Genetics & Genomics 2019

In 2014, the Undiagnosed Diseases Network (UDN), which is funded by the NIH, was established as a network of seven clinical sites, two sequencing cores, and a coordinating center. Later, a central biorepository, a metabolomics core, and a model organisms screening center were added. The aim of the UDN was to provide wider access to cross-disciplinary expertise and to leverage specific advantages of the collaborative network, such as deep subspecialty expertise. The sequencing cores were divided by methodology; the sequencing core at MCW, which subsequently moved to HudsonAlpha made use of WGS, whilst the other core at Baylor College of Medicine (BCM) made use of ES. 

The hypothesis set out by the WGS site was that application of WGS would lead to an increase in molecular diagnoses of 25%. This talk will explore whether this hypothesis was proven out and will discuss the challenges faced and lessons learned during the first four years of the UDN.

Learning Objectives: 

1. To define the meaning of genomic medicine.
2. To review benefits, challenges, and potential limitations of genomic medicine.
3. To describe the challenges faced when interpreting genomic data.
4. To understand how clinical WGS has a number of applied case example within the rare disease space.
 

Screening to identify all known viruses and other pathogenic microorganisms including bacteria, fungus and parasites in human tumor tissues will provide a more comprehensive understanding of the contributory role of the microbiome in the associated cancer. Metagenomic assays will require rapid and economical processing of large numbers of samples and data analysis pipeline. The PathoChip platform was developed by targeting viral, prokaryotic and eukaryotic genomes with multiple DNA probes in a microarray format that can be combined with a variety of upstream sample preparation protocols and downstream data analyses. PathoChip screening of DNA and RNA from formalin-fixed, paraffin embedded tumor tissues demonstrated the utility of this platform, and the detection of oncogenic viruses was validated using independent PCR and deep sequencing methods. These studies demonstrate the use of the PathoChIP technology combined with PCR and deep sequencing as a valuable strategy for identification of microbial pathogens in human cancers and other diseases. Furthermore, we were able to rapidly identify a zygomycetous fungi, Rhizomucor, a challenging organism for microbial testing laboratories, in a subject with acute myelogenous leukemia using this pan pathogen array technology. We highlight the value of PathoChip as a tool to identify micro-organisms to the species or strain level, especially for those difficult to identify in most microbial testing laboratories. Additionally, human cancers are associated with a wide range of microbes making infections the third highest cancer risk. We used the PathoChIP technology to identify the viruses, bacteria, fungi and parasites associated with breast cancer using formalin-fixed paraffin embedded (FFPE) archival tissues. Results were validated by PCR and next-generation sequencing. We define the signatures in a specific type of breast cancer tissue which was underrepresented in normal tissue. These include viruses as well as families of bacteria, fungi and eukaryotic parasites. Hierarchical clustering analysis identified two signature patterns, one with predominant bacteria and parasites and the other predominant for viruses. The contribution of these microbial signatures to cancer is yet to be determined. However, these microbes may contribute or exist in the tumor microenvironment and impact cancer biology. 

For Research Use Only. Not for use in diagnostic procedures.

Learning Objectives: 

1. Understand the importance of screening as it relates to the microbiome in cancer. 
2. Gain an understanding as to how the PathoChIP technology can be used as a valuable tool to identify micro-organisims during testing. 

Real-time PCR, or quantitative qPCR, is a commonly used molecular biology lab technique to determine the actual amount of PCR product at a given cycle.  For quantitative reverse transcription PCR (RT-qPCR), the starting material is RNA, which is then transcribed to cDNA by a reverse transcriptase prior to the qPCR assay. Gene expression is a common application for qPCR, and amplification through RT-qPCR is necessary to detect and quantify gene expression from small amounts of RNA. This webinar will present the basics of RT-qPCR and tips and tricks for performing RT-qPCR experiments.

Learning Objectives: 

1. Understand the basics of RT-PCR, including how it is used for quantitating gene expression and its advantages over other methods
2. Explore successful optimization strategies and troubleshooting tips to achieve better results

Speculations that some form DNA alteration might be utilized by the brain date to the 1960s [1] wherein hypotheses for genomic alterations of germline DNA were proposed for immunoglobulins and, by analogy, nervous system development.  In the mid 1970s, the first evidence for what is now known as V(D)J recombination of immunoglobulins was reported [2]. However, brain recombination processes were not reported.  Circumstantial evidence for possible recombination processes emerged in the 1990s with brain expression of immune recombination genes like RAG1 [3], however no genomic locus was found.  However, related work identified the first evidence for pervasive genomic alterations – “Genomic Mosaicism” - in the form of aneuploidies of neural progenitor cells [4] and a growing range of genomic elements are known to be altered in single cells of the vertebrate brain:  chromosomes, smaller copy number variations (CNVs), Line1 elements, and single nucleotide variations (SNVs), all reported globally as DNA content variation (DCV) (reviewed in [5]).  Gene recombination, however, remained speculative.  The realization that the brain is a complex genomic mosaic led to identification of APP as a gene possibly affected by SGR [6, 7], which was recently demonstrated [8].  Unlike V(D)J recombination, SGR is (thus far) most evident in post-mitotic neurons and involves reverse transcription of APP spliced RNA that produces a “genomic complementary DNA” or “gencDNA” in neurons, ranging from 0-to-multiple copies, with integration in places distinct from germline alleles.  It appears to be dysregulated in Alzheimer’s disease with more copies of different variant forms including species with SNVs that are identical to mutations reported in familial AD yet occurring mosaically and somatically.  The involvement of reverse transcription implicates reverse transcriptase inhibition as a possible therapeutic:  literature and epidemiological assessments of patient databases for aged persons taking these medicines supports their use as near-term therapeutics for treating AD.  SGR likely affects other genes and diseases.

Learning Objectives: 

1. The brain is composed of cells, particularly neurons, having distinct if not unique genomic DNA sequences.  This is referred to as “Genomic Mosaicism.”
2. Somatic gene recombination (SGR) occurs in human brain neurons (and likely other cells), and is dysregulated in sporadic Alzheimer’s disease.  It involves reverse transcription, implicating reverse transcriptase inhibitors used now to treat HIV and hepatitis B, in the near-term treatment of AD.

Release of the first human genome assembly was a landmark achievement, and after nearly two decades of improvements, the current human reference genome (GRCh38) is the most accurate and complete vertebrate genome ever produced. However, no one chromosome has yet been finished end to end, and hundreds of gaps persist across the genome. This is a fundamental problem because these gaps vary in repeat structure and copy number between individuals, which can affect genome stability and health. 

To address this challenge, I will present a whole-genome de novo assembly that surpasses the continuity of GRCh38, along with the first complete, telomere-to-telomere assembly of a human X chromosome. We have collected 40X coverage of ultra-long Oxford Nanopore sequencing for the CHM13hTERT cell line, including 44 Gb of sequence in reads >100 kb and a maximum read length exceeding 1 Mb.  This unprecedented coverage of ultra-long reads enabled the resolution of most repeats in the genome, including large fractions of the centromeric satellite arrays and short arms of the acrocentrics. Using this assembly as a basis, we chose to manually finish the X chromosome. These results demonstrate that it is now possible to finish entire human chromosomes without gaps, and our future work (Telomere-to-telomere, T2T Consortium) will focus on completing and validating the remainder of the genome.

Finally, centromeric sequences are expected to vary in repeat composition and copy number between individuals in the population. To study the extent of this variation, I have performed a comprehensive study of centromere sequence structural variation using a panel of high-coverage, long read datasets from individuals from diverse populations. Efforts to increase production of UL-read sequencing – thereby dramatically increasing our ability to characterize satellite array structure – using the PromethION sequencing platform from Oxford Nanopore will be discussed.

Learning Objectives: 

1. Human centromere sequence structure and organization.
2. Long-read sequencing and scaffolding assembly strategies to complete human chromosome assemblies.
 

The advent of precision medicine largely depends on the creation of precise and accurate predictive tools. While most late-onset diseases are moderately to highly heritable, using genetic information to make individual predictions about risk of disease has proven to be challenging. Recent technological and methodological advances have established the importance of the polygenic model of inheritance for complex diseases, whereby dozens of thousands of functional variants might contribute to disease risk. Genome-wide association studies have identified thousands of variants associated with disease, yet each individual variant tends to only have a modest effect on risk. We and others have proposed using polygenic risk scores combining information from hundreds to millions of variants to improve genetic risk prediction and identify individuals at very high risk of disease.  We illustrate this approach by studying coronary artery disease (CAD), a leading cause of mortality worldwide. A genetic cause is identified in less than 5% of patients with early onset disease, mainly Mendelian mutations leading to Familial Hypercholesterolemia. Our data show that belonging to the extreme tail of polygenic risk is associated with a greater risk of CAD than carrying a Mendelian mutation, both in terms of risk and prevalence. Indeed, more individuals are at high risk of CAD because of polygenic risk, and such individuals have higher risk than carriers of routinely tested Familial Hypercholesterolemia mutations. Similar findings have been reported for other common diseases such as breast and prostate cancer, among others. These findings support clinical use of polygenic risk scores for common complex disease in the near future.

Learning Objectives: 

1. Understand the polygenic nature of complex traits
2. Understand principles of polygenic risk scores
3. Clinical applications of polygenic risk scores

Recent technological advances as well as longitudinal monitoring not only have the potential to improve the treatment of disease (Precision Medicine) but also empower people to stay healthy (Precision Health). We profiled 107 participants using multiomics technologies (genomics, immunomics, transcriptomics, proteomics, metabolomics, microbiomics and wearables etc) for up to eight years and made 49 major health discoveries. Altogether, we conclude that deep longitudinal profiling using advanced technologies can lead to actionable health discoveries and provide important information relevant for precision health.

Learning Objectives: 

1. To demonstrate how advanced technologies can be useful on managing health
2. To demonstrate the power of longitudinal profiling for individualized health
 

Variant interpretation presents a bottleneck for many labs, posing a challenge to the broader adoption of precision medicine. The ACMG/AMP variant classification framework has provided a foundation for this process, but several key obstacles remain. In particular, the identification and prioritization of key publications presents a time- and resource-intensive challenge for many labs.

In this presentation, Dr. Mark Kiel, CSO and Co-Founder of Genomenon will discuss challenges to automating variant interpretation while improving the accuracy and reproducibility of the process using Mastermind - the world's most comprehensive literature-based Genomic Search Engine. He will guide the viewer through examples of Mastermind users, including the Rare Genomics Institute who were able to change their patients' diagnoses using findings that were only identified through Mastermind.

Learning Objectives: 

1. How to more efficiently identify and prioritize publications by ACMG variant classification guidelines.
2. How increased specificity and immediate access to annotated search results accelerates variant interpretation workflow.
3. How increased sensitivity in literature search results in fewer false negatives.
 

Recent improvements in sequencing chemistry and instrument performance combine to create a new PacBio data type of highly accurate (HiFi), long insert reads. Increased read length and improvement in library construction enables average read lengths of 10-20 kb with average sequence identity greater than 99% from raw single molecule reads. The resulting reads have the accuracy comparable to short read NGS but with 50-100 times longer read length. These highly accurate long reads allow for comprehensive variation detection from single nucleotide polymorphism to large structural variation with a single data type at 15- to 30-fold coverage. Using existing variation detection pipelines (e.g. GATK) to call variants and construct phase blocks, we achieve state of the art sensitivity and specificity for small nucleotide polymorphisms while preserving high sensitivity to detect larger structural variation (>50 bp) at single base resolution and delineate haplotype linkages. Additionally, the lack of sequence context bias and the unambiguous mappability of the longer HiFi reads allow a more complete survey of the human genome, expanding the detection of variants outside of the GIAB high confidence regions. We demonstrate the utility of this data type by sequencing to 15- to 30-fold coverage and calling all variants in the well-characterized HG002 genome. In addition to human resequencing analysis, HiFi reads may be used to assemble and call variants in plant or animal genomes, with assembly results rivaling the current long read sequencing approaches. The highly accurate raw data is directly compatible with many existing bioinformatics tools.

Learning Objectives: 

1. Understand the generation of HiFi sequence reads, and the uniqueness of this data type among sequencing technologies
2. Learn about the different use cases uniquely enabled by the new HiFi sequence data type

Next-generation transcriptome and miRNome sequencing are routinely performed on traditional cell or tissue samples, as well as more difficult samples such as FFPE samples and biofluids. Regardless of starting material, high-performance solutions are required for sample prep, library prep, sequencing and data analysis to unlock the secrets hidden in your samples – whether they are differentially-expressed genes/miRNAs, gene fusions or SNPs. Furthermore, it is often prudent to outsource the entire workflow to a service provider, to achieve actionable insights quickly. In this webinar, you will learn about best practices, considerations and tips for sample prep, library prep, sequencing and data analysis/interpretation. Also, we will discuss what to look for when selecting a sequencing core.

Whatever your interest in RNA sequencing, this webinar will be of great interest!  Join us for a Sample to Insight discussion of RNA sequencing with Dr. Jonathan Shaffer, Associate Director of NGS Assay Technologies.

Learning Objectives: 

1. Attendees will learn critical parameters associated with sample prep and sample QC for subsequent NGS
2. Attendees will understand the variety of NGS library prep and sequencing solutions available for transcriptome and miRNome NGS
3. Attendees will be exposed to the primary, secondary and tertiary NGS data analysis offerings
 

The last two decades have seen an explosion in the volume of oncology data generated using next-generation sequencing (NGS) and multi-omics techniques. As a result, there is a growing need for computational tools that are powerful enough to generate meaningful hypotheses about the biological mechanisms underlying cancer. In this presentation, we demonstrate how two of QIAGEN’s bioinformatics solutions – Ingenuity Pathway Analysis (IPA) software and the OmicSoft OncoLand database – can be combined to generate testable hypotheses in an immuno-oncology case study (GSE67501). This study sought to understand the mechanism underlying the failure of anti-PD-1 therapy in advanced renal cell carcinoma patients. The study data, along with tens of thousands of other public-domain datasets (including TCGA, SRA, GEO, GTEx and others), have been reprocessed using OmicSoft’s bioinformatics pipeline, and are readily available in OmicSoft’s OncoLand platform. Analysis of these data in OncoLand revealed that UGT1 family members and immune genes were differentially regulated in responder vs non-responder populations, similar to as described in the original publication. These data were then interpreted in IPA, which has millions of findings from peer-reviewed publications. Immunologic, cell cycle, metabolite transport and solute related pathways and functions were found to be enriched. Using IPA’s analytic and predictive capabilities, along with the public data curated from TCGA and GEO and other sources present in OncoLand, we demonstrate how these QIAGEN solutions can be used to generate hypotheses for mechanism of action, and to discover potential therapeutic targets and biomarkers.

Learning Objectives: 

1. Introduction to databases backing Ingenuity Pathway Analysis and Oncoland
2. Studying potential biomarkers and targets through Oncoland’s data mining and comparison tools
3. Generation of peer-reviewed literature backed hypotheses through Ingenuity Pathway Analysis
 

One of the central tenants of biology is that our genetics—our genotype—influences the physical characteristics we manifest—our phenotype. But with more than 25,000 human genes and more than 6,000,000 common genetic variants mapped in our genome, finding associations between our genotype and phenotype is an ongoing challenge. Indeed, genome-wide association studies have found thousands of small effect size genetic variants that are associated with phenotypic traits and disease. The simplest explanation is that these genetic variants work synergistically to help define phenotype and to regulate processes that are responsible for phenotypic state transitions. We will use gene expression and genetic data to explore gene regulatory networks, to study phenotypic state transitions, and to analyze the connections between genotype, gene expression, and phenotyope, and to explore how cancer-risk SNPs exert an influence on the disease risk that extends beyond one locus.

Learning Objectives: 

1. Genes and genetic variants work together in complex networks that are associated with individual phenotypes and change as phenotype evolves and changes.
2. Differences in network structure can help us to better understand the drivers of health and disease.
3. Networks and their structures can help us understand how small-effect genetic variants can work collectively to influence disease risk.
 

CRISPR/Cas gene editing has become the gold standard for individual gene perturbations as well as high-throughput functional screenings, with a rapidly increasing demand for high-quality CRISPR/Cas reagents requiring novel and innovative technologies to satisfy all user groups. The recently described 3Cs technology to generate bias-free gRNA and shRNA reagents addressees this issue and provides a robust and rapid, PCR- and cloning-free protocol for the generation of single and multiplexed gRNAs for functional interrogations. In my lecture, I will present selected single and multiplex gene editing applications to demonstrate the general applicability and power of 3Cs reagents.

Learning Objectives: 

1. What defines 3Cs gene editing reagents
2. What are the current forms of 3Cs reagents

Arrayed gene knockout (KO) libraries represent a valuable resource for performing functional genomics screening. Current generation arrayed KO libraries for the whole human genome rely on either single CRISPR sgRNAs to generate frameshift-causing indels or a mixture of several sgRNAs pooled together using only top ranked on-target predicted sgRNAs with no geographical consideration. However, there are considerable drawbacks to both of these approaches, since many indels do not cause frameshifts and current on-target prediction is unreliable. We designed a next generation library for generating gene KOs in an arrayed library format by multiplexing sgRNAs to localized, early exon regions of genes. We found that these multiplexed sgRNAs work cooperatively to generate larger sequence deletions thank indels at an efficient rate, allowing researchers to de-risk an arrayed approach to CRISPR library screening and assuring that each well within a library can produce a strong KO phenotype. 

Learning Objectives: 

1. Understand how optimizing sgRNA design and CRISPR protocols can yield the most effective methods for generating gene knockouts.
2. Learn how these methods have been adapted to generating next-generation whole human genome gene knockout libraries, that can be purposed for functional genomics screening. 

To interpret genome sequence from telomere to telomere, it is helpful  end-to-end haplotypes with single-molecule epigenetics overlays, via in situ omics at sub-cellular (20 nm) resolution and multicellular (connectome) context.  To test putative causative alleles and therapies, we need faithfully representative organoids and accurate editing of these (not merely NHEJ knockouts).   We also benefit from comprehensive precision medicine datasets on individuals broadly shareable by virtue of either "open consent" protocols (personalgenomes.org) or provably secure queries via homomorphic encryption and blockchain (Nebula.org).  This goals are aided by recent  reduction in cost of human and environmental DNA sequencing by over 10 million fold and new editing tools capable of over 26,000 edits per cell (including tools to read, alter and test changes in DNA repeats).

Learning Objectives: 

1. New technologies -- In situ & Nanopore sequencing are moving us toward portable, real-time, inexpensive (0-$999) & gap-less clinical-grade genomes.
2. Interpretation benefits from testing variants in organoids & large shareable cohorts – enabled by either open consent or secure via homomorphic encryption queries & blockchain public ledgers. 

Genetic drivers of cancer can be dysregulated through epigenetic modifications of DNA. Although the critical role of DNA 5-methylcytosine (5mC) in the regulation of transcription is recognized, the functions of other non-canonical DNA modifications remain obscure. We report the identification of novel DNA N6-methyladenine (N6-mA) modifications in human tissues and implicate this epigenetic mark in human disease, specifically the highly malignant brain cancer glioblastoma. Glioblastoma markedly upregulated N6-mA levels, which co-localized with heterochromatic histone modifications, predominantly H3K9me3. N6-mA levels were dynamically regulated by the DNA demethylase ALKBH1, depletion of which led to transcriptional silencing of oncogenic pathways through decreasing chromatin accessibility. Targeting the N6-mA regulator ALKBH1 in patient-derived human glioblastoma models inhibited tumor cell proliferation and extended the survival of tumor-bearing mice, supporting this novel DNA modification as a potential therapeutic target for glioblastoma. 

Furthermore, ALKBH1 controls the hypoxia responding genes in glioblastoma. Collectively, our results uncover a novel epigenetic node in cancer through the DNA modification N6-mA. The regulators of this new modification could serve as novel therapeutic targets in cancer therapy.

Learning Objectives: 

1. Get cutting-edge knowledge of epigenetics
2. Learning the application of 3rd generation SMRT sequencing in novel DNA methylation discovery