Search

Intrachromosomal amplification of chromosome 21 defines a subtype of high-risk childhood acute lymphoblastic leukemia (iAMP21-ALL) characterized by copy number changes and complex rearrangements of chromosome 21. The genomic basis of iAMP21-ALL and the pathogenic role of the region of amplification of chromosome 21 to leukemogenesis remain incompletely understood. Here, using integrated whole genome and transcriptome sequencing of 124 iAMP21-ALL patients, including rare cases arising in the context of constitutional chromosomal aberrations, we identified subgroups of iAMP21-ALL according to patterns of copy number alteration and structural variation. This large dataset enabled formal delineation of a 7.8 Mb common region of amplification harboring 71 genes, 43 of which are differentially expressed compared to non-iAMP21-ALL cases, and including multiple genes implicated in the pathogenesis of acute leukemia: CHAF1B, DYRK1A, ERG, HMGN1 and RUNX1. Using multimodal single cell genomic profiling, including single cell whole genome sequencing of two cases, we documented clonal heterogeneity and genomic evolution, formally demonstrating that acquisition of the iAMP21-chromosome is an early event that may undergo progressive amplification during disease ontogeny. We show that UV mutational signatures and high mutation load are characteristic secondary genetic features. Although the genomic alterations of chromosome 21 are variable, these integrated genomic analyses and demonstration of an extended common minimal region of amplification broaden the definition of iAMP21-ALL for more precise diagnosis using cytogenetic or genomic methods to inform clinical management.

Strong nuclear staining of mutant TP53-R337H protein in normal tissue of carriers who developed ACC.

Mouse Cohorts and Associated Pathology

The TP53-R337H founder mutation exists at a high frequency throughout southern Brazil and represents one of the most common germline TP53 mutations reported to date. It was identified in pediatric adrenocortical tumors in families with a low incidence of cancer. The R337H mutation has since been found in association with early-onset breast cancers and Li–Fraumeni syndrome (LFS). To study this variability in tumor susceptibility, we generated a knockin mutant p53 mouse model (R334H). Endogenous murine p53-R334H protein was naturally expressed at high levels in multiple tissues and was functionally compromised in a tissue- and stress-specific manner. Mutant p53-R334H mice developed tumors with long latency and incomplete penetrance, consistent with many human carriers being at a low but elevated risk for cancer. These findings suggest the involvement of additional cooperating genetic alterations when TP53-R337H occurs in the context of LFS, which has important implications for genetic counseling and long-term clinical follow-up.Significance:A p53-R334H knockin mouse serves as an important model for studying the most common inherited germline TP53 mutation (R337H) that is associated with variable tumor susceptibility.

Supplemental Materials and Methods and Supplementary Figure Legends

The TP53-R337H founder mutation exists at a high frequency throughout southern Brazil and represents one of the most common germline TP53 mutations reported to date. It was identified in pediatric adrenocortical tumors in families with a low incidence of cancer. The R337H mutation has since been found in association with early-onset breast cancers and Li–Fraumeni syndrome (LFS). To study this variability in tumor susceptibility, we generated a knockin mutant p53 mouse model (R334H). Endogenous murine p53-R334H protein was naturally expressed at high levels in multiple tissues and was functionally compromised in a tissue- and stress-specific manner. Mutant p53-R334H mice developed tumors with long latency and incomplete penetrance, consistent with many human carriers being at a low but elevated risk for cancer. These findings suggest the involvement of additional cooperating genetic alterations when TP53-R337H occurs in the context of LFS, which has important implications for genetic counseling and long-term clinical follow-up. Significance: A p53-R334H knockin mouse serves as an important model for studying the most common inherited germline TP53 mutation (R337H) that is associated with variable tumor susceptibility.

A lack of relevant genetic models and cell lines hampers our understanding of hepatoblastoma pathogenesis and the development of new therapies for this neoplasm. We report a liver-specific MYC-driven hepatoblastoma murine model that faithfully recapitulates the pathological features of mixed fetal and embryonal hepatoblastoma, with transcriptomics resembling the high-risk gene signatures of the human disease. Single-cell RNA-sequencing and spatial transcriptomics identified distinct subpopulations of hepatoblastoma cells. After deriving cell lines from the mouse model, we mapped the cancer dependency genes using CRISPR-Cas9 screening and identified druggable targets shared with human hepatoblastoma (i.e., CDK7, CDK9, PRMT1, PRMT5). Our screen also revealed oncogenes and tumor suppressor genes in hepatoblastoma that engage multiple, druggable cancer signaling pathways. Chemotherapy is critical for human hepatoblastoma treatment. A genetic mapping of doxorubicin response by CRISPR-Cas9 screening identified modifiers whose loss-of-function synergizes with (e.g., PRKDC) or antagonizes (e.g., apoptosis genes) with the effect of chemotherapy. The combination of PRKDC inhibition and doxorubicin-based chemotherapy greatly enhances therapeutic efficacy. These studies have provided a useful set of resources including disease models suitable to identify and validate potential therapeutic targets in human high-risk hepatoblastoma.

Sequencing cases without matched healthy controls hinders prioritization of germline disease-predisposition genes. To circumvent this problem, genotype summary counts from public data sets can serve as controls. However, systematic inflation and false positives can arise if confounding factors are not controlled. We propose a framework, consistent summary counts based rare variant burden test (CoCoRV), to address these challenges. CoCoRV implements consistent variant quality control and filtering, ethnicity-stratified rare variant association test, accurate estimation of inflation factors, powerful FDR control, and detection of rare variant pairs in high linkage disequilibrium. When we applied CoCoRV to pediatric cancer cohorts, the top genes identified were cancer-predisposition genes. We also applied CoCoRV to identify disease-predisposition genes in adult brain tumors and amyotrophic lateral sclerosis. Given that potential confounding factors were well controlled after applying the framework, CoCoRV provides a cost-effective solution to prioritizing disease-risk genes enriched with rare pathogenic variants.

Individuals with monogenic disorders can experience variable phenotypes that are influenced by genetic variation. To investigate this in sickle cell disease (SCD), we performed whole-genome sequencing (WGS) of 722 individuals with hemoglobin HbSS or HbSβ0-thalassemia from Baylor College of Medicine and from the St. Jude Children’s Research Hospital Sickle Cell Clinical Research and Intervention Program (SCCRIP) longitudinal cohort study. We developed pipelines to identify genetic variants that modulate sickle hemoglobin polymerization in red blood cells and combined these with pain-associated variants to build a polygenic score (PGS) for acute vaso-occlusive pain (VOP). Overall, we interrogated the α-thalassemia deletion −α3.7 and 133 candidate single-nucleotide polymorphisms (SNPs) across 66 genes for associations with VOP in 327 SCCRIP participants followed longitudinally over 6 years. Twenty-one SNPs in 9 loci were associated with VOP, including 3 (BCL11A, MYB, and the β-like globin gene cluster) that regulate erythrocyte fetal hemoglobin (HbF) levels and 6 (COMT, TBC1D1, KCNJ6, FAAH, NR3C1, and IL1A) that were associated previously with various pain syndromes. An unweighted PGS integrating all 21 SNPs was associated with the VOP event rate (estimate, 0.35; standard error, 0.04; P = 5.9 × 10−14) and VOP event occurrence (estimate, 0.42; standard error, 0.06; P = 4.1 × 10−13). These associations were stronger than those of any single locus. Our findings provide insights into the genetic modulation of VOP in children with SCD. More generally, we demonstrate the utility of WGS for investigating genetic contributions to the variable expression of SCD-associated morbidities.

Amyotrophic lateral sclerosis (ALS) is a multi‐system disease characterized primarily by progressive muscle weakness. Cognitive dysfunction is commonly observed in patients; however, factors influencing risk for cognitive dysfunction remain elusive. Using sparse canonical correlation analysis (sCCA), an unsupervised machine‐learning technique, we observed that single nucleotide polymorphisms collectively associate with baseline cognitive performance in a large ALS patient cohort (N = 327) from the multicenter Clinical Research in ALS and Related Disorders for Therapeutic Development (CReATe) Consortium. We demonstrate that a polygenic risk score derived using sCCA relates to longitudinal cognitive decline in the same cohort and also to in vivo cortical thinning in the orbital frontal cortex, anterior cingulate cortex, lateral temporal cortex, premotor cortex, and hippocampus (N = 90) as well as post‐mortem motor cortical neuronal loss (N = 87) in independent ALS cohorts from the University of Pennsylvania Integrated Neurodegenerative Disease Biobank. Our findings suggest that common genetic polymorphisms may exert a polygenic contribution to the risk of cortical disease vulnerability and cognitive dysfunction in ALS., Single nucleotide polymorphisms (SNPs) previously identified through genome‐wide association studies as risk factors for amyotrophic lateral sclerosis (ALS) and/or frontotemporal dementia (FTD) further associate in a polygenic manner with risk for cognitive dysfunction in ALS and related disorders.

Significance Primary template-directed amplification (PTA) is a major improvement in whole genome amplification, which is required to study intratissue cellular evolution. As presented in the manuscript, PTA produces significantly improved and reproducible genome sequencing coverage and variant detection from a single genome of a single cell. Applications of measuring genetic diversity from single cells with PTA presented in the manuscript include examining the acquisition of genetic changes during normal development and aging, measuring the consequences of specific perturbations such as genome editing, and characterizing the evolution of clonal populations during cancer formation., Improvements in whole genome amplification (WGA) would enable new types of basic and applied biomedical research, including studies of intratissue genetic diversity that require more accurate single-cell genotyping. Here, we present primary template-directed amplification (PTA), an isothermal WGA method that reproducibly captures >95% of the genomes of single cells in a more uniform and accurate manner than existing approaches, resulting in significantly improved variant calling sensitivity and precision. To illustrate the types of studies that are enabled by PTA, we developed direct measurement of environmental mutagenicity (DMEM), a tool for mapping genome-wide interactions of mutagens with single living human cells at base-pair resolution. In addition, we utilized PTA for genome-wide off-target indel and structural variant detection in cells that had undergone CRISPR-mediated genome editing, establishing the feasibility for performing single-cell evaluations of biopsies from edited tissues. The improved precision and accuracy of variant detection with PTA overcomes the current limitations of accurate WGA, which is the major obstacle to studying genetic diversity and evolution at cellular resolution.

The TP53-R337H founder mutation exists at a high frequency throughout southern Brazil and represents one of the most common germline TP53 mutations reported to date. It was identified in pediatric adrenocortical tumors in families with a low incidence of cancer. The R337H mutation has since been found in association with early-onset breast cancers and Li-Fraumeni syndrome (LFS). To study this variability in tumor susceptibility, we generated a knockin mutant p53 mouse model (R334H). Endogenous murine p53-R334H protein was naturally expressed at high levels in multiple tissues and was functionally compromised in a tissue- and stress-specific manner. Mutant p53-R334H mice developed tumors with long latency and incomplete penetrance, consistent with many human carriers being at a low but elevated risk for cancer. These findings suggest the involvement of additional cooperating genetic alterations when TP53-R337H occurs in the context of LFS, which has important implications for genetic counseling and long-term clinical follow-up. SIGNIFICANCE: A p53-R334H knockin mouse serves as an important model for studying the most common inherited germline TP53 mutation (R337H) that is associated with variable tumor susceptibility.

The XAF1-E134* variant increases the cancer risk for carriers of the TP53-R337H allele., Cancer risk is highly variable in carriers of the common TP53-R337H founder allele, possibly due to the influence of modifier genes. Whole-genome sequencing identified a variant in the tumor suppressor XAF1 (E134*/Glu134Ter/rs146752602) in a subset of R337H carriers. Haplotype-defining variants were verified in 203 patients with cancer, 582 relatives, and 42,438 newborns. The compound mutant haplotype was enriched in patients with cancer, conferring risk for sarcoma (P = 0.003) and subsequent malignancies (P = 0.006). Functional analyses demonstrated that wild-type XAF1 enhances transactivation of wild-type and hypomorphic TP53 variants, whereas XAF1-E134* is markedly attenuated in this activity. We propose that cosegregation of XAF1-E134* and TP53-R337H mutations leads to a more aggressive cancer phenotype than TP53-R337H alone, with implications for genetic counseling and clinical management of hypomorphic TP53 mutant carriers.

Amyotrophic lateral sclerosis (ALS) is a multi-system disease characterized primarily by progressive muscle weakness. Cognitive dysfunction is commonly observed in patients, however factors influencing risk for cognitive dysfunction remain elusive. Using sparse canonical correlation analysis (sCCA), an unsupervised machine-learning technique, we observed that single nucleotide polymorphisms collectively associate with baseline cognitive performance in a large ALS patient cohort (N=327) from the multicenter Clinical Research in ALS and Related Disorders for Therapeutic Development (CReATe) Consortium. We demonstrate that a polygenic risk score derived using sCCA relates to longitudinal cognitive decline in the same cohort, and also to in vivo cortical thinning in the orbital frontal cortex, anterior cingulate cortex, lateral temporal cortex, premotor cortex, and hippocampus (N=90) as well as post mortem motor cortical neuronal loss (N=87) in independent ALS cohorts from the University of Pennsylvania Integrated Neurodegenerative Disease Biobank. Our findings suggest that common genetic polymorphisms may exert a polygenic contribution to the risk of cortical disease vulnerability and cognitive dysfunction in ALS.

Graphical abstract, Accounting for batch effects, especially latent batch effects, in differential expression (DE) analysis is critical for identifying true biological effects. Single-cell RNA sequencing (scRNA-seq) is a powerful tool for quantifying cell-to-cell variation in transcript abundance and characterizing cellular dynamics. Although many scRNA-seq DE analysis methods accommodate known batch variables, their performance has not been systematically evaluated. Moreover, the challenge of accounting for latent batch variables in scRNA-seq DE analysis is largely unmet. In contrast, many methods have been developed to account for batch variables (either known or latent) in other high-dimensional data, especially bulk RNA-seq. We extensively evaluate 11 methods for batch variables in different scRNA-seq DE analysis scenarios, with a primary focus on latent batch variables. We demonstrate that for known batch variables, incorporating them as covariates into a regression model outperformed approaches using a batch-corrected matrix. For latent batches, fixed effects models have inflated FDRs, whereas aggregation-based methods and mixed effects models have significant power loss. Surrogate variable based methods generally control the FDR well while achieving good power with small group effects. However, their performance (except that of SVA) deteriorated substantially in scenarios involving large group effects and/or group label impurity. In these settings, SVA achieves relatively good performance despite an occasionally inflated FDR (up to 0.2). Finally we make the following recommendations for scRNA-seq DE analysis: 1) incorporate known batch variables instead of using batch-corrected data; and 2) employ SVA for latent batch correction. However, better methods are still needed to fully unleash the power of scRNA-seq.

Single-cell RNA sequencing (scRNA-seq) is a powerful tool for characterizing the cell-to-cell variation and cellular dynamics in populations which appear homogeneous otherwise in basic and translational biological research. However, significant challenges arise in the analysis of scRNA-seq data, including the low signal-to-noise ratio with high data sparsity, potential batch effects, scalability problems when hundreds of thousands of cells are to be analyzed among others. The inherent complexities of scRNA-seq data and dynamic nature of cellular processes lead to suboptimal performance of many currently available algorithms, even for basic tasks such as identifying biologically meaningful heterogeneous subpopulations. In this study, we developed the Latent Cellular Analysis (LCA), a machine learning–based analytical pipeline that combines cosine-similarity measurement by latent cellular states with a graph-based clustering algorithm. LCA provides heuristic solutions for population number inference, dimension reduction, feature selection, and control of technical variations without explicit gene filtering. We show that LCA is robust, accurate, and powerful by comparison with multiple state-of-the-art computational methods when applied to large-scale real and simulated scRNA-seq data. Importantly, the ability of LCA to learn from representative subsets of the data provides scalability, thereby addressing a significant challenge posed by growing sample sizes in scRNA-seq data analysis.

Patients harboring germline pathogenic biallelic variants in genes involved in the recognition and repair of DNA damage are known to have a substantially increased cancer risk. Emerging evidence suggests that individuals harboring heterozygous variants in these same genes may also be at heightened, albeit lesser, risk for cancer. Herein, we sought to determine whether heterozygous variants in RECQL4, the gene encoding an essential DNA helicase that is defective in children with the autosomal recessive cancer-predisposing condition Rothmund–Thomson syndrome (RTS), are associated with increased risk for childhood cancer. To address this question, we interrogated germline sequence data from 4435 pediatric cancer patients at St. Jude Children's Research Hospital and 1127 from the National Cancer Institute Therapeutically Applicable Research to Generate Effective Treatment (TARGET) database and identified 24 (0.43%) who harbored loss-of-function (LOF) RECQL4 variants, including five of 249 (2.0%) with osteosarcoma (OS). These RECQL4 variants were significantly overrepresented in children with OS, the cancer most frequently observed in patients with RTS, as compared to 134,187 noncancer controls in the Genome Aggregation Database (gnomAD v2.1; P = 0.00087, odds ratio [OR] = 7.1, 95% CI, 2.9–17). Nine of the 24 (38%) individuals possessed the same c.1573delT (p.Cys525Alafs) variant located in the highly conserved DNA helicase domain, suggesting that disruption of this domain is central to oncogenesis. Altogether these data expand our understanding of the genetic factors predisposing to childhood cancer and reveal a novel association between heterozygous RECQL4 LOF variants and development of pediatric OS.

Functional annotations have been shown to improve both the discovery power and fine-mapping accuracy in genome-wide association studies. However, the optimal strategy to incorporate the large number of existing annotations is still not clear. In this study, we propose a Bayesian framework to incorporate functional annotations in a systematic manner. We compute the maximum a posteriori solution and use cross validation to find the optimal penalty parameters. By extending our previous fine-mapping method CAVIARBF into this framework, we require only summary statistics as input. We also derived an exact calculation of Bayes factors using summary statistics for quantitative traits, which is necessary when a large proportion of trait variance is explained by the variants of interest, such as in fine mapping expression quantitative trait loci (eQTL). We compared the proposed method with PAINTOR using different strategies to combine annotations. Simulation results show that the proposed method achieves the best accuracy in identifying causal variants among the different strategies and methods compared. We also find that for annotations with moderate effects from a large annotation pool, screening annotations individually and then combining the top annotations can produce overly optimistic results. We applied these methods on two real data sets: a meta-analysis result of lipid traits and a cis-eQTL study of normal prostate tissues. For the eQTL data, incorporating annotations significantly increased the number of potential causal variants with high probabilities.

Kernel machine based association tests (KAT) have been increasingly used in testing the association between an outcome and a set of biological measurements due to its power to combine multiple weak signals of complex relationship with the outcome through the specification of a relevant kernel. Human genetic and microbiome association studies are two important applications of KAT. However, the classic KAT framework relies on large sample theory, and conservativeness has been observed for small sample studies, especially for microbiome association studies. The common approach for addressing the small sample problem relies on computationally intensive resampling methods. Here, we derive an exact test for KAT with continuous traits, which resolve the small sample conservatism of KAT without the need for resampling. The exact test has significantly improved power to detect association for microbiome studies. For binary traits, we propose a similar approximate test, and we show that the approximate test is very powerful for a wide range of kernels including common variant- and microbiome-based kernels, and the approximate test controls the type I error well for these kernels. In contrast, the sequence kernel association tests have slightly inflated genomic inflation factors after small sample adjustment. Extensive simulations and application to a real microbiome association study are used to demonstrate the utility of our method.

There are currently no reliable approaches for correctly identifying which patients with major depressive disorder (MDD) will respond well to antidepressant therapy. However, recent genetic advances suggest that Polygenic Risk Scores (PRS) could allow MDD patients to be stratified for antidepressant response. We used PRS for MDD and PRS for neuroticism as putative predictors of antidepressant response within three treatment cohorts: The Genome-based Therapeutic Drugs for Depression (GENDEP) cohort, and 2 sub-cohorts from the Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomics Study PRGN-AMPS (total patient number = 760). Results across cohorts were combined via meta-analysis within a random effects model. Overall, PRS for MDD and neuroticism did not significantly predict antidepressant response but there was a consistent direction of effect, whereby greater genetic loading for both MDD (best MDD result, p < 5*10–5 MDD-PRS at 4 weeks, β = -0.019, S.E = 0.008, p = 0.01) and neuroticism (best neuroticism result, p < 0.1 neuroticism-PRS at 8 weeks, β = -0.017, S.E = 0.008, p = 0.03) were associated with less favourable response. We conclude that the PRS approach may offer some promise for treatment stratification in MDD and should now be assessed within larger clinical cohorts.

The interaction between extrinsic factors and intrinsic signal strength governs thymocyte development, but the mechanisms linking them remain elusive. We report that mechanistic target of rapamycin complex 1 (mTORC1) couples microenvironmental cues with metabolic programs to orchestrate the reciprocal development of two fundamentally distinct T cell lineages, the αβ and γδ T cells. Developing thymocytes dynamically engage metabolic programs including glycolysis and oxidative phosphorylation, as well as mTORC1 signaling. Loss of RAPTOR-mediated mTORC1 activity impairs the development of αβ T cells but promotes γδ T cell generation, associated with disrupted metabolic remodeling of oxidative and glycolytic metabolism. Mechanistically, we identify mTORC1-dependent control of reactive oxygen species production as a key metabolic signal in mediating αβ and γδ T cell development, and perturbation of redox homeostasis impinges upon thymocyte fate decisions and mTORC1-associated phenotypes. Furthermore, single-cell RNA sequencing and genetic dissection reveal that mTORC1 links developmental signals from T cell receptors and NOTCH to coordinate metabolic activity and signal strength. Our results establish mTORC1-driven metabolic signaling as a decisive factor for reciprocal αβ and γδ T cell development and provide insight into metabolic control of cell signaling and fate decisions.

Read counting and unique molecular identifier (UMI) counting are the principal gene expression quantification schemes used in single-cell RNA-sequencing (scRNA-seq) analysis. By using multiple scRNA-seq datasets, we reveal distinct distribution differences between these schemes and conclude that the negative binomial model is a good approximation for UMI counts, even in heterogeneous populations. We further propose a novel differential expression analysis algorithm based on a negative binomial model with independent dispersions in each group (NBID). Our results show that this properly controls the FDR and achieves better power for UMI counts when compared to other recently developed packages for scRNA-seq analysis. Electronic supplementary material The online version of this article (10.1186/s13059-018-1438-9) contains supplementary material, which is available to authorized users.

Advancing from statistical associations of complex traits with genetic markers to understanding the functional genetic variants that influence traits is often a complex process. Fine-mapping can select and prioritize genetic variants for further study, yet the multitude of analytical strategies and study designs makes it challenging to choose an optimal approach. We review the strengths and weaknesses of different fine-mapping approaches, emphasizing the main factors that affect performance. Topics include interpreting results from genome-wide association studies (GWAS), the role of linkage disequilibrium, statistical fine-mapping approaches, trans-ethnic studies, genomic annotation and data integration, and other analysis and design issues.

Purpose Childhood cancer survivors are at increased risk of subsequent neoplasms (SNs), but the germline genetic contribution is largely unknown. We assessed the contribution of pathogenic/likely pathogenic (P/LP) mutations in cancer predisposition genes to their SN risk. Patients and Methods Whole-genome sequencing (30-fold) was performed on samples from childhood cancer survivors who were ≥ 5 years since initial cancer diagnosis and participants in the St Jude Lifetime Cohort Study, a retrospective hospital-based study with prospective clinical follow-up. Germline mutations in 60 genes known to be associated with autosomal dominant cancer predisposition syndromes with moderate to high penetrance were classified by their pathogenicity according to the American College of Medical Genetics and Genomics guidelines. Relative rates (RRs) and 95% CIs of SN occurrence by mutation status were estimated using multivariable piecewise exponential regression stratified by radiation exposure. Results Participants were 3,006 survivors (53% male; median age, 35.8 years [range, 7.1 to 69.8 years]; 56% received radiotherapy), 1,120 SNs were diagnosed among 439 survivors (14.6%), and 175 P/LP mutations were identified in 5.8% (95% CI, 5.0% to 6.7%) of survivors. Mutations were associated with significantly increased rates of breast cancer (RR, 13.9; 95% CI, 6.0 to 32.2) and sarcoma (RR, 10.6; 95% CI, 4.3 to 26.3) among irradiated survivors and with increased rates of developing any SN (RR, 4.7; 95% CI, 2.4 to 9.3), breast cancer (RR, 7.7; 95% CI, 2.4 to 24.4), nonmelanoma skin cancer (RR, 11.0; 95% CI, 2.9 to 41.4), and two or more histologically distinct SNs (RR, 18.6; 95% CI, 3.5 to 99.3) among nonirradiated survivors. Conclusion The findings support referral of all survivors for genetic counseling for potential clinical genetic testing, which should be prioritized for nonirradiated survivors with any SN and for those with breast cancer or sarcoma in the field of prior irradiation.

Single-cell RNA sequencing has emerged as a powerful tool for characterizing the cell-to-cell variation and dynamics. We present Latent Cellular Analysis (LCA), a machine learning– based analytical pipeline that features a dual-space model search with inference of latent cellular states, control of technical variations, cosine similarity measurement, and spectral clustering. LCA has proved to be robust, accurate, scalable, and powerful in revealing subtle diversity in cell populations.

There are currently no reliable approaches for correctly identifying which patients with major depressive disorder (MDD) will respond well to antidepressant therapy. However, recent genetic advances suggest that Polygenic Risk Scores (PRS) could allow MDD patients to be stratified for antidepressant response. We used PRS for MDD and PRS for neuroticism as putative predictors of antidepressant response within three treatment cohorts: The Genome-based Therapeutic Drugs for Depression (GENDEP) cohort, and 2 sub-cohorts from the Pharmacogenomics Research Network Antidepressant Medication Pharmacogenomics Study PRGN-AMPS (total patient number = 783). Results across cohorts were combined via meta-analysis within a random effects model. Overall, PRS for MDD and neuroticism did not significantly predict antidepressant response but there was a consistent direction of effect, whereby greater genetic loading for both MDD (best MDD result, p < 5*10-5 MDD-PRS at 4 weeks, β = -0.019, S.E = 0.008, p = 0.01) and neuroticism (best neuroticism result, p < 0.1 neuroticism-PRS at 8 weeks, β = -0.017, S.E = 0.008, p = 0.03) were associated with less favourable response. We conclude that the PRS approach may offer some promise for treatment stratification in MDD and should now be assessed within larger clinical cohorts.

A defining feature of adaptive immunity is the development of long-lived memory T cells to curtail infection. Recent studies have identified a unique stem-like T-cell subset amongst exhausted CD8-positive T cells in chronic infection1-3, but it remains unclear whether CD4-positive T-cell subsets with similar features exist in chronic inflammatory conditions. Amongst helper T cells, TH17 cells have prominent roles in autoimmunity and tissue inflammation and are characterized by inherent plasticity4-7, although how such plasticity is regulated is poorly understood. Here we demonstrate that TH17 cells in a mouse model of autoimmune disease are functionally and metabolically heterogeneous; they contain a subset with stemness-associated features but lower anabolic metabolism, and a reciprocal subset with higher metabolic activity that supports transdifferentiation into TH1-like cells. These two TH17-cell subsets are defined by selective expression of the transcription factors TCF-1 and T-bet, and by discrete levels of CD27 expression. We also identify signalling via the kinase complex mTORC1 as a central regulator of TH17-cell fate decisions by coordinating metabolic and transcriptional programmes. TH17 cells with disrupted mTORC1 signalling or anabolic metabolism fail to induce autoimmune neuroinflammation or to develop into TH1-like cells, but instead upregulate TCF-1 expression and acquire stemness-associated features. Single-cell RNA sequencing and experimental validation reveal heterogeneity in fate-mapped TH17 cells, and a developmental arrest in the TH1 transdifferentiation trajectory upon loss of mTORC1 activity or metabolic perturbation. Our results establish that the dichotomy of stemness and effector function underlies the heterogeneous TH17 responses and autoimmune pathogenesis, and point to previously unappreciated metabolic control of plasticity in helper T cells.

Recently, Wen and Stephens (Wen and Stephens [2010] Ann Appl Stat 4(3):1158-1182) proposed a linear predictor, called BLIMP, that uses conditional multivariate normal moments to impute genotypes with accuracy similar to current state-of-the-art methods. One novelty is that it regularized the estimated covariance matrix based on a model from population genetics. We extended multivariate moments to impute genotypes in pedigrees. Our proposed method, PedBLIMP, utilizes both the linkage-disequilibrium (LD) information estimated from external panel data and the pedigree structure or identity-by-descent (IBD) information. The proposed method was evaluated on a pedigree design where some individuals were genotyped with dense markers and the rest with sparse markers. We found that incorporating the pedigree/IBD information can improve imputation accuracy compared to BLIMP. Because rare variants usually have low LD with other single-nucleotide polymorphisms (SNPs), incorporating pedigree/IBD information largely improved imputation accuracy for rare variants. We also compared PedBLIMP with IMPUTE2 and GIGI. Results show that when sparse markers are in a certain density range, our method can outperform both IMPUTE2 and GIGI.

OBJECTIVES We have previously reported a top-ranked risk gene [i.e., serine incorporator 2 gene (SERINC2)] for alcohol dependence in individuals of European descent by analyzing the common variants in a genome-wide association study. In the present study, we comprehensively examined the rare variants [minor allele frequency (MAF)

Brain ideal midline estimation is vital in medical image processing, especially in analyzing the severity of a brain injury in clinical environments. We propose an automated computer-aided ideal midline estimation system with a two-step process. First, a CT Slice Selection Algorithm (SSA) can automatically select an appropriate subset of slices from a large number of raw CT images using the skull’s anatomical features. Next, an ideal midline detection is implemented on the selected subset of slices. An exhaustive symmetric position search is performed based on the anatomical features in the detection. In order to enhance the accuracy of the detection, a global rotation assumption is applied to determine the ideal midline by fully considering the connection between slices. Experimental results of the multi-stage algorithm were assessed on 3313 CT slices of 70 patients. The accuracy of the proposed system is 96.9%, which makes it viable for use under clinical settings.

Background/Objectives: For genome-wide association studies (GWAS) with case-control designs, one of the most widely used association tests is the Cochran-Armitage (CA) trend test assuming an additive mode of inheritance. The CA trend test often has higher power than other association tests under additive and multiplicative disease models. However, it can have very low power under a recessive disease model in GWAS. Although tests (such as MAX3) robust to different genetic models have been developed, they often have relatively lower power than the CA trend test under additive and multiplicative models. The goal of this study is to propose an efficient method that not only has higher power than the CA trend test under dominant and recessive models but also maintains the power of the CA trend test under additive and multiplicative models. Methods: We employed the generalized sequential Bonferroni (GSB) procedure of Holm to incorporate information from a Hardy-Weinberg disequilibrium (HWD) test into the CA trend test based on estimating weights from the p values of the HWD test. We proposed to smooth the weights to reduce possible noise. Results and Conclusions: Results from extensive simulation studies showed that the proposed GSB procedure can achieve the goal described above.

Although sickle cell disease (SCD) is a monogenic disorder, the severity and specific organ dysfunction and failure are strongly influenced by genetic modifiers. Rapid identification of all modifiers in patients and well-phenotyped cohorts will better define the impact of relevant variants on clinical status, inform disease biology, and identify new therapeutic strategies. We created the Sickle Genome Project (SGP), a whole genome sequencing (WGS) strategy, to define genomic variation and modifiers of SCD. We performed WGS on 871 African American SCD patients from St. Jude Children's Research Hospital who participated in the Sickle Cell Clinical Research and Intervention Program (SCCRIP, Hankins et al. Pediatr Blood Cancer, 2018) and Texas Children's Hospital Hematology Center (TCHC). We developed robust pipelines for accurate detection of single nucleotide polymorphisms (SNPs), identification of structural variants and data retrieval/sharing via the St. Jude Cloud platform (to be described elsewhere). Notable findings include: 1) Confirmed associations of common genetic modifiers with SCD phenotypes, including levels of fetal hemoglobin (BCL11A, HBS1L-MYB, HBB), bilirubin (UGT1A1), and microalbuminuria (APOL1). Additional associations approaching genome-wide significance require further investigation, including replication in independent samples. 2) Improved determination of the SCD modifier α-thalassemia. The most common α-thalassemia mutations in SCD are 3.7 kb or 4.2 kb deletions (-α3.7 and -α4.2 alleles), which arose from recombination between homologous HBA1 and HBA2 genes and are difficult to map using standard WGS reads. Three independent crossover events are described for -α3.7 and one for -α4.2 in SCD cohorts. We developed a novel approach to identify α-globin gene deletions by local de novo assembly of WGS data and coverage depth analysis. We identified 5 -α3.7 alleles (frequencies 0.77-32.12%) and 7 -α4.2 alleles (frequencies 0.19-5.77%). Collectively, the frequency of all -α alleles was 57%, reflecting at least 12 distinct recombination events, greatly exceeding previously published counts. These findings better define the evolution of α-globin genes to allow improved understanding of their regulation and influence on SCD. 3) Characterization of β0-thalassemia alleles. Mutations in the extended β-globin locus influence SCD phenotypes. Five SGP patients had large β-globin (HBB) deletions associated with elevated fetal hemoglobin, which ameliorates symptoms of SCD. Twenty-three patients had HbSβ0-thalassemia, which reduces the severity of some SCD phenotypes. Overall, 48.6% (18/37) of patients clinically designated as HbSβ0 -thalassemia had no identified β-thalassemia mutation. Moreover, 4/680 patients (0.6%) designated HbSS were identified to be β0-thalassemia heterozygotes. The MCV, RBC and %HbA2 distributions overlapped substantially in correct vs. incorrect genotype assignments. Improved discrimination of HbSβ0 vs HbSS genotypes by WGS will better define associated phenotype differences to impact clinical care. 4) Determination of a genetic variant linked to vaso-occlusive crisis (VOC). Previously, a single GWAS study linked rs3115229, located 63.7 kb 5′ upstream of the KIAA1109 gene, with VOC at borderline significance (P = 5.63 × 10−8) (Chaturvedi et al, Blood 130, 2017). Using WGS data for 327 SGP participants (HbSS or HbSβ0-thalassemia) enrolled in the SCCRIPP study, we found strong association (p = 7 x 10-5) between the onset of VOC and a 4-SNP diplotype within an adjacent LD block of the KIAA1109-TENR-IL2-IL21 region (chr4: 122.8Mb - 123.8Mb) which has been previously associated with numerous inflammatory disorders. We validated this association using imputed genome-wide array data in an independent group of SCD patients (Sleep and Asthma Cohort, n= 181 patients, p = 0.05) (Cohen et al, Ann Am Thorac Soc, 2016). This works provides confirmation that the region surrounding KIAA1109 is associated with pain crisis in SCD. Our studies provide new information on the genomic architecture of SCD patients and delineate a consolidated approach for future applications of precision medicine. Disclosures Hankins: Novartis: Research Funding; Global Blood Therapeutics: Research Funding; NCQA: Consultancy; bluebird bio: Consultancy. Estepp:Global Blood Therapeutics: Consultancy, Research Funding; ASH Scholar: Research Funding; NHLBI: Research Funding; Daiichi Sankyo: Consultancy.

Single-cell RNA sequencing (scRNA-seq) emerges as a powerful tool to characterize cell-to-cell variation and dynamics in a seemingly homogenous population. Efficient and affordable, scRNA-seq is gaining in popularity in both basic and translational biological research areas. However, significant challenges arise in the analysis of scRNA-seq data, including low signal-to-noise ratio with high data sparsity, rising scalability hurdles with hundreds of thousands of cells, and more. Due to inherent complexities in scRNA-seq data, the performance of currently available algorithms may not always be optimal even for fundamental tasks such as identifying heterogeneous subpopulations in the data. In this study, we developed Latent Cellular Analysis (LCA), a machine learning based analytical pipeline that combines similarity measurement by latent cellular states with a graph-based clustering algorithm. LCA features a dual-space model search for both the optimal number of subpopulations and the informative cellular states distinguishing them. LCA provides heuristic solutions for population number inference, dimension reduction, feature selection and confounding factor removal without explicit gene filtering. LCA has proved to be robust, accurate and powerful by comparison to multiple state-of-the-art computational methods on large-scale real and simulated scRNA-seq data. Importantly, LCA's ability to learn from representative subsets of the data provides scalability, thereby addressing a significant challenge for growing sample size in scRNA-seq data analysis. Citation Format: Changde Cheng, John Easton, Celeste Rosencrance, Yan Li, Bensheng Ju, Wenan Chen, Xiang Chen. LCA: A robust and scalable algorithm to reveal subtle diversity in large-scale single-cell RNA-Seq data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5297.

The allelic spectrum of the genetic architecture of breast cancer (BC) susceptibility includes at least 172 common variants with small effect sizes (per-allele odds ratio range: 1.03-1.31), plus rare variants with high (BRCA1, BRCA2, CHD1, PTEN, STK11, TP53) or moderate penetrance (ATM, CHEK2, NBN, NF1, PALB2). While these common variants confer modest risk individually, their combined effect in the form of a polygenic risk score (PRS) may be substantial. The SJLIFE whole-genome sequencing (WGS) data provide a unique opportunity to evaluate common and rare sets of genetic variants jointly, along with treatment exposures, for their contributions to subsequent BC risk in adult survivors of childhood cancer. This analysis utilized WGS data from 1131 females of European ancestry [median age at last follow-up: 34.9 years (range: 6.2-68.6)] of whom 47 were diagnosed with a subsequent BC (median age at BC 40.3 years, range: 25.5-53.0). The PRS (mean, 10.1; range, 8.3-12.2) was calculated using a weighted sum of the number of risk alleles and their log per-allele odds ratio from Michailidou et al. (Nature, Nov. 2017). A total of 34 (3.0%) survivors were carriers of pathogenic or likely pathogenic (P/LP) variants in the 11 BC predisposition genes (listed above). The standardized incidence ratio (SIR) for BC was 6.7 (95% CI, 5.0-8.9) for survivors relative to the SEER population. The SIR varied from 3.7 (95% CI, 1.4-8.1) for survivors with PRS in the 1st quintile to 3.6 (95% CI, 1.2-8.3), 7.3 (95% CI, 3.8-12.7), 7.6 (95% CI, 3.6-14.0), and 11.4 (95% CI, 6.8-18.1) in the 2nd, 3rd, 4th, and 5th quintiles, respectively. In the multivariable model adjusting for age at diagnosis, chest irradiation, alkylating agents, anthracyclines, attained age, and significant genotype eigenvectors, the relative rates (RR) of BC were 16.5 (95% CI, 6.4 - 42.6), 11.5 (95% CI, 4.4-29.9), and 47.8 (95% CI, 8.2-278.3) for carriers vs. non-carriers of the P/LP variants among all survivors, and survivors with and without chest irradiation, respectively. The RR per one standard deviation of PRS were 1.5 (95% CI, 1.1-1.9), 1.6 (95% CI, 1.2-2.0) and 1.3 (95% CI, 0.7-2.2), respectively, for the same three groups. Importantly, PRS was significantly associated with the rate of subsequent BC under the age of 45 (RR, 1.7; 95% CI, 1.3-2.2) but not over 45 (RR, 0.9; 95% CI, 0.6-1.5). To our knowledge, this is the first assessment of the joint effects of rare and common genetic variations implicated in the etiology of BC in the general population, among long-term survivors of childhood cancer. Clinically, we anticipate that an individual genetic profile utilizing common susceptibility loci in combination with rare P/LP variants will inform an improved strategy for personalized BC risk stratification and management for childhood cancer survivors. Further replication studies are warranted to confirm and refine our findings. Citation Format: Zhaoming Wang, Carmen L. Wilson, Qi Liu, John Easton, Heather L. Mulder, Michael Rusch, Michael Edmonson, Shawn Levy, Aman Patel, Ying Shao, Ti-Cheng Chang, Stephen V. Rice, Yadav Sapkota, Russell J. Brooke, Wonjong Moon, Evadnie Rampersaud, Xiaotu Ma, Cynthia Pepper, Xin Zhou, Xiang Chen, Wenan Chen, Angela Jones, Braden Boone, Matthew J. Ehrhardt, Rebecca M. Howell, Nicholas Phillips, Courtney Lewis, Chimene A. Kesserwan, Gang Wu, Kim E. Nichols, James R. Downing, Melissa M. Hudson, Jinghui Zhang, Yutaka Yasui, Leslie L. Robison. Monogenic and polygenic associations with subsequent breast cancer risk in survivors of childhood cancer: The St. Jude Lifetime Cohort Study (SJLIFE) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3007.