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Is Medicine ready for Genome Sequencing?

Genetics news has been popular for many years, complete with premature promises of revolutionary new medical treatments. These inflated expectations are certain to disappoint the uninitiated. The unfolding science reveals more complexity and uncertainty with each discovery. New methods emerge quickly that empower scientists to discover more, faster and at lower cost, but there is no assurance that new discoveries will take us closer to practical applications, rather than farther away from affordable medical miracles.

In their description of the National Human Genome Research Institute’s Encyclopedia of DNA Elements (ENCODE) project, Guigó and Reese stated: “Finding human genes is a complex task because of the peculiar anatomy of the eukaryotic genome. Eukaryotic genes lie within long stretches of intergenic DNA, and within the genes only a few short fragments—the exons—are spliced together, often in alternative configurations, to form the mRNAs. Sequence signals in the genome are degenerate, and computational programs using them are able to identify the exons and link them into genes with relative success. But only through the sequencing of the corresponding mRNA molecule can a gene be unequivocally identified. It is unclear, however, what fraction of genes can be ascertained through mRNA sequencing. In addition, genes are only one type of functional elements. It is likely that most of the functionality of the human genome sequence remains largely unexplored.”The modest aim of the first phase of ENCODE was to identify all functional elements in about 1% of the genome sequence through the collaborative effort of computational and laboratory-based scientists.

In time, the genomes of many species and many individuals within a species will be determined. Sophisticated comparative analyses of genomes will reveal more about the evolution of species. Computers with increasingly sophisticated software are essential to using genomic information in meaningful ways. DNA sequencing, brilliant programming and digital computing are perfect matches.

Personal Genome

The idea that each person will have their entire genome sequenced and that someone, somehow can read the genome and predict the future is both intriguing and misleading. Sequencing technology is advancing rapidly toward cheaper, faster, somewhat reliable genome analysis. However the brutal truth is that having access to 3 million base pairs in a sequence is having a surplus of mostly useless information. The intriguing aspect of having abundant genome information is that the doors open to a century of new research, new methods of computing large data sets and work for armies of researches who can run studies of populations of humans to find out what the genomic information really means. In other words, genomes are just a beginning of a journey of discovery, not an endpoint.

A nature editorial reviewing the 10 years since the fist human genome was reported stated:" The first post-genome decade saw spectacular advances in science. The success of the original genome project inspired many other 'big biology' efforts — notably the International HapMap Project, which charted the points at which human genomes commonly differ, and the Encyclopedia of DNA Elements (ENCODE), which aims to identify every functional element in the human genome. Dramatic leaps in sequencing technology and a precipitous drop in costs have helped generate torrents of genetic data, including more than two dozen published human genomes and close to 200 unpublished ones. Along the way, geneticists have discovered that such basic concepts as 'gene' and 'gene regulation' are far more complex than they ever imagined. But for all the intellectual ferment of the past decade, has human health truly benefited from the sequencing of the human genome? Francis Collins and Craig Venter both say 'not much'. Granted, there has been some progress, in the form of drugs targeted against specific genetic defects identified in a few types of cancer, for example, and in some rare inherited disorders. But the complexity of post-genome biology has dashed early hopes that this trickle of therapies would rapidly become a flood. Witness the multitude of association studies that aimed to find connections between common genetic variants and common diseases, with only limited success, or the discovery that most cancers have their own unique genetic characteristics, making widely applicable therapies hard to find. (Editorial. The human genome. Nature464, 649-650 (1 April 2010) | doi:10.1038/464649a; Published online 31 March 2010)

George Church, a professor of genetics at Harvard Medical School, founded the Personal Genome Project with the intention of sequencing selected individuals who were willing to share their genomes and medical histories in a public database. Personal held a conference in Boston April, 2010, inviting prominent individuals who have already been sequenced to share their experiences. Personal Genomics stated that there are fewer than 25 individuals to-date with public genome sequences, we expect that in this decade, there may be 1 million or more individuals with complete genome sequences worldwide. Genome sequenced speakers included James Watson, Jay Flatley, Skip Gates, Esther Dyson, Stephen Quake, Misha Angrist, George Church, James Lupski, Dan Stoicescu, Seong-Jin Kim, Greg Lucier and Rosalynn Gill.

Medical Genetics Old Fashioned

In medical papers, old ideas of genes often prevail. Phrases such as genetic tendency, genetic component, and genes play a role in are typical of obsolete generalities that confuse rather than inform. The new appreciation that genes are not solid, real entities is difficult for physicians to understand. Part of the problem is that medical education pretends that humans are static entities and that diseases are discrete phenomena.

A dynamic, interactive systems model better accounts for what actually happens. Rather than solid, reliable genes, you can imagine segments of DNA as codes that are read differently depending on circumstances. Much of the coding deals with getting food, digesting it, distributing and using nutrients, and excreting waste products. Food intake to the body is a major player in determining gene expression.

Beyond the genome lies epigenetics - the study of how the expression of the DNA code is altered as dynamic processes that can change in minutes. The expression of DNA is balanced between stability mechanisms that preserve the long-term species memory in the genome and adaptive mechanisms that change the expression, depending on circumstances. It is the adaptive mechanisms and changing DNA expressions that make individual predictions based on genome analysis alone an act of faith rather than a reliable expression of science.

Epigenetics began with the discovery that DNA nucleotides can be silenced by adding methyl groups. Somehow, somebody in cells or cell to cell communications adds or subtracts methyl code to change the expression of DNA. Methylation was just the beginning of discoveries that revealed more and more mechanism that alter the expression of the genome. The DNA code is translated into many different kinds of RNA. The emphasis has been on messenger RNA that is transferred to ribosomes where it acts as a template for protein synthesis. The meaning of gene has be limited to protein encoding sequences of DNA, but already this understanding is seen as a partial truth at best. Short RNA pieces, for example, can interfere with messenger RNA encoding.

Single-nucleotide polymorphisms (SNPs)

A screening technology that identifiers single-nucleotide polymorphisms (SNPs) has developed rapidly and is less expensive and more accessible than complete genome sequencing. As a tool of basic science SNP scanning is interesting, promising. Databases have developed that associated SNPs with diseases in thousands of cases and provide a preliminary view of complex traits and diseases caused by many genetic and environmental factors working together. SNP screens have been offered commercially as tests for disease risk. Their value is doubtful. In a 2010 review of SNP research Monolo stated:" What is becoming clear from these early attempts at genetically based risk assessment is that currently known variants explain too little about the risk of disease occurrence to be of clinically useful predictive value. One can anticipate that as sample sizes increase and more risk variants are identified, the predictive value of cumulative genotypic scores will increase. It has also been argued that the use of dense genotyping information, from tens of thousands of SNPs with only nominal associations with disease, may improve the accuracy of phenotypic prediction. Care is needed in evaluating genetic predictive models, since they are often specific to the population in which they were developed, and their value can vary with genotypic frequencies, effect sizes, and disease incidence. Possible clinical uses of predictive scores — for example, in deciding which patients should be screened more intensively for breast cancer with the use of mammography or for statin-induced myopathy with the use of muscle enzyme assays — will require rigorous, preferably prospective, evaluation before being accepted into clinical practice. Genome wide scans permit screening for many conditions at once. If probabilities were applied to 40 independent diseases, for example, roughly 90% of the population would be placed in the top 5% of those at genetic risk for at least one of the diseases, 33% would be in the top 1%, and 4% would be in the top 0.1%. Expanding such screening to 120 diseases would nearly triple the proportion in the top 0.001% at risk and identify 1.2% at the top 0.01%, levels that could justify population-based screening if appropriate interventions were available. The ability to assess risk for 120 conditions at the same time also raises the concern that predictive models will yield conflicting recommendations; if implemented, they could reduce a person's risk for development of one condition and exacerbate the risk for development of another. Such considerations are timely and important, since several commercial ventures are marketing genome wide association–based screening directly to consumers. Patients inquiring about genome wide association testing should be advised that at present the results of such testing have no value in predicting risk and are not clinically directive. Clinicians would do well to use the discussion as an opportunity to point out other identifiable, modifiable risk factors that motivated patients can control. Whether to heed such advice or instead undergo testing and present the physician with the test results as a fait accompli is the choice of the individual patient. A decision to undergo genome wide association testing may result in the diversion of scarce time and resources to counseling or follow-up investigation of findings." Teri A. Manolio. Genomewide Association Studies and Assessment of the Risk of Disease. NEJM Volume 363:166-176 July 8, 2010 Number 2

While there is great interest in developing rapid, inexpensive genome sequencing, we lack even the most basic understanding of how to read the genomic data. The predication of disease risk is the least likely result of genomic data. Since the code is interactive, the expression of any sequence associated with a disease may be altered by changing food intake or other variables in the environment.

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