A novel parallel approach to the likelihood-based estimation of admixture in population genetics

Inferring population admixture from genetic data and quantifying it is a difficult but crucial task in evolutionary and conservation biology. Unfortunately state-of-the-art probabilistic approaches are computationally demanding. Effectively exploiting the computational power of modern multiprocessor systems can thus have a positive impact to Monte Carlo-based simulation of admixture modeling. A novel parallel approach is briefly described and promising results on its message passing interface (MPI)-based C implementation are reported.

100 years on: a century of genetics

In 2006 we celebrated the centenary of a remarkable year that saw the birth of genetics as a scientific discipline. This birth had its origins in horticulture and was supervised by a remarkable Cambridge academic, accompanied by a loyal group of female colleagues who worked together in underfunded conditions with little institutional support. Despite this deprivation, they established the foundations of an ongoing revolution, with huge academic and commercial consequences that we can recognize today in the shape of genomics and its application to biomedicine.

Development of a reverse genetics system enabling the rescue of recombinant avian influenza virus A/Turkey/England/50-92/91 (H5N1)

We previously described the use of an established reverse genetics system for the generation of recombinant human influenza A viruses from cloned cDNAs. Here, we have assembled a set of plasmids to allow recovery of the avian H5N1 influenza virus A/Turkey/England/50-92/91 entirely from cDNA. This system enables us to introduce mutations or truncations into the cDNAs to create mutant viruses altered specifically in a chosen gene. These mutant viruses can then be used in future pathogenesis studies in chickens and in studies to understand the host range restrictions of avian influenza viruses in humans.

Domestication of plants in the americas: Insights from mendelian and molecular genetics

Background Plant domestication occurred independently in four different regions of the Americas. In general, different species were domesticated in each area, though a few species were domesticated independently in more than one area. The changes resulting from human selection conform to the familiar domestication syndrome, though different traits making up this syndrome, for example loss of dispersal, are achieved by different routes in crops belonging to different families. Genetic and Molecular Analyses of Domestication Understanding of the genetic control of elements of the domestication syndrome is improving as a result of the development of saturated linkage maps for major crops, identification and mapping of quantitative trait loci, cloning and sequencing of genes or parts of genes, and discoveries of widespread orthologies in genes and linkage groups within and between families. As the modes of action of the genes involved in domestication and the metabolic pathways leading to particular phenotypes become better understood, it should be possible to determine whether similar phenotypes have similar underlying genetic controls, or whether human selection in genetically related but independently domesticated taxa has fixed different mutants with similar phenotypic effects. Conclusions Such studies will permit more critical analysis of possible examples of multiple domestications and of the origin(s) and spread of distinctive variants within crops. They also offer the possibility of improving existing crops, not only major food staples but also minor crops that are potential export crops for developing countries or alternative crops for marginal areas.

The Bayesian revolution in genetics

Bayesian statistics allow scientists to easily incorporate prior knowledge into their data analysis. Nonetheless, the sheer amount of computational power that is required for Bayesian statistical analyses has previously limited their use in genetics. These computational constraints have now largely been overcome and the underlying advantages of Bayesian approaches are putting them at the forefront of genetic data analysis in an increasing number of areas.