More accurate and precise phenotyping strategies are necessary to empower high-resolution linkage mapping and genome-wide association studies and for training genomic selection models in plant improvement. Within this framework, the objective of modern phenotyping is to increase the accuracy, precision and throughput of phenotypic estimation at all levels of biological organization while reducing costs and minimizing labor through automation, remote sensing, improved data integration and experimental design. Much like the efforts to optimize genotyping during the 1980s and 1990s, designing effective phenotyping initiatives today requires multi-faceted collaborations between biologists, computer scientists, statisticians and engineers. Robust phenotyping systems are needed to characterize the full suite of genetic factors that contribute to quantitative phenotypic variation across cells, organs and tissues, developmental stages, years, environments, species and research programs. Next-generation phenotyping generates significantly more data than previously and requires novel data management, access and storage systems, increased use of ontologies to facilitate data integration, and new statistical tools for enhancing experimental design and extracting biologically meaningful signal from environmental and experimental noise. To ensure relevance, the implementation of efficient and informative phenotyping experiments also requires familiarity with diverse germplasm resources, population structures, and target populations of environments. Today, phenotyping is quickly emerging as the major operational bottleneck limiting the power of genetic analysis and genomic prediction. The challenge for the next generation of quantitative geneticists and plant breeders is not only to understand the genetic basis of complex trait variation, but also to use that knowledge to efficiently synthesize twenty-first century crop varieties.

Genealogical patterns in different genomic regions may be different due to the joint influence of gene flow and selection. The existence of two subspecies of cultivated rice provides a unique opportunity for analyzing these effects during domestication. We chose 66 accessions from the three rice taxa (about 22 each from Oryza sativa indica, O. sativa japonica, and O. rufipogon) for whole-genome sequencing. In the search for the signature of selection, we focus on low diversity regions (LDRs) shared by both cultivars. We found that the genealogical histories of these overlapping LDRs are distinct from the genomic background. While indica and japonica genomes generally appear to be of independent origin, many overlapping LDRs may have originated only once, as a result of selection and subsequent introgression. Interestingly, many such LDRs contain only one candidate gene of rice domestication, and several known domestication genes have indeed been “rediscovered” by this approach. In summary, we identified 13 additional candidate genes of domestication. The origin of two cultivated rice Oryza sativa indica and O. sativa japonica has been an interesting topic in evolutionary biology. Through whole-genome sequencing, we show that the rice genome embodies two different evolutionary trajectories. Overall genome-wide pattern supports a history of independent origin of two cultivars from their wild population. However, genomic segments bearing important agronomic traits originated only once in one population and spread across all cultivars through introgression and human selection. Population genetic analysis allows us to pinpoint 13 additional candidate domestication genes.

Reference collections of multiple Drosophila lines with accumulating collections of "omics" data have proven especially valuable for the study of population genetics and complex trait genetics. Here we present a description of a resource collection of 84 strains of Drosophila melanogaster whose genome sequences were obtained after 12 generations of full-sib inbreeding. The initial rationale for this resource was to foster development of a systems biology platform for modeling metabolic regulation, using natural polymorphisms as perturbations. As reference lines, they are amenable to repeated phenotypic measurements, and already a large collection of metabolic traits have been assayed. Another key feature of these strains is their widespread geographic origin, coming from Beijing, Ithaca, Netherlands, Tasmania and Zimbabwe. After obtaining 12.5x coverage of paired-end Illumina sequence reads, SNP and indel calls were made with the GATK platform. Thorough quality control was enabled by deep sequencing one line to >100x, and SNPs and indels were validated using ddRAD-sequencing as an orthogonal platform. In addition, a series of preliminary population genetic tests were performed with these SNP data for assessment of data quality. We found 83 segregating inversions among the lines, and as expected these were especially abundant in the African sample. We anticipate that this will make a useful addition to the set of reference D. melanogaster strains, thanks to its geographic structuring and unusually high level of genetic diversity.

To understand the role of adaptation in speciation, one must characterize the ecologically relevant phenotypic effects of naturally occurring alleles at loci potentially causing reproductive isolation. The desaturase2 gene of Drosophila melanogaster is such a locus. Two geographically differentiated ds2 alleles underlie a pheromonal difference between the Zimbabwe and Cosmopolitan races. We used a site-directed gene replacement technique to introduce an allele of ds2 from the Zimbabwe population into Cosmopolitan flies. We show that the Cosmopolitan allele confers resistance to cold as well as susceptibility to starvation when the entire genetic background is otherwise identical. We conclude that ecological adaptation likely accompanies sexual isolation between the two behavioral races of D. melanogaster.

Variation in inflorescence development is an important target of selection for numerous crop species, including many members of the Poaceae (grasses). In Asian rice (Oryza sativa), inflorescence (panicle) architecture is correlated with yield and grain-quality traits. However, many rice breeders continue to use composite phenotypes in selection pipelines, because measuring complex, branched panicles requires a significant investment of resources. We developed an open-source phenotyping platform, PANorama, which measures multiple architectural and branching phenotypes from images simultaneously. PANorama automatically extracts skeletons from images, allows users to subdivide axes into individual internodes, and thresholds away structures, such as awns, that normally interfere with accurate panicle phenotyping. PANorama represents an improvement in both efficiency and accuracy over existing panicle imaging platforms, and flexible implementation makes PANorama capable of measuring a range of organs from other plant species. Using high-resolution phenotypes, a mapping population of recombinant inbred lines, and a dense single-nucleotide polymorphism data set, we identify, to our knowledge, the largest number of quantitative trait loci (QTLs) for panicle traits ever reported in a single study. Several areas of the genome show pleiotropic clusters of panicle QTLs, including a region near the rice Green Revolution gene SEMIDWARF1. We also confirm that multiple panicle phenotypes are distinctly different among a small collection of diverse rice varieties. Taken together, these results suggest that clusters of small-effect QTLs may be responsible for varietal or subpopulation-specific panicle traits, representing a significant opportunity for rice breeders selecting for yield performance across different genetic backgrounds.