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Genome in a Bottle benchmarks are widely used to help validate clinical sequencing pipelines and develop variant calling and sequencing methods. Here we use accurate linked and long reads to expand benchmarks in 7 samples to include difficult-to-map regions and segmental duplications that are challenging for short reads. These benchmarks add more than 300,000 SNVs and 50,000 insertions or deletions (indels) and include 16% more exonic variants, many in challenging, clinically relevant genes not covered previously, such as

Modeling with longitudinal electronic health record (EHR) data proves challenging given the high dimensionality, redundancy, and noise captured in EHR. In order to improve precision medicine strategies and identify predictors of disease risk in advance, evaluating meaningful patient disease trajectories is essential. In this study, we develop the algorithmDiseasE Trajectory fEature extraCTion (DETECT)for feature extraction and trajectory generation in high-throughput temporal EHR data. This algorithm can 1) simulate longitudinal individual-level EHR data, specified to user parameters of scale, complexity, and noise and 2) use a convergent relative risk framework to test intermediate codes occurring between a specified index code(s) and outcome code(s) to determine if they are predictive features of the outcome. We benchmarked our method on simulated data and generated real-world disease trajectories using DETECT in a cohort of 145,575 individuals diagnosed with hypertension in Penn Medicine EHR for severe cardiometabolic outcomes.

Motivation Understanding comorbidity is essential for disease prevention, treatment and prognosis. In particular, insight into which pairs of diseases are likely or unlikely to co-occur may help elucidate the potential relationships between complex diseases. Here, we introduce the use of an inter-disease interactivity network to discover/prioritize comorbidities. Specifically, we determine disease associations by accounting for the direction of effects of genetic components shared between diseases, and categorize those associations as synergistic or antagonistic. We further develop a comorbidity scoring algorithm to predict whether diseases are more or less likely to co-occur in the presence of a given index disease. This algorithm can handle networks that incorporate relationships with opposite signs. Results We finally investigate inter-disease associations among 427 phenotypes in UK Biobank PheWAS data and predict the priority of comorbid diseases. The predicted comorbidities were verified using the UK Biobank inpatient electronic health records. Our findings demonstrate that considering the interaction of phenotype associations might be helpful in better predicting comorbidity. Availability and implementation The source code and data of this study are available at https://github.com/dokyoonkimlab/DiseaseInteractiveNetwork. Supplementary information Supplementary data are available at Bioinformatics online.

The 1000 Genomes Project (1kGP) is the largest fully open resource of whole-genome sequencing (WGS) data consented for public distribution without access or use restrictions. The final, phase 3 release of the 1kGP included 2,504 unrelated samples from 26 populations and was based primarily on low-coverage WGS. Here, we present a high-coverage 3,202-sample WGS 1kGP resource, which now includes 602 complete trios, sequenced to a depth of 30X using Illumina. We performed single-nucleotide variant (SNV) and short insertion and deletion (INDEL) discovery and generated a comprehensive set of structural variants (SVs) by integrating multiple analytic methods through a machine learning model. We show gains in sensitivity and precision of variant calls compared to phase 3, especially among rare SNVs as well as INDELs and SVs spanning frequency spectrum. We also generated an improved reference imputation panel, making variants discovered here accessible for association studies.

Amyotrophic lateral sclerosis (ALS) is a progressively fatal neurodegenerative disease affecting motor neurons in the brain and spinal cord. We used 380 post-mortem tissue RNA-seq transcriptomes from 154 ALS cases and 49 control individuals from cervical, thoracic, and lumbar spinal cord segments to investigate the gene expression response to ALS. We observed an increase in microglia and astrocyte expression, accompanied by a decrease in oligodendrocytes. By creating a gene co-expression network in the ALS samples, we identify several activated microglia modules that negatively correlate with retrospective disease duration.We map molecular quantitative trait loci and find several potential ALS risk loci that may act through gene expression or splicing in the spinal cord and assign putative cell-types for FNBP1, ACSL5, SH3RF1 and NFASC. Finally, we outline how repeat expansions that alter splicing of C9orf72 are tagged by common variants, and use this to suggest ATXN3 as a putative risk gene.

Amyotrophic lateral sclerosis (ALS) is a progressively fatal neurodegenerative disease affecting motor neurons in the brain and spinal cord. In this study, we investigated gene expression changes in ALS via RNA sequencing in 380 postmortem samples from cervical, thoracic and lumbar spinal cord segments from 154 individuals with ALS and 49 control individuals. We observed an increase in microglia and astrocyte gene expression, accompanied by a decrease in oligodendrocyte gene expression. By creating a gene co-expression network in the ALS samples, we identified several activated microglia modules that negatively correlate with retrospective disease duration. We mapped molecular quantitative trait loci and found several potential ALS risk loci that may act through gene expression or splicing in the spinal cord and assign putative cell types for FNBP1, ACSL5, SH3RF1 and NFASC. Finally, we outline how common genetic variants associated with splicing of C9orf72 act as proxies for the well-known repeat expansion, and we use the same mechanism to suggest ATXN3 as a putative risk gene.

SUMMARYThe 1000 Genomes Project (1kGP) is the largest fully open resource of whole genome sequencing (WGS) data consented for public distribution of raw sequence data without access or use restrictions. The final release of the 1kGP included 2,504 unrelated samples from 26 populations and was based primarily on low coverage WGS. Here, we present a new,high coverage3,202-sample WGS 1kGP resource, sequenced to a targeted depth of 30X using the Illumina NovaSeq 6000 system, which now includes 602 complete trios. We performed SNV/INDEL calling against the GRCh38 reference using GATK’s HaplotypeCaller, and generated a comprehensive set of SVs by integrating multiple analytic methods through a sophisticated machine learning model. We make all the data generated as part of this project publicly available and we envision it to become the new de facto public resource for the worldwide genomics and genetics community.

Assessing the reproducibility, accuracy and utility of massively parallel DNA sequencing platforms remains an ongoing challenge. Here the Association of Biomolecular Resource Facilities (ABRF) Next-Generation Sequencing Study benchmarks the performance of a set of sequencing instruments (HiSeq/NovaSeq/paired-end 2 × 250-bp chemistry, Ion S5/Proton, PacBio circular consensus sequencing (CCS), Oxford Nanopore Technologies PromethION/MinION, BGISEQ-500/MGISEQ-2000 and GS111) on human and bacterial reference DNA samples. Among short-read instruments, HiSeq 4000 and X10 provided the most consistent, highest genome coverage, while BGI/MGISEQ provided the lowest sequencing error rates. The long-read instrument PacBio CCS had the highest reference-based mapping rate and lowest non-mapping rate. The two long-read platforms PacBio CCS and PromethION/MinION showed the best sequence mapping in repeat-rich areas and across homopolymers. NovaSeq 6000 using 2 × 250-bp read chemistry was the most robust instrument for capturing known insertion/deletion events. This study serves as a benchmark for current genomics technologies, as well as a resource to inform experimental design and next-generation sequencing variant calling. Next-generation sequencing platforms are benchmarked using human, bacterial and metagenomics reference materials.

Long-read and strand-specific sequencing technologies together facilitate the de novo assembly of high-quality haplotype-resolved human genomes without parent–child trio data. We present 64 assembled haplotypes from 32 diverse human genomes. These highly contiguous haplotype assemblies (average contig N50: 26 Mbp) integrate all forms of genetic variation across even complex loci such as the major histocompatibility complex. We focus on 107,590 structural variants (SVs), of which 68% are inaccessible by short-read sequencing. We identify new SV hotspots (spanning megabases of gene-rich sequence), characterize 130 of the most active mobile element source elements, and find that 63% of all SVs arise by homology-mediated mechanisms—a twofold increase from previous studies. Our resource now enables reliable graph-based genotyping from short reads of up to 50,340 SVs, resulting in the identification of 1,525 expression quantitative trait loci (SV-eQTLs) as well as SV candidates for adaptive selection within the human population.

Resolving genomic structural variationMany human genomes have been reported using short-read technology, but it is difficult to resolve structural variants (SVs) using these data. These genomes thus lack comprehensive comparisons among individuals and populations. Ebertet al.used long-read structural variation calling across 64 human genomes representing diverse populations and developed new methods for variant discovery. This approach allowed the authors to increase the number of confirmed SVs and to describe the patterns of variation across populations. From this dataset, they identified quantitative trait loci affected by these SVs and determined how they may affect gene expression and potentially explain genome-wide association study hits. This information provides insights into patterns of normal human genetic variation and generates reference genomes that better represent the diversity of our species.Science, this issue p.eabf7117

Author(s): Padhi, Evin M; Hayeck, Tristan J; Mannion, Brandon; Chatterjee, Sumantra; Byrska-Bishop, Marta; Musunuri, Rajeeva; Narzisi, Giuseppe; Abhyankar, Avinash; Cheng, Zhang; Hunter, Riana D; Akiyama, Jennifer; Fries, Lauren E; Ng, Jeffrey; Stong, Nick; Allen, Andrew S; Dickel, Diane E; Bernier, Raphael A; Gorkin, David U; Pennacchio, Len A; Zody, Michael C; Turner, Tychele N | Abstract: AbstractPrevious research in autism and other neurodevelopmental disorders (NDDs) has indicated an important contribution of de novo protein-coding variants within specific genes. The role of de novo noncoding variation has been observable as a general increase in genetic burden but has yet to be resolved to individual functional elements. In this study, we assessed whole-genome sequencing data in 2,671 families with autism, with a specific focus on de novo variation in enhancers with previously characterized in vivo activity. We identified three independent de novo mutations limited to individuals with autism in the enhancer hs737. These mutations result in similar phenotypic characteristics, affect enhancer activity in vitro, and preferentially occur in AAT motifs in the enhancer with predicted disruptions of transcription factor binding. We also find that hs737 is enriched for copy number variation in individuals with NDDs, is dosage sensitive in the human population, is brain-specific, and targets the NDD gene EBF3 that is genome-wide significant for protein coding de novo variants, demonstrating the importance of understanding all forms of variation in the genome.One Sentence SummaryWhole-genome sequencing in thousands of families reveals variants relevant to simplex autism in a brain enhancer of the well-established neurodevelopmental disorder gene EBF3.

Genome in a Bottle (GIAB) benchmarks have been widely used to help validate clinical sequencing pipelines and develop new variant calling and sequencing methods. Here we use accurate long and linked reads to expand the prior benchmark to include difficult-to-map regions and segmental duplications that are not readily accessible to short reads. Our new benchmark adds more than 300,000 SNVs, 50,000 indels, and 16 % new exonic variants, many in challenging, clinically relevant genes not previously covered (e.g., PMS2). We increase coverage of the autosomal GRCh38 assembly from 85 % to 92 %, while excluding problematic regions for benchmarking small variants (e.g., copy number variants and assembly errors) that should not have been in the previous version. Our new benchmark reliably identifies both false positives and false negatives across multiple short-, linked-, and long-read based variant calling methods. As an example of its utility, this benchmark identifies eight times more false negatives in a short read variant call set relative to our previous benchmark, mostly in difficult-to-map regions. To enable robust small variant benchmarking, we still exclude 3.6% of GRCh37 and 5.0% of GRCh38 in (1) highly repetitive regions such as large, highly similar segmental duplications and the centromere not accessible to our data and (2) regions where our sample is highly divergent from the reference due to large indels, structural variation, copy number variation, and/or errors in the reference (e.g., some KIR genes that have duplications in HG002). We have demonstrated the utility of this benchmark to assess performance in more challenging regions, which enables benchmarking in more difficult genes and continued technology and bioinformatics development. The v4.2.1 benchmarks are available under ftp://ftp-trace.ncbi.nlm.nih.gov/ReferenceSamples/giab/release/AshkenazimTrio/.

Massively parallel DNA sequencing is a critical tool for genomics research and clinical diagnostics. Here, we describe the Association of Biomolecular Resource Facilities (ABRF) Next-Generation Sequencing Phase II Study to measure quality and reproducibility of DNA sequencing. Replicates of human and bacterial reference DNA samples were generated across multiple sequencing platforms, including well-established technologies such as Illumina, ThermoFisher Ion Torrent, and Pacific Biosciences, as well as emerging technologies such as BGI, Genapsys, and Oxford Nanopore. A total of 202 datasets were generated to investigate the performance of a total of 16 sequencing platforms, including mappability of reads, coverage and error rates in difficult genomic regions, and detection of small-scale polymorphisms and large-scale structural variants. This study provides a comprehensive baseline resource for continual benchmarking as chemistries, methods, and platforms evolve for DNA sequencing.

Phenome-wide association studies (PheWASs) have been a useful tool for testing associations between genetic variations and multiple complex traits or diagnoses. Linking PheWAS-based associations between phenotypes and a variant or a genomic region into a network provides a new way to investigate cross-phenotype associations, and it might broaden the understanding of genetic architecture that exists between diagnoses, genes, and pleiotropy. We created a network of associations from one of the largest PheWASs on electronic health record (EHR)-derived phenotypes across 38,682 unrelated samples from the Geisinger's biobank; the samples were genotyped through the DiscovEHR project. We computed associations between 632,574 common variants and 541 diagnosis codes. Using these associations, we constructed a "disease-disease" network (DDN) wherein pairs of diseases were connected on the basis of shared associations with a given genetic variant. The DDN provides a landscape of intra-connections within the same disease classes, as well as inter-connections across disease classes. We identified clusters of diseases with known biological connections, such as autoimmune disorders (type 1 diabetes, rheumatoid arthritis, and multiple sclerosis) and cardiovascular disorders. Previously unreported relationships between multiple diseases were identified on the basis of genetic associations as well. The network approach applied in this study can be used to uncover interactions between diseases as a result of their shared, potentially pleiotropic SNPs. Additionally, this approach might advance clinical research and even clinical practice by accelerating our understanding of disease mechanisms on the basis of similar underlying genetic associations.

Autism is a highly heritable complex disorder in which de novo mutation (DNM) variation contributes significantly to risk. Using whole-genome sequencing data from 3,474 families, we investigate another source of large-effect risk variation, ultra-rare variants. We report and replicate a transmission disequilibrium of private, likely gene-disruptive (LGD) variants in probands but find that 95% of this burden resides outside of known DNM-enriched genes. This variant class more strongly affects multiplex family probands and supports a multi-hit model for autism. Candidate genes with private LGD variants preferentially transmitted to probands converge on the E3 ubiquitin–protein ligase complex, intracellular transport and Erb signaling protein networks. We estimate that these variants are approximately 2.5 generations old and significantly younger than other variants of similar type and frequency in siblings. Overall, private LGD variants are under strong purifying selection and appear to act on a distinct set of genes not yet associated with autism. Analysis of whole-genome sequence data from 3,474 families finds an excess of private, likely gene-disrupting variants in individuals with autism. These variants are under purifying selection and suggest candidate genes not previously associated with autism.

Autism is a highly heritable, complex disorder where de novo mutation (DNM) variation contributes significantly to disease risk. Using whole-genome sequencing data from 3,474 families, we investigate another source of large-effect risk variation, ultra-rare mutations. We report and replicate a transmission disequilibrium of private likely-gene disruptive (LGD) mutations in probands but find that 95% of this burden resides outside of known DNM-enriched genes. This variant class more strongly affects multiplex family probands and supports a multi-hit model for autism. Candidate genes with private LGD variants preferentially transmitted to probands converge on the E3 ubiquitin-protein ligase complex, intracellular transport, and Erb signaling protein networks. We estimate these mutations are ~2.5 generations old and significantly younger than other mutations of similar type and frequency in siblings. Overall, private LGD variants are under strong purifying selection and act on a distinct set of genes not yet associated with autism.One sentence summaryUltra-rare autism variants preferentially transmitted to probands are younger and identify distinct gene candidates and functional networks.

The analysis of large biomedical data often presents with various challenges related to not just the size of the data, but also to data quality issues such as heterogeneity, multidimensionality, noisiness, and incompleteness of the data. The data-intensive nature of computational genomics problems in biomedical informatics warrants the development and use of massive computer infrastructure and advanced software tools and platforms, including but not limited to the use of cloud computing. Our session aims to address these challenges in handling big data for designing a study, performing analysis, and interpreting outcomes of these analyses. These challenges have been prevalent in many studies including those which focus on the identification of novel genetic variant-phenotype associations using data from sources like Electronic Health Records (EHRs) or multi-omic data. One of the biggest challenges to focus on is the imperfect nature of the biomedical data where a lot of noise and sparseness is observed. In our session, we will present research articles that can help in identifying innovative ways to recognize and overcome newly arising challenges associated with pattern recognition in biomedical data.

Motivation BioBin is an automated bioinformatics tool for the multi-level biological binning of sequence variants. Herein, we present a significant update to BioBin which expands the software to facilitate a comprehensive rare variant analysis and incorporates novel features and analysis enhancements. Results In BioBin 2.3, we extend our software tool by implementing statistical association testing, updating the binning algorithm, as well as incorporating novel analysis features providing for a robust, highly customizable, and unified rare variant analysis tool. Availability and implementation The BioBin software package is open source and freely available to users at http://www.ritchielab.com/software/biobin-download Supplementary information Supplementary data are available at Bioinformatics online.

Given the exponential growth of biomedical data, researchers are faced with numerous challenges in extracting and interpreting information from these large, high-dimensional, incomplete, and often noisy data. To facilitate addressing this growing concern, the "Patterns in Biomedical Data-How do we find them?" session of the 2017 Pacific Symposium on Biocomputing (PSB) is devoted to exploring pattern recognition using data-driven approaches for biomedical and precision medicine applications. The papers selected for this session focus on novel machine learning techniques as well as applications of established methods to heterogeneous data. We also feature manuscripts aimed at addressing the current challenges associated with the analysis of biomedical data.

Patients with Down syndrome (trisomy 21, T21) have hematologic abnormalities throughout life. Newborns frequently exhibit abnormal blood counts and a clonal preleukemia. Human T21 fetal livers contain expanded erythro-megakaryocytic precursors with enhanced proliferative capacity. The impact of T21 on the earliest stages of embryonic hematopoiesis is unknown and nearly impossible to examine in human subjects. We modeled T21 yolk sac hematopoiesis using human induced pluripotent stem cells (iPSCs). Blood progenitor populations generated from T21 iPSCs were present at normal frequency and proliferated normally. However, their developmental potential was altered with enhanced erythropoiesis and reduced myelopoiesis, but normal megakaryocyte production. These abnormalities overlap with those of T21 fetal livers, but also reflect important differences. Our studies show that T21 confers distinct developmental stage- and species-specific hematopoietic defects. More generally, we illustrate how iPSCs can provide insight into early stages of normal and pathological human development.

Dynamic cell–microenvironment interactions regulate many biological events and play a critical role in tissue regeneration. Cell homing to targeted tissues requires well balanced interactions between cells and adhesion molecules on blood vessel walls. However, many stem cells lack affinity with adhesion molecules. It is challenging and clinically important to engineer these stem cells to modulate their dynamic interactions with blood vessels. In this study, a new chemical strategy was developed to engineer cell–microenvironment interactions. This method allowed the conjugation of peptides onto stem cell membranes without affecting cell viability, proliferation or multipotency. Mesenchymal stem cells (MSCs) engineered in this manner showed controlled firm adhesion and rolling on E-selectin under physiological shear stresses. For the first time, these biomechanical responses were achieved by tuning the binding kinetics of the peptide-selectin interaction. Rolling of engineered MSCs on E-selectin is mediated by a Ca[superscript 2+] independent interaction, a mechanism that differs from the Ca[superscript 2+] dependent physiological process. This further illustrates the ability of this approach to manipulate cell–microenvironment interactions, in particular for the application of delivering cells to targeted tissues. It also provides a new platform to engineer cells with multiple functionalities., National Heart, Lung, and Blood Institute (Programs of Excellence in Nanotechnology Award Contract HHSN268201000045C), National Institutes of Health (U.S.) (Grant 2-P30-CA14051), Armed Forces Institute of Regenerative Medicine (Award W81XWH-08-2-0034)

Germline GATA1 mutations that result in the production of an amino-truncated protein termed GATA1s (where s indicates short) cause congenital hypoplastic anemia. In patients with trisomy 21, similar somatic GATA1s-producing mutations promote transient myeloproliferative disease and acute megakaryoblastic leukemia. Here, we demonstrate that induced pluripotent stem cells (iPSCs) from patients with GATA1-truncating mutations exhibit impaired erythroid potential, but enhanced megakaryopoiesis and myelopoiesis, recapitulating the major phenotypes of the associated diseases. Similarly, in developmentally arrested GATA1-deficient murine megakaryocyte-erythroid progenitors derived from murine embryonic stem cells (ESCs), expression of GATA1s promoted megakaryopoiesis, but not erythropoiesis. Transcriptome analysis revealed a selective deficiency in the ability of GATA1s to activate erythroid-specific genes within populations of hematopoietic progenitors. Although its DNA-binding domain was intact, chromatin immunoprecipitation studies showed that GATA1s binding at specific erythroid regulatory regions was impaired, while binding at many nonerythroid sites, including megakaryocytic and myeloid target genes, was normal. Together, these observations indicate that lineage-specific GATA1 cofactor associations are essential for normal chromatin occupancy and provide mechanistic insights into how GATA1s mutations cause human disease. More broadly, our studies underscore the value of ESCs and iPSCs to recapitulate and study disease phenotypes.

Summary In moving cells dynamic microtubules (MTs) target and disassemble substrate adhesion sites (focal adhesions; FAs) in a process that enables the cell to detach from the substrate and propel itself forward. The short-range interactions between FAs and MT plus ends have been observed in several experimental systems, but the spatial overlap of these structures within the cell has precluded analysis of the putative long-range mechanisms by which MTs growing through the cell body reach FAs in the periphery of the cell. In the work described here cell geometry was controlled to remove the spatial overlap of cellular structures thus allowing for unambiguous observation of MT guidance. Specifically, micropatterning of living cells was combined with high-resolution in-cell imaging and gene product depletion by means of RNA interference to study the long-range MT guidance in quantitative detail. Cells were confined on adhesive triangular microislands that determined cell shape and ensured that FAs localized exclusively at the vertices of the triangular cells. It is shown that initial MT nucleation at the centrosome is random in direction, while the alignment of MT trajectories with the targets (i.e. FAs at vertices) increases with an increasing distance from the centrosome, indicating that MT growth is a non-random, guided process. The guided MT growth is dependent on the presence of FAs at the vertices. The depletion of either myosin IIA or myosin IIB results in depletion of F-actin bundles and spatially unguided MT growth. Taken together our findings provide quantitative evidence of a role for long-range MT guidance in MT targeting of FAs.

To complement the human Encyclopedia of DNA Elements (ENCODE) project and to enable a broad range of mouse genomics efforts, the Mouse ENCODE Consortium is applying the same experimental pipelines developed for human ENCODE to annotate the mouse genome.

Mammalian erythropoiesis during embryogenesis occurs in several distinct stages or "waves" that vary according to timing, site of production, gene expression and physiology. The ontogeny of mammalian erythropoiesis is most thoroughly studied in mice where the earliest circulating erythroblasts released from the yolk sac are termed primitive. Later, the first definitive erythroid lineage is established by erythro-myeloid progenitors (EMPs) that originate in the yolk sac and migrate to the fetal liver for terminal differentiation. A second wave of definitive erythropoiesis is established from hematopoietic stem/progenitor cells that originate in the dorsal aorta and migrate to later stage fetal liver for terminal differentiation. Finally around birth, definitive erythropoiesis shifts to the bone marrow. The ontogeny of erythropoiesis overlaps in mice and humans, although less is known about the latter, as hematopoietic tissues from precisely staged early human embryos are difficult to obtain. We hypothesized that the initial steps of human erythroid ontogeny could be recapitulated by induced pluripotent stem cells (iPSCs) induced to undergo hematopoietic differentiation. We used a serum- and feeder-free protocol to differentiate iPSCs into embryoid bodies (EBs) that produced two sequential waves of distinctly different erythroid precursors. At day 8 of differentiation, EBs began to release hematopoietic precursors. Thereafter, erythroid precursors were released from the EBs in the presence of stem cell factor (SCF), erythropoietin (EPO) and insulin-like growth factor 1 (IGF-1). Erythroid precursors produced during wave 1 (days 12-23 of differentiation) were relatively large and expressed embryonic-type globins (zeta and epsilon), resembling those produced during primitive erythropoiesis. In contrast, wave 2 erythroblasts (day 27 and later) were smaller and expressed mainly gamma and alpha globins with some beta globin, suggestive of fetal-type definitive erythropoiesis. To investigate further the similarity of wave 1 and wave 2 erythroblasts to cells at the primitive and definitive stages of ontogeny, respectively, we used Affymetrix Genechips to analyze the global transcriptomes of stage-matched (CD235+ CD71high) cells. As primary human primitive erythroblasts were not available for comparison, we compared the transcriptomes from the iPSC-derived erythroblasts with those of primary murine definitive and primitive erythroblasts that were flow cytometry-purified from embryonic day 15.5 (E15.5) fetal liver and E10.5 bloodstream, respectively. The comparisons showed that wave 1 erythroblasts from human pluripotent cells resembled more closely the erythroid primitive lineage from mice, while wave 2 erythroblasts from the human cells resembled the erythroid definitive lineage of mice (P-value < 0.05 by a modified Kolmogorov-Smirnov test). For example, SOX6 and BCL11A, preferentially expressed during definitive erythropoiesis, were expressed at relatively high levels in wave 2 erythroblasts. In addition, gene set enrichment analysis (GSEA) demonstrated that wave 2 human iPSC-derived erythroblasts and primary murine definitive erythroblasts expressed numerous genes related to immune/inflammatory pathways that were shown previously to be important for the formation of definitive hematopoietic stem and progenitor cells in zebrafish and mouse embryos. Our findings demonstrate that human iPSC-derived embryoid bodies recapitulate early stages of erythroid ontogeny with respect to the timing of emerging lineages and their gene expression. Additionally, gene expression studies of human iPSC-derived primitive and definitive erythroblasts indicate inflammatory signaling as a potential regulator of the later stage of erythroid development. Disclosures No relevant conflicts of interest to declare.