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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.

Background Methylome-wide association (MWAS) studies present a new way to advance the search for biological correlates for alcohol use. A challenge with methylation studies of alcohol involves the causal direction of significant methylation–alcohol associations. One way to address this issue is to combine MWAS data with genomewide association study (GWAS) data. Methods Here, we combined MWAS and GWAS results for alcohol use from 619 individuals. Our MWAS data were generated by next-generation sequencing of the methylated genomic DNA fraction, producing over 60 million reads per subject to interrogate methylation levels at ~27 million autosomal CpG sites in the human genome. Our GWAS included 5,571,786 single nucleotide polymorphisms (SNPs) imputed with 1000 Genomes. Results When combining the MWAS and GWAS data, our top finding was a region in an intron of CNTN4 (p = 2.55 × 10−8), located between chr3: 2,555,403 and 2,555,524, encompassing SNPs rs1382874 and rs1382875. This finding was then replicated in an independent sample of 730 individuals. We used bisulfite pyrosequencing to measure methylation and found significant association with regular alcohol use in the same direction as the MWAS (p = 0.021). Rs1382874 and rs1382875 were genotyped and found to be associated in the same direction as the GWAS (p = 0.008 and p = 0.009). After integrating the MWAS and GWAS findings from the replication sample, we replicated our combined analysis finding (p = 0.0017) in CNTN4. Conclusions Through combining methylation and SNP data, we have identified CNTN4 as a risk factor for regular alcohol use.

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 resolves 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 produces correct null distribution of association p-values for these kernels. In contrast, the sequence kernel association tests (SKAT) 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.

Two recently developed fine-mapping methods, CAVIAR and PAINTOR, demonstrate better performance over other fine-mapping methods. They also have the advantage of using only the marginal test statistics and the correlation among SNPs. Both methods leverage the fact that the marginal test statistics asymptotically follow a multivariate normal distribution and are likelihood based. However, their relationship with Bayesian fine mapping, such as BIMBAM, is not clear. In this study, we first show that CAVIAR and BIMBAM are actually approximately equivalent to each other. This leads to a fine-mapping method using marginal test statistics in the Bayesian framework, which we call CAVIAR Bayes factor (CAVIARBF). Another advantage of the Bayesian framework is that it can answer both association and fine-mapping questions. We also used simulations to compare CAVIARBF with other methods under different numbers of causal variants. The results showed that both CAVIARBF and BIMBAM have better performance than PAINTOR and other methods. Compared to BIMBAM, CAVIARBF has the advantage of using only marginal test statistics and takes about one-quarter to one-fifth of the running time. We applied different methods on two independent cohorts of the same phenotype. Results showed that CAVIARBF, BIMBAM, and PAINTOR selected the same top 3 SNPs; however, CAVIARBF and BIMBAM had better consistency in selecting the top 10 ranked SNPs between the two cohorts. Software is available at https://bitbucket.org/Wenan/caviarbf.

Childhood cancer survivors are at increased risk of subsequent neoplasms (SN), largely considered to be therapy-related. Studies of cancer predisposition genes (CPGs) and risk of SN among long-term survivors are lacking. We characterized germline mutations in CPGs in childhood cancer survivors to determine their contribution to SN risk. Whole genome (30x) and exome (100x) sequencing was performed for 2988 5+ year survivors of childhood cancer (1629 leukemia/lymphoma, 332 CNS, 1027 other solid tumors, 53% male, median follow-up 28 [range 6-55] years). Survivors underwent a comprehensive clinical assessment, treatment exposures were abstracted from medical records, and SN were validated by pathology reports. Germline mutations in 63 CPGs were classified using the American College of Medical Genetics and Genomics guidelines as previously described (Zhang et al. NEJM 2015). Logistic regression, adjusting for age, sex and race, was used to evaluate associations between mutation status, cancer therapy and the SN risk. 1062 SNs were diagnosed in 437 survivors, of whom 98 developed ≥2 histologically distinct SNs. Median age at SN and time to first SN was 38.2 (range 3.3-67.4) and 29.2 (0.9-48.4) years, respectively. Common SNs were basal cell carcinoma (542 in 153 survivors), meningioma (201 in 100), thyroid (64 in 64), and breast cancer (58 in 50). Cumulative incidence of SN at age 45 was 25.5% (95% CI: 22.9-27.9). 169 survivors (5.7%) had a pathogenic/likely pathogenic (P/LP) mutation in a CPG, consisting of 97 single nucleotide variations, 63 insertion/deletions and 9 copy number alterations (49% of mutations not in ClinVar). Frequently mutated genes were: RB1 (n=41), NF1 (n=22), BRCA2 (n=13), BRCA1 (n=12) and TP53 (n=10). Our data confirmed known associations between CPG mutations and specific primary diagnoses including RB1 mutations in 32 of 41 (78%) of bilateral and 7 of 57 (12%) of unilateral retinoblastoma survivors, 22 NF1 (20 of 332 CNS survivors), 4 SUFU (all in medulloblastoma survivors) and 5 WT1 mutations (all in Wilms’ tumor survivors). Analyses revealed novel associations between CPG mutations and SN risk. Among 1326 survivors not exposed to radiation therapy (non-RT), 62 SNs developed in 54 survivors, of which 15 (24.2%) occurred in P/LP mutation carriers. Non-RT exposed survivors with a P/LP mutation had an increased risk of SN (OR=5.6, 95% CI=2.6-12.0, P Citation Format: Zhaoming Wang, Carmen L. Wilson, John Easton, Dale Hedges, Qi Liu, Gang Wu, Michael Rusch, Michael Edmonson, Shawn Levy, Jennifer Q. Lanctot, Eric Caron, Kyla Shelton, Kelsey Currie, Matthew Lear, Heather L. Mulder, Donald Yergeau, Celeste Rosencrance, Bhavin Vadodaria, Yadav Sapkota, Russell J. Brooke, Wonjong Moon, Evadnie Rampersaud, Xiaotu Ma, Shuoguo Wang, Ti-Cheng Chang, Stephen Rice, Andrew Thrasher, Aman Patel, Cynthia Pepper, Xin Zhou, Xiang Chen, Wenan Chen, Angela Jones, Braden Boone, Deo Kumar Srivastava, Chimene A. Kesserwan, Kim E. Nichols, James R. Downing, Melissa M. Hudson, Yutaka Yasui, Leslie L. Robison, Jinghui Zhang. Germline mutations in cancer predisposition genes and risk for subsequent neoplasms among long-term survivors of childhood cancer in the St. Jude Lifetime Cohort [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3001. doi:10.1158/1538-7445.AM2017-3001

10502 Background: CCS are at increased risk of therapy-related PM but contribution of genetic factors is unknown. Methods: Using Affymetrix 6.0 SNP array, treatment exposures [cumulative alkylating agents (AA), ovarian radiotherapy (RT) dose] and clinically-assessed PM status (menopause < 40 years), a genome-wide association analysis was conducted using logistic regression in SJLIFE. A cluster of most statistically significant SNPs on chr4 was further examined, stratifying by ovarian RT and AA. Replication was performed using self-reported PM in CCSS. Results: PM was diagnosed in 30 of 805 SJLIFE female survivors. A loci of 13 SNPs in 4 linkage disequilibrium blocks (mean r2 = 0.51) in the upstream regulatory region of Neuropeptide Receptor 2 ( NPY2R) was identified with a minimum p-value of 3.3x10-7 (all -5). ENCODE gene expression, motifs, and chromatin remodeling data suggest these SNPs alter transcription factor binding sites, potentially disrupting neuroendocrine events necessary for ovulation. Among CCS exposed to ovarian RT, homozygous carriers of a risk profile (RP) defined by 4 of the 13 SNPs, found in over half of the survivors with clinically-diagnosed PM and 1 in 7 in the general population, significantly increased PM risk (odds ratio (OR) 25.8, p=5.4x10-5) (Table). This finding was replicated using self-reported PM status of 1644 survivors in CCSS (OR 4.2, p=4.6x10-4). Prediction of clinically-diagnosed PM (in the SJLIFE discovery cohort) improved by adding the RP to the model with age and treatment (area under ROC curve 0.84 vs. 0.93, p=0.011). Conclusions: The common RP is associated with PM risk in pediatric cancer survivors and may have potential for clinical application. [Table: see text]

Objective: To develop effective methods for GWAS in admixed populations such as African Americans. Methods: We show that, when testing the null hypothesis that the test SNP is not in background linkage disequilibrium with the causal variants, several existing methods cannot control well the family-wise error rate (FWER) in the strong sense in GWAS. These existing methods include association tests adjusting for global ancestry and joint association tests that combine statistics from admixture mapping tests and association tests that correct for local ancestry. Furthermore, we describe a generalized sequential Bonferroni (smooth-GSB) procedure for GWAS that incorporates smoothed weights calculated from admixture mapping tests into association tests that correct for local ancestry. We have applied the smooth-GSB procedure to analyses of GWAS data on American Africans from the Atherosclerosis Risk in Communities (ARIC) Study. Results: Our simulation studies indicate that the smooth-GSB procedure not only control the FWER, but also improves statistical power compared with association tests correcting for local ancestry. Conclusion: The smooth-GSB procedure can result in a better performance than several existing methods for GWAS in admixed populations.

This paper attempts to predict Intracranial Pressure (ICP) based on features extracted from non-invasively collected patient data. These features include midline shift measurement and textural features extracted from Computed axial Tomography (CT) images. A statistical analysis is performed to examine the relationship between ICP and midline shift. Machine learning is also applied to estimate ICP levels with a two-stage feature selection scheme. To avoid overfitting, all feature selections and parameter selections are performed using a nested 10-fold cross validation within the training data. The classification results demonstrate the effectiveness of the proposed method in ICP prediction.

Importance Epigenetic studies present unique opportunities to advance schizophrenia research because they can potentially account for many of its clinical features and suggest novel strategies to improve disease management. Objective To identify schizophrenia DNA methylation biomarkers in blood. Design, Setting, and Participants The sample consisted of 759 schizophrenia cases and 738 controls (N = 1497) collected in Sweden. We used methyl-CpG–binding domain protein-enriched genome sequencing of the methylated genomic fraction, followed by next-generation DNA sequencing. We obtained a mean (SD) number of 68 (26.8) million reads per sample. This massive data set was processed using a specifically designed data analysis pipeline. Critical top findings from our methylome-wide association study (MWAS) were replicated in independent case-control participants using targeted pyrosequencing of bisulfite-converted DNA. Main Outcomes and Measures Status of schizophrenia cases and controls. Results Our MWAS suggested a considerable number of effects, with 25 sites passing the highly conservative Bonferroni correction and 139 sites significant at a false discovery rate of 0.01. Our top MWAS finding, which was located in FAM63B , replicated with P = 2.3 × 10 −10 . It was part of the networks regulated by microRNA that can be linked to neuronal differentiation and dopaminergic gene expression. Many other top MWAS results could be linked to hypoxia and, to a lesser extent, infection, suggesting that a record of pathogenic events may be preserved in the methylome. Our findings also implicated a site in RELN , one of the most frequently studied candidates in methylation studies of schizophrenia. Conclusions and Relevance To our knowledge, the present study is one of the first MWASs of disease with a large sample size using a technology that provides good coverage of methylation sites across the genome. Our results demonstrated one of the unique features of methylation studies that can capture signatures of environmental insults in peripheral tissues. Our MWAS suggested testable hypotheses about disease mechanisms and yielded biomarkers that can potentially be used to improve disease management.

This chapter provides an overview of various machine learning algorithms which are typically adopted into many predictive computer-assisted decision making systems for traumatic injuries. The objective here is to compare some existing machine learning methods using an aggregated database of traumatic injuries. These methods are used towards the development of rule-based computer-assisted decision-making systems that provide recommendations to physicians for the course of treatment of the patients. Since physicians in trauma centers are constantly required to make quick yet difficult decisions for patient care using a multitude of patient information, such computer assisted decision support systems are bound to play a vital role in improving healthcare. The content of this chapter also presents a novel image processing method to assess traumatic brain injuries (TBI).

Raised intracranial pressure (ICP) causes serious problem on traumatic brain injury patient. Automated and non-intrusive ICP level prediction saves cost and enhances efficiency. An automated ICP level prediction model based on machine learning method is proposed in this paper. Multiple features, including midline shift, intracranial air cavities, ventricle size, texture patterns, and blood amount, are selected, extracted and aggregated using different methods. Some demographic information, such as age and injury severity score, is also considered as candidate features. After the feature aggregation, the most important features are selected by a feature selection scheme applied on 10 fold nested cross validation. The final support vector machine classification result using RapidMiner shows the effectiveness of the proposed method in ICP level prediction.

In this paper we present an automated system based mainly on the computed tomography (CT) images consisting of two main components: the midline shift estimation and intracranial pressure (ICP) pre-screening system. To estimate the midline shift, first an estimation of the ideal midline is performed based on the symmetry of the skull and anatomical features in the brain CT scan. Then, segmentation of the ventricles from the CT scan is performed and used as a guide for the identification of the actual midline through shape matching. These processes mimic the measuring process by physicians and have shown promising results in the evaluation. In the second component, more features are extracted related to ICP, such as the texture information, blood amount from CT scans and other recorded features, such as age, injury severity score to estimate the ICP are also incorporated. Machine learning techniques including feature selection and classification, such as Support Vector Machines (SVMs), are employed to build the prediction model using RapidMiner. The evaluation of the prediction shows potential usefulness of the model. The estimated ideal midline shift and predicted ICP levels may be used as a fast pre-screening step for physicians to make decisions, so as to recommend for or against invasive ICP monitoring.

Objective: For genome-wide association studies (GWAS) using case-control data with stratification, a commonly used association test is the generalized Armitage (GA) trend test implemented in the software EIGENSTRAT. The GA trend test uses principal component analysis to correct for population stratification. It usually assumes an additive disease model and can have high power when the underlying disease model is additive or multiplicative, but may have relatively low power when the underlying disease model is recessive or dominant. The purpose of this paper is to provide a test procedure for GWAS with increased power over the GA trend test under the recessive and dominant models, while maintaining the power of the GA trend test under the additive and multiplicative models. Methods: We extend a Hardy-Weinberg disequilibrium (HWD) trend test for a homogeneous population to account for population stratification, and then propose a robust association test procedure for GWAS that incorporates information from the extended HWD trend test into the GA trend test. Results and Conclusions: Our simulation studies and application of our method to a GWAS data set indicate that our proposed method can achieve the purpose described above.

We evaluate four association tests for rare variants—the combined multivariate and collapsing (CMC) method, two weighted-sum methods, and a variable threshold method—by applying them to the simulated data sets of unrelated individuals in the Genetic Analysis Workshop 17 (GAW17) data. The family-wise error rate (FWER) and average power are used as criteria for evaluation. Our results show that when all nonsynonymous SNPs (rare variants and common variants) in a gene are jointly analyzed, the CMC method fails to control the FWER; when only rare variants (single-nucleotide polymorphisms with minor allele frequency less than 0.05) are analyzed, all four methods can control FWER well. All four methods have comparable power, which is low for the analysis of the GAW17 data sets. Three of the methods (not including the CMC method) involve estimation of p-values using permutation procedures that either can be computationally intensive or generate inflated FWERs. We adapt a fast permutation procedure into these three methods. The results show that using the fast permutation procedure can produce FWERs and average powers close to the values obtained from the standard permutation procedure on the GAW17 data sets. The standard permutation procedure is computationally intensive.

The coagulation and fibrinolytic systems are complex, inter-connected biological systems with major physiological roles. The complex, nonlinear multi-point relationships between the molecular and cellular constituents of two systems render a comprehensive and simultaneous study of the system at the microscopic and macroscopic level a significant challenge. We have created an Agent Based Modeling and Simulation (ABMS) approach for simulating these complex interactions. As the scale of agents increase, the time complexity and cost of the resulting simulations presents a significant challenge. As such, in this paper, we also present a high-speed framework for the coagulation simulation utilizing the computing power of graphics processing units (GPU). For comparison, we also implemented the simulations in NetLogo, Repast, and a direct C version. As our experiments demonstrate, the computational speed of the GPU implementation of the million-level scale of agents is over 10 times faster versus the C version, over 100 times faster versus the Repast version and over 300 times faster versus the NetLogo simulation.

Computer assisted medical image processing can extract vital information that may be elusive to human eyes. In this paper, an algorithm is proposed to automatically estimate the position of the actual midline from the brain CT scans using multiple regions shape matching. The method matches feature points identified from a set of ventricle templates, extracted from MRI, with the corresponding feature points in the segmented ventricles from CT images. Then based on the matched feature points, the position of the actual midline is estimated. The proposed multiple regions shape matching algorithm addresses the deformation problem arising from the intrinsic multiple regions nature of the brain ventricles. Experiments on the CT scans from patients with traumatic brain injuries (TBI) show promising results, particularly the proposed algorithm proves to be quite robust.

VCU Theses and Dissertations

Elevated Intracranial Pressure (ICP) is a significant cause of mortality and long-term functional damage in traumatic brain injury (TBI). Current ICP monitoring methods are highly invasive, presenting additional risks to the patient. This paper describes a computerized non-invasive screening method based on texture analysis of computed tomography (CT) scans of the brain, which may assist physicians in deciding whether to begin invasive monitoring. Quantitative texture features extracted using statistical, histogram and wavelet transform methods are used to characterize brain tissue windows in individual slices, and aggregated across the scan. Support Vector Machine (SVM) is then used to predict high or normal levels of ICP using the most significant features from the aggregated set. Results are promising, providing over 80% predictive accuracy and good separation of the two ICP classes, confirming the suitability of the approach and the predictive power of texture features in screening patients for high ICP.

This paper introduces a hierarchical method of recognizing bone tissue from regions extracted from Pelvic CT Images. The method allows distinguishing among segmented objects with similar grey level values, such as bone tissue and regions of active hemorrhage. The method addresses the challenge of correctly segmenting and classifying bone as well as assessing presence of active hemorrhage.

Background Accurate analysis of CT brain scans is vital for diagnosis and treatment of Traumatic Brain Injuries (TBI). Automatic processing of these CT brain scans could speed up the decision making process, lower the cost of healthcare, and reduce the chance of human error. In this paper, we focus on automatic processing of CT brain images to segment and identify the ventricular systems. The segmentation of ventricles provides quantitative measures on the changes of ventricles in the brain that form vital diagnosis information. Methods First all CT slices are aligned by detecting the ideal midlines in all images. The initial estimation of the ideal midline of the brain is found based on skull symmetry and then the initial estimate is further refined using detected anatomical features. Then a two-step method is used for ventricle segmentation. First a low-level segmentation on each pixel is applied on the CT images. For this step, both Iterated Conditional Mode (ICM) and Maximum A Posteriori Spatial Probability (MASP) are evaluated and compared. The second step applies template matching algorithm to identify objects in the initial low-level segmentation as ventricles. Experiments for ventricle segmentation are conducted using a relatively large CT dataset containing mild and severe TBI cases. Results Experiments show that the acceptable rate of the ideal midline detection is over 95%. Two measurements are defined to evaluate ventricle recognition results. The first measure is a sensitivity-like measure and the second is a false positive-like measure. For the first measurement, the rate is 100% indicating that all ventricles are identified in all slices. The false positives-like measurement is 8.59%. We also point out the similarities and differences between ICM and MASP algorithms through both mathematically relationships and segmentation results on CT images. Conclusion The experiments show the reliability of the proposed algorithms. The novelty of the proposed method lies in its incorporation of anatomical features for ideal midline detection and the two-step ventricle segmentation method. Our method offers the following improvements over existing approaches: accurate detection of the ideal midline and accurate recognition of ventricles using both anatomical features and spatial templates derived from Magnetic Resonance Images.

Traumatic pelvic injuries are complex and difficult to treat, due to the high risk of complications. Prompt and accurate medical treatment is therefore vital. Computer-aided decision-making systems can assist physicians in this task, but none of those proposed so far incorporate features extracted from medical images. The study in this paper uses demographic information, standard medical measurements, and features extracted from X-ray images to predict a patient's length of stay in ICU via rules extracted from decision trees generated by the CART algorithm. The X-ray features are extracted by using a spline/ASM segmentation technique to detect structure position, then calculating measures of displacement. The results are promising and compare well with SVM and C4.5 algorithms, indicating that the rules represent true data patterns. Significantly, an X-ray feature is selected as highly important to injury severity, indicating that medical image features are important in providing accurate recommendations and predictions.

In this paper, a segmentation method using Gaussian Mixture Model (GMM) combined with template match is proposed for analysis of brain CT images. The specific aim of this method is to extract ventricles from brain CT images. These can then be used for automated detection of the midline shift in brain. In the method, different types of brain tissue, of which the ventricles form the region of interest, are segmented using multiple Gaussian mixtures. Expectation Maximization (EM) method is used to train the GMM. Ventriclular tissue is then detected in the segmented regions using template matching. Other segmentation methods, including K-means clustering and Iterated Conditional Modes (ICM), are also implemented and their results are compared with those of the proposed method. The algorithms are evaluated against a dataset of brain CT images captured from both normal and TBI cases. The segmentation results show the advantages of the proposed GMM-based method for brain tissue modeling. The computational complexity of the proposed method is also discussed, as well as the means to address this issue. The proposed GMM-based method allows accurate segmentation of ventricles required for detection of the shift in the midline.

Rapid detection and treatment of hemorrhagic injuries are important factors in decreasing mortality in the battlefield and civilian trauma settings. In this study, novel features based on discrete wavelet transformation (DWT) were used to analyze physiological signals for prediction of central hypovolemia severity in humans. These features were defined based on approximate and detailed DWT coefficients extracted from physiological signals such as the electrocardiogram (ECG), arterial blood pressure (ABP), and thoracic impedance (IZT and DZT) signals, collected on healthy humans exposed to a hemorrhage model called lower body negative pressure (LBNP). The LBNP protocol consisted of applying 0, -15, -30, -45, -60, -70 mm Hg pressure to the lower half of the body, for 5 minutes at each stage. These LBNP levels were divided into three classes: mild, moderate, and severe. Machine learning algorithms were applied to predict the severity of blood loss based on the features extracted from the physiological signals. One of the objectives of this study was to compare the utility of using multiple physiological signals in prediction of the severity of hypovolemia as opposed to only using ECG. The classification results indicate that SVM has the highest accuracy at 82%. SVM's average precision and recall for all three classes are 79.2% and 79.8%, respectively. This shows that the wavelet-based method using multiple signals has the ability of rapidly determining the degree of volume loss, providing a potential tool for real-time remote triage and decision making in victims of trauma.

It is estimated that every year, 1.5 million people in the United States sustain a traumatic brain injury (TBI). Over 50,000 of these patients will not survive, and many others will be left permanently disabled. TBI is known to be accompanied by an increase in intracranial pressure (ICP), as the presence of hematomas compresses the brain tissue. Severe ICP can be fatal, and so must be monitored. This typically requires cranial trepanation, a risky procedure for the patient. However, some signs of increased ICP are visible on medical scans. For example, the lateral ventricles may change in size and position, depending on the location of the original injury. In this paper, we focus on automatic processing of CT brain images to segment and identify the lateral ventricles, using both iterated conditional models (ICM) and maximum a posteriori spatial probability (MASP). The ideal midline of the brain is found via exhaustive search based on skull symmetry and tissue features. The horizontal shift in the ventricles associated with increased ICP can then later be calculated based on the ideal midline. The novelty of the proposed method lies in its combination of anatomical features with template matching against MRI images, its stepwise improvement of the detected actual midline, and its comparison of two existing methods, ICM and MASP, for ventricle detection. The relatively large size of the CT dataset used for testing increases the reliability of the results.

Background In methylome-wide association studies (MWAS) there are many possible differences between cases and controls (e.g. related to life style, diet, and medication use) that may affect the methylome and produce false positive findings. An effective approach to control for these confounders is to first capture the major sources of variation in the methylation data and then regress out these components in the association analyses. This approach is, however, computationally very challenging due to the extremely large number of methylation sites in the human genome. Result We introduce MethylPCA that is specifically designed to control for potential confounders in studies where the number of methylation sites is extremely large. MethylPCA offers a complete and flexible data analysis including 1) an adaptive method that performs data reduction prior to PCA by empirically combining methylation data of neighboring sites, 2) an efficient algorithm that performs a principal component analysis (PCA) on the ultra high-dimensional data matrix, and 3) association tests. To accomplish this MethylPCA allows for parallel execution of tasks, uses C++ for CPU and I/O intensive calculations, and stores intermediate results to avoid computing the same statistics multiple times or keeping results in memory. Through simulations and an analysis of a real whole methylome MBD-seq study of 1,500 subjects we show that MethylPCA effectively controls for potential confounders. Conclusions MethylPCA provides users a convenient tool to perform MWAS. The software effectively handles the challenge in memory and speed to perform tasks that would be impossible to accomplish using existing software when millions of sites are interrogated with the sample sizes required for MWAS.

This file includes: (1) supplementary methods describing details in single cell quality control and preprocessing, application details of other DE methods, and a statistical model linking UMI and read counts; (2) all supplementary figures. (PDF 2338 kb)

This file includes: (1) supplementary methods describing details in single cell quality control and preprocessing, application details of other DE methods, and a statistical model linking UMI and read counts; (2) all supplementary figures. (PDF 2338 kb)