Millions of individuals have been sequenced or genotyped and linked with medical records, providing an exciting opportunity for therapeutic target discovery. My lab has been using a resilience approach to learn from success instead of failure, and analyzed hundreds of thousands of genomes with electronic medical records to drive target discovery for childhood Mendelian diseases, Alzheimer’s disease, and cardiovascular traits.

I will describe how we analyzed 589,305 genomes and identified 13 genetic super heroes who carry fully penetrant childhood Mendelian diseases but are healthy in our recent resilience project, and how we decoded individuals who carry APOE e4/e4 risk variants but are resilient for Alzheimer’s disease. I will further describe how we analyzed individuals who have a loss of gene but carry favorable cardiovascular traits, identified potential therapeutic targets to lower fasting glucose levels, and validated the effects in mice.

Learning objectives:

• How to interpret genetic variants using 150,000 genomes from diseased and healthy cohorts, and variants with validated molecular functional impact from 30 million literature.
• How to use resilient approach to interpret thousands of genomes with medical records for therapeutic target discovery.

Canon BioMedical was established last year to develop innovative technologies and solutions that empower biomedical research and healthcare communities. Our mission is broad and encompassing, but what are the results that can help you right now? In the past year, we have continuously launched novel genotyping assays based on Canon’s expertise with high-resolution melting (HRM). 

Are there DNA targets in your lab which are difficult to genotype using standard PCR or sequencing? HRM is an alternative solution that addresses focused genotyping needs. We have developed a library of common and unique targets for identifying single nucleotide polymorphisms (SNPs), small indels, and large deletions. Using any HRM-enabled thermocycler you can get conclusive results in a variety of research areas including cancer, neuroscience, and pharmacogenetics. 

Join us to discuss how our Novallele™ assays can answer your difficult genotyping questions.

Targeted sequencing assays are increasingly used to identify tumor mutations that guide therapeutic decisions. Interpretation of a cancer variant’s origin and therapeutic impact poses analytical challenges. Recent studies have indicated that jointly analyzing a tumor with its matched normal can accurately discriminate between somatic and inherited mutations. However, procurement of a matched sample is often logistically impractical. In the absence of a matched normal, large databases and analytical techniques are currently used to identify cancer variants in tumor sequencing data. Whether the benefits outweigh the additional burden of sequencing the matched normal for accurate detection of cancer-relevant mutations remains an open question. For NGS-based cancer interpretation to guide clinical decisions in a practical and cost-effective manner, highly optimized tumor-only and tumor/normal analyses must be available with proper attention to germline consent, classification and education.

Learning Objectives:

• Understanding the differences in pipelines, filtering, and use of large population databases necessary depending on the availability of a matched normal
• Consideration of the burden of secondary findings when sequencing a matched normal 
• Defining your “actionable” set of variants drastically affects analysis results

In order to realize the benefits of personalized cancer therapy, increasing demands are placed upon clinical laboratories to provide timely, comprehensive, clinically actionable, and analytically valid information regarding the genomic profile of individual patients’ tumor specimens. We have employed both next generation sequencing (NGS) technologies as well as DNA microarrays to comprehensively profile solid tumors for diagnostic, prognostic, and therapeutic indications. We have developed a custom NGS-based panel test to interrogate all exons for 397 genes, as well as most introns for a subset of 28 genes. The NGS panel detects several types of genomic alterations, including coding missense, nonsense, splice variants, insertions/deletions, focal gene amplifications, homozygous deletions, and selected rearrangements. Reportable variants are classified as either clinically significant with in-depth annotation regarding clinical relevance, relevant therapies and clinical trials, or variants of uncertain clinical significance with limited annotation. The DNA microarray complements the NGS assay by assessing genome-wide chromosome structural changes including gains, losses, focal gene amplifications, and copy-neutral events such as loss of heterozygosity (LOH). Direct involvement of clinical lab directors, pathologists, oncologists, and radiologists in our institution’s working tumor conferences via the presentation of in-house laboratory findings enhances both patient care and the educational environment via better informed discussion of unique patient specific findings.

Learning objectives:

• Understand technical advantages and limitations for next-generation sequencing and DNA microarrays in a clinical test setting.
• Understand how next-generation sequencing and DNA microarrays can be used to aid in diagnosis, prognosis and therapeutic decision-making in oncology.

Network models are an invaluable tool for integrating multiple data types and for modeling interactions between biological elements. One common question that arises, however, is what to do with such a network beyond making a pretty picture. In this talk, I will describe two applications in which we have used network structure to explain features of biological systems. In the first, we construct bipartite eQTL networks from genotype and gene expression data collected by the GTEx consortium. In the second, we explore the response of gene regulatory networks in Mycobacterium tuberculosis to a targeted drug treatment. In both cases, we find that the network structural properties reflect constraints operating on each biological system, and that each network has its own unique, informative feature set. For example, in the eQTL networks, we find the global hubs are not very informative but degree is very useful in interpreting the tuberculosis regulatory network. I will also discuss statistical testing and validation of these networks using functional annotation and wet lab experiments.

The extravagant expectations around emerging technologies often gives way to a sense of disillusionment when the lofty expectations are not fulfilled. For genomics and precision precision to escape this cycle, it will be essentially to demonstrate palpable utility, tangible impact. We'll be there when sequencing is embraced not because it’s interesting or edgy, but because it’s so obviously useful that once you’ve seen the benefits you can’t imagine not availing yourself of it.  Achieving impact will require data, collaboration, and champions.

Learning Objectives:

• Recognize the cycle in emerging technologies of extravagant expectation followed by disappointment and disillusionment
• Review examples where precision medicine is impacting patients today

Working with biology currently takes too long, costs too much, and fails too often. At the core of this is the complexity of the systems we are trying to understand, compounded by a lack of reproducibility in our daily lab practices and poor traceability of what was actually done to perform an experiment. Antha is a high level, open source programming language for unambiguously defining lab protocols, which can then be combined into sophisticated workflows for biological investigations.  Antha operating system (AnthaOS) takes these workflows and compiles them into the computer code required to directly run the lab automation and analytical equipment needed for each experiment.  AnthaOS tracks the flow of information and physical samples through the workflows, so that every piece of data generated is linked to full provenance of the experimental procedure that generated it.  Antha relies on a range of drivers to talk to different lab equipment, meaning that it is interoperable between different makes and models of lab hardware.

By linking diverse lab equipment and defined protocols in this way, Antha enables the rapid and flexible programming of lab automation, so large and sophisticated experiments can be easily designed, run and analysed.  These include high dimensional experimental designs, which are an exceptionally powerful way of addressing biological complexity and enabling the development of efficient and robust processes.

This talk will present case studies on how high dimensional experimental designs can unpick high order interactions between genetics and environment in biological systems, and how these methods can also be used to optimize lab protocols.

Learning objectives:

• An overview of the power of using high dimensional experimental designs for characterization and optimization of biological processes and protocols.
• How high level languages combined with lab automation can enable these complex experiments that unpick biological complexity.

The biological state of the cell is characterized by a complex network of interacting genes, gene products, proteins, microRNAs, as well as other molecules. Microarrays and next generation sequencing technologies have been widely applied to study alterations of these molecules in complex diseases such as cancer. However, most of the standard, widely-used methods for bioinformatics analysis treat these various sources of information independently, looking for overlaps in feature sets rather than directly modeling their interactions.
It has become clear that, to understand what drives (complex) disease, we need to integrate multiple types of ‘omics data in a natural way that allows us to gain insight into the molecular interactions that occur in disease development and progression. Gene regulatory network reconstruction algorithms infer such interactions by drawing on large numbers of measured expression samples to estimate an “aggregate” network model, which represents single estimates for the likelihood of molecular interactions. While informative, aggregate models fail to capture the heterogeneity represented in a disease population. In this presentation, I will introduce a computational framework for single-sample network reconstruction that allows us to “extract” individual patient networks from aggregate networks. I will demonstrate the strengths of this method in multiple big 'omics datasets, and will highlight newly identified gene regulatory interactions that play a role in cancer survival.

Learning objectives:

• Participants will learn how to integrate big 'omics data using gene regulatory networks
• Participants will learn how to reconstruct and analyze patient-specific gene regulatory networks

DNA methylation is an essential mechanism of epigenetic gene regulation with broad relevance in development and disease. Its localization on genomic DNA and general stability make this epigenetic mark an attractive target for large-scale studies in cancer research, developmental biology and ecology. In recent years, microarray-based technologies have been gradually replaced by more robust, accurate and versatile next generation sequencing-based methods. 
    
This presentation will introduce highly-multiplexed reduced representation bisulfite sequencing as a cost-effective means for large DNA methylation studies. The protocol provides a single-base resolution read-out of 5-methylcytosine, while avoiding the cost of whole-genome sequencing. Based on an enzymatic enrichment step, the method is cost-effective, but provides excellent coverage of promoter regions, CpG islands, and other genomic elements such as enhancers and CpG island shores. At the same time, the protocol supports high-throughput applications, is suitable for any vertebrate species and has been optimized specifically for formalin-fixed, paraffin-embedded samples.

In our lab, we have successfully used RRBS on over 2000 samples comprising many different vertebrate species, various cancers, FFPE and low-input samples, and are happy to share our experience with the epigenomics community.

Learning objectives:

• the talk will introduce highly multiplexed reduced representation bisulfite sequencing as a cost-effective means for large DNA methylation studies
• get an overview of the required bioinformatic workflow, for an easy transition from microarrays to next generation sequencing-based analyses

As millions of people all over the world get their genome sequenced, physicians and researchers as well as the individuals themselves will want to ask questions of these data. To ask questions at a planet scale, however, we must re-imagine the computational and storage infrastructure required. We also need a consistent naming scheme for parts of the genome. To address this we invented tiling – a technique that divides the genome into about 10 million overlapping, variable-length sequences, or “tiles”, each with a unique 24-base tag at each end. We use examples from public data to show tiling supports simple and consistent names, annotation, queries, machine learning,
and clinical screening.    Someday soon the general public may get to
know the tiles in their own genomes, while researchers and doctors may use the information to realize precision medicine.

• Learn about free and open source software (FOSS), open-standards and open-data that can help you build necessary informatics infrastructure
• Use Tiling to facilitate planet scale analyses of genomic data

With the ever increasing value of circulating cell-free and FFPE applications in the genomics industry today, we will present sample prep and collection guidelines to ensure that the quantity and quality of the input DNA for downstream assays involving PCR and NGS, is maximized. The quality of this input is further maximized by the use of chemagen technology, which is designed for scalability in volume and throughput, such as that required for cell-free applications. We will also explore collaborations with Swift Biosciences and Covaris for our key applications in addition to the PerkinElmer workflows available to address your needs.

     Wolframin is a major protein of the endoplasmic reticulum, it is expressed in most tissues and clinical data demonstrate its significant connection to diabetes mellitus. Loss of function mutations of the WFS1 gene result in the monogenic Wolfram-syndrome, characterized by optic atrophy, diabetes insipidus, early onset diabetes mellitus and deafness. Accordingly, polymorphic variants of the gene, which cause only minor alterations in protein function, are putative risk factors of diabetes. This presentation focuses on the association and molecular analysis of two SNPs (rs1046322 and rs9457) in the 3’ UTR region of the WFS1 gene, which are supposed to alter the binding of miRNA-668 and miRNA-185, respectively, based on in-silico data.

     Association analysis of the polymorphic loci and diabetes mellitus was carried out on 617 patients and 1147 health controls in the study. Genotype analysis was carried out using PCR and single base extension (SBE) reaction using the GenomeLab GeXP™ Genetic Analyzer. Functional analysis of miRNA binding was investigated by luciferase reporter system.  The results suggested that rs9457 SNP “C” allele was significantly more frequent among patients with type 2 diabetes mellitus (p = 0.0008), whereas the rs1046322 variant showed a significant association with the type 1 form of the disease. Haplotype analysis confirmed the roles of the polymorphic variants in the genetic background of diabetes. In addition, the luciferase reporter experiments confirmed the data of the sequence analysis in where the rs1046322 and the rs9457 SNPs altered the binding of miRNA-668 and miRNA-185, respectively. Earlier studies indicated an association between a third variant of the 3’ UTR (rs1046320) and diabetes; however no biological function of the SNP could be observed. This is probably due to the strong linkage disequilibrium between rs9457 and rs1046322, thus the latter polymorphism could be a potential genetic marker of rs9457 miRNA-SNP.

Circulating tumor cells (CTCs) is an emerging source used molecular cancer diagnostics. Through expression profiling of CTCs, it allows a deeper understanding about which metabolic pathways enable tumor cells to survive in the circulation, how they become resistant to a drug regimen, how they transform and adapt and, finally, which cellular markers should targeted for future therapies. 
This webinar will introduce the AdnaTest CTC detection platform which has been proven in several clinical trials to provide prognostic and predictive information in breast, ovarian and prostate cancer. The platform by itself is still open for research and allows access to any potential target of interest. Join us to learn more about this novel platform, its technology and applications in liquid biopsy.

Technological advances allow for the large scale sequencing of the whole human genome. Most studies have generated population-based information on human diversity using low to intermediate coverage of the genome (4x to 20x sequencing depth). The recent release of the Illumina HiseqX-Ten allows the sequencing of up to 160 genomes at 30x mean depth in 3 day cycles, at an average cost of $1,000 to $2,000 per genome. We evaluated the capabilities of this new technology by sequencing 10,545 human genomes at high depth. This allowed for the development of a reliable representation of human single nucleotide variation, the reporting of clinically relevant single nucleotide variants and the identification of additional non-reference and of putative human-like sequences.

Learning objectives
1.    Evaluating the impact of deep sequencing for the reporting on single genomes
2.    Understanding the pace of discovery afforded by large scale sequencing of human genomes

It is estimated that by the end of this year more than 1 million people worldwide will have had their genome sequenced. Genomic sequencing is increasingly being integrated into clinical care, in both adult and pediatric populations. In this talk I will discuss two major ethical and policy issues related to the clinical integration of genomics: (1) the psychosocial risks of genomic testing, and (2) the need for robust databases to ensure accurate and useful variant interpretation.

Learning Objectives:

By the end of the session, participants will be able to:
1.    Identify ethical and policy issues related to the clinical integration of genomics.
2.    Discuss the importance of data sharing for genomic medicine.

Human breast tumors have been shown to exhibit extensive inter- and intra-tumor heterogeneity. While recent advances in genomic technologies have allowed us to deconvolute this heterogeneity, few studies have addressed the functional consequences of diversity within tumor populations. We performed single cell RNA and exome sequencing of treatment resistant breast tumor derived xenografts (PDX) to identify population structure. Genes differentially expressed between these subpopulations are involved in proliferation and differentiation. Microfluidic whole genome amplification followed by whole exome capture of single cells, identified driver mutations as well as a number of sub-clonal mutations that are being investigated further. Loss of heterozygocity was observed in 16 TCGA cancer driver genes and novel mutations in 7 known cancer driver genes. Careful comparison of the exome sequencing data allowed the association of driver gene mutation prevalence with tumor progression. These findings are important in our understanding the functional consequences of intra-tumor heterogeneity with respect to clinically important phenotypes such as invasion, metastasis and drug-resistance.

Learning Objectives:

• Develop an understanding of the technology behind single cell genomics 
• Understand the data analysis and quality control processes for single cell data
• Understand single cell expression profiling approaches using high throughput quantitative PCR
• Understand the relevance of single cell genomics in the development of personalized medicine approaches

The rapid implementation of next generation sequencing is changing how genetics and genomics are applied in a clinical setting. However, the quickly evolving technology can be challenging to implement. The Center for Pediatric Genomic Medicine at Children's Mercy Hospital has been using NGS in both a research and clinical setting since its inception. Currently the center offers targeted panels, symptom driven exome, and whole genome sequencing clinically. This presentation will outline applications of next generation sequencing in a pediatric hospital. Real case examples will be used to highlight best use practices. The limitations and benefits of NGS in the clinic will be discussed. Additionally, the intersection of research and clinical testing, and how it is particularly applicable in genomic medicine will be discussed.

At the end of this presentation participants will:

• Understand different applications of targeted, whole exome, and whole genome testing.
• Recognize how NGS is impacting clinical care. 

The clinical utility of high-dimensional molecular profiling has been provisionally established in several domains of medicine.  Today, interrogation of scores of genes and even whole exomes and genomes is an increasingly common practice that is informing diagnostic, theranostic, reproductive and other decisions by patients and physicians. Progress has been substantial, but vast potential remains untapped.
 
As seen in biological research, genomic profiling technologies will proliferate in the clinic, providing insight across a myriad of molecular actors, processes, tissues and time points.  Indeed laboratory medicine is evolving from a bench science to an information science.
 
One key objective of this presentation is to identify common challenges and roadblocks faced in deploying precision medicine programs at scale: Integrating the numerous complex and fragmented data sources necessary to inform clinical reporting; structuring biomarker and other association details such that they can be appropriately matched to observed events; and meeting the performance requirements necessary to support highly interconnected operational clinical systems necessary at high volumes. Here we will present specific technical solutions that can help move these programs from pilot to full production deployment.
 
Beyond the immediate clinical utility, a remarkable aspect of precision medicine is the ability to apply aggregated data sets for a whole host of applications across the spectrum of research, development and clinical care.
 
The second objective will be to explore examples of these myriad additional uses, and to discuss how a new breed of technical capabilities can provide the data agility necessary to maximize the value of these data assets.
 
Finally, we will explore why molecular medicine is an ideal proving ground for the data agility the entire healthcare system so urgently needs.

DNASTAR offers an integrated suite of software for accurate assembly and analysis of sequence data from all major next-generation sequencing platforms supporting key workflows including de novo transcriptome and RNA-Seq analysis.  Using DNASTAR’s new patented de novo assembly algorithm, large transcriptome data sets, containing hundreds of millions of reads can be assembled using a desktop computer producing full length, annotated mRNA sequences.  Downstream analysis includes review and editing of assembled transcripts, and RNA-Seq expression analysis of both annotated and novel transcripts.

Technological advances have made genomic sequencing more affordable, efficient, and available. Questions related to the implementation and effects of large-scale sequencing in healthcare have been approached in a largely theoretical manner.  To address this knowledge gap, we describe our experience and preliminary results after implementation of several longitudinal genome-based studies.

We are approximately five years post-initiation of studies; the initial goal for the main study is 5,000 trio-based families.  This study focuses on trio-based whole-genome sequencing (WGS) and related ‘omics.  We enroll in the 2nd trimester via 5 obstetric clinics.  We perform WGS from samples taken concurrently with routine newborn screening (infant) and as part of prenatal care (parents).  We follow participants longitudinally to investigate the causes of a variety of conditions and to determine best practices for prospective genomic medicine.  All data and analysis are cloud-based, allowing storage and management of petabytes of data while maximizing security.  We gather data into a unified system by collecting standard HER information, and combine these data with study-specific data.  To address study needs and provide services for our healthcare system, we have built a dedicated clinical genomics unit.  In addition to our large study, we have smaller studies that focus on unearthing Mendelian explanations.

Over 3000 participating parent-infant trios have been enrolled to date (~60 new families/month). The participation rate of eligible, approached families is ~35%.   Our study-specific survey return rate is ~84% per month.  Our cohort is extremely diverse, which has enabled us to construct population-specific algorithms for the filtering of variants.  We describe a number of Mendelian and non-Mendelian case studies to illustrate our findings.

Learning Objectives:

• Understand the resources required to initiate longitudinal genomic studies within an active healthcare system.
• Describe examples of Mendelian genomic findings through a disease-focused study.
• Describe examples of analyses focused on complex diseases as well as more basic genomic hypotheses.

Emerging technologies facilitate basic science research, but their value in regulatory applications requires rigorous assessment and consensus within the research community. Next-generation sequencing (NGS) represents an advanced genomic approach with a far-reaching impact in drug development, personalized medicine, food safety and beyond. Rapid progress in NGS over the last few years has demonstrated its translation from an exploratory to an applied stage. As a result, the FDA has received an increasing amount of NGS data supporting the evidence for the safety and efficacy of new medical products and the agency is now facing challenges with respect to developing standards for receiving data and ensuring reproducible results. This presentation will discuss the FDA-led community wide consortium project, known as the SEquencing Quality Control (SEQC) project, which developed a comprehensive plan to assess the power and limitations of RNA-seq. The project involved >180 participants from ~80 organizations and generated large RNA-seq data sets covering a broad range of biological samples (human, rat, and reference samples) with which many critical issues of applying RNA-Seq in clinical application and safety assessment were evaluated and discussed. The presentation will provide an overview and main conclusions of the SEQC project. In addition, the new SEQC project (SEQC2) which is in the planning stage will be also explained. SEQC2 is focused on whole-genome sequencing and target gene sequencing.

Learner objectives:

• Learn about the FDA’s Voluntary eXploratory Data Submission (VXDS) program and its impact to the FDA guidance to industry for genomic data submission
• Learn about the FDA led community wide MicroArray Quality Control (MAQC) consortium to develop standards and bioinformatics solutions for microarrays and next-generation sequencing 

We are at a time where traditionally risk-adverse health care systems and their IT architecture are confronted with the deluge of data at a massive scale and tremendous variability. In many Health Systems, genomic and molecular datasets are already being generated and the amount of data is escalating quickly. In parallel, the widespread adoption of Electronic Health Records generates large amounts of data that adds a complicated layer of variability that is difficult to standardize. Lastly, there is a high-level of privacy and security needed that protect patient data as well as adhere to IT best practice guidelines. To help with this daunting challenge, the last 5 years has brought an onslaught of highly scalable and agile technologies built specifically for these large and diverse set of challenges, though not necessarily with health care in mind. This presentation will highlight the challenges faced in building and supporting petabyte scale molecular data, combined with EHR data within the constraints of a health system, both in research and clinical settings. In particular, it will outline the IT and informatics challenges of scale, standardization, security, staff and data sharing. As narrative, the presentation will give tangible examples of the work being done at the Inova Translational Medicine Institute over the past 5 years and lessons learned. The presentation will also discuss the convergence of these new technologies such as Cloud, High Performance Compute (HPC) and Massive Parallel Processing (MPP) systems built within a Hybrid Cloud and other next generation scalable IT architectures.

Learner Objectives:

• Learn about the challenges of building and support large genomic and clinical data projects within a health system.
• Learn about emerging IT and Informatics technologies that can be implemented to help address challenges in IT and informatics architectures.

Imaging-based techniques have traditionally been restricted to the diagnosis and staging of cancer. But technological advances are moving imaging modalities into the heart of patient care. Imaging can address a critical barrier in precision medicine as solid tumors can be spatial and temporal heterogeneous, and the standard approach to tumor sampling, often invasive needle biopsy, is unable to fully capture the spatial state of the tumor. Image-based phenotyping, which represents quantification of tumor phenotype on medical imaging, is a promising development for precision medicine. Medical imaging can provide a comprehensive macroscopic picture of tumor phenotype and its environment, ideally suited to quantifying the development of tumor phenotype before, during, and after treatment. As a noninvasive technology, medical imaging can be performed at low risk and inconvenience to the patient. Radiomics can quantify this phenotype using advanced data characterization algorithms that can be used to develop biomarkers which complement those derived from biopsies. The ultimate goal of radiomics is to improve precision medicine strategies by allowing clinicians to monitor disease in real time as patients move through treatment. In this talk, Dr. Aerts will discuss recent developments from his group and collaborators performing research at the intersection of radiology and bioinformatics. Also, he will discuss recent work of building a computational image analysis system to extract a rich radiomics set and use these features to build prognostic radiomics signatures. The presentation will conclude with a discussion of future work on building integrative systems incorporating both molecular and phenotypic data to improve cancer therapies.

Learning objectives:

• Learn about the motivation and methodology for Computational Imaging & Radiomics.
• Learn about the existing and future potential role of radiomics with other –omics data and within precision medicine.

New approaches to generate high-quality representations of human genomes and transcriptomes are now becoming available. In contrast to short-read “Whole-Genome” re-sequencing methods, these de novo genome assemblies capture genomes more completely, and provide resolution of genetic variation of a much larger size and type range, including structural variants and phased allelic variation. Similarly in RNA-seq, full-length mRNA sequencing directly allows for more comprehensive views of transcript isoforms, which are commonly very difficult to reconstruct from splice-junction spanning short reads.

Learning Objectives:

• recognize the limitations associated with short-read approaches for high-quality de novo genome assemblies and transcriptome architectures
• learn about new, long-read sequencing approaches for more complete, reference-free genomes and transcriptomes

In this time of incredible promise for genomic medicine, the Genomes2People (G2P) Research Program in Translational Genomics and Health Outcomes, directed by Robert C. Green, MD, MPH, is creating a deep body of knowledge on the medical, behavioral and economic implications of genomic analysis. Through its research, G2P is helping to set the stage for the use of genomic information in promoting health, preventing or mitigating disease, and reducing healthcare costs.

Dr. Green will summarize his research program that for 15 years has focused on the collection of clinical outcomes data using multiple modalities, including randomized controlled trials (RCT), surveys, demonstration projects and other methodologies. Dr. Green will summarize key research studies and results that comprise the G2P Research Program. He will discuss the history of returning predictive genetic risk information, beginning with the REVEAL Study of returning APOE-related risks, and will present an overview of the projects that have rigorously tracked medical, behavioral and economic outcomes associated with evolving applications of genetic and genomic testing. These include: the PGen Study of consumer genetic testing; the MedSeq Project, a RCT exploring the integration of genome sequencing (GS) into the clinical care of healthy adults and adults with cardiomyopathy; the BabySeq Project, a RCT investigating the use of GS in screening healthy and sick infants for childhood risks and newborn illness; and the PeopleSeq Consortium, a longitudinal survey of ostensibly healthy cohorts that have received GS results. He will end by previewing upcoming initiatives and collaborations that are poised to inform the future implementation of genomic medicine, particularly in understudied populations and contexts with unique ELSI challenges.

Advances in DNA sequencing technology are about to transform healthcare.    Since completion of the human genome reference sequence ten years ago, there has been a 1-million fold improvement in the rate of sequence data generation (bases/day/instrument).  As a result, it is now possible to decode a person’s genome and return the annotated sequence for biological and clinical interpretation in a few days.  Current research is focussed on refining methods for sequencing real-world clinical samples, benchmarking accuracy, refining variant calling, improving annotation and reporting, and in developing process workflows to enable whole genome sequencing at a national scale.  Early applications of clinical whole genome sequencing include detection of mutations in patients suffering rare genetic disease or cancer. Aggregation of individual genome sequences alongside clinical and other phenotypic information will create an integrated knowledgebase that underpins a new era of genomic medicine.

Library preparation for Nanopore sequencing is a very flexible process. There are many ways in which a sequencing template, or other analyte, can be presented to a nanopore for analysis. PCR may or may not be performed, shearing or size selection is not necessary but can be helpful in specific circumstances. You can sequence both strands in a duplex, or just one ... and what’s more, the platform is not limited to just sequencing DNA, but also RNA, and has been used to sense proteins and a wide variety of small molecules. The versatility of the system gives users access to a wide variety of library preparation options, depending on the needs of their experiments: ultra-long reads (>50 kb), low inputs (<50 ng), rapid and simple preps (10 minutes). In this presentation, I discuss the myriad options that are available and enabled with Nanopore sequencing.

It has been estimated that every human being carries ~20 rare “natural human gene knockouts”-DNA variants in protein-coding regions of the genome that partially or completely inactivate gene products. When coupled with information about clinical phenotypes, these natural human gene knockouts can illuminate biological function of geness and further our understanding of diseass. In select cases, “protective” gene-inactivating mutations may guide the way to new therapeutic targets or confirm existing targets. With the advent of next generation sequencing facilitating genomics on massive scales, properly powered studies of human knockouts are finally possible. Here, we describe an integrated approach to sequencing-based discovery of naturally occurring human knockouts that spans genetic trait architectures, from small collections of highly related individuals with extreme phenotypic traits, to geographically- and reproductively-isolated “founder” populations harboring frequent instances of highly impactful alleles, to large scale sampling of outbred populations with rich “real-world” phenotypic data captured in electronic medical records. In collaboration with the Geisinger Health System and academic groups worldwide, we report early findings from whole exome sequencing of over 100,000 individuals spanning these population architectures and study designs. We find that the majority of genes encoding drug targets harbor mutations that are predicted to partially or completely inactivate their gene products. We highlight examples of protective and harmful clinical associations with inactivating mutations in these genes that support and invalidate therapeutic targets. We also describe exome-wide scans for gene-inactivating mutations that nominate novel candidate genes and targets in multiple clinical traits and diseases. These early insights suggest that large-scale discovery of human gene knockouts will contribute to the next wave of drug target discovery and validation.