized as a powerful formalism in the bioinformatics field, especially in gene expression studies and linkage analysis. Although less well known in association genetics, many successful methods have recently emerged to dissect the genetic architecture of complex diseases. In this review article, we cover the applications of these models to the population association studies' context, such as linkage disequilibrium modeling, fine mapping and candidate gene studies, and genome-scale association studies. Significant breakthroughs of the corresponding methods are highlighted, but emphasis is also given to their current limitations, in particular, to the issue of scalability. Finally, we give promising directions for future research in this field.     

Acetobacter methanolicus--a new organism for genetic studies

A new bacterial strain is described belonging to Acetobacter methanolicus species. It is of industrial value as a producer of protein and methanol products. The strain is acidophile and this feature comprises a conspicuous technological advantage. The results of bacteriophage and cell interactions are reported. They might be potentially useful for elaboration of the transduction technique for the strain. The collection of mutants was obtained including those utilizing methanol, having auxotrophic markers as well as streptomycin and rifampicin resistances. The transfer of plasmids RSF1010 and R68 to Acetobacter methanolicus from other bacteria has been demonstrated.     

Statistical genetic issues for genome-wide association studies

Genotyping technology now allows the rapid and affordable generation of million-SNP profiles for humans, leading to considerable activity in association mapping. Similar activity is anticipated for many plant species, including Brassica. These plant association mapping activities will require the same care in quality control and quality assurance as for humans. The subsequent analyses may draw upon the same body of theory that is described here in the language of quantitative genetics.