Phytophthora infestans is the most destructive pathogen of potato and a model organism for the oomycetes, a distinct lineage of fungus-like eukaryotes that are related to organisms such as brown algae and diatoms. As the agent of the Irish potato famine in the mid-nineteenth century, P. infestans has had a tremendous effect on human history, resulting in famine and population displacement. To this day, it affects world agriculture by causing the most destructive disease of potato, the fourth largest food crop and a critical alternative to the major cereal crops for feeding the world's population. Current annual worldwide potato crop losses due to late blight are conservatively estimated at $6.7 billion. Management of this devastating pathogen is challenged by its remarkable speed of adaptation to control strategies such as genetically resistant cultivars. Here we report the sequence of the P. infestans genome, which at approximately 240 megabases (Mb) is by far the largest and most complex genome sequenced so far in the chromalveolates. Its expansion results from a proliferation of repetitive DNA accounting for approximately 74% of the genome. Comparison with two other Phytophthora genomes showed rapid turnover and extensive expansion of specific families of secreted disease effector proteins, including many genes that are induced during infection or are predicted to have activities that alter host physiology. These fast-evolving effector genes are localized to highly dynamic and expanded regions of the P. infestans genome. This probably plays a crucial part in the rapid adaptability of the pathogen to host plants and underpins its evolutionary potential.

Cataloging the very large number of undescribed species of insects could be greatly accelerated by automated DNA based approaches, but procedures for large-scale species discovery from sequence data are currently lacking. Here, we use mitochondrial DNA variation to delimit species in a poorly known beetle radiation in the genus Rivacindela from arid Australia. Among 468 individuals sampled from 65 sites and multiple morphologically distinguishable types, sequence variation in three mtDNA genes (cytochrome oxidase subunit 1, cytochrome b, 16S ribosomal RNA) was strongly partitioned between 46 or 47 putative species identified with quantitative methods of species recognition based on fixed unique ("diagnostic") characters. The boundaries between groups were also recognizable from a striking increase in branching rate in clock-constrained calibrated trees. Models of stochastic lineage growth (Yule models) were combined with coalescence theory to develop a new likelihood method that determines the point of transition from species-level (speciation and extinction) to population-level (coalescence) evolutionary processes. Fitting the location of the switches from speciation to coalescent nodes on the ultrametric tree of Rivacindela produced a transition in branching rate occurring at 0.43 Mya, leading to an estimate of 48 putative species (confidence interval for the threshold ranging from 47 to 51 clusters within 2 logL units). Entities delimited in this way exhibited biological properties of traditionally defined species, showing coherence of geographic ranges, broad congruence with morphologically recognized species, and levels of sequence divergence typical for closely related species of insects. The finding of discontinuous evolutionary groupings that are readily apparent in patterns of sequence variation permits largely automated species delineation from DNA surveys of local communities as a scaffold for taxonomy in this poorly known insect group.

SUMMARYThe majority of agronomically important crop traits are quantitative, meaning that they are controlled by multiple genes each with a small effect (quantitative trait loci, QTLs). Mapping and isolation of QTLs is important for efficient crop breeding by marker-assisted selection (MAS) and for a better understanding of the molecular mechanisms underlying the traits. However, since it requires the development and selection of DNA markers for linkage analysis, QTL analysis has been time-consuming and labor-intensive. Here we report the rapid identification of plant QTLs by whole-genome resequencing of DNAs from two populations each composed of 20-50 individuals showing extreme opposite trait values for a given phenotype in a segregating progeny. We propose to name this approach QTL-seq as applied to plant species. We applied QTL-seq to rice recombinant inbred lines and F 2 populations and successfully identified QTLs for important agronomic traits, such as partial resistance to the fungal rice blast disease and seedling vigor. Simulation study showed that QTL-seq is able to detect QTLs over wide ranges of experimental variables, and the method can be generally applied in population genomics studies to rapidly identify genomic regions that underwent artificial or natural selective sweeps.