The giant sequoia (Sequoiadendron giganteum) of California are massive, long-lived trees that grow along the U.S. Sierra Nevada mountains. Genomic data are limited in giant sequoia and producing a reference genome sequence has been an important goal to allow marker development for restoration and management. Using deep-coverage Illumina and Oxford Nanopore sequencing, combined with Dovetail chromosome conformation capture libraries, the genome was assembled into eleven chromosome-scale scaffolds containing 8.125 Gbp of sequence. Iso-Seq transcripts, assembled from three distinct tissues, was used as evidence to annotate a total of 41,632 protein-coding genes. The genome was found to contain, distributed unevenly across all 11 chromosomes and in 63 orthogroups, over 900 complete or partial predicted NLR genes, of which 375 are supported by annotation derived from protein evidence and gene modeling. This giant sequoia reference genome sequence represents the first genome sequenced in the Cupressaceae family, and lays a foundation for using genomic tools to aid in giant sequoia conservation and management.

Simulations of close relatives and identical by descent (IBD) segments are common in genetic studies, yet most past efforts have utilized sex averaged genetic maps and ignored crossover interference, thus omitting features known to affect the breakpoints of IBD segments. We developed Ped-sim, a method for simulating relatives that can utilize either sex-specific or sex averaged genetic maps and also either a model of crossover interference or the traditional Poisson model for inter-crossover distances. To characterize the impact of previously ignored mechanisms, we simulated data for all four combinations of these factors. We found that modeling crossover interference decreases the standard deviation of pairwise IBD proportions by 10.4% on average in full siblings through second cousins. By contrast, sex-specific maps increase this standard deviation by 4.2% on average, and also impact the number of segments relatives share. Most notably, using sex-specific maps, the number of segments half-siblings share is bimodal; and when combined with interference modeling, the probability that sixth cousins have non-zero IBD sharing ranges from 9.0 to 13.1%, depending on the sexes of the individuals through which they are related. We present new analytical results for the distributions of IBD segments under these models and show they match results from simulations. Finally, we compared IBD sharing rates between simulated and real relatives and find that the combination of sex-specific maps and interference modeling most accurately captures IBD rates in real data. Ped-sim is open source and available from https://github.com/williamslab/ ped-sim.

Simulations of close relatives and identical by descent (IBD) segments are common in genetic studies, yet most past efforts have utilized sex averaged genetic maps and ignored crossover interference, thus omitting features known to affect the breakpoints of IBD segments. We developed Ped-sim, a method for simulating relatives that can utilize either sex-specific or sex averaged genetic maps and also either a model of crossover interference or the traditional Poisson model for inter-crossover distances. To characterize the impact of previously ignored mechanisms, we simulated data for all four combinations of these factors. We found that modeling crossover interference decreases the standard deviation of the IBD proportion by 10.4% on average in full siblings through second cousins. By contrast, sex-specific maps increase this standard deviation by 4.2% on average, and also impact the number of segments relatives share. Most notably, using sex-specific maps, the number of segments half-siblings share is bimodal; and when combined with interference modeling, the probability that sixth cousins have non-zero IBD ranges from 9.0 to 13.1%, depending on the sexes of the individuals through which they are related. We present new analytical results for the distributions of IBD segments under these models and show they match results from simulations. Finally, we compared IBD sharing rates between simulated and real relatives and find that the combination of sex-specific maps and interference modeling most accurately captures IBD rates in real data. Ped-sim is open source and available from https://github.com/williamslab/ped-sim. Short title: Crossover interference and sex-specific maps shape IBD distributionsSimulations are ubiquitous throughout statistical genetics in order to generate data with known properties, enabling tests of inference methods and analyses of real world processes in settings where experimental data are challenging to collect. Simulating genetic data for relatives in a pedigree requires the synthesis of chromosomes parents transmit to their children. These chromosomes form as a mosaic of a given parent's two chromosomes, with the location of switches between the two parental chromosomes known as crossovers. Detailed information about crossover generation based on real data from humans now exists, including the fact that men and women have overall different rates (women produce ∼1.6 times more crossovers) and that real crossovers are subject to interference-whereby crossovers are further apart from one another than expected under a model that selects their locations randomly. Our new method, Ped-sim, can simulate pedigree data using these less commonly modeled crossover features, and we used it to evaluate the importance of sex-specific rates and interference in real data. These comparisons show that both factors shape the amount of DNA two relatives share identically, and that their inclusion in models of crossover better fit data from real relatives.

† These authors are joint first authors. SUMMARYJuglans (walnuts), the most speciose genus in the walnut family (Juglandaceae), represents most of the family's commercially valuable fruit and wood-producing trees. It includes several species used as rootstock for their resistance to various abiotic and biotic stressors. We present the full structural and functional genome annotations of six Juglans species and one outgroup within Juglandaceae (Juglans regia, J. cathayensis, J. hindsii, J. microcarpa, J. nigra, J. sigillata and Pterocarya stenoptera) produced using BRAKER2 semi-unsupervised gene prediction pipeline and additional tools. For each annotation, gene predictors were trained using 19 tissue-specific J. regia transcriptomes aligned to the genomes. Additional functional evidence and filters were applied to multi-exonic and mono-exonic putative genes to yield between 27 000 and 44 000 high-confidence gene models per species. Comparison of gene models to the BUSCO embryophyta dataset suggested that, on average, genome annotation completeness was 85.6%. We utilized these high-quality annotations to assess gene family evolution within Juglans, and among Juglans and selected Eurosid species. We found notable contractions in several gene families in J. hindsii, including disease resistance-related wall-associated kinase (WAK), Catharanthus roseus receptor-like kinase (CrRLK1L) and others involved in abiotic stress response. Finally, we confirmed an ancient whole-genome duplication that took place in a common ancestor of Juglandaceae using site substitution comparative analysis.

Published genomes frequently contain erroneous gene models that represent issues associated with identification of open reading frames, start sites, splice sites, and related structural features. The source of these inconsistencies is often traced back to integration across text file formats designed to describe long read alignments and predicted gene structures. In addition, the majority of gene prediction frameworks do not provide robust downstream filtering to remove problematic gene annotations, nor do they represent these annotations in a format consistent with current file standards. These frameworks also lack consideration for functional attributes, such as the presence or absence of protein domains that can be used for gene model validation. To provide oversight to the increasing number of published genome annotations, we present a software package, the Gene Filtering, Analysis, and Conversion (gFACs), to filter, analyze, and convert predicted gene models and alignments. The software operates across a wide range of alignment, analysis, and gene prediction files with a flexible framework for defining gene models with reliable structural and functional attributes. gFACs supports common downstream applications, including genome browsers, and generates extensive details on the filtering process, including distributions that can be visualized to further assess the proposed gene space. gFACs is freely available and implemented in Perl with support from BioPerl libraries at https://gitlab.com/PlantGenomicsLab/gFACs.

Premise An informatics approach was used for the construction of an Axiom genotyping array from heterogeneous, high‐throughput sequence data to assess the complex genome of loblolly pine ( Pinus taeda ). Methods High‐throughput sequence data, sourced from exome capture and whole genome reduced‐representation approaches from 2698 trees across five sequence populations, were analyzed with the improved genome assembly and annotation for the loblolly pine. A variant detection, filtering, and probe design pipeline was developed to detect true variants across and within populations. From 8.27 million variants, a total of 642,275 were evaluated and 423,695 of those were screened across a range‐wide population. Results The final informatics and screening approach delivered an Axiom array representing 46,439 high‐confidence variants to the forest tree breeding and genetics community. Based on the annotated reference genome, 34% were located in or directly upstream or downstream of genic regions. Discussion The Pita50K array represents a genome‐wide resource developed from sequence data for an economically important conifer, loblolly pine. It uniquely integrates independent projects that assessed trees sampled across the native range. The challenges associated with the large and repetitive genome are addressed in the development of this resource.

The giant sequoia ( Sequoiadendron giganteum ) of California are massive, long-lived trees that grow along the U.S. Sierra Nevada mountains. As they grow primarily in isolated groves within a narrow range, conservation of existing trees has been a national goal for over 150 years.Genomic data are limited in giant sequoia, and the assembly and annotation of the first giant sequoia genome has been an important goal to allow marker development for restoration and management. Using Illumina and Oxford Nanopore sequencing combined with Dovetail chromosome conformation capture libraries, 8.125 Gbp of sequence was assembled into eleven chromosome-scale scaffolds. This giant sequoia assembly represents the first genome sequenced in the Cupressaceae family, and lays a foundation for using genomic tools to aid in giant sequoia conservation and management. Beyond conservation and management applications, the giant sequoia assembly is a resource for answering questions about the life history of this enigmatic and robust species. Here we provide an example by taking an inventory of the large and complex family of NLR type disease resistance genes.assembly and annotation presented here is an unprecedented resource in conifer genomics, both for the quality of the assembly and because it represents an understudied branch of the gymnosperm tree of life. MATERIALS AND METHODS Sequencing and assembly Megagametophyte DNA extraction and sequencingCones were collected from a 1,360-year-old giant sequoia (SEGI21, Sillett et al., 2015) in Sequoia/Kings Canyon National Park in 2012. As in previous conifer genome sequencing projects (e.g. Zimin et al., 2014), the megagametophyte from a single fertilized seed was dissected out and its haploid DNA extracted with a Qiagen DNeasy Plant Kit (Hilden, Germany), followed by library preparation with an Illumina TruSeq Nano kit using the low throughput protocol. This megagametophyte library was then sequenced on 10 lanes of an Illumina HiSeq 4000 with 150 bp paired-end reads at the UC Davis Genome Center DNA Technologies Core facility. Foliage DNA extraction and Nanopore sequencingIn 2017 foliage was collected from the upper canopy of the same giant sequoia tree (SEGI21).From this foliage, high molecular weight DNA was extracted following the protocol described here (dx.doi.org/10.17504/protocols.io.4vbgw2n) . Briefly, purified genomic DNA was isolated through a nuclei extraction and lysis protocol. First, mature leaf tissue was homogenized in liquid nitrogen until well-ground, then added to a gentle lysis buffer (after Zhang et al.,

We performed gene and genome targeted SNP discovery towards the development of a genome-wide, multispecies genotyping array for tropical pines. Pooled RNA-seq data from shoots of seedlings from five tropical pine species was used to identify transcript-based SNPs resulting in 1.3 million candidate Affymetrix SNP probe sets.