Major impacts of widespread structural variation on sorghum.

Genetic diversity is critical to crop breeding and improvement, and dissection of the genomic variation underlying agronomic traits can both assist breeding and give insight into basic biological mechanisms. Although recent genome analyses in plants reveal many structural variants (SVs), most current studies of crop genetic variation are dominated by single-nucleotide polymorphisms (SNPs). The extent of the impact of SVs on global trait variation, as well as their utility in genome-wide selection, is not yet understood. In this study, we built an SV data set based on whole-genome resequencing of diverse sorghum lines (n = 363), validated the correlation of photoperiod sensitivity and variety type, and identified SV hotspots underlying the divergent evolution of cellulosic and sweet sorghum. In addition, we showed the complementary contribution of SVs for heritability of traits related to sorghum adaptation. Importantly, inclusion of SV polymorphisms in association studies revealed genotype-phenotype associations not observed with SNPs alone. Three-way genome-wide association studies (GWAS) based on whole-genome SNP, SV, and integrated SNP + SV data sets showed substantial associations between SVs and sorghum traits. The addition of SVs to GWAS substantially increased heritability estimates for some traits, indicating their important contribution to functional allelic variation at the genome level. Our discovery of the widespread impacts of SVs on heritable gene expression variation could render a plausible mechanism for their disproportionate impact on phenotypic variation. This study expands our knowledge of SVs and emphasizes the extensive impacts of SVs on sorghum.

The genomic case against genetic determinism.

Animal studies reveal that the molecular wiring of the brain can be altered by heredity, the environment, and their interaction. A deeper molecular understanding of these interactions could be a potent antidote to societal concerns of genetic determinism for human behavior, but this requires a paradigm that extends beyond traditional genome-wide association study (GWAS).

Common signatures of selection reveal target loci for breeding across soybean populations.

Understanding the underlying genetic bases of yield-related selection and distinguishing these changes from genetic drift are critical for both improved understanding and future success of plant breeding. Soybean [Glycine max (L.) Merr.] is a key species for world food security, yet knowledge of the mechanism of selective breeding in soybean, such as the century-long program of artificial selection in U.S. soybean germplasm, is currently limited to certain genes and loci. Here, we identify genome-wide signatures of selection in separate populations of soybean subjected to artificial selection for increased yield by multiple breeding programs in the United States. We compared the alternative soybean breeding population (AGP) created by USDA-ARS to the conventional public soybean lines (CGP) developed at three different stages of breeding (ancestral, intermediate, and elite) to identify shared signatures of selection and differentiate these from drift. The results showed a strong selection for specific haplotypes identified by single site frequency and haplotype homozygosity methods. A set of common selection signatures was identified in both AGP and CGP that supports the hypothesis that separate breeding programs within similar environments coalesce on the fixation of the same key haplotypes. Signatures unique to each breeding program were observed. These results raise the possibility that selection analysis can allow the identification of favorable alleles to enhance directed breeding approaches.

Promoter deletion in the soybean Compact mutant leads to overexpression of a gene with homology to the C20-gibberellin 2-oxidase family.

Height is a critical component of plant architecture, significantly affecting crop yield. The genetic basis of this trait in soybean remains unclear. In this study, we report the characterization of the Compact mutant of soybean, which has short internodes. The candidate gene was mapped to chromosome 17, and the interval containing the causative mutation was further delineated using biparental mapping. Whole-genome sequencing of the mutant revealed an 8.7 kb deletion in the promoter of the Glyma.17g145200 gene, which encodes a member of the class III gibberellin (GA) 2-oxidases. The mutation has a dominant effect, likely via increased expression of the GA 2-oxidase transcript observed in green tissue, as a result of the deletion in the promoter of Glyma.17g145200. We further demonstrate that levels of GA precursors are altered in the Compact mutant, supporting a role in GA metabolism, and that the mutant phenotype can be rescued with exogenous GA3. We also determined that overexpression of Glyma.17g145200 in Arabidopsis results in dwarfed plants. Thus, gain of promoter activity in the Compact mutant leads to a short internode phenotype in soybean through altered metabolism of gibberellin precursors. These results provide an example of how structural variation can control an important crop trait and a role for Glyma.17g145200 in soybean architecture, with potential implications for increasing crop yield.

An NBS-LRR protein in the Rpp1 locus negates the dominance of Rpp1-mediated resistance against Phakopsora pachyrhizi in soybean.

The soybean Rpp1 locus confers resistance to Phakopsora pachyrhizi, causal agent of rust, and resistance is usually dominant over susceptibility. However, dominance of Rpp1-mediated resistance is lost when a resistant genotype (Rpp1 or Rpp1b) is crossed with susceptible line TMG06_0011, and the mechanism of this dominant susceptibility (DS) is unknown. Sequencing the Rpp1 region reveals that the TMG06_0011 Rpp1 locus has a single nucleotide-binding site leucine-rich repeat (NBS-LRR) gene (DS-R), whereas resistant PI 594760B (Rpp1b) is similar to PI 200492 (Rpp1) and has three NBS-LRR resistance gene candidates. Evidence that DS-R is the cause of DS was reflected in virus-induced gene silencing of DS-R in Rpp1b/DS-R or Rpp1/DS-R heterozygous plants with resistance partially restored. In heterozygous Rpp1b/DS-R plants, expression of Rpp1b candidate genes was not significantly altered, indicating no effect of DS-R on transcription. Physical interaction of the DS-R protein with candidate Rpp1b resistance proteins was supported by yeast two-hybrid studies and in silico modeling. Thus, we conclude that suppression of resistance most likely does not occur at the transcript level, but instead probably at the protein level, possibly with Rpp1 function inhibited by binding to the DS-R protein. The DS-R gene was found in other soybean lines, with an estimated allele frequency of 6% in a diverse population, and also found in wild soybean (Glycine soja). The identification of a dominant susceptible NBS-LRR gene provides insight into the behavior of NBS-LRR proteins and serves as a reminder to breeders that the dominance of an R gene can be influenced by a susceptibility allele.

Leveraging orthology within maize and Arabidopsis QTL to identify genes affecting natural variation in gravitropism.

Plants typically orient their organs with respect to the Earth's gravity field by a dynamic process called gravitropism. To discover conserved genetic elements affecting seedling root gravitropism, we measured the process in a set of Zea mays (maize) recombinant inbred lines with machine vision and compared the results with those obtained in a similar study of Arabidopsis thaliana. Each of the several quantitative trait loci that we mapped in both species spanned many hundreds of genes, too many to test individually for causality. We reasoned that orthologous genes may be responsible for natural variation in monocot and dicot root gravitropism. If so, pairs of orthologous genes affecting gravitropism may be present within the maize and Arabidopsis QTL intervals. A reciprocal comparison of sequences within the QTL intervals identified seven pairs of such one-to-one orthologs. Analysis of knockout mutants demonstrated a role in gravitropism for four of the seven: CCT2 functions in phosphatidylcholine biosynthesis, ATG5 functions in membrane remodeling during autophagy, UGP2 produces the substrate for cellulose and callose polymer extension, and FAMA is a transcription factor. Automated phenotyping enabled this discovery of four naturally varying components of a conserved process (gravitropism) by making it feasible to conduct the same large-scale experiment in two species.

Impact of multiple selective breeding programs on genetic diversity in soybean germplasm.

KEY MESSAGE: Independent soybean breeding programs shape genetic diversity from unimproved germplasm to modern cultivars in similar ways, but distinct breeding populations retain unique genetic variation, preserving additional diversity. From the domestication of wild soybean (Glycine soja Sieb. & Zucc.), over 3,000 years ago, to the modern soybean (Glycine max L. Merr) cultivars that provide much of the world's oil and protein, soybean populations have undergone fundamental changes. We evaluated the molecular impact of breeding and selection using 391 soybean accessions including US cultivars and their progenitors from the USDA Soybean Germplasm Collection (CGP), plus two new populations specifically developed to increase genetic diversity and high yield in two alternative gene pools: one derived from exotic G. max germplasm (AGP) and one derived from G. soja (SGP). Reduction in nucleotide genetic diversity (π) was observed with selection within gene pools, but artificial selection in the AGP maintained more diversity than in the CGP. The highest FST levels were seen between ancestral and elite lines in all gene pools, but specific nucleotide-level patterns varied between gene pools. Population structure analyses support that independent selection resulted in high-yielding elite lines with similar allelic compositions in the AGP and CGP. SGP, however, produced elite progeny that were well differentiated from, but lower yielding than, CGP elites. Both the AGP and SGP retained a significant number of private alleles that are absent in CGP. We conclude that the genomic diversity shaped by multiple selective breeding programs can result in gene pools of highly productive elite lines with similar allelic compositions in a genome-wide perspective. Breeding programs with different ancestral lines, however, can retain private alleles representing unique genetic diversity.

CROPSR: an automated platform for complex genome-wide CRISPR gRNA design and validation.

BACKGROUND: CRISPR/Cas9 technology has become an important tool to generate targeted, highly specific genome mutations. The technology has great potential for crop improvement, as crop genomes are tailored to optimize specific traits over generations of breeding. Many crops have highly complex and polyploid genomes, particularly those used for bioenergy or bioproducts. The majority of tools currently available for designing and evaluating gRNAs for CRISPR experiments were developed based on mammalian genomes that do not share the characteristics or design criteria for crop genomes. RESULTS: We have developed an open source tool for genome-wide design and evaluation of gRNA sequences for CRISPR experiments, CROPSR. The genome-wide approach provides a significant decrease in the time required to design a CRISPR experiment, including validation through PCR, at the expense of an overhead compute time required once per genome, at the first run. To better cater to the needs of crop geneticists, restrictions imposed by other packages on design and evaluation of gRNA sequences were lifted. A new machine learning model was developed to provide scores while avoiding situations in which the currently available tools sometimes failed to provide guides for repetitive, A/T-rich genomic regions. We show that our gRNA scoring model provides a significant increase in prediction accuracy over existing tools, even in non-crop genomes. CONCLUSIONS: CROPSR provides the scientific community with new methods and a new workflow for performing CRISPR/Cas9 knockout experiments. CROPSR reduces the challenges of working in crops, and helps speed gRNA sequence design, evaluation and validation. We hope that the new software will accelerate discovery and reduce the number of failed experiments.

WI12Rhg1 interacts with DELLAs and mediates soybean cyst nematode resistance through hormone pathways.

The soybean cyst nematode (SCN) is one of the most important causes of soybean yield loss. The major source of genetic resistance to SCN is the Rhg1 repeat, a tandem copy number polymorphism of three genes. The roles of these genes are only partially understood. Moreover, nematode populations virulent on Rhg1-carrying soybeans are becoming more common, increasing the need to understand the most successful genetic resistance mechanism. Here, we show that a Rhg1-locus gene (Glyma.18G02270) encoding a wound-inducible protein (WI12Rhg1 ) is needed for SCN resistance. Furthermore, knockout of WI12Rhg1 reduces the expression of DELLA18, and the expression of WI12Rhg1 is itself induced by either JA, SA or GA. The content of the defence hormone SA is significantly lower whilst GA12 and GA53 are increased in WI12Rhg1 knockout roots compared with unedited hairy roots. We find that WI12Rhg1 directly interacts with DELLA18 (Glyma.18G040000) in yeast and plants and that double knockout of DELLA18 and its homeolog DELLA11 (Glyma.11G216500) significantly reduces SCN resistance and alters the root morphology. As DELLA proteins are implicated in hormone signalling, we explored the content of defence hormones (JA and SA) in DELLA knockout and unedited roots, finding reduced levels of JA and SA after the knockout of DELLA. Additionally, the treatment of DELLA-knockout roots with JA or SA rescues SCN resistance lost by the knockout. Meanwhile, the SCN resistance of unedited roots decreases after the treatment with GA, but increases with JA or SA. Our findings highlight the critical roles of WI12Rhg1 and DELLA proteins in SCN resistance through interconnection with hormone signalling.

Design considerations for workflow management systems use in production genomics research and the clinic.

The changing landscape of genomics research and clinical practice has created a need for computational pipelines capable of efficiently orchestrating complex analysis stages while handling large volumes of data across heterogeneous computational environments. Workflow Management Systems (WfMSs) are the software components employed to fill this gap. This work provides an approach and systematic evaluation of key features of popular bioinformatics WfMSs in use today: Nextflow, CWL, and WDL and some of their executors, along with Swift/T, a workflow manager commonly used in high-scale physics applications. We employed two use cases: a variant-calling genomic pipeline and a scalability-testing framework, where both were run locally, on an HPC cluster, and in the cloud. This allowed for evaluation of those four WfMSs in terms of language expressiveness, modularity, scalability, robustness, reproducibility, interoperability, ease of development, along with adoption and usage in research labs and healthcare settings. This article is trying to answer, which WfMS should be chosen for a given bioinformatics application regardless of analysis type?. The choice of a given WfMS is a function of both its intrinsic language and engine features. Within bioinformatics, where analysts are a mix of dry and wet lab scientists, the choice is also governed by collaborations and adoption within large consortia and technical support provided by the WfMS team/community. As the community and its needs continue to evolve along with computational infrastructure, WfMSs will also evolve, especially those with permissive licenses that allow commercial use. In much the same way as the dataflow paradigm and containerization are now well understood to be very useful in bioinformatics applications, we will continue to see innovations of tools and utilities for other purposes, like big data technologies, interoperability, and provenance.

Impact of variant-level batch effects on identification of genetic risk factors in large sequencing studies.

Genetic studies have shifted to sequencing-based rare variants discovery after decades of success in identifying common disease variants by Genome-Wide Association Studies using Single Nucleotide Polymorphism chips. Sequencing-based studies require large sample sizes for statistical power and therefore often inadvertently introduce batch effects because samples are typically collected, processed, and sequenced at multiple centers. Conventionally, batch effects are first detected and visualized using Principal Components Analysis and then controlled by including batch covariates in the disease association models. For sequencing-based genetic studies, because all variants included in the association analyses have passed sequencing-related quality control measures, this conventional approach treats every variant as equal and ignores the substantial differences still remaining in variant qualities and characteristics such as genotype quality scores, alternative allele fractions (fraction of reads supporting alternative allele at a variant position) and sequencing depths. In the Alzheimer's Disease Sequencing Project (ADSP) exome dataset of 9,904 cases and controls, we discovered hidden variant-level differences between sample batches of three sequencing centers and two exome capture kits. Although sequencing centers were included as a covariate in our association models, we observed differences at the variant level in genotype quality and alternative allele fraction between samples processed by different exome capture kits that significantly impacted both the confidence of variant detection and the identification of disease-associated variants. Furthermore, we found that a subset of top disease-risk variants came exclusively from samples processed by one exome capture kit that was more effective at capturing the alternative alleles compared to the other kit. Our findings highlight the importance of additional variant-level quality control for large sequencing-based genetic studies. More importantly, we demonstrate that automatically filtering out variants with batch differences may lead to false negatives if the batch discordances come largely from quality differences and if the batch-specific variants have better quality.

Genome biology of the paleotetraploid perennial biomass crop Miscanthus.

Miscanthus is a perennial wild grass that is of global importance for paper production, roofing, horticultural plantings, and an emerging highly productive temperate biomass crop. We report a chromosome-scale assembly of the paleotetraploid M. sinensis genome, providing a resource for Miscanthus that links its chromosomes to the related diploid Sorghum and complex polyploid sugarcanes. The asymmetric distribution of transposons across the two homoeologous subgenomes proves Miscanthus paleo-allotetraploidy and identifies several balanced reciprocal homoeologous exchanges. Analysis of M. sinensis and M. sacchariflorus populations demonstrates extensive interspecific admixture and hybridization, and documents the origin of the highly productive triploid bioenergy crop M. × giganteus. Transcriptional profiling of leaves, stem, and rhizomes over growing seasons provides insight into rhizome development and nutrient recycling, processes critical for sustainable biomass accumulation in a perennial temperate grass. The Miscanthus genome expands the power of comparative genomics to understand traits of importance to Andropogoneae grasses.

t-SNAREs bind the Rhg1 α-SNAP and mediate soybean cyst nematode resistance.

Soybean cyst nematode (SCN; Heterodera glycines) is the largest pathogenic cause of soybean yield loss. The Rhg1 locus is the most used and best characterized SCN resistance locus, and contains three genes including one encoding an α-SNAP protein. Although the Rhg1 α-SNAP is known to play an important role in vesicle trafficking and SCN resistance, the protein's binding partners and the molecular mechanisms underpinning SCN resistance remain unclear. In this report, we show that the Rhg1 α-SNAP strongly interacts with two syntaxins of the t-SNARE family (Glyma.12G194800 and Glyma.16G154200) in yeast and plants; importantly, the genes encoding these syntaxins co-localize with SCN resistance quantitative trait loci. Fluorescent visualization revealed that the α-SNAP and the two interacting syntaxins localize to the plasma membrane and perinuclear space in both tobacco epidermal and soybean root cells. The two syntaxins and their two homeologs were mutated, individually and in combination, using the CRISPR-Cas9 system in the SCN-resistant Peking and SCN-susceptible Essex soybean lines. Peking roots with deletions introduced into syntaxin genes exhibited significantly reduced resistance to SCN, confirming that t-SNAREs are critical to resisting SCN infection. The results presented here uncover a key step in the molecular mechanism of SCN resistance, and will be invaluable to soybean breeders aiming to develop highly SCN-resistant soybean varieties.

Genomic regions influencing aggressive behavior in honey bees are defined by colony allele frequencies.

For social animals, the genotypes of group members affect the social environment, and thus individual behavior, often indirectly. We used genome-wide association studies (GWAS) to determine the influence of individual vs. group genotypes on aggression in honey bees. Aggression in honey bees arises from the coordinated actions of colony members, primarily nonreproductive "soldier" bees, and thus, experiences evolutionary selection at the colony level. Here, we show that individual behavior is influenced by colony environment, which in turn, is shaped by allele frequency within colonies. Using a population with a range of aggression, we sequenced individual whole genomes and looked for genotype-behavior associations within colonies in a common environment. There were no significant correlations between individual aggression and specific alleles. By contrast, we found strong correlations between colony aggression and the frequencies of specific alleles within colonies, despite a small number of colonies. Associations at the colony level were highly significant and were very similar among both soldiers and foragers, but they covaried with one another. One strongly significant association peak, containing an ortholog of the Drosophila sensory gene dpr4 on linkage group (chromosome) 7, showed strong signals of both selection and admixture during the evolution of gentleness in a honey bee population. We thus found links between colony genetics and group behavior and also, molecular evidence for group-level selection, acting at the colony level. We conclude that group genetics dominates individual genetics in determining the fatal decision of honey bees to sting.

Bacterial steroid-17,20-desmolase is a taxonomically rare enzymatic pathway that converts prednisone to 1,4-androstanediene-3,11,17-trione, a metabolite that causes proliferation of prostate cancer cells.

The adrenal gland has traditionally been viewed as a source of "weak androgens"; however, emerging evidence indicates 11-oxy-androgens of adrenal origin are metabolized in peripheral tissues to potent androgens. Also emerging is the role of gut bacteria in the conversion of C21 glucocorticoids to 11-oxygenated C19 androgens. Clostridium scindens ATCC 35,704 is a gut microbe capable of converting cortisol into 11-oxy-androgens by cleaving the side-chain. The desA and desB genes encode steroid-17,20-desmolase. Our prior study indicated that the urinary tract bacterium, Propionimicrobium lymphophilum ACS-093-V-SCH5 encodes desAB and converts cortisol to 11ß-hydroxyandrostenedione. We wanted to determine how widespread this function occurs in the human microbiome. Phylogenetic and sequence similarity network analyses indicated that the steroid-17,20-desmolase pathway is taxonomically rare and located in gut and urogenital microbiomes. Two microbes from each of these niches, C. scindens and Propionimicrobium lymphophilum, respectively, were screened for activity against endogenous (cortisol, cortisone, and allotetrahydrocortisol) and exogenous (prednisone, prednisolone, dexamethasone, and 9-fluorocortisol) glucocorticoids. LC/MS analysis showed that both microbes were able to side-chain cleave all glucocorticoids, forming 11-oxy-androgens. Pure recombinant DesAB from C. scindens showed the highest activity against prednisone, a commonly prescribed glucocorticoid. In addition, 0.1 nM 1,4-androstadiene-3,11,17-trione, bacterial side-chain cleavage product of prednisone, showed significant proliferation relative to vehicle in androgen-dependent growth LNCaP prostate cancer cells after 24 h (2.3 fold; P <  0.01) and 72 h (1.6 fold; P < 0.01). Taken together, DesAB-expressing microbes may be an overlooked source of androgens in the body, potentially contributing to various disease states, such as prostate cancer.

Correction to: Recommendations for performance optimizations when using GATK3.8 and GATK4.

Following publication of the original article [1], the author explained that Table 2 is displayed incorrectly. The correct Table 2 is given below. The original article has been corrected.

Recommendations for performance optimizations when using GATK3.8 and GATK4.

BACKGROUND: Use of the Genome Analysis Toolkit (GATK) continues to be the standard practice in genomic variant calling in both research and the clinic. Recently the toolkit has been rapidly evolving. Significant computational performance improvements have been introduced in GATK3.8 through collaboration with Intel in 2017. The first release of GATK4 in early 2018 revealed rewrites in the code base, as the stepping stone toward a Spark implementation. As the software continues to be a moving target for optimal deployment in highly productive environments, we present a detailed analysis of these improvements, to help the community stay abreast with changes in performance. RESULTS: We re-evaluated multiple options, such as threading, parallel garbage collection, I/O options and data-level parallelization. Additionally, we considered the trade-offs of using GATK3.8 and GATK4. We found optimized parameter values that reduce the time of executing the best practices variant calling procedure by 29.3% for GATK3.8 and 16.9% for GATK4. Further speedups can be accomplished by splitting data for parallel analysis, resulting in run time of only a few hours on whole human genome sequenced to the depth of 20X, for both versions of GATK. Nonetheless, GATK4 is already much more cost-effective than GATK3.8. Thanks to significant rewrites of the algorithms, the same analysis can be run largely in a single-threaded fashion, allowing users to process multiple samples on the same CPU. CONCLUSIONS: In time-sensitive situations, when a patient has a critical or rapidly developing condition, it is useful to minimize the time to process a single sample. In such cases we recommend using GATK3.8 by splitting the sample into chunks and computing across multiple nodes. The resultant walltime will be nnn.4 hours at the cost of $41.60 on 4 c5.18xlarge instances of Amazon Cloud. For cost-effectiveness of routine analyses or for large population studies, it is useful to maximize the number of samples processed per unit time. Thus we recommend GATK4, running multiple samples on one node. The total walltime will be ∼34.1 hours on 40 samples, with 1.18 samples processed per hour at the cost of $2.60 per sample on c5.18xlarge instance of Amazon Cloud.

Sentieon DNASeq Variant Calling Workflow Demonstrates Strong Computational Performance and Accuracy.

As reliable, efficient genome sequencing becomes ubiquitous, the need for similarly reliable and efficient variant calling becomes increasingly important. The Genome Analysis Toolkit (GATK), maintained by the Broad Institute, is currently the widely accepted standard for variant calling software. However, alternative solutions may provide faster variant calling without sacrificing accuracy. One such alternative is Sentieon DNASeq, a toolkit analogous to GATK but built on a highly optimized backend. We conducted an independent evaluation of the DNASeq single-sample variant calling pipeline in comparison to that of GATK. Our results support the near-identical accuracy of the two software packages, showcase optimal scalability and great speed from Sentieon, and describe computational performance considerations for the deployment of DNASeq.

Genome Sequence of the Soybean Cyst Nematode (Heterodera glycines) Endosymbiont "Candidatus Cardinium hertigii" Strain cHgTN10.

In this study, we present the genome sequence of the "Candidatus Cardinium hertigii" strain cHgTN10, an endosymbiotic bacterium of the plant-parasitic nematode Heterodera glycines This is the first genome assembly reported for an endosymbiont directly sequenced from a tylenchid nematode.