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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.

Characterizing how variation at the level of individual nucleotides contributes to traits and diseases has been an area of growing interest since the completion of sequencing the first human genome. Our understanding of how a single nucleotide polymorphism (SNP) leads to a pathogenic phenotype on a genome-wide scale is a fruitful endeavor for anyone interested in developing diagnostic tests, therapeutics, or simply wanting to understand the etiology of a disease or trait. To this end, many datasets and algorithms have been developed as resources/tools to annotate SNPs. One of the most common practices is to annotate coding SNPs that affect the protein sequence. Synonymous variants are often grouped as one type of variant, however there are in fact many tools available to dissect their effects on gene expression. More recently, large consortiums like ENCODE and GTEx have made it possible to annotate non-coding regions. Although annotating variants is a common technique among human geneticists, the constant advances in tools and biology surrounding SNPs requires an updated summary of what is known and the trajectory of the field. This review will discuss the history behind SNP annotation, commonly used tools, and newer strategies for SNP annotation. Additionally, we will comment on the caveats that distinguish approaches from one another, along with gaps in the current state of knowledge, and potential future directions. We do not intend for this to be a comprehensive review for any specific area of SNP annotation, but rather it will be an excellent resource for those unfamiliar with computational tools used to functionally characterize SNPs. In summary, this review will help illustrate how each SNP annotation method impacts the way in which the genetic and molecular etiology of a disease is explored in-silico.

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.

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.

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

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.

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.

The purpose of this review is to provide an analysis of the latest developments on the functions of the carbon catabolite-repression 4-Not (Ccr4-Not) complex in regulating eukaryotic gene expression. Ccr4-Not is a nine-subunit protein complex that is conserved in sequence and function throughout the eukaryotic kingdom. Although Ccr4-Not has been studied since the 1980s, our understanding of what it does is constantly evolving. Once thought to solely regulate transcription, it is now clear that it has much broader roles in gene regulation, such as in mRNA decay and quality control, RNA export, translational repression and protein ubiquitylation. The mechanism of actions for each of its functions is still being debated. Some of the difficulty in drawing a clear picture is that it has been implicated in so many processes that regulate mRNAs and proteins in both the cytoplasm and the nucleus. We will describe what is known about the Ccr4-Not complex in yeast and other eukaryotes in an effort to synthesize a unified model for how this complex coordinates multiple steps in gene regulation and provide insights into what questions will be most exciting to answer in the future.

It is now known that the synthesis and decay of mRNAs are coordinated; however, the factors involved in this process and the mechanism of how these two distinct events are linked are not known. The...