Temple Grandin's TED Talk on Autism

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.

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.

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.