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yes / Epigenetic information is available from contemporary organisms, but is difficult to track back in evolutionary time. Here, we show that genome-wide epigenetic information can be gathered directly from next-generation sequence reads of DNA isolated from ancient remains. Using the genome sequence data generated from hair shafts of a 4000-yr-old Paleo- Eskimo belonging to the Saqqaq culture, we generate the first ancient nucleosome map coupled with a genome-wide survey of cytosine methylation levels. The validity of both nucleosome map and methylation levels were confirmed by the recovery of the expected signals at promoter regions, exon/intron boundaries, and CTCF sites. The top-scoring nucleosome calls revealed distinct DNA positioning biases, attesting to nucleotide-level accuracy. The ancient methylation levels exhibited high conservation over time, clustering closely with modern hair tissues. Using ancient methylation information, we estimated the age at death of the Saqqaq individual and illustrate how epigenetic information can be used to infer ancient gene expression. Similar epigenetic signatures were found in other fossil material, such as 110,000- to 130,000-yr-old bones, supporting the contention that ancient epigenomic information can be reconstructed from a deep past. Our findings lay the foundation for extracting epigenomic information from ancient samples, allowing shifts in epialleles to be tracked through evolutionary time, as well as providing an original window into modern epigenomics.

The human genome is incredibly information dense, consisting of approximately 25,000 protein-coding genes contained within 24 unique chromosomes. An aspect of the genome that is critically important is maintaining spatial context which assists in understanding gene interactions and relationships. Existing methods of genome visualization that utilize spatial awareness are inefficient and prone to limitations in gene information and spatial context. The solution proposed in this thesis was the development and evaluation of alternative methods of genome visualization and exploration using virtual reality and desktop. To determine the optimal location of gene information within virtual reality and the influence of virtual reality, three interaction methods were implemented that interact with the ideograms. Multi-focus was applied to the ideogram interaction design to assist in visualizing multiple locations within the genome without sacrificing gene information detail or spatial awareness of the user. Two interaction methods were developed in virtual reality to determine if gene information is better suited embedded within the chromosome ideogram or separate from the ideogram. The final interaction method was implemented as a desktop application to evaluate if virtual reality provided an advantage. Results from the user study conducted determined that the use of virtual reality gave users a higher degree of confidence when navigating the chromosome ideograms and was preferred over desktop. In addition, depending on the type of task, the placement of gene information within the visualization had a notable impact on the ability of a user to work the task. / Master of Science / From the viewpoint of a dataset, the human genome is incredibly information dense. It consists of approximately 25,000 protein-coding genes contained within 24 unique chromosomes. An aspect of the genome that is critically important is maintaining spatial context which assists in understanding gene interactions and relationships. Existing methods of genome visualization that utilize spatial awareness are inefficient and prone to limitations in gene information and spatial context. In this work, an alternative method of genome visualization and interaction utilizing virtual reality and desktop was proposed. To determine the optimal location of gene information within virtual reality and the influence of virtual reality, three genome interaction methods were implemented that operate through interactions with chromosome ideograms. Two interaction methods were developed in virtual reality to determine if gene information is better suited embedded within the chromosome ideogram or separate from the ideogram. The final interaction method was implemented as a desktop application to evaluate if virtual reality provided an advantage. Results from the user study conducted determined that the inclusion of virtual reality gave users a higher degree of confidence when navigating the chromosome ideograms and was preferred over desktop. In addition, depending on the type of task, the placement of gene information within the visualization had a notable impact on the ability of a user to work the task.

Predicting the effect of missense variants is critically important in population and medical genetics. It is essential to interpret genetic variation in population screening and clinical diagnostic sequencing, to reach optimal statistical power of risk gene discovery in genetic studies of diseases and traits. A quantitative analysis of the fitness effect of all possible missense variants can provide a foundation for understanding how proteins evolve in humans and other species. In this thesis, I describe new methods to infer the effect of missense variants using various machine learning techniques. First, I worked on a ResNet-based supervised model to predict pathogenicity trained on curated databases. The curated clinical databases have uneven quality and uncertain bias across genes. To address this issue, I developed a new method, MisFit, to separately model the molecular effect and population fitness effect of missense variants, and to estimate them jointly using a probabilistic graphical model. The architecture of MisFit follows the biological causality of the variant effect, that is, for a missense variant, the protein sequence and structure context determine its molecular effect, which in turn determines its fitness effect given how the protein is involved in various conditions and traits. The latter is a latent factor encapsulated in a sigmoid-shaped function with gene-specific parameters. The fitness effect determines the expected allele counts in human populations. This model can be trained using large-scale population genome data without known pathogenicity labels. I investigated how informative allele counts are for inferring fitness effect using simulations with realistic demographic parameters. To take advantage of the latest deep learning techniques and large population genome data sets, I use a Poisson-Inverse-Gaussian distribution, which is differentiable, to approximate the probability of allele counts given fitness effect and sample size. We show that MisFit estimated heterozygous selection coefficient of missense variants is consistent with ratio of de novo mutations among observed variants in a population with child-parents trio data. Furthermore, de novo missense variants with selection coefficient >0.01 are significantly enriched in neurodevelopmental disorders cases, achieving the best performance in prioritization of de novos for new risk gene discovery compared to previous methods. We also show that the estimated molecular effect reached the state-of-the-art performance in the classification of damaging variants in deep mutational scanning assays, with improved consistency of the score scale across genes. Finally, I analyzed the transmission disequilibrium of inherited variants in autism using a new empirical Bayesian method to identify risk genes, which models relative risk as a continuous function of variant effect in each gene.

M.Comm. / A revolution in genetic research, known as the Human Genome Project (HGP), is taking place. This project, initiated in 1984, is a twenty-year, six billion-dollar science project designed to map the entire genetic structure (Genome) of the human species (Brockett and Tankersley, 1995). In 1998, the HGP leaders expected to complete the project by 2003 (Lowden, J. A., 1999:33). The Human Genome Project is designed to sequence the human genome (the blue print of genetic information) and to identify the estimated 100000 genesherein. This has added a new dimension to the technology available to underwriters in the life and health insurance industry for the selection of medical risks. Genetic testing can identify inherited diseases and predict illnesses that might not manifest for decades (Brackenridge & Elder, 1998:89). Genome research has opened up new opportunities for diagnosis and in some cases, early treatment of medical conditions. This new basis of knowledge is referred to as the advent of the molecular age in medicine. Medical journals, the mass media and genetic interest groups are treating human genetics and the opportunities it presents as a high-profile issue, with great attention being paid to the complex and emotive topics of life insurance and genetic testing (Regenauer & Schmidtke, 1998:5). The Insurance Industry can use genetic testing to identify high-risk applicants more accurately and price products accordingly, thereby improving risk assessment and profitability. These potential advantages, however, are counter-balanced by ethical considerations that are much more difficult to address (Lowden, J. A., 1999:33). Many consumers, ethicists and geneticists fear that insurers will use this data for unfair discriminatory purposes, identifying a genetic underclass of people who, although clinically well, will be uninsurable. Genetic testing could invade the privacy of applicants and their families. There are concerns about the confidential handling of genetic information as well as the accurate interpretation of genetic tests. The uncertainty about the predictive value of genetic tests, the shortage of trained geneticists and counsellors and the psychological impact of that knowledge of a predictable serious disease might have, have lead to much opposition to the use of genetic information by third parties. In the United States most Americans receive health insurance through their place of employment. There are fears that genetic testing will be used to discriminate against prospective employees and render many people unemployable and uninsurable (Council for responsible Genetics, 1997: http://www.gene-watch.org/genclisc htuil Consumer groups have lobbied effectively for the prohibition of testing or the use of testing by insurers in the United States and Europe and legislators aim to ban the use of genetic information on a broad basis. Insurers, on the other hand, are assuming that the new laws will cause untold damage to the fiscal stability of their companies (Lowden, J. A., 1999:33). However, it seems inevitable that genetic testing will affect risk classification sooner rather than later and to a greater extent than most believe (Chambers, 1997: http://www.Inrc.com/epirr/issues/143/143-4.htm).

Xiang Hai Liao. / "July 2002." / Thesis (Ph.D.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (p. 180-200). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. Ann Arbor, MI : ProQuest Information and Learning Company, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Mode of access: World Wide Web. / Abstracts in English and Chinese.

The human genome contains ~1.5% coding sequence, with the remaining 98.5% being non-coding. The functional potential of the majority of this non-coding sequence remains unknown. Much of this non-coding sequence is derived from transposable element (TE) sequences. These TE sequences contain their own regulatory information, e.g. promoter and transcription factor binding sites. Given the large number of these sequences, over 4 million in the human genome, it would be expected that the regulatory information that they contain would affect the expression of nearby genes. This dissertation describes research that characterizes that alternation of and contribution to the human transcriptome by non-coding elements, including TE sequences.

published_or_final_version / Medicine / Doctoral / Doctor of Philosophy

In the past century, the scope of patentable objects has greatly expanded. Patents are now being granted on living organisms, human biological material and genes. What are the consequences of such practices for scientific research and health care? One of the fundamental philosophical questions behind this issue is the following: are we justified in patenting human genetic material? An examination of the traditional philosophical justification of intellectual property will allow us to critically explore whether or not this practice is ethically justifiable. It will be argued that the consequentialist justification of intellectual property requires, in this present case, that we modify the patent regimes in order to maximise social benefits and minimize public burdens.

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