Personal Genome Sequencing Research Articles

Statistical model for assessing the predictive capacity of personal genome sequencing

Statistical model for assessing the predictive capacity of personal genome sequencing

Exome and whole-genome sequencing for gene discovery: The future is now!

Exome and whole-genome sequencing for gene discovery: The future is now!

A Case for Pharmacogenomics in Management of Cardiac Arrhythmias

Disorders of the cardiac rhythm are quite prevalent in clinical practice. Though the variability in drug response between individuals has been extensively studied, this information has not been widely used in clinical practice. Rapid advances in the field of pharmacogenomics have provided us with crucial insights on inter-individual genetic variability and its impact on drug metabolism and action. Technologies for faster and cheaper genetic testing and even personal genome sequencing would enable clinicians to optimize prescription based on the genetic makeup of the individual, which would open up new avenues in the area of personalized medicine. We have systematically looked at literature evidence on pharmacogenomics markers for anti-arrhythmic agents from the OpenPGx consortium collection and reason the applicability of genetics in the management of arrhythmia. We also discuss potential issues that need to be resolved before personalized pharmacogenomics becomes a reality in regular clinical practice.

Catching Education Up with Technology: Preparing the Public to Make Informed Choices about Personal Genetics

Dear Editor: As we approach an era in which personal genome sequencing will be inexpensive and rapid enough for routine use in the clinic, genetic information will play an increasingly prominent role in healthcare. Therefore, the fields of personalized medicine and pharmacogenomics seek to utilize personal genetic information to develop individualized diagnostic and therapeutic strategies. For their potentially transformative effects to be fully realized, a public that is informed about genetics is essential. In their recent article, Dougherty and colleagues (2011) highlight this critical educational need. They demonstrate that statewide standards for genetics education at the high school level are largely inadequate, particularly for concepts related to complex traits, which are fundamental for comprehending the potential and the intricacies of interpreting personal genetic information. This report is alarming, as it is critical that upcoming generations know about the promise of personalized medicine, our current ability to interpret genome sequences, and the limitations of what this information can predict about future health. To complement the improvements in genetics education recommended by Dougherty and colleagues, our society must also be made aware of the complex ethical, legal, and social implications (ELSI) of the choices we will face. Therefore, the most effective preparation of our society for the benefits and challenges of modern genetics should include a comprehensive program addressing the ELSI of personal genetics. To make informed decisions about obtaining and sharing personal genetic information, individuals will also need to consider many complex questions, such as: What do you want to learn from your genome sequence? Predictions about your health? Predictions about your behavior? Would you ask your family's permission before having your genome analyzed, since it may potentially reveal information about the genetic makeup of your relatives? Would a person's genetic predisposition to a disease affect your decision to date that individual? How private is your genetic information? How are you protected against genetic discrimination? As the cost of personal genome sequencing rapidly declines, we must act quickly to integrate these and other important topics into the curriculum. At the Personal Genetics Education Project (pgEd), we suggest that educating high school students offers a cost-effective and comprehensive strategy to broadly reach a generation of young adults who will become independent healthcare consumers just as genome sequencing becomes a low-cost, widely available option. Issues in personal genetics involve multiple fields, including genetics, ethics, sociology, policy, and health. Determining what students should learn will require interdisciplinary dialogue and will yield opportunities to include units on personal genetics in multiple classrooms, including those of biology, health, social studies, psychology, and law. A program that incorporates ELSI discussions across the curriculum is likely to have the broadest impact, reaching students regardless of their inclination toward science. Promoting a coordinated national effort to incorporate personal genetics education into high school curricula is precisely our mission at pgEd. Our curriculum is freely available on our website (www.pgEd.org) to help teachers start discussions about personal genetics in their classrooms. We envision that efforts such as these will help to cultivate a community of informed individuals prepared to face highly personal choices about their genetic information and therefore optimally poised to benefit from this new era in science and medicine.

Human Genome Sequencing in Health and Disease

Following the "finished," euchromatic, haploid human reference genome sequence, the rapid development of novel, faster, and cheaper sequencing technologies is making possible the era of personalized human genomics. Personal diploid human genome sequences have been generated, and each has contributed to our better understanding of variation in the human genome. We have consequently begun to appreciate the vastness of individual genetic variation from single nucleotide to structural variants. Translation of genome-scale variation into medically useful information is, however, in its infancy. This review summarizes the initial steps undertaken in clinical implementation of personal genome information, and describes the application of whole-genome and exome sequencing to identify the cause of genetic diseases and to suggest adjuvant therapies. Better analysis tools and a deeper understanding of the biology of our genome are necessary in order to decipher, interpret, and optimize clinical utility of what the variation in the human genome can teach us. Personal genome sequencing may eventually become an instrument of common medical practice, providing information that assists in the formulation of a differential diagnosis. We outline herein some of the remaining challenges.

Detecting Rare Variants for Quantitative Traits Using Nuclear Families

With the advent of sequencing technology opening up a new era of personal genome sequencing, huge amounts of rare variant data have suddenly become available to researchers seeking genetic variants related to human complex disorders. There is an urgent need for the development of novel statistical methods to analyze rare variants in a statistically powerful manner. While a number of statistical tests have already been developed to analyze collapsed rare variants identified by association tests in case-control studies, to date, only two FBAT tests-for-rare (described in the updated FBAT version v2.0.4) have applied collapsing methods analogously in family-based designs. For further research in this area, this study aims to introduce three new beta-determined weight tests for detecting rare variants for quantitative traits in nuclear families. In addition to evaluating the performance of these new methods, it also evaluates that of the two FBAT tests-for-rare, using extensive simulations of situations with and without linkage disequilibrium. Results from these simulations suggest that the four tests using beta-determined weights outperform the two collapsing methods used in FBAT (-v0 and -v1). In addition, both the linear combination method (detailed in the FBAT menu v2.0.4) and the multiple regression method (mixing LASSO and Ridge penalties) performed better than the other two beta-determined weight tests we proposed. Following testing and evaluation, we submitted four new beta-determined weight methods of statistical analysis in a computer program to the Comprehensive R Archive Network (CRAN) for general use.

Mapping personal functional data to personal genomes

The sequencing of personal genomes enabled analysis of variation in transcription factor (TF) binding, chromatin structure and gene expression and indicated how they contribute to phenotypic variation. It is hypothesized that using the reference genome for mapping ChIP-seq or RNA-seq reads may introduce errors, especially at polymorphic genomic regions. We developed a Personal Genome Editor (perEditor) that changes the reference human genome (NCBI36/hg18) into an individual genome, taking into account single nucleotide polymorphisms (SNPs), insertions and deletions, copy number variation, and chromosomal rearrangements. perEditor outputs two alleles (maternal, paternal) of the individual genome that is ready for mapping ChIP-seq and RNA-seq reads, and enabling the analyses of allele specific binding, chromatin structure and gene expression. perEditor is available at http://biocomp.bioen.uiuc.edu/perEditor. szhong@illinois.edu.

Will Gene Patents Impede Whole Genome Sequencing?: Deconstructing the Myth that Twenty Percent of the Human Genome is Patented

A 2005 Science article by Jensen and Murray is widely cited for the proposition that 20% of human genes are patented, and has led to a pervasive assumption that thousands of human genes cannot be used, studied or even 'looked at' by researchers and healthcare providers without infringing a gene patent. Many have voiced concern that this perceived thicket of gene patents will impede the implementation of next-generation genetic technologies, particularly personal whole genome sequencing (WGS). In fact, Jensen and Murray only showed that, with respect to 20% of human genes known at the time they conducted their study, either (1) the DNA sequence of the gene, or (2) the amino acid sequence encoded by the gene, was mentioned in a US patent claim. The myth that 20% of human genes are “patented” has taken root because too many have incorrectly inferred that the mere “mention” of a gene in a patent claim precludes all uses of the gene. To better understand the actual implications of Jensen and Murray’s findings, I analyzed the claims from a random sampling of 533 of the 4270 patents identified in their article as “gene patents.” I found that, under any reasonable interpretation, 140 of the 533 patents would not be infringed by any form of genetic testing. The remaining 393 patents include claims with respect to which I cannot rule out the possibility that at least some form of genetic testing would be found infringing. These claims fall into two categories - products claims directed to polynucleotides (e.g., DNA molecules), and method claims. The language used in these claims is extremely heterogeneous, and it is impossible to predict with any certainty exactly how broadly a court would interpret their scope if they were ever asserted in litigation, but to varying degrees a majority of these patents would appear not to be infringed by at least some, perhaps all, forms of genetic testing. In particular, few (if any) of these patents would appear likely to be infringed by some next-generation WGS technologies, particularly those that do not require DNA amplification. In short, there is absolutely no basis to infer from the Jensen & Murray article that personal WGS, and other multiplex genetic diagnostic testing technologies, would result in the infringement of a large number of human gene patents. To the contrary, it appears that a vast majority of these patents were drafted in a manner that would not encompass WGS.

Accurate and comprehensive sequencing of personal genomes

As whole-genome sequencing becomes commoditized and we begin to sequence and analyze personal genomes for clinical and diagnostic purposes, it is necessary to understand what constitutes a complete sequencing experiment for determining genotypes and detecting single-nucleotide variants. Here, we show that the current recommendation of ∼30× coverage is not adequate to produce genotype calls across a large fraction of the genome with acceptably low error rates. Our results are based on analyses of a clinical sample sequenced on two related Illumina platforms, GAII(x) and HiSeq 2000, to a very high depth (126×). We used these data to establish genotype-calling filters that dramatically increase accuracy. We also empirically determined how the callable portion of the genome varies as a function of the amount of sequence data used. These results help provide a "sequencing guide" for future whole-genome sequencing decisions and metrics by which coverage statistics should be reported.

A probabilistic disease-gene finder for personal genomes

VAAST (the Variant Annotation, Analysis & Search Tool) is a probabilistic search tool for identifying damaged genes and their disease-causing variants in personal genome sequences. VAAST builds on existing amino acid substitution (AAS) and aggregative approaches to variant prioritization, combining elements of both into a single unified likelihood framework that allows users to identify damaged genes and deleterious variants with greater accuracy, and in an easy-to-use fashion. VAAST can score both coding and noncoding variants, evaluating the cumulative impact of both types of variants simultaneously. VAAST can identify rare variants causing rare genetic diseases, and it can also use both rare and common variants to identify genes responsible for common diseases. VAAST thus has a much greater scope of use than any existing methodology. Here we demonstrate its ability to identify damaged genes using small cohorts (n = 3) of unrelated individuals, wherein no two share the same deleterious variants, and for common, multigenic diseases using as few as 150 cases.

Translocation of biomolecules through solid‐state nanopores: Theory meets experiments

AbstractThe interest in polynucleotide translocation through nanopores has moved from purely biological to the need of realizing nanobiotechnological applications related to personalized genome sequencing. Polynucleotide translocation is a process in which biomolecules, like DNA or RNA, are electrophoretically driven through a narrow pore and their passage can be monitored by the change in the ionic current through the pore. Such a translocation process, which will be described here offers a very promising technology aiming at ultra‐fast low‐cost sequencing of DNA, though its realization is still confronted with challenges and drawbacks. In this review, we present the main aspects involved in the polynucleotide translocation through solid‐state nanopores by discussing the most relevant experimental, theoretical, and computational approaches and the way these can supplement each other. The discussion will expose the goals that have been reached so far, the open questions, and contains an outlook to the future of nanopore sequencing. © 2011 Wiley Periodicals, Inc. J Polym Sci Part B: Polym Phys 49: 985–1011, 2011

Genomic medicine and neurological disease.

"Genomic medicine" refers to the diagnosis, optimized management, and treatment of disease--as well as screening, counseling, and disease gene identification--in the context of information provided by an individual patient's personal genome. Genomic medicine, to some extent synonymous with "personalized medicine," has been made possible by recent advances in genome technologies. Genomic medicine represents a new approach to health care and disease management that attempts to optimize the care of a patient based upon information gleaned from his or her personal genome sequence. In this review, we describe recent progress in genomic medicine as it relates to neurological disease. Many neurological disorders either segregate as Mendelian phenotypes or occur sporadically in association with a new mutation in a single gene. Heritability also contributes to other neurological conditions that appear to exhibit more complex genetics. In addition to discussing current knowledge in this field, we offer suggestions for maximizing the utility of genomic information in clinical practice as the field of genomic medicine unfolds.

CNVnator: An approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing

Copy number variation (CNV) in the genome is a complex phenomenon, and not completely understood. We have developed a method, CNVnator, for CNV discovery and genotyping from read-depth (RD) analysis of personal genome sequencing. Our method is based on combining the established mean-shift approach with additional refinements (multiple-bandwidth partitioning and GC correction) to broaden the range of discovered CNVs. We calibrated CNVnator using the extensive validation performed by the 1000 Genomes Project. Because of this, we could use CNVnator for CNV discovery and genotyping in a population and characterization of atypical CNVs, such as de novo and multi-allelic events. Overall, for CNVs accessible by RD, CNVnator has high sensitivity (86%-96%), low false-discovery rate (3%-20%), high genotyping accuracy (93%-95%), and high resolution in breakpoint discovery (<200 bp in 90% of cases with high sequencing coverage). Furthermore, CNVnator is complementary in a straightforward way to split-read and read-pair approaches: It misses CNVs created by retrotransposable elements, but more than half of the validated CNVs that it identifies are not detected by split-read or read-pair. By genotyping CNVs in the CEPH, Yoruba, and Chinese-Japanese populations, we estimated that at least 11% of all CNV loci involve complex, multi-allelic events, a considerably higher estimate than reported earlier. Moreover, among these events, we observed cases with allele distribution strongly deviating from Hardy-Weinberg equilibrium, possibly implying selection on certain complex loci. Finally, by combining discovery and genotyping, we identified six potential de novo CNVs in two family trios.

A novel compression tool for efficient storage of genome resequencing data

With the advent of DNA sequencing technologies, more and more reference genome sequences are available for many organisms. Analyzing sequence variation and understanding its biological importance are becoming a major research aim. However, how to store and process the huge amount of eukaryotic genome data, such as those of the human, mouse and rice, has become a challenge to biologists. Currently available bioinformatics tools used to compress genome sequence data have some limitations, such as the requirement of the reference single nucleotide polymorphisms (SNPs) map and information on deletions and insertions. Here, we present a novel compression tool for storing and analyzing Genome ReSequencing data, named GRS. GRS is able to process the genome sequence data without the use of the reference SNPs and other sequence variation information and automatically rebuild the individual genome sequence data using the reference genome sequence. When its performance was tested on the first Korean personal genome sequence data set, GRS was able to achieve ∼159-fold compression, reducing the size of the data from 2986.8 to 18.8 MB. While being tested against the sequencing data from rice and Arabidopsis thaliana, GRS compressed the 361.0 MB rice genome data to 4.4 MB, and the A. thaliana genome data from 115.1 MB to 6.5 KB. This de novo compression tool is available at http://gmdd.shgmo.org/Computational-Biology/GRS.

AGE: defining breakpoints of genomic structural variants at single-nucleotide resolution, through optimal alignments with gap excision.

Motivation: Defining the precise location of structural variations (SVs) at single-nucleotide breakpoint resolution is an important problem, as it is a prerequisite for classifying SVs, evaluating their functional impact and reconstructing personal genome sequences. Given approximate breakpoint locations and a bridging assembly or split read, the problem essentially reduces to finding a correct sequence alignment. Classical algorithms for alignment and their generalizations guarantee finding the optimal (in terms of scoring) global or local alignment of two sequences. However, they cannot generally be applied to finding the biologically correct alignment of genomic sequences containing SVs because of the need to simultaneously span the SV (e.g. make a large gap) and perform precise local alignments at the flanking ends.Results: Here, we formulate the computations involved in this problem and describe a dynamic-programming algorithm for its solution. Specifically, our algorithm, called AGE for Alignment with Gap Excision, finds the optimal solution by simultaneously aligning the 5′ and 3′ ends of two given sequences and introducing a ‘large-gap jump’ between the local end alignments to maximize the total alignment score. We also describe extensions allowing the application of AGE to tandem duplications, inversions and complex events involving two large gaps. We develop a memory-efficient implementation of AGE (allowing application to long contigs) and make it available as a downloadable software package. Finally, we applied AGE for breakpoint determination and standardization in the 1000 Genomes Project by aligning locally assembled contigs to the human genome.Availability and Implementation: AGE is freely available at http://sv.gersteinlab.org/age.Contact: pi@gersteinlab.orgSupplementary information: Supplementary data are available at Bioinformatics online.

Human genetics and genomics a decade after the release of the draft sequence of the human genome

Substantial progress has been made in human genetics and genomics research over the past ten years since the publication of the draft sequence of the human genome in 2001. Findings emanating directly from the Human Genome Project, together with those from follow-on studies, have had an enormous impact on our understanding of the architecture and function of the human genome. Major developments have been made in cataloguing genetic variation, the International HapMap Project, and with respect to advances in genotyping technologies. These developments are vital for the emergence of genome-wide association studies in the investigation of complex diseases and traits. In parallel, the advent of high-throughput sequencing technologies has ushered in the 'personal genome sequencing' era for both normal and cancer genomes, and made possible large-scale genome sequencing studies such as the 1000 Genomes Project and the International Cancer Genome Consortium. The high-throughput sequencing and sequence-capture technologies are also providing new opportunities to study Mendelian disorders through exome sequencing and whole-genome sequencing. This paper reviews these major developments in human genetics and genomics over the past decade.

AlleleSeq: analysis of allele‐specific expression and binding in a network framework

To study allele-specific expression (ASE) and binding (ASB), that is, differences between the maternally and paternally derived alleles, we have developed a computational pipeline (AlleleSeq). Our pipeline initially constructs a diploid personal genome sequence (and corresponding personalized gene annotation) using genomic sequence variants (SNPs, indels, and structural variants), and then identifies allele-specific events with significant differences in the number of mapped reads between maternal and paternal alleles. There are many technical challenges in the construction and alignment of reads to a personal diploid genome sequence that we address, for example, bias of reads mapping to the reference allele. We have applied AlleleSeq to variation data for NA12878 from the 1000 Genomes Project as well as matched, deeply sequenced RNA-Seq and ChIP-Seq data sets generated for this purpose. In addition to observing fairly widespread allele-specific behavior within individual functional genomic data sets (including results consistent with X-chromosome inactivation), we can study the interaction between ASE and ASB. Furthermore, we investigate the coordination between ASE and ASB from multiple transcription factors events using a regulatory network framework. Correlation analyses and network motifs show mostly coordinated ASB and ASE.

The reference human genome demonstrates high risk of type 1 diabetes and other disorders.

Personal genome resequencing has provided promising lead to personalized medicine. However, due to the limited samples and the lack of case/control design, current interpretation of personal genome sequences has been mainly focused on the identification and functional annotation of the DNA variants that are different from the reference genome. The reference genome was deduced from a collection of DNAs from anonymous individuals, some of whom might be carriers of disease risk alleles. We queried the reference genome against a large high-quality disease-SNP association database and found 3,556 disease-susceptible variants, including 15 rare variants. We assessed the likelihood ratio for risk for the reference genome on 104 diseases and found high risk for type 1 diabetes (T1D) and hypertension. We further demonstrated that the risk of T1D was significantly higher in the reference genome than those in a healthy patient with a whole human genome sequence. We found that the high T1D risk was mainly driven by a R260W mutation in PTPN22 in the reference genome. Therefore, we recommend that the disease-susceptible variants in the reference genome should be taken into consideration and future genome sequences should be interpreted with curated and predicted disease-susceptible loci to assess personal disease risk.

Overview of personalized medicine in the disease genomic era

Sir William Osler (1849-1919) recognized that "variability is the law of life, and as no two faces are the same, so no two bodies are alike, and no two individuals react alike and behave alike under the abnormal conditions we know as disease". Accordingly, the traditional methods of medicine are not always best for all patients. Over the last decade, the study of genomes and their derivatives (RNA, protein and metabolite) has rapidly advanced to the point that genomic research now serves as the basis for many medical decisions and public health initiatives. Genomic tools such as sequence variation, transcription and, more recently, personal genome sequencing enable the precise prediction and treatment of disease. At present, DNA-based risk assessment for common complex diseases, application of molecular signatures for cancer diagnosis and prognosis, genome-guided therapy, and dose selection of therapeutic drugs are the important issues in personalized medicine. In order to make personalized medicine effective, these genomic techniques must be standardized and integrated into health systems and clinical workflow. In addition, full application of personalized or genomic medicine requires dramatic changes in regulatory and reimbursement policies as well as legislative protection related to privacy. This review aims to provide a general overview of these topics in the field of personalized medicine.

The bioinformatics of next generation sequencing: a meeting report

scientists after finishing human genome aimed to develop tailor made drugs based on genome information. However, this requires personal genome sequencing, knowledge of genome level SNPs and other variations. More to add, there were some very serious domains of life about which our knowledge remained very limited and full of myths till the arrival of application of next generation sequencing. Metagenomics, Epigenomics, importance of non-coding elements like Alu and microRNAs, reports like high transcriptional activities of retroelements and transposons in plant system, all picking up after the advent of next generation sequencing, just with in the past 5 years or so (Shendure and Ji, 2008). Sensing the relevance and importance of thetopic,SCBB,IHBTplannedanddesigned this workshop to create awareness about NGS and associated Bioinformatics activitiesatthenationallevel.Itcarefullyselected the speakers at national platform and invited specialists in Bioinformatics and NGS. The workshop was designed to benefit the community with first hand personal experiences of each other, discuss, present their work, problems and create awareness. Total eight speakers were invited to deliver a lecture series, give demonstrations, hands-on training and participate in brainstorming session with participants selected from all over India. Most of the selected participants were associated with research problems involving next generation sequencing application. The lecture session was kept open for interested scientists.