The first long-read nuclear genome assembly of Oryza australiensis, a wild rice from northern Australia

Oryza australiensis is a wild rice native to monsoonal northern Australia. The International Oryza Map Alignment Project emphasises its significance as the sole representative of the EE genome clade. Assembly of the O. australiensis genome has previously been challenging due to its high Long Terminal Repeat (LTR) retrotransposon (RT) content. Oxford Nanopore long reads were combined with Illumina short reads to generate a high-quality ~ 858 Mbp genome assembly within 850 contigs with 46× long read coverage. Reference-guided scaffolding increased genome contiguity, placing 88.2% of contigs into 12 pseudomolecules. After alignment to the Oryza sativa cv. Nipponbare genome, we observed several structural variations. PacBio Iso-Seq data were generated for five distinct tissues to improve the functional annotation of 34,587 protein-coding genes and 42,329 transcripts. We also report SNV numbers for three additional O. australiensis genotypes based on Illumina re-sequencing. Although genetic similarity reflected geographical separation, the density of SNVs also correlated with our previous report on variations in salinity tolerance. This genome re-confirms the genetic remoteness of the O. australiensis lineage within the O. officinalis genome complex. Assembly of a high-quality genome for O. australiensis provides an important resource for the discovery of critical genes involved in development and stress tolerance.Aaron L. Phillips, Scott Ferguson, Nathan S. Watson, Haigh, Ashley W. Jones, Justin O. Borevitz, Rachel A. Burton, Brian J. Atwel

Climatic drivers of silicon accumulation in a model grass operate in low- but not high-silicon soils

Grasses are hyper-accumulators of silicon (Si), which is known to alleviate diverse environmental stresses, prompting speculation that Si accumulation evolved in response to unfavourable climatic conditions, including seasonally arid environments. We conducted a common garden experiment using 57 accessions of the model grass Brachypodium distachyon, sourced from different Mediterranean locations, to test relationships between Si accumulation and 19 bioclimatic variables. Plants were grown in soil with either low or high (Si supplemented) levels of bioavailable Si. Si accumulation was negatively correlated with temperature variables (annual mean diurnal temperature range, temperature seasonality, annual temperature range) and precipitation seasonality. Si accumulation was positively correlated with precipitation variables (annual precipitation, precipitation of the driest month and quarter, and precipitation of the warmest quarter). These relationships, however, were only observed in low-Si soils and not in Si-supplemented soils. Our hypothesis that accessions of B. distachyon from seasonally arid conditions have higher Si accumulation was not supported. On the contrary, higher temperatures and lower precipitation regimes were associated with lower Si accumulation. These relationships were decoupled in high-Si soils. These exploratory results suggest that geographical origin and prevailing climatic conditions may play a role in predicting patterns of Si accumulation in grasses

An Adaptive and Memory Efficient Algorithm for Genotype Imputation

Genome wide association studies have proven to be a highly successful method for identification of genetic loci for complex phenotypes in both humans and model organisms. These large scale studies rely oil the collection of hundreds of thousands of single nucleotide polymorphisms (SNPs) across the genome. Standard high-throughput genotyping technologies capture only a fraction of the total genetic variation. Recent efforts have shown that it is possible to "impute" with high accuracy the genotypes of SNPs that are not collected ill the study provided that they are present in a reference data set which contains both SNPs collected in the Study as well as other SNPs. We here introduce a novel HMM based technique to solve the imputation problem that addresses several shortcomings of existing methods. First, our method is adaptive which lets it estimate population genetic parameters from the data and be applied to model organisms that have very different evolutionary histories. Compared to traditional methods. Our method is tip to tell times more accurate on model organisms such as mouse. Second, our algorithm scales in memory usage in the number of collected markers as opposed to the number of known SNPs. This issue is very relevant due to the size of the reference data sets currently being generated. We compare our method over mouse and human data sets to existing methods and show that each has either comparable or better performance and much lower memory usage. The method is available for download at http://genetics.cs.ucla.edu/eminim.N

Managed relocation: Integrating the scientific, regulatory, and ethical challenges

Managed relocation is defined as the movement of species, populations, or genotypes to places outside the areas of their historical distributions to maintain biological diversity or ecosystem functioning with changing climate. It has been claimed that a major extinction event is under way and that climate change is increasing its severity. Projections indicating that climate change may drive substantial losses of biodiversity have compelled some scientists to suggest that traditional management strategies are insufficient. The managed relocation of species is a controversial management response to climate change. The published literature has emphasized biological concerns over difficult ethical, legal, and policy issues. Furthermore, ongoing managed relocation actions lack scientific and societal engagement. Our interdisciplinary team considered ethics, law, policy, ecology, and natural resources management to identify the key issues of managed relocation relevant for developing sound policies that support decisions for resource management. We recommend that government agencies develop and adopt best practices for managed relocation

Genome-wide association study of 107 phenotypes in a common set of Arabidopsis thaliana inbred lines

International audienceAlthough pioneered by human geneticists as a potential solution to the challenging problem of finding the genetic basis of common human diseases1,2, genome-wide association (GWA) studies have, owing to advances in genotyping and sequencing technology, become an obvious general approach for studying the genetics of natural variation and traits of agricultural importance. They are particularly useful when inbred lines are available, because once these lines have been genotyped they can be phenotyped multiple times, making it possible (as well as extremely cost effective) to study many different traits in many different environments, while replicating the phenotypic measurements to reduce environmental noise. Here we demonstrate the power of this approach by carrying out a GWA study of 107 phenotypes in Arabidopsis thaliana, a widely distributed, predominantly self-fertilizingmodel plant known to harbour considerable genetic variation for many adaptively important traits3. Our results are dramatically different from those of human GWA studies, in that we identify many common alleles of major effect, but they are also, in many cases, harder to interpret because confounding by complex genetics and population structure make it difficult to distinguish true associations from false. However, a-priori candidates are significantly over-represented among these associations as well, making many of themexcellent candidates for follow-up experiments. Our study demonstrates the feasibility of GWA studies in A. thaliana and suggests that the approach will be appropriate for many other organisms