After listening to Temple Grandin’s TED talk, “The world needs all kinds of minds,” I spent a lot of time learning more about Temple and her accomplishments. As a child, Temple Grandin suffered from severe autism, but as an adult she became famous for developing more effective and humane protocols in the cattle industry and she become a prominent speaker to help others understand autism. She posits that her acute visual thinking (and hampered verbal abilities) helped her gain insight into the animal mind and this is the cause for her success in the cattle industry.
In addition to her TED talk, I also listened to the BBC special “The Woman Who Thinks like a Cow.” I would strongly recommend watching this video if you want to gain some insight into Temple’s life and accomplishments. I also plan to check out one of her books sometime in the near future.
I think Temple does a fantastic job of accomplishing two things: 1) helping the audience understand how autistic people think and 2) emphasizing the need for individuals with “autistic-like” traits and encouraging those with “unique [minds]” to find jobs that suit their abilities.
For reasons of brevity, I will simply encourage you to watch the TED talk if you want to gain insight into how an autistic mind works. However, I would like to think more carefully about Temple’s second point.
Temple emphasizes that society needs different types of thinkers for different types of jobs. For example, she often remarks that an autistic mind is well-suited for a job in Silicon Valley. Now, I do agree that we should be aware that people (such as highly functioning autistic individuals) can very talented at certain things but bad at other tasks. However, I think she occasionally takes this argument a little too far.
Namely, I think Temple overestimates the role of autistic individuals in society and underestimates the tragic life of severely autistic individuals. Previous to this TED talk, she claimed “[society] would still be socializing around the fire if it was not for autistic individuals.” When questioned about this claim during the question and answer session after her TED talk, she responded “Who do you think made the first spears? An Asperger guy.” While it is true that many highly functioning autistic individuals can have a heightened sense of perception and analytical abilities, it is not safe to assume that social skills are always antagonistic to analytical thinking. There are many individuals who have both verbal and visual talents. For example, a good physician needs both analytical and communication skills in order to succeed at his or her job. Although I agree that most people have a dominant style of thinking, overall aptitude varies significantly between individuals and it is not necessarily safe to assume that verbal and visual skills are mutually exclusive. To be fair, Temple does mention that about one-half of autistic individuals will never learn how to talk and therefore cannot maintain a job and independent lifestyle. For this reason, I question Temple’s broad claim that autism genes are good for society. Autism is a spectrum of disorders, and severe autism devastates the lives of many individuals. Even Temple has to take antidepressants in order to conduct everyday functions (as I learned from the BBC special). Therefore, I think it is good to have individuals with a “brush” of autism, but I also think that society would benefit from the development of novel therapeutics that could correct the developmental delays in communication associated with autism and/or genetic diagnostics that can help predict if a child is likely to develop a case of severe autism.
Friday, February 26, 2010
Tuesday, February 23, 2010
Traditional versus Genetic Risk Factors
Yesterday, I started reading this GenomeWeb article discussing the utility of genome-wide association studies (GWAS) in testing drug safety. This article largely agreed with my earlier post. In fact, the article mentions the utility of genetic diagnostics for prescribing abacavir (Ziagen, a HIV drug), as described in “The Language of Life.” The article also mentions new studies to determine genetic risk factors for flucloxacillin (an antibiotic) and clozapine (an antipsychotic). In general, this article provides good support for my earlier claim that drug sensitivity is the most exciting area of GWAS research.
However, the article also mentions that scientists may have overestimated the strength of GWAS in predicting risk to diseases such as heart disease and type II diabetes. This slightly contradicts my most recent post where I claim that genetic predictors of type II diabetes are probably “pretty good.” Therefore, I decided it might be worthwhile to reevaluate my claims.
I think the recent type II diabetes study is well designed, and I was surprised by the results. The authors calculate multiple models for traditional (e.g. age, sex, family history, waist circumference, body mass index, smoking behavior, cholesterol levels) and genetic risk factors. They found that traditional models identified onset of type II diabetes with a 20-30% success rate (at 5% false positive rate), while the gene-based model had only about a 6.5% success rate (at 5% false positive rate). The GenomeWeb article also mentions an interview with the senior director of research at 23andMe (a genetic diagnostic company), who claims that some rare variants may impart especially high risk to certain individuals and genetic tests should therefore complement risk assessment using traditional risk factors. Readers should also take a look at Web Table B of the diabetes study, which calculates predictive power of individual variants. For example, TCF7L2 had one of the strongest associations with type II diabetes in the GWAS Catalog, and this gene did in fact have a significantly higher rate of incidence for diabetes for individuals with a certain set of variants. However, the risk of developing diabetes only increased from 5.4% to 8.6%. Therefore, I think these time consuming diagnostic studies (lasting 10+ years) are unfortunately essential to determine the practicality of genetic tests for diseases whose genetic basis is either complex or unclear.
On the other hand, I don’t think the results of the heart disease study are very surprising. After checking the GWAS Catalog for genetic associations with myocardial infarction and stroke (the diseases examined in the study), I only found a small amount of information on these diseases. There was only one study on myocardial infarction, which only had one significant variant (rs10757278-G, p-value = 1 x 10-20), and two studies on stroke with weak associations (p-values between 9 x 10-6 and 1 x 10-9) and no overlapping associations between the two studies. The heart disease study also used a gene model with 101 variants, which is much larger than the number of significant associations listed in the GWAS catalog (unless the authors considered diseases not explicitly described in the abstract). Unlike the diabetes study, the authors did not provide a table describing how accurately individual variants could predict the onset of heart disease.
Of course, genetic models could improve when novel variants are discovered and/or better models for complex diseases are developed. I also think genetic models would naturally be more accurate for simple diseases that are associated with mutations in single gene. In the meantime, I think consumers need to currently interpret genetic risk factors for complex diseases with a grain of salt, but I think it is still worth getting excited about using genomic tools to predict novel drug targets and discover genetic sensitivities to drugs that are currently on the market or in clinical trials.
However, the article also mentions that scientists may have overestimated the strength of GWAS in predicting risk to diseases such as heart disease and type II diabetes. This slightly contradicts my most recent post where I claim that genetic predictors of type II diabetes are probably “pretty good.” Therefore, I decided it might be worthwhile to reevaluate my claims.
I think the recent type II diabetes study is well designed, and I was surprised by the results. The authors calculate multiple models for traditional (e.g. age, sex, family history, waist circumference, body mass index, smoking behavior, cholesterol levels) and genetic risk factors. They found that traditional models identified onset of type II diabetes with a 20-30% success rate (at 5% false positive rate), while the gene-based model had only about a 6.5% success rate (at 5% false positive rate). The GenomeWeb article also mentions an interview with the senior director of research at 23andMe (a genetic diagnostic company), who claims that some rare variants may impart especially high risk to certain individuals and genetic tests should therefore complement risk assessment using traditional risk factors. Readers should also take a look at Web Table B of the diabetes study, which calculates predictive power of individual variants. For example, TCF7L2 had one of the strongest associations with type II diabetes in the GWAS Catalog, and this gene did in fact have a significantly higher rate of incidence for diabetes for individuals with a certain set of variants. However, the risk of developing diabetes only increased from 5.4% to 8.6%. Therefore, I think these time consuming diagnostic studies (lasting 10+ years) are unfortunately essential to determine the practicality of genetic tests for diseases whose genetic basis is either complex or unclear.
On the other hand, I don’t think the results of the heart disease study are very surprising. After checking the GWAS Catalog for genetic associations with myocardial infarction and stroke (the diseases examined in the study), I only found a small amount of information on these diseases. There was only one study on myocardial infarction, which only had one significant variant (rs10757278-G, p-value = 1 x 10-20), and two studies on stroke with weak associations (p-values between 9 x 10-6 and 1 x 10-9) and no overlapping associations between the two studies. The heart disease study also used a gene model with 101 variants, which is much larger than the number of significant associations listed in the GWAS catalog (unless the authors considered diseases not explicitly described in the abstract). Unlike the diabetes study, the authors did not provide a table describing how accurately individual variants could predict the onset of heart disease.
Of course, genetic models could improve when novel variants are discovered and/or better models for complex diseases are developed. I also think genetic models would naturally be more accurate for simple diseases that are associated with mutations in single gene. In the meantime, I think consumers need to currently interpret genetic risk factors for complex diseases with a grain of salt, but I think it is still worth getting excited about using genomic tools to predict novel drug targets and discover genetic sensitivities to drugs that are currently on the market or in clinical trials.
Tuesday, February 16, 2010
Playing with the GWAS Catalog
When I was looking at an article in PLoS Biology, I noticed that the abstract listed a comprehensive government database for genome-wide association studies (the “GWAS Catalog”). This database provides a lot of interesting information. In order to get a feel for the data in the GWAS Catalog, I looked at the data for four specific diseases (autism, prostate cancer, type I diabetes, and type II diabetes).
[If you are a non-scientist looking at this database, stronger genetic associations (which should more accurately predict genetic predisposition to a disease) should have low p-values and should be reproducible between different studies.]
1) Autism - There were 3 studies included in the GWAS Catalog. The first two studies identified the same exact region (but with slightly different variants), and the third study identified a different but nearby region. Although I think that there is probably something interesting going on in this region of chromosome 5, I don’t think it is worth getting very excited about the specific variants identified in these studies. For example, the p-values for the autism studies are the lowest out of the four diseases that I analyzed (meaning autism has the weakest genetic component and/or the genetic component of autism is the most complex to model). Furthermore, the most recent study showed that the expression levels of SEMA5A (one of the genes listed in the GWAS Catalog for autism) are very similar for autistic and normal people (see Fig 2. if you have access to this article). The authors of this study claim that gene expression in autistic patients is significantly lower than in normal patients, but I think the statistical significance may be due to an over-fitting problem because they only look at 20 autism patients and 10 control patients (and I have a hard time believing this was enough data to adjust for “age at brain acquisition, post-mortem interval and sex”). The genes with the strongest genetic association in the first study (CDH10 and CDH9) also have similar expression patterns in both autistic and control patents, and the authors of this first study report that this difference is not statistically significant. Of course, the autism variants may be non-functional yet retain similar gene expression levels, but I would still seriously question the strength of any of the specific variants listed in these studies.
2) Prostate Cancer – I looked at the data for prostate cancer, type I diabetes, and type II diabetes because variants for these three diseases are included in at least two of the three major genomic tests listed in “The Language of Life.” More specifically, the three major genomic testing companies gave completely different predictions regarding Dr. Collins’ risk of getting prostate cancer. The GWAS Catalog lists 11 studies (10 of which have significant associations), and the genetic associations for prostate cancer were much stronger than for autism (p-values equal 3 x 10-33 vs. 2 x 10-10, respectively). Highly significant genetic associations were found within the 8q24.21 and 17q12 regions in several independent studies, but many associations are only found in individual studies. According to “The Language of Life”, deCODE has 13 variants for prostate cancer, Navigenics has 9 variants, and 23andMe has 5 variants. Based upon what I’ve seen in the GWAS Catalog, I think that there probably are at least 5 strong, reproducible variants that could be used to calculate genetic predisposition to prostate cancer, but I am not certain if there 13 variants with well-established genetic associations. However, calculating genetic association for several variants at the same time can be tricky, and the difference in test results may be a problem with the underlying models for calculating genetic association more so than the individual variants considered for the analysis.
3) Type I Diabetes – Type I diabetes has a very strong genetic component, and the molecular basis for this disease is well understood. In these respects, the data in the GWAS Catalog are a good reflection of what is known about this disease. The strongest associations had the lowest p-value out of all the diseases considered (5 x 10-134 for a variant within the Major Histocompatibility Complex, or MHC), and either MHC or HLA (which is part of the MHC) had the strongest genetic association for 4 out of the 8 studies in the GWAS Catalog. This makes a lot of sense because the MHC displays antigens to immune system (thereby telling the body which cells to attack) and type I diabetes is due to due to an autoimmune response where the immune system attacks and destroys the insulin-producing beta cells in the pancreas. It bothered me that some studies reported pretty different results, but that is why I think that it is necessary to only use reproducible associations for genetic testing.
4) Type II Diabetes – The GWAS Catalog contained 15 studies on type II diabetes (12 of which had significant results), which is the highest number of studies listed for the four diseases that I looked at. The strongest associations for type II diabetes had p-values similar to prostate cancer, but higher than type I diabetes. This makes sense because type I diabetes has a stronger genetic component than type II diabetes, so type II diabetes should have weaker associations than type I diabetes. The 8 genes listed as predictors of type II diabetes in “The Language of Life” (TCF7L2, IGF2BP2, CDKN2A, CDKAL1, KCNJ11, HHEX, SLC20A8, and PPARG) were pretty well represented among the different studies listed in the GWAS Catalog, so I bet the predictors of genetic predisposition to type II diabetes are pretty good.
Monday, February 15, 2010
PLoS Medicine Debate on Medical Patents
The most recent issue of PLoS Medicine contains an article with three mini-essays regarding the debate “Are Patents Impeding Medical Care and Innovation?”.
In a nutshell, I would say this paper describes four main arguments from those who argue for refinement of the current patent system for drugs and medical devices. First, drug companies only develop a small proportion of drugs for diseases affecting developing counties. Second, many existing drugs are often too expensive for use in developing counties. Third, pharmaceutical companies are producing relatively fewer drugs at higher cost, and some attribute this to patent law. Fourth, patents can dissuade newcomers from developing technologies in a market that already contains patented products. There are other arguments that have been made against certain types of medical patents, but I think these are the four main arguments discussed throughout the article. Of course, proponents of medical patents argue that patents are necessary in order to provide the proper incentives for innovation.
Evidence within the article (especially by the author of the second mini-essay) indicates that patent law may not be responsible for the third argument (fewer new drugs at higher cost) and the fourth argument (barriers to entry/innovation) may be exaggerated. Furthermore, I think the fourth argument applies to all industries, at least to some extent. Therefore, I will mostly focus on the arguments regarding the impact of patent law on developing countries.
The authors of the third mini-essay cite “malaria, pneumonia, diarrhea, and tuberculosis…account for 21% of the global disease burden, [but] receive 0.31% of all public and private funds devoted for health research.” I think the fact that the problems of developing countries receive a low proportion of funds from both public and private funds indicates that this problem is not completely caused by patent laws. To be honest, I think it makes sense for people to want to spend a higher proportion of their money on problems that directly affect them, so I would probably expect global needs to exceed funding no matter what.
The authors of the third mini-essay provide a good solution to cutting costs of drugs for developing countries. The non-profit Drugs for Diseases Initiative “finances R&D up front and offers the outcome of its research on a nonexclusive basis to generic producers”. Universities also hold the patent on a number of important drugs, so scientists from various non-profit organizations (such as universities) could negotiate deals with companies to produce and sell their drugs at a reduced cost. Although it was not mentioned in this article, some drug companies already offer drugs to developing countries at a reduced cost or donate patents to non-profit organizations.
The author of the first mini-essay mentions that some people believe that a prize system could replace the current patent system. I completely disagree with implementing a prize system to replace patent law, but it is possible that a prize system could complement innovation in the non-profit sector (this is already done on various scales, but more prizes certainly wouldn’t hurt).
That said, I don’t think patent laws are absolutely perfect. For example, I think we may reach a point where limitations need to be set for patents on genetic information. A popular example of this problem (also mentioned in this article) would be Myriad Genetics’ patent on BRCA1/2.
In general, I really like this article format. I wish the mini-essays more cleanly divided into “pro,” “con,” and “unsure” categories, but this is a very minor issue that does not significantly detract from a well-written and organized article. On a different note, I would like to mention that a debate article like this has not been published in PLoS Medicine since August 2009 (although several were published in the months prior to that), and I would personally like to see a lot more articles like this.
I am a huge fan of the PLoS journals in general, and I think PLoS journals make excellent choices for reviews of scientific journal articles in blog entries because they are open-source. So you can definitely count on more PLoS journal reviews from me in the future!
In a nutshell, I would say this paper describes four main arguments from those who argue for refinement of the current patent system for drugs and medical devices. First, drug companies only develop a small proportion of drugs for diseases affecting developing counties. Second, many existing drugs are often too expensive for use in developing counties. Third, pharmaceutical companies are producing relatively fewer drugs at higher cost, and some attribute this to patent law. Fourth, patents can dissuade newcomers from developing technologies in a market that already contains patented products. There are other arguments that have been made against certain types of medical patents, but I think these are the four main arguments discussed throughout the article. Of course, proponents of medical patents argue that patents are necessary in order to provide the proper incentives for innovation.
Evidence within the article (especially by the author of the second mini-essay) indicates that patent law may not be responsible for the third argument (fewer new drugs at higher cost) and the fourth argument (barriers to entry/innovation) may be exaggerated. Furthermore, I think the fourth argument applies to all industries, at least to some extent. Therefore, I will mostly focus on the arguments regarding the impact of patent law on developing countries.
The authors of the third mini-essay cite “malaria, pneumonia, diarrhea, and tuberculosis…account for 21% of the global disease burden, [but] receive 0.31% of all public and private funds devoted for health research.” I think the fact that the problems of developing countries receive a low proportion of funds from both public and private funds indicates that this problem is not completely caused by patent laws. To be honest, I think it makes sense for people to want to spend a higher proportion of their money on problems that directly affect them, so I would probably expect global needs to exceed funding no matter what.
The authors of the third mini-essay provide a good solution to cutting costs of drugs for developing countries. The non-profit Drugs for Diseases Initiative “finances R&D up front and offers the outcome of its research on a nonexclusive basis to generic producers”. Universities also hold the patent on a number of important drugs, so scientists from various non-profit organizations (such as universities) could negotiate deals with companies to produce and sell their drugs at a reduced cost. Although it was not mentioned in this article, some drug companies already offer drugs to developing countries at a reduced cost or donate patents to non-profit organizations.
The author of the first mini-essay mentions that some people believe that a prize system could replace the current patent system. I completely disagree with implementing a prize system to replace patent law, but it is possible that a prize system could complement innovation in the non-profit sector (this is already done on various scales, but more prizes certainly wouldn’t hurt).
That said, I don’t think patent laws are absolutely perfect. For example, I think we may reach a point where limitations need to be set for patents on genetic information. A popular example of this problem (also mentioned in this article) would be Myriad Genetics’ patent on BRCA1/2.
In general, I really like this article format. I wish the mini-essays more cleanly divided into “pro,” “con,” and “unsure” categories, but this is a very minor issue that does not significantly detract from a well-written and organized article. On a different note, I would like to mention that a debate article like this has not been published in PLoS Medicine since August 2009 (although several were published in the months prior to that), and I would personally like to see a lot more articles like this.
I am a huge fan of the PLoS journals in general, and I think PLoS journals make excellent choices for reviews of scientific journal articles in blog entries because they are open-source. So you can definitely count on more PLoS journal reviews from me in the future!
Sunday, February 14, 2010
Review of "The Language of Life"
I have wanted to learn more about the current status of personalized genomics for some time, and I was hoping the release of Dr. Francis Collins’ new book “The Language of Life” could help bring me up to speed. Dr. Collins is the current director of the NIH, and he was also the head of the Human Genome Project.
Overall, I like the book, and I think Dr. Collins does a good job presenting facts objectively, providing both optimistic and pessimistic evidence. However, readers should be careful to distinguish between “potential” applications and current applications of personalized medicine; the potential applications greatly outnumber the tools currently in widespread use.
I have included relatively brief summaries of the main applications of personalized medicine and some cool factoids that I gleaned from the book:
Applications:
1) Personalized drug treatments - This is the aspect of personalized medicine that I find most exciting. Adverse drug reactions are the fifth leading cause of death in the United States (although some problems are due to human error, rather than genetic sensitivities). Dr. Collins discusses the current use of diagnostic tests to guide prescriptions for 6-MP (leukemia), Warfarin (blod clot/heart attack), Ziagen (HIV), and Herceptin (breast cancer). There are also several drug sensitivities that can be revealed using genetic tests (such as 23andMe), but such genetic testing is not currently standard practice. Many other potential applications of personalized drug treatments are discussed, and Dr. Collins also discusses the numerous drugs that have been developed after discovering the genetic basis for various diseases (using genetic/genomic tools).
2) Assessment of risk factors in your own genome – This is the topic of the book’s introduction. Dr. Collins discusses his own family history and his interpretation of genetic tests provided by 23andMe, deCODE, and Navigenics. He also provides a list of his positive results at the end of the book. Although exciting progress has been made in this area, I think these tests need to be more accurate. For example, the three tests were not even in agreement as to whether or not Dr. Collins should have either increased or decreased disposition to prostate cancer (the difference was due to which genetic variants were considered as part of the test). 23andMe also predicted Dr. Collins would have brown eyes, when in fact he had blue eyes. New genetic associations are constantly being published, and I think companies need to be conservative and only test for reproducible associations discovered by independent studies.
3) Assessing risk factors when planning children – This is certainly the most controversial aspect of personalized medicine. Although couples can get individual genomic tests and simply forgo having children (or staying together) if they are both carriers for a severe, recessive disease (like cystic fibrosis), a more aggressive and controversial route would be pre-implantation genetic analysis (PGD). PGD involves in vitro fertilization, conducting genetic tests on the fertilized embryos, and only implanting the embryos that are free from serious diseases. This technology is already in use, but it will obviously raise concerns for those who either believe that life begins at conception as well as those who fear a GATTACA-esque future of designer babies. In fact, Dr. Collins reports that 42% of PGD clinics would be willing to apply this procedure for sex selection, and a California lab currently advertises providing selection for eye and hair color. Dr. Collins proposes regulating which traits can or cannot be selected for during this process, and he also raises the point that defining every trait genetically (as presented in GATTACA) would be impossible because a number of traits are determined more by environment than genetics and the number of embryos needed to produce the right combination of desired traits would be enormous and impractical. I also think it is worth recalling the current accuracy of genetic tests (recall that Dr. Collins was supposed to have brown eyes when in fact he had blue eyes). I am generally not a fan of government regulation, but I do see how this thing can get out of hand and would at least advocate giving people the facts necessary to view these tests with a critical eye.
4) Gene therapy and stem cells – Although I wouldn’t usually consider this to be “personalized medicine”, these therapies are disused in depth in the final chapter of the book. Collins discusses case studies for gene therapy treating LCA (a disease that causes blindness) and X-linked SCID (“bubble boy”) patients. In an earlier chapter, Dr. Collins discusses a case study where stem cells (containing double mutants for CCR5) implanted in the bone marrow of a leukemia patient was able to confer resistance to HIV. Collins also discusses the use of iPS stem cells (engineered from normal cells, not embryonic stem cells) to cure sickle-cell anemia in mice, but he also notes potential complications (i.e. one of the four genes used to induce these cells is an oncogene, and may cause cancer in human patients). However, it is important to remember that case studies can sometimes provide atypically good results.
Cool Factoids:
1) There is currently a free government website that allows people to record and analyze their family history. Dr. Collins describes this as currently “the single most important source of information about your future health”, and I personally think this would be a cool extra credit activity for high school students to see how they can apply genetics to real-world problems…that is assuming students can prove they have used the website without handing their medical history over to their biology teacher.
2) Free tools already exist that allow people to keep digital copies of their medical records, which can be rapidly accessed by designated health care providers. Two such tools are provided in the book: Google Health and Microsoft HealthVault
3) In addition to the discussion of Dr. Collin’s family history and genomic analysis, he also describes catching malaria and TB (on separate occasions) as a volunteer physician and a medical intern, respectively. Of course, he successfully recovered in both cases.
In conclusion, I think “The Language of Life” provides a good review of the progress of genomics in biomedical research, and I would especially recommend it to nonscientists who want to learn more genomic medicine.
Overall, I like the book, and I think Dr. Collins does a good job presenting facts objectively, providing both optimistic and pessimistic evidence. However, readers should be careful to distinguish between “potential” applications and current applications of personalized medicine; the potential applications greatly outnumber the tools currently in widespread use.
I have included relatively brief summaries of the main applications of personalized medicine and some cool factoids that I gleaned from the book:
Applications:
1) Personalized drug treatments - This is the aspect of personalized medicine that I find most exciting. Adverse drug reactions are the fifth leading cause of death in the United States (although some problems are due to human error, rather than genetic sensitivities). Dr. Collins discusses the current use of diagnostic tests to guide prescriptions for 6-MP (leukemia), Warfarin (blod clot/heart attack), Ziagen (HIV), and Herceptin (breast cancer). There are also several drug sensitivities that can be revealed using genetic tests (such as 23andMe), but such genetic testing is not currently standard practice. Many other potential applications of personalized drug treatments are discussed, and Dr. Collins also discusses the numerous drugs that have been developed after discovering the genetic basis for various diseases (using genetic/genomic tools).
2) Assessment of risk factors in your own genome – This is the topic of the book’s introduction. Dr. Collins discusses his own family history and his interpretation of genetic tests provided by 23andMe, deCODE, and Navigenics. He also provides a list of his positive results at the end of the book. Although exciting progress has been made in this area, I think these tests need to be more accurate. For example, the three tests were not even in agreement as to whether or not Dr. Collins should have either increased or decreased disposition to prostate cancer (the difference was due to which genetic variants were considered as part of the test). 23andMe also predicted Dr. Collins would have brown eyes, when in fact he had blue eyes. New genetic associations are constantly being published, and I think companies need to be conservative and only test for reproducible associations discovered by independent studies.
3) Assessing risk factors when planning children – This is certainly the most controversial aspect of personalized medicine. Although couples can get individual genomic tests and simply forgo having children (or staying together) if they are both carriers for a severe, recessive disease (like cystic fibrosis), a more aggressive and controversial route would be pre-implantation genetic analysis (PGD). PGD involves in vitro fertilization, conducting genetic tests on the fertilized embryos, and only implanting the embryos that are free from serious diseases. This technology is already in use, but it will obviously raise concerns for those who either believe that life begins at conception as well as those who fear a GATTACA-esque future of designer babies. In fact, Dr. Collins reports that 42% of PGD clinics would be willing to apply this procedure for sex selection, and a California lab currently advertises providing selection for eye and hair color. Dr. Collins proposes regulating which traits can or cannot be selected for during this process, and he also raises the point that defining every trait genetically (as presented in GATTACA) would be impossible because a number of traits are determined more by environment than genetics and the number of embryos needed to produce the right combination of desired traits would be enormous and impractical. I also think it is worth recalling the current accuracy of genetic tests (recall that Dr. Collins was supposed to have brown eyes when in fact he had blue eyes). I am generally not a fan of government regulation, but I do see how this thing can get out of hand and would at least advocate giving people the facts necessary to view these tests with a critical eye.
4) Gene therapy and stem cells – Although I wouldn’t usually consider this to be “personalized medicine”, these therapies are disused in depth in the final chapter of the book. Collins discusses case studies for gene therapy treating LCA (a disease that causes blindness) and X-linked SCID (“bubble boy”) patients. In an earlier chapter, Dr. Collins discusses a case study where stem cells (containing double mutants for CCR5) implanted in the bone marrow of a leukemia patient was able to confer resistance to HIV. Collins also discusses the use of iPS stem cells (engineered from normal cells, not embryonic stem cells) to cure sickle-cell anemia in mice, but he also notes potential complications (i.e. one of the four genes used to induce these cells is an oncogene, and may cause cancer in human patients). However, it is important to remember that case studies can sometimes provide atypically good results.
Cool Factoids:
1) There is currently a free government website that allows people to record and analyze their family history. Dr. Collins describes this as currently “the single most important source of information about your future health”, and I personally think this would be a cool extra credit activity for high school students to see how they can apply genetics to real-world problems…that is assuming students can prove they have used the website without handing their medical history over to their biology teacher.
2) Free tools already exist that allow people to keep digital copies of their medical records, which can be rapidly accessed by designated health care providers. Two such tools are provided in the book: Google Health and Microsoft HealthVault
3) In addition to the discussion of Dr. Collin’s family history and genomic analysis, he also describes catching malaria and TB (on separate occasions) as a volunteer physician and a medical intern, respectively. Of course, he successfully recovered in both cases.
In conclusion, I think “The Language of Life” provides a good review of the progress of genomics in biomedical research, and I would especially recommend it to nonscientists who want to learn more genomic medicine.
Subscribe to:
Posts (Atom)
Posts
