This book highlights selected current data and its relevance in the human health care system, offering a fundamental primer on genetics and human health. With the advent of new high-throughput technologies (for the whole genome including exome sequencing), the conventional focus on genetics and individual genes is now shifting toward the analysis of complex genes, gene-gene interactions and the association between genes and environment, including epigenetics. The rapidly changing scientific research landscape, with the ever-growing influx of data on one hand and emergence of newer and more complicated diseases on the other, has created a dilemma for researchers and caregivers, who are still hopeful that advances in genetics and genomics will provide avenues for the understanding, prevention and possible cure of human diseases. The book focuses on the interactions between genes and proteins at both the transcriptome and proteome levels, which in turn affect the human genome and health. Additionally, it covers the domain that must be explored in order to understand the gene-gene and protein-protein interactions that contribute to human health. The book offers a valuable guide for all students and researchers working in the area of molecular genetics and genomics.

Geneticists now stand on the threshold of sequencing the genome in its entirety. The unprecedented insights into human disease and evolution offered by mapping and sequencing are transforming medicine and agriculture. This revolution depends vitally on the contributions made by applied mathematicians, statisticians, and computer scientists. Kenneth Lange has written a book to enable graduate students in the mathematical sciences to understand and model the epidemiological and experimental data encountered in genetics research. Mathematical, statistical, and computational principles relevant to this task are developed hand-in-hand with applications to gene mapping, risk prediction, and the testing of epidemiological hypotheses. The book covers many topics previously only accessible in journal articles, such as pedigree analysis algorithms, Markov chain, Monte Carlo methods, reconstruction of evolutionary trees, radiation hybrid mapping, and models of recombination. The whole is backed by numerous exercise sets.

"As Lee so wisely and eloquently cautions, there may be perils along this pathway as well as miraculous discoveries. Do dangers lurk in this new technological approach to nature? May we unwittingly be doing irreparable harm to individuals, not to mention the biosphere? This perceptive author even-handedly assesses the controversies surrounding the perils that may await us as molecular science moves out of the laboratory and into our homes and environment. This fascinating and comprehensive volume shows that the time has come to confront our gene future, because our gene future is now."--Jacket

The book introduces the bioinformatics tools, databases and strategies for the translational research, focuses on the biomarker discovery based on integrative data analysis and systems biological network reconstruction. With the coming of personal genomics era, the biomedical data will be accumulated fast and then it will become reality for the personalized and accurate diagnosis, prognosis and treatment of complex diseases. The book covers both state of the art of bioinformatics methodologies and the examples for the identification of simple or network biomarkers. In addition, bioinformatics software tools and scripts are provided to the practical application in the study of complex diseases. The present state, the future challenges and perspectives were discussed. The book is written for biologists, biomedical informatics scientists and clinicians, etc. Dr. Bairong Shen is Professor and Director of Center for Systems Biology, Soochow University; he is also Director of Taicang Center for Translational Bioinformatics.

After sequencing the human genome a decade ago, researchers have continued their projects, but now to try to better understand how, and when, genes are expressed in health and disease. Efforts have been concentrated on the measurement of the expression of RNA transcripts. In an analogy to the genome, the term "transcriptome" was created to refer to the complete set of RNAs in a cell type or tissue in a particular situation. Transcriptomics is the science that studies this issue and it is a branch of functional genomics. Transcriptomics in Heath and Disease provides a comprehensive overview of the science of transcriptomics initially in health, focusing on the concept of the transcriptome and the main methods to evaluate it. The authors discuss the concept and use of gene expression signatures and transcriptional biomarkers in normal development and diseased tissues and organs. As the transcriptome changes depending on the pathology, there is also a focus on the variations in the gene expression in different diseases such as autoimmune, inflammation, cancer and infections. This book should be very useful for researchers in molecular biology focusing on gene expression, human genetics, immunology, and genomics.

Over the past century, we have made great strides in reducing rates of disease and enhancing people's general health. Public health measures such as sanitation, improved hygiene, and vaccines; reduced hazards in the workplace; new drugs and clinical procedures; and, more recently, a growing understanding of the human genome have each played a role in extending the duration and raising the quality of human life. But research conducted over the past few decades shows us that this progress, much of which was based on investigating one causative factor at a time—often, through a single discipline or by a narrow range of practitioners—can only go so far. Genes, Behavior, and the Social Environment examines a number of well-described gene-environment interactions, reviews the state of the science in researching such interactions, and recommends priorities not only for research itself but also for its workforce, resource, and infrastructural needs.

Technologies collectively called omics enable simultaneous measurement of an enormous number of biomolecules; for example, genomics investigates thousands of DNA sequences, and proteomics examines large numbers of proteins. Scientists are using these technologies to develop innovative tests to detect disease and to predict a patient's likelihood of responding to specific drugs. Following a recent case involving premature use of omics-based tests in cancer clinical trials at Duke University, the NCI requested that the IOM establish a committee to recommend ways to strengthen omics-based test development and evaluation. This report identifies best practices to enhance development, evaluation, and translation of omics-based tests while simultaneously reinforcing steps to ensure that these tests are appropriately assessed for scientific validity before they are used to guide patient treatment in clinical trials.