Anthony J. Greenberg

Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype–phenotype relationships and its relevance to crop improvement

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.

Evolution of protein-coding genes in Drosophila

Several contributing factors have been implicated in evolutionary rate heterogeneity among proteins, but their evolutionary mechanisms remain poorly characterized. The recently sequenced 12 Drosophila genomes provide a unique opportunity to shed light on these unresolved issues. Here, we focus on the role of natural selection in shaping evolutionary rates. We use the Drosophila genomic data to distinguish between factors that increase the strength of purifying selection on proteins and factors that affect the amount of positive selection experienced by proteins. We confirm the importance of translational selection in shaping protein evolution in Drosophila and show that factors such as tissue bias in expression, gene essentiality, intron number, and recombination rate also contribute to evolutionary rate variation among proteins.

Two Evolutionary Histories in the Genome of Rice: the Roles of Domestication Genes

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.

Global Diversity Lines–A Five-Continent Reference Panel of Sequenced Drosophila melanogaster Strains

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 by the use of 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 12.5× 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 >100×, and single-nucleotide polymorphisms and indels were validated using ddRAD-sequencing as an orthogonal platform. In addition, a series of preliminary population genetic tests were performed with these single-nucleotide polymorphism 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.

Genome-wide misexpression of X-linked versus autosomal genes associated with hybrid male sterility

Postmating reproductive isolation is often manifested as hybrid male sterility, for which X-linked genes are overrepresented (the so-called large X effect). In contrast, X-linked genes are significantly under-represented among testis-expressing genes. This seeming contradiction may be germane to the X:autosome imbalance hypothesis on hybrid sterility, in which the X-linked effect is mediated mainly through the misexpression of autosomal genes. In this study, we compared gene expression in fertile and sterile males in the hybrids between two Drosophila species. These hybrid males differ only in a small region of the X chromosome containing the Ods-site homeobox (OdsH) (also known as Odysseus) locus of hybrid sterility. Of genes expressed in the testis, autosomal genes were, indeed, more likely to be misexpressed than X-linked genes under the sterilizing action of OdsH. Since this mechanism of X:autosome interaction is only associated with spermatogenesis, a connection between X:autosome imbalance and the high rate of hybrid male sterility seems plausible.

GAGA Factor Isoforms Have Distinct but Overlapping Functions In Vivo

ABSTRACT The Drosophila melanogaster GAGA factor (encoded by the Trithorax-like [Trl] gene) is required for correct chromatin architecture at diverse chromosomal sites. The Trl gene encodes two alternatively spliced isoforms of the GAGA factor (GAGA-519 and GAGA-581) that are identical except for the length and sequence of the C-terminal glutamine-rich (Q) domain. In vitro and tissue culture experiments failed to find any functional difference between the two isoforms. We made a set of transgenes that constitutively express cDNAs coding for either of the isoforms with the goal of elucidating their roles in vivo. Phenotypic analysis of the transgenes in Trl mutant background led us to the conclusion that GAGA-519 and GAGA-581 perform different, albeit largely overlapping, functions. We also expressed a fusion protein with LacZ disrupting the Q domain of GAGA-519. This LacZ fusion protein compensated for the loss of wild-type GAGA factor to a surprisingly large extent. This suggests that the Q domain either is not required for the essential functions performed by the GAGA protein or is exclusively used for tetramer formation. These results are inconsistent with a major role of the Q domain in chromatin remodeling or transcriptional activation. We also found that GAGA-LacZ was able to associate with sites not normally occupied by the GAGA factor, pointing to a role of the Q domain in binding site choice in vivo.

The Drosophila GAGA factor is required for dosage compensation in males and for the formation of the male-specific-lethal complex chromatin entry site at 12DE.

Drosophila melanogaster males have one X chromosome, while females have two. To compensate for the resulting disparity in X-linked gene expression between the two sexes, most genes from the male X chromosome are hyperactivated by a special dosage compensation system. Dosage compensation is achieved by a complex of at least six proteins and two noncoding RNAs that specifically associate with the male X. A central question is how the X chromosome is recognized. According to a current model, complexes initially assemble at ∼35 chromatin entry sites on the X and then spread bidirectionally along the chromosome where they occupy hundreds of sites. Here, we report that mutations in Trithorax-like (Trl) lead to the loss of a single chromatin entry site on the X, male lethality, and mislocalization of dosage compensation complexes.

High-Resolution Inflorescence Phenotyping Using a Novel Image-Analysis Pipeline, PANorama

Image analysis facilitates high-resolution phenotyping and complements dense genetic marker data to improve quantitative trait locus mapping of inflorescence traits in rice. 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.

Environmental and genetic perturbations reveal different networks of metabolic regulation

Progress in systems biology depends on accurate descriptions of biological networks. Connections in a regulatory network are identified as correlations of gene expression across a set of environmental or genetic perturbations. To use this information to predict system behavior, we must test how the nature of perturbations affects topologies of networks they reveal. To probe this question, we focused on metabolism of Drosophila melanogaster. Our source of perturbations is a set of crosses among 92 wild‐derived lines from five populations, replicated in a manner permitting separate assessment of the effects of genetic variation and environmental fluctuation. We directly assayed activities of enzymes and levels of metabolites. Using a multivariate Bayesian model, we estimated covariance among metabolic parameters and built fine‐grained probabilistic models of network topology. The environmental and genetic co‐regulation networks are substantially the same among five populations. However, genetic and environmental perturbations reveal qualitative differences in metabolic regulation, suggesting that environmental shifts, such as diet modifications, produce different systemic effects than genetic changes, even if the primary targets are the same.

A Hierarchical Bayesian Model for a Novel Sparse Partial Diallel Crossing Design

Partial diallel crossing designs are in common use among evolutionary geneticists, as well as among plant and animal breeders. When the goal is to make statements about populations represented by a given set of lines, it is desirable to maximize the number of lines sampled given a set number of crosses among them. We propose an augmented round-robin design that accomplishes this. We develop a hierarchical Bayesian model to estimate quantitative genetic parameters from our scheme. For example, we show how to partition genetic effects into specific and general combining abilities, and the method provides estimates of heritability, dominance, and genetic correlations in the face of complex and unbalanced designs. We test our approach with simulated and real data. We show that although the models slightly overestimate genetic variances, main effects are assessed accurately and precisely. We also illustrate how our approach allows the construction of posterior distributions of combinations of parameters by calculating narrow-sense heritability and a genetic correlation between activities of two enzymes.