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Background: Rare variants play an essential role in the etiology of cancer. In this study, we aim to characterize rare germline variants that impact the risk of cancer. Methods: We performed a genome-wide rare variant analysis using germline whole exome sequencing (WES) data derived from the Geisinger MyCode initiative to discover cancer predisposition variants. The case–control association analysis was conducted by binning variants in 5,538 patients with cancer and 7,286 matched controls in a discovery set and 1,991 patients with cancer and 2,504 matched controls in a validation set across nine cancer types. Further, The Cancer Genome Atlas (TCGA) germline data were used to replicate the findings. Results: We identified 133 significant pathway–cancer pairs (85 replicated) and 90 significant gene–cancer pairs (12 replicated). In addition, we identified 18 genes and 3 pathways that were associated with survival outcome across cancers (Bonferroni P < 0.05). Conclusions: In this study, we identified potential predisposition genes and pathways based on rare variants in nine cancers. Impact: This work adds to the knowledge base and progress being made in precision medicine.

Plasma lipids are known heritable risk factors for cardiovascular disease, but increasing evidence also supports shared genetics with diseases of other organ systems. We devised a comprehensive three-phase framework to identify novel lipid-associated genes and study the relationships between lipids, genotypes, gene expression and hundreds of complex human diseases from electronic Medical Records and Genomics (347 traits) and UK Biobank (549 traits) cohorts. Aside from 67 novel lipid-associated genes with strong replication, we found evidence for pleiotropic SNPs/genes between lipids and diseases across the phenome. These include discordant pleiotropy in the HLA region between lipids and multiple sclerosis and putative causal paths between triglycerides and gout, among several others. Our findings give insights into the genetic basis of the relationship between plasma lipids and diseases on a phenome-wide scale and can provide context for future prevention and treatment strategies.

Nonalcoholic fatty liver disease is common and highly heritable. Genetic studies of hepatic fat have not sufficiently addressed non-European and rare variants. In a medical biobank, we quantitate hepatic fat from clinical computed tomography (CT) scans via deep learning in 10,283 participants with whole-exome sequences available. We conduct exome-wide associations of single variants and rare predicted loss-of-function (pLOF) variants with CT-based hepatic fat and perform cross-modality replication in the UK Biobank (UKB) by linking whole-exome sequences to MRI-based hepatic fat. We confirm single variants previously associated with hepatic fat and identify several additional variants, including two (FGD5 H600Y and CITED2 S198_G199del) that replicated in UKB. A burden of rare pLOF variants in LMF2 is associated with increased hepatic fat and replicates in UKB. Quantitative phenotypes generated from clinical imaging studies and intersected with genomic data in medical biobanks have the potential to identify molecular pathways associated with human traits and disease.

Our software framework, Proteus, treats adaptation as a first-class object, enabling rapid development of robust, adaptive applications. Proteus developers specify their programs' intent and adaptable components (or knobs). A control-theoretic runtime continually monitors the running application, adjusting knobs so that the specified intent is met.

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

Population growth has led to increased global discharges of wastewater. Contaminants that are not fully removed during wastewater treatment, such as pharmaceuticals and personal care products (PPCPs), may negatively affect aquatic ecosystems. PPCPs can bioaccumulate causing adverse health effects and behavioural changes in exposed fish. To assess the impact of PPCPs on wild fish, and to assess whether caged fish could be used as a surrogate for resident wild fish in future monitoring, we caged goldfish in a marsh affected by discharges of wastewater effluents (Cootes Paradise, Lake Ontario, Canada). We collected plasma from resident wild goldfish, and from goldfish that we caged in the marsh for three weeks. We analyzed the plasma proteome and metabolome of both wild and caged fish. We also compared proteomic and metabolic responses in caged and wild fish from the marsh to fish caged at a reference site (Jordan Harbour Conservation Area). We identified significant changes in expression of over 250 molecules that were related to liver necrosis, accumulation and synthesis of lipids, synthesis of cyclic AMP, and the quantity of intracellular calcium in fish from the wastewater affected marsh. Our results suggest that PPCPs could be affecting the health of wild fish populations.

Rare variants play an essential role in the etiology of cancer and characterizing rare germline variants that impact the risk of cancer is an ongoing challenge. We performed a genome-wide rare variant analysis using germline whole exome sequencing (WES) data derived from the Geisinger MyCode initiative to discover cancer predisposition variants. The analysis was conducted using 5,538 cancer patients and 7,286 matched controls in a discovery set and 1,991 cancer patients and 2,504 matched controls in a validation set across nine cancer types. The variants in the WES data were annotated and filtered for pathogenic and likely pathogenic variants using ClinVar and Variant Effect Predictor (VEP). After filtering, case-control association tests were conducted on each cancer type separately by binning the pathogenic and likely pathogenic rare variants into genes and pathways using SKAT-O. We discovered 87 genes and 106 pathways significantly associated with cancer (Bonferroni-corrected P < 0.05) out of which seven genes and 26 pathways replicated from the validation set (suggestive threshold P < 0.05). Further, four genes and 21 pathways were discovered to be associated with multiple cancers (Bonferroni-corrected P < 0.05). Additionally, we identified 13 genes and two pathways associated with survival outcome across seven cancers (Bonferroni-corrected P < 0.05), where two genes, PCDHB8 and DCHS2, were also associated with survival outcome in TCGA data. In summary, we conducted one of the largest pan-cancer association studies using germline data derived from a single hospital system to find novel predisposition genes and pathways associated with nine cancers. Our results can inform future guidelines for germline genetic testing in cancer, which will be helpful in screening for cancer high-risk patients. This work adds to the knowledge base and progress being made in precision medicine.

Since prostate cancer is highly heritable, common variants associated with prostate cancer have been studied in various populations, including those in Korea. However, rare and low-frequency variants have a significant influence on the heritability of the disease. The contributions of rare variants to prostate cancer susceptibility have not yet been systematically evaluated in a Korean population. In this work, we present a large-scale exome-wide rare variant analysis of 7,258 individuals (985 cases with prostate cancer and 6,273 controls). In total, 19 rare variant loci spanning 7 genes contributed to an association with prostate cancer susceptibility. In addition to replicating previously known susceptibility genes (e.g., CDYL2, MST1R, GPER1, and PARD3B), 3 novel genes were identified (FDR q ENTPD3-AS1, LOC102724438, and protein-coding gene SPATA3. Additionally, 6 pathways were identified based on identified variants and genes, including estrogen signaling pathway, signaling by MST1, IL-15 production, MSP-RON signaling pathway, and IL-12 signaling and production in macrophages, which are known to be associated with prostate cancer. In summary, we report novel genes and rare variants that potentially play a role in prostate cancer susceptibility in the Korean population. These observations demonstrated a path towards one of the fundamental goals of precision medicine, which is to identify biomarkers for a subset of the population with a greater risk of disease than others.

Rigid body dynamics calculations are needed for many tasks in robotics, including online control. While there currently exist several competing software implementations that are sufficient for use in traditional control approaches, emerging sophisticated motion control techniques such as nonlinear model predictive control demand orders of magnitude more frequent dynamics calculations. Current software solutions are not fast enough to meet that demand for complex robots. The goal of this work is to examine the performance of current dynamics software libraries in detail. In this paper, we (i) survey current state-of-the-art software implementations of the key rigid body dynamics algorithms (RBDL, Pinocchio, RigidBodyDynamics.jl, and RobCoGen), (ii) establish a methodology for benchmarking these algorithms, and (iii) characterize their performance through real measurements taken on a modern hardware platform. With this analysis, we aim to provide direction for future improvements that will need to be made to enable emerging techniques for real-time robot motion control. To this end, we are also releasing our suite of benchmarks to enable others to help contribute to this important task.

[Purpose] To present a case of non-surgical reduction of thoracic hyperkyphosis utilizing a multimodal rehabilitation program emphasizing the mirror image® concept. [Subject and Methods] A 15-year-old female presented to a rehabilitation office suffering from back and neck pains and headaches. The patient was treated sporadically over a period of 13-months. Treatment consisted of anterior thoracic translation and thoracic extension exercises, spinal traction and spinal manipulation. [Results] After 13-months of treatment the patient displayed a significant reduction in hyperkyphosis and a dramatic correction of her overall posture and spine alignment corresponding to the reduction in back/neck pains, headaches and the simultaneous improvement of various other health issues. [Conclusion] Thoracic hyperkyphosis can be reduced through a multimodal rehabilitation program emphasizing mirror image thoracic extension procedures.

Background At least 90% of human genes are alternatively spliced. Alternative splicing has an important function regulating gene expression and miss-splicing can contribute to risk for human diseases, including Alzheimer’s disease (AD). Methods We developed a splicing decision model as a molecular mechanism to identify functional exon skipping events and genetic variation affecting alternative splicing on a genome-wide scale by integrating genomics, transcriptomics, and neuroimaging data in a systems biology approach. In this study, we analyzed RNA-Seq data of hippocampus brain tissue from Alzheimer’s disease (AD; n = 24) and cognitively normal elderly controls (CN; n = 50) and identified three exon skipping events in two genes (RELN and NOS1) as significantly associated with AD (corrected p-value 1.5). Next, we identified single-nucleotide polymorphisms (SNPs) affecting exon skipping events using the splicing decision model and then performed an association analysis of SNPs potentially affecting three exon skipping events with a global cortical measure of amyloid-β deposition measured by [18F] Florbetapir position emission tomography (PET) scan as an AD-related quantitative phenotype. A whole-brain voxel-based analysis was also performed. Results Two exons in RELN and one exon in NOS1 showed significantly lower expression levels in the AD participants compared to CN participants, suggesting that the exons tend to be skipped more in AD. We also showed the loss of the core protein structure due to the skipped exons using the protein 3D structure analysis. The targeted SNP-based association analysis identified one intronic SNP (rs362771) adjacent to the skipped exon 24 in RELN as significantly associated with cortical amyloid-β levels (corrected p-value

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Background The Cancer Genome Atlas (TCGA) project is a public resource that provides transcriptomic, DNA sequence, methylation, and clinical data for 33 cancer types. Transforming the large size and high complexity of TCGA cancer genome data into integrated knowledge can be useful to promote cancer research. Alternative splicing (AS) is a key regulatory mechanism of genes in human cancer development and in the interaction with epigenetic factors. Therefore, AS-guided integration of existing TCGA data sets will make it easier to gain insight into the genetic architecture of cancer risk and related outcomes. There are already existing tools analyzing and visualizing alternative mRNA splicing patterns for large-scale RNA-seq experiments. However, these existing web-based tools are limited to the analysis of individual TCGA data sets at a time, such as only transcriptomic information. Results We implemented CAS-viewer (integrative analysis of Cancer genome data based on Alternative Splicing), a web-based tool leveraging multi-cancer omics data from TCGA. It illustrates alternative mRNA splicing patterns along with methylation, miRNAs, and SNPs, and then provides an analysis tool to link differential transcript expression ratio to methylation, miRNA, and splicing regulatory elements for 33 cancer types. Moreover, one can analyze AS patterns with clinical data to identify potential transcripts associated with different survival outcome for each cancer. Conclusions CAS-viewer is a web-based application for transcript isoform-driven integration of multi-omics data in multiple cancer types and will aid in the visualization and possible discovery of biomarkers for cancer by integrating multi-omics data from TCGA. Electronic supplementary material The online version of this article (10.1186/s12920-018-0348-8) contains supplementary material, which is available to authorized users.

Alzheimer’s disease (AD) is a neurodegenerative disorder with few biomarkers even though it impacts a relatively large portion of the population and is predicted to affect significantly more individuals in the future. Neuroimaging has been used in concert with genetic information to improve our understanding in relation to how AD arises and how it can be potentially diagnosed. Additionally, evidence suggests synonymous variants can have a functional impact on gene regulatory mechanisms, including those related to AD. Some synonymous codons are preferred over others leading to a codon bias. The bias can arise with respect to codons that are more or less frequently used in the genome. A bias can also result from optimal and non-optimal codons, which have stronger and weaker codon anti-codon interactions, respectively. Although association tests have been utilized before to identify genes associated with AD, it remains unclear how codon bias plays a role and if it can improve rare variant analysis. In this work, rare variants from whole-genome sequencing from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort were binned into genes using BioBin. An association analysis of the genes with AD-related neuroimaging biomarker was performed using SKAT-O. While using all synonymous variants we did not identify any genome-wide significant associations, using only synonymous variants that affected codon frequency we identified several genes as significantly associated with the imaging phenotype. Additionally, significant associations were found using only rare variants that contains an optimal codon in among minor alleles and a non-optimal codon in the major allele. These results suggest that codon bias may play a role in AD and that it can be used to improve detection power in rare variant association analysis.

The bioaccumulation of a broad range of pharmaceuticals and personal care product chemicals (PPCPs) was studied in Cootes Paradise Marsh (CPM), an urban wetland that receives tertiary treated municipal waste waters as well as urban storm runoff. We measured PPCPs in caged and wild goldfish, as well as wild carp, and compared observed bioaccumulation factors (BAFP) using concentrations in surface waters and fish blood plasma, with modeled BAFs. Thirty-two PPCPs were detected in water from the central CPM site (CPM3) while 64 PPCPs were found at higher concentrations at a site immediately downstream of the effluent outflow (CPM1). Following a 3-week deployment, 15 PPCPs were detected in the plasma of caged goldfish at CPM1, and 14 at CPM3, compared to only 3 in goldfish caged at a reference site. The highest BAFP in goldfish were for the antidepressant Σfluoxetine averaging 386 L/kg in caged and 906 L/kg in wild goldfish, respectively. In carp, ΣDiazepam (diazepam and oxazepam) had the highest BAFP (927 L/kg). This study identified a broader range of PPCPs in fish and surface waters than previously reported. However, modeled BAFs did not show good agreement with observed whole body or plasma BAFs, demonstrating that more work is needed to better explain bioaccumulation of PPCPs.

The Ccr4 (carbon catabolite repression 4)-Not complex is a major regulator of stress responses that controls gene expression at multiple levels, from transcription to mRNA decay. Ccr4, a “core” subunit of the complex, is the main cytoplasmic deadenylase in Saccharomyces cerevisiae; however, its mRNA targets have not been mapped on a genome-wide scale. Here, we describe a genome-wide approach, RNA immunoprecipitation (RIP) high-throughput sequencing (RIP-seq), to identify the RNAs bound to Ccr4, and two proteins that associate with it, Dhh1 and Puf5. All three proteins were preferentially bound to lowly abundant mRNAs, most often at the 3′ end of the transcript. Furthermore, Ccr4, Dhh1, and Puf5 are recruited to mRNAs that are targeted by other RNA-binding proteins that promote decay and mRNA transport, and inhibit translation. Although Ccr4-Not regulates mRNA transcription and decay, Ccr4 recruitment to mRNAs correlates better with decay rates, suggesting it imparts greater control over transcript abundance through decay. Ccr4-enriched mRNAs are refractory to control by the other deadenylase complex in yeast, Pan2/3, suggesting a division of labor between these deadenylation complexes. Finally, Ccr4 and Dhh1 associate with mRNAs whose abundance increases during nutrient starvation, and those that fluctuate during metabolic and oxygen consumption cycles, which explains the known genetic connections between these factors and nutrient utilization and stress pathways.

Alzheimer's disease (AD) is a neurodegenerative disorder with few biomarkers even though it impacts a relatively large portion of the population and is predicted to affect significantly more individuals in the future. Neuroimaging has been used in concert with genetic information to improve our understanding in relation to how AD arises and how it can be potentially diagnosed. Additionally, evidence suggests synonymous variants can have a functional impact on gene regulatory mechanisms, including those related to AD. Some synonymous codons are preferred over others leading to a codon bias. The bias can arise with respect to codons that are more or less frequently used in the genome. A bias can also result from optimal and non-optimal codons, which have stronger and weaker codon anti-codon interactions, respectively. Although association tests have been utilized before to identify genes associated with AD, it remains unclear how codon bias plays a role and if it can improve rare variant analysis. In this work, rare variants from whole-genome sequencing from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort were binned into genes using BioBin. An association analysis of the genes with AD-related neuroimaging biomarker was performed using SKAT-O. While using all synonymous variants we did not identify any genomewide significant associations, using only synonymous variants that affected codon frequency we identified several genes as significantly associated with the imaging phenotype. Additionally, significant associations were found using only rare variants that contains an optimal codon in among minor alleles and a non-optimal codon in the major allele. These results suggest that codon bias may play a role in AD and that it can be used to improve detection power in rare variant association analysis.

Eukaryotic organisms have developed a variety of mechanisms to regulate translation post-transcriptionally, including but not limited to the use of miRNA silencing in many species. One method of post-transcriptional regulation is through miRNAs that bind to the 3' UTRs to regulate mRNA abundance and influence protein expression. Therefore, the diversity of mRNA 3' UTRs mediating miRNA binding sites influence miRNA-mediated regulation. Alternative polyadenylation, by shortening mRNA isoforms, increases the diversity of 3' UTRs; moreover, short mRNA isoforms elude miRNA-medicated repression. Because no current prediction methods for putative miRNA target sites consider whether or not 1) splicing-informed miRNA binding sites and/or 2) the use of 3' UTRs provide higher resolution or functionality, we sought to identify not only the genome-wide impact of using exons in mRNA 3' UTRs but also their functional connection to miRNA regulation and clinical outcomes in cancer. With a genome-wide expression of mRNA and miRNA quantified by 395 bladder cancer cases from The Cancer Genome Atlas (TCGA), we 1) demonstrate the diversity of 3' UTRs affecting miRNA efficiency and 2) identify a set of genes clinically associated with mRNA expression in bladder cancer. Knowledge of 3' UTR diversity will not only be a useful addition to current miRNA target prediction algorithms but also enhance the clinical utility of mRNA isoforms in the expression of mRNA in cancer. Thus, variability among cancer patient's variability in molecular signatures based on these exon usage events in 3' UTR along with miRNAs in bladder cancer may lead to better prognostic/treatment strategies for improved precision medicine.