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Yearly Archives: 2015
There is an allure to making the invisible visible, especially when it is about us. We carry many things around with us every day, beneath the surface: stories, histories, thoughts, and beliefs. Our bodies also carry around a total of several trillion copies of our genome, two per cell and these, too, remain invisible to us. (Also invisible to me, as an undergraduate biology major, unable to work a microscope: “All I see is clear liquid!”)
There is a popular science fair and classroom experiment where, with nothing more than a few household ingredients, you can extract and make visible some of your own DNA (usually from a sample of cheek cells). I’ve seen this experiment done where you can put your own DNA in a vial of liquid and wear it as a necklace. Some of you may think this is a “vial” proposition, and granted the DNA usually looks a bit like snot, but many people get into it. There is just something cool about being able to turn some of your cells inside out and spill the hidden contents, the “secret code” that drives the cellular machinery of your body.
The first time I saw physical evidence of my own genome was while studying abroad in college, at Deakin University in Melbourne, Australia. I was taking a Human Genetics course and some folks from the local public health department came in to use our class as guinea pigs for a new genetic screening program. (Historically, Australia has been a bit ahead of the curve in terms of thinking population level applications of genetic information.) The program they were piloting was to test everyone for genetic variants that cause a disease called hereditary hemochromatosis, which is characterized by a buildup of excess iron in the body.
Hemochromatosis is an excellent candidate for population level screening. First, the genetic basis is well understood and easy to test for. Second, it’s pretty common, so you’re not wasting a lot of resources by just testing everyone for it like you might for a rarer condition. Third, there is an inexpensive and straightforward treatment: give blood. Though it was the go-to “treatment” for many diseases from ancient times up to the late 1800’s, bloodletting is usually a bad idea – except for treating hemochromatosis. A final reason that genetic screening makes sense is that, if left untreated, the effects can be pretty serious. So: easy to detect + easy to treat + bad to overlook = reasonable population genetic screening program. Which is likely why these Aussies wanted to try it out.
So I gave a sample, probably signed a form, and a few weeks later got a letter in the mail (a letter, isn’t’ that quaint?) with my results. There, on the paper, was my genotype for two of the most common hemochromatosis variants. I had two normal copies of both variants tested, meaning I was not expected to have the disease or to pass on a risk variant to my child. I felt relief, for sure, but more than that I just felt a sense of awe. I wasn’t wearing a pendant with my own viscous DNA molecules, but I was holding in my hand some tangible piece of information about my own genome. Granted it was only two positions out of the 3 billion in my whole genome, but that 0.000000067% was neato to see, hold, and then fold back into that envelope for safe keeping.
It’s not necessarily rational or logically defensible to feel the allure of the DNA mystique, to get transfixed in the DNA looking glass. But it happens to people – it has happened to me and I think it will continue to happen to people in the future. Understanding what all those A’s, C’s, T’s and G’s actually do is one laudable and long-term goal, but even just holding a few letters in your hand and watching them glint in the sunlight is – I think – pretty cool.
I introduced myself earlier as a graduate student in Public Health Genetics, an interdisciplinary program. But what exactly is “interdisciplinary?” It’s a nice-sounding word that gets thrown around a lot, but can be tough to define — sort of like “zumba.” (Ethnic dance style fusion? Funky line dancing?) So I’m going to take this post as an opportunity to explore this mysterious word, “interdisciplinary,” both as a general concept and as a personal and professional aspiration of mine. (Don’t worry, I have taken a class and read some papers on the topic, so I’m not totally winging it here.)
A taxonomy of disciplinarity
To understand interdisciplinarity it helps to start with disciplinarity and build out a taxonomy from there. Disciplines are individual subjects or fields of study, such as philosophy, biology, or history. Monodisciplinary is remaining in one discipline. Combining multiple disciplines can occur with varying degrees of integration, creating a kind of taxonomy that I’ll describe with an extended metaphor borrowed from Dr. Paula Nurius (UW School of Social Work). Think of individual disciplines as pieces of fruit. Initially they’re separate, maybe divided into individual bins at the supermarket. There’s little exchange of people, ideas, problems, or approaches. When you start to put the disciplines in closer proximity to each other, you have a multidisciplinary fruit plate. Disciplinarians are starting to talk, but they’re still distinct and kinda doing their own thing. Cut up the fruit and put it in a salad and now we’re in the interdisciplinary zone, where different disciplines are starting to have a greater influence on each other. Take that fruit salad and throw it in a blender and now we’ve got a transdisciplinary smoothie, where the product is wholly different than the sum of its parts. Inspiring and nutritious. But quite rare and difficult to achieve in practice.
Interdisciplinary scholars typically have a disciplinary “home” in which they’ve achieved a certain level of mastery and knowledge. In addition to this home, they also have the cultural sensitivity and language skills needed to travel to other “homes.” These other homes might be different internal mental spaces or externally different places: the disciplinary “homes” of their colleagues. Cultural sensitivity and language skills are hardly even metaphorical here. Some of the key challenges of interdisciplinary work are to find a common language and to maintain respect for the ways of other disciplines. Because in addition to having different objects of study, disciplines are characterized by different ways of studying (methodologies) and different ways of knowing (epistemologies). But there’s a growing recognition both within and beyond academia that the world’s contemporary, complex problems need interdisciplinary and collaborative approaches. We can’t keep poking at things with our individual sticks. We need Swiss Army knife, fruit smoothie approaches.
Public Health Genetics as an interdisciplinary field
So how is interdisciplinarity manifest in Public Health Genetics, my field of study? Well, the applications of genetic information and technologies are complex and multi-faceted. Genetic information is being increasingly integrated into health care. Researchers are using more and more types of genetic data to understand human health and disease (see Big Data, Big Deal (?)). Genetic analysis is being used to track infectious disease outbreaks. Direct-to-consumer genetic testing is changing how people conceptualize and investigate their personal and familial identities (see OTC genetics). Just to name a few. To understand how all this is happening and with what effects, we benefit from considering and perhaps synthesizing the approaches and ideas of multiple disciplines.
My graduate training and work experiences have encompassed genetic epidemiology, biostatistics, policy, law, social science, and bioethics. I have studied and applied both quantitative and qualitative research methods. I approach scholarship from both an empirical (what is?) and a normative (what should be?) standpoint. My goal is to bring all this to bear in an academic research career that looks at how genetic information is shaping our everyday experiences. Most immediately, I’m embarking on an interdisciplinary dissertation project that weaves together data science, social science, and policy to examine how people are accessing and using their own genetic data obtained from direct-to-consumer genetic testing .
Assembling an interdisciplinary outfit
In the interdisciplinary career class I mentioned earlier, I was asked to create a visual to describe how I see my interdisciplinarity developing (reproduced above). When asked what I do, I commonly respond that I wear two hats: one as a research scientist in human genetics and one as a graduate student in Public Health Genetics. I built my visual off this phrase, to illustrate how I want to continue integrating these different aspects of my interests and skills, to move from a series of monodisciplinary “hats” to an interdisciplinary “outfit”. The way I envision doing that is to continue seeking collaboration and cross-pollination of ideas across different groups of peers, mentors, friends and families. Plus some fruit and a blender.
Genetic testing has been around for decades. Early examples include state-run newborn screening programs, the first of which was for the metabolic disorder phenylketonuria (PKU). Whether you know it or not, in the first few hours of your life a little heel prick of blood was taken from you and tested for probably a dozen or so diseases, PKU among them. Unlike most genetic tests, newborn screening is a public health program administered population-wide with the justification that identifying early-onset, treatable conditions in newborns is a pressing public health concern. Apart from newborn screening, genetic testing is carried out in a variety of clinical contexts, including carrier screening (for couples planning to conceive), prenatal care (in the early stages of pregnancy), and increasingly for cancer patients to guide treatment strategies.
I won’t give an exhaustive history of genetic testing and its evolution here, as others have already done a good job of that (e.g., here and here). Instead, I’d like to fast-forward to about ten years ago, when a new mode of genetic testing came onto the scene: direct-to-consumer, or “DTC” for short. Previously genetic testing always involved some intermediary — usually a health care provider or perhaps a public health department, as with newborn screening. DTC testing is, as the name implies, directly ordered by the consumer without any physician intermediary. It’s analogous to the difference between prescription and over the counter medications.
What precipitated the rise of DTC genetic testing? My impression is that it was a marriage of the falling costs of genetic technologies with the growing interest in learning about one’s own genome. It was a definite Wild West situation, where the first wave of DTC genetic testing companies were operating with apparently very little regulatory oversight. This initial set of DTC companies had different niches. Some were focused on genetic genealogy and ancestry testing. These are where you would turn if you wanted to know how much Asian ancestry you had, for example, or to trace a paternal genetic line alongside a known surname. Some tests were more health-related, returning your risk for common diseases or for responding to certain medications. Then there were decidedly scammy things like “nutrigenomics” companies (I quote because it seems like such a ridiculous word to me). These guys would tell you your genetic predisposition for needing certain types of vitamins and supplements…and then turn around and offer to sell you those very products. Most of these “nutrigenomics” companies have since fallen by the wayside, and I’m not shedding any tears on their behalf.
But some of the ancestry and health-related DTC genetic companies have stood the test of (albeit a relatively short period) time. The top two contenders these days are 23andMe and Ancestry.com, with about 1 million and 1.5 million customers, respectively. Note I’m glossing over volumes of history and debate and research about DTC genetic testing, including an evolving “regulatory landscape,” to keep the Wild West metaphor going. I’ll likely return to these issues in future posts, or if you can’t wait you can get some excellent coverage here.
These two companies have been in the news as of late and both because of health information. First, 23andMe recently changed their service quite significantly where they are once again returning health information (carrier status for 35+ conditions). They’ve also doubled the price from $99 to $199. They are now the first DTC genetic test to have the FDA’s blessing to return health information, FDA approval being part of the contentious and evolving “regulatory landscape” noted above. And Ancestry.com got some media attention recently due to rumors that they were considering adding some health information to their previously ancestry and genealogically-focused product.
This is the point where I come clean and say that I am a 23andMe customer. I ordered the test in 2012 at a time when it cost $99 and included information on genetic ancestry, drug response, non-health traits (stuff like do you smell asparagus in your pee and does cilantro taste soapy to you), and risk information for ~200 common diseases. When I got the test I wasn’t very vocal about it to my academic and professional colleagues, because many viewed (and still view) DTC testing as a frivolous waste of time and money — especially those in the genetic research and medicine communities.
But I took the leap for a few reasons. First, I’ve got 99 problems a genetic test ain’t one — er, in other words, $99 seemed like a relatively inexpensive gamble. Second, I know from my family history that the likelihood I’d find out something really devastating was quite low. There’s no early onset Alzheimer’s or familiar breast cancer in my family; had there been, I’m not sure I’d want a website to tell me about it while I’m sitting in my fleece pants eating pretzels. Third, I was curious how the site looked, how results were displayed and how probabilities were visualized and explained. Last, the type of genetic data 23andMe produces and bases its interpretations on is the exact same type of data often generated on genetic research participants. (It’s the microarray genotyping technology I mentioned in my previous post.) I work with those data on a daily basis in my research scientist position. I figured if all those research participants were willing to put that type of data on themselves out there, and even in my hands, then it was time I “walked the walk” and had that same type of data generated about me. I recognize DTC is different than traditional research, but it was more about the “stuff” of the data, the technology used and the scope of genetic information produced. It just didn’t seem that scary to me.
On that last point — the “scariness” of it — I was also perplexed at how the genetics community at the time seemed to be putting forth two disparate discourses about DTC genetic testing. On the one hand it was regarded as dangerous, premature, and in need of stricter regulation. On the other hand it was “recreational,” “consumer” genomics and ultimately meaningless. I understand how something could be simultaneously meaningless and dangerous, but nevertheless — I saw some contradiction there and that made me even more curious.
I won’t go into details about my results other than to say that, as for many, it was bit anticlimactic. But also very cool to see that type of information on myself. By “see” I mean literally — in the ancestry component of the test, they produce this visual called “chromosome paintings” where your 23 pairs of chromosomes are laid out and color-coded by genetic ancestry. This is possible because our genomes are mosaics of ancestral populations. Some people have pretty homogenous mosaics – e.g., mostly all Northern European ancestry, in my case – but other chromosomes paintings can look much more like colorful patchwork quilts.
I don’t look at my 23andMe results all that much, and I’ve chosen to opt out of many of the site’s features. For example, you can allow the company to search for your relatives in the company database and allow those people to contact you. For people interested in that, it can be like a virtual family reunion. 23andMe writes about those types of stories in their blogs all the time, but I also know people very close to me who have had these types of experiences. And felt very rewarded and connected by it.
So that’s a quick and dirty overview of DTC genetic testing with a little peppering of personal experience. If you’re related to me and tried to email me on 23andMe, I’m sorry – but I just turned all that stuff off, nothing personal! 🙂
Quick free association exercise: I say “big data” and you think…maybe, Google? NSA? The bane of my existence? The promise of tomorrow? I have a little bit of all of the above. But firstly I think of big genetic data, which is all the rage in biomedical research. Big data in genetics has come about due to advances in both computing technology (which underlies most of the “big data” we talk/hear about) and in laboratory equipment that measures genetic variation. A few decades ago it took a whole painstaking experiment to look at one single genetic variant. In the mid-2000’s genotyping arrays came about, which enabled one experiment to measure hundreds of thousands of variants in many people all at once. Now there are arrays that measure up to 5 million variants in one go (~0.1% of the 3 billion sites in the genome).
But presently the poster child technology of big genetic data is next generation sequencing. It’s “next generation” compared to the earlier sequencing technology used during the Human Genome Project in the 1990’s. Sequencing means that you’re looking at each letter in a stretch of DNA (perhaps the whole genome), rather than a priori selecting letters to look at as you do in a genotyping array experiment. It’s similar to strategically sampling a subset of people to survey (genotyping arrays) versus doing a census of the whole population (sequencing). Sequencing is now fast and relatively inexpensive – we’ve heard for several years about the imminent “$1,000 genome,” though my laboratory colleagues will have to tell me if that’s in fact a reality. But it does mean that sequencing many genomes whole is becoming more commonplace in research and, in some limited ways, in medicine.
There’s lots to be said about these trends, but I want to focus on one question: what makes genetic data — especially “big data” — valuable? In market speak, where and when is the “value add,” because presumably just the bucket loads of A’s, C’s, G’s and T’s aren’t getting people up in the morning. A few years ago during a TEDx talk (and my apologies to the presenter, whose name I don’t have a record of) I was introduced to the “knowledge hierarchy,” also called “knowledge pyramid” or “data-information-knowledge-wisdom” (DIKW) framework.
It’s a relatively intuitive way to think of the relationship between data, information, knowledge, and wisdom: it’s a hierarchy and one level builds off or assumes the previous. The concept is usually traced back to a 1989 article by organizational theorist Richard Ackoff, published in the Journal of Applied Systems Analysis (sounds like a page turner).
I liked the framework so much I made a folder on my laptop called “DIKW” where I started to collect articles and jot down thoughts on DIKW issues in genetics. Now the folder is called “DIKW_dissertation” and it’s where I store everything related to my Public Health Genetics dissertation project.
With genetic data, we inevitably start at the bottom of the period. Certain practices, such as variant annotation and interpretation, guided by bioinformatics tools and by research initiatives, allow us to move the data further up into information and perhaps knowledge. Wisdom? I’m not sure if and how that’s possible, but it’s another open question for genetics. I’m interested in how and why genetic data moves up the hierarchy not just as an abstract concept but because there are real controversies in the communities of genetics research and genetics medicine that tie into this framework.
More on all of that in future posts, but I want to encourage you to think about this DIKW framework when you encounter these discourses of “big data” in other arenas. My sense is that we sometimes get enamored of big data because we feel there is an inevitability to at least a partial trajectory up the knowledge hierarchy. But I’d be very interested to hear how you see DIKW around you.
The early days
I’m not sure I would be in the field of genetics if I didn’t have blue eyes. I remember my first introduction to genetics, in my 7th grade “life sciences” class, which is just biology for middle schoolers. We were learning about Mendel, his peas, and the laws of genetic inheritance. One way to depict the transmission of genes and traits from parents to offspring is with a Punnett Square.
In its simplest form, the square illustrates how the form of one gene in the parents, call the two forms “A” and “a,” can be passed on to an offspring. Let’s just say “kid,” lest we sound like cold, hard scientists. Different forms of the gene, or genotypes, in the parents lead to different expected proportions of genotypes in the kids. If both parents are Aa, then you’d expect 25% of the kids to be AA, 50% to be Aa, and 25% to be aa. This is because each parent only passes one of their forms to the offspring: the father gives one copy in the sperm and the mother gives one copy in the egg. Each time an egg or sperm goes down the chute, it has an equal chance of being either of the parent’s two forms, meaning each kid has the same chance of being either AA, Aa, or aa.
Don’t get hung up on the AA/Aa/aa stuff, especially since that can be a really non-intuitive way to think of genes (the DNA molecule is made of 4 chemicals nicknamed A, C, T, and G – all uppercase). More important for this story is that my teacher drew a Punnet Square for the trait of eye color. Eye color is used all the time to demonstrate genetic principles, which is ironic given that it’s actually a very complex trait and not well-understood. (If you want to pass yourself off as a human genetics scientist, just say something like “we’re still trying to elucidate the genetic architecture of eye color,” and they’ll let you into all the parties, journals, grants, etc.) But in the toy example, “A” represents a form of the eye color gene for brown eyes and “a” for blue. Aa individuals might either have a blended form, such as hazel, or just have a really loud A form that drowns out the blue “a” form, resulting in brown eyes.
I have blue eyes, so what I saw in that little quadrant of the Punnet Square up on the chalkboard was an opportunity for uniqueness, for exclusivity. I am the youngest in a family of three daughters, my first name was arguably the most popular of all girls born in 1983, I have brown hair and grew up middle class in suburban East Tennessee. Granted many people have blue eyes, but no one in my immediate family. Granny (my paternal grandmother) did. My mom has brown, my dad has hazel, and both my sisters have brown. So let’s remember that eye color is not as simple as one gene with two forms A and a, but something happened in the making of me that caused a previously hidden blue eyed “gene” in my mom to combine with my dad’s partially observable blue eyed “gene,” such that I have 100% blue eyes. Step aside loud brown A genes, and let that “aa” shine.
So fast forward almost two decades. Now I’m in something like 21st grade and pursuing a PhD in Public Health Genetics, an interdisciplinary field that studies the science of genetics, but also the ethical, legal, and social implications (“ELSI”, pronounced else-ee) of using genetic information – in research, in health care, and in everyday life. My research is particularly concerned with that latter piece, the “everyday life” part. Many people, not just blue eyed 7th graders, have noted that genetics (DNA, genes, genomes) has a captivating mystique. DNA is generally hidden to us yet integral to our existence and function as living beings. It is part of who we are, where we came from, and to some extent where we are going.* The first time we knew the full sequence of the human genome was in ~2001, after over 10 years and $3 billion devoted to the Human Genome Project (alongside some private ventures). The cost of sequencing has fallen so hard and so fast that sequencing whole genomes is becoming an increasingly common part of research and, in some currently limited ways, in medical practice. But for most people, at least our own genome still remains hidden to us, though we carry it around with us all the time.
*I do not endorse these “isms”
Above I wrote that DNA is “part of who we are, where we came from, and to some extent where we are going.” A quick but important aside to emphasize “part of” and “to some extent” in that sentence and to clarify what I am not saying about genetics. I don’t think genetics is a crystal ball or sacred text or panacea for all our personal and communal ills on this planet. Many of the societal and health related problems we face as a nation and as a planet have very little to do with genetics. In fact, efforts to understand genetics can often detract (attention, funding, etc.) from more pressing needs and problems. Social inequalities in health and access to health care are traceable to assaults much bigger and much further upstream than genetics. Basically, genetics isn’t deterministic in the sense that having certain DNA sequences translates 100% of the time into certain outcomes. Rather, there is a complex web of ecosystems, communities, families, and individuals that all interact to yield certain social and health outcomes. So genetic determinism is not something I subscribe to or espouse.
Another “ism” I do not intend to promote with my comments here is “genetic essentialism”, or the idea that we can be reduced down to, or “essentialized” as our genetic make-up. We are way more complicated than that (maybe this is a good time to check your Facebook news feed and confirm that last statement).
Open Reading Frame
I’ll talk more about what I think about genetics and what I’m doing in my PhD research in future posts. I’m still working out what the right blog frequency is, both for me and for you, my esteemed reader. But my initial thought is to post at least every week or two. Sign up to get notified of new posts via RSS feed or stuff like Digg Reader and then you’ll automatically receive your next dose.
Ps – Thanks to Ms. DeRoos, my 7th grade life sciences teacher, for teaching me about Punnett Squares, albeit through the intuitive but slightly misleading example of eye color.