Here Is a Human Being

by Misha Angrist

  My team included a fifty-something guy from Lawton, Oklahoma, named Dennis Pollock. In 1993 Dennis was diagnosed with alpha-1-antitrypsin deficiency; he had served on the board of the Alpha-1 Association for many years. Alpha-1-antitrypsin is a protein produced mostly in the liver. Its main job is to protect the lungs from an enzyme that digests damaged or aging cells and bacteria. Without alpha-1, the enzyme will attack healthy lung tissue.70 As a young man Dennis had a double lung transplant. He described himself as completely healthy, although it was clear that schlepping from one congressional office building to another in the July Washington heat took its toll upon him. Being from Oklahoma, Dennis was represented by Republican senator Tom Coburn,

  aka “Dr. No.” Among legislators, Coburn was probably the one most allergic to legislation. In 2007 he single-handedly blocked or slowed more than ninety bills, including GINA.71 Dennis repeatedly came to D.C. to press Coburn about GINA, going so far as to organize a phone blitz on his office. Not long after this—and following a couple of concessions on the part of the bill’s sponsor—Coburn lifted his hold on GINA. Dennis’s activism won him admirers, including Francis Collins. The two became friends, Dennis told me; Pollock stayed with Collins and his wife whenever he was in the area.72 I had heard Collins talk about reducing health disparities on many occasions. It was gratifying to know that it wasn’t just talk.

  As we collected our trays, Collins asked what I would do with my genome. “What are you going to do when you encounter a SNP no one’s ever seen? You will have some breathtaking mutations … because we all do. You’ll be thinking [about what they mean for] yourself, for your kids. What are you going to do with that?”73

  What indeed.

  * This problem became less acute as more genomes were sequenced to higher accuracy that could serve as benchmarks for subsequent genomes.

  12 Charity Begins at Home

  By the end of the summer of 2009, I was in a pickle. Not only was I on deadline—that is to say, a year late—for this book, but I was getting regular invitations to speak. “O Great Genomeboy, yea, though thou hast walked through the valley of the shadow of death, thou hast feared no evil. We beseech you. Tell us, please: What is it like to have Your Genome Sequenced?”

  The truth was, I still had no idea. It had become clear to me that a complete exome, much less a complete genome, was not going to be forthcoming from the Church lab any time soon. While the quality of the Harvard sequence data had gotten better, after two and a half years, I had access to only some 5 percent of my exome, or about 0.002 percent of my genome. This was unlikely to yield the stuff from which deep insights into oneself would be gained. George’s lab was an incubator—a place where ideas were born, tested, and published. It was a workshop, not an assembly line. The notion that it would generate thousands of exome sequences like clockwork was simply unrealistic, at least in the near term. It was clear that George needed technical help to bring his grand visions to fruition, but at the moment it was unclear as to when and from whom he would get it outside of his own group. For my own selfish reasons (personal genomics: it’s all about me), I needed to look elsewhere.

  I began to bug David Goldstein. David is a colleague and friend, and for both relationships I am grateful. He’s one of the smartest people I’ve ever met, and to be honest, I don’t have very many really good friends, probably because I have not made time for them, and because I am insecure and afraid that they will reject me. I suspect David doesn’t have many close friends, either, though not out of fear of rejection. He is a brilliant geneticist but extremely competitive, sure of himself, convinced that he’s right, not one to shy away from a scrap, and not always blessed with the gift of tact. Indeed, he really doesn’t “give a toss” (he lived in the United Kingdom for many years and has retained some of the vernacular) about whether you like him. God knows that if he did give a toss, he’d be a mess: over one two-week period, half a dozen people on campus complained to me about him. The general tenor of their complaints was “Who does he think he is?” When I told him of this, he seemed curious about who the aggrieved parties were, but assumed that they were people who, at least on a professional level, did not really matter to him all that much (for the most part he was right).

  David grew up in California in a middle-class but broken home, and on the way to fulfilling a passion for marine biology he stumbled into genetics in college. He completed a graduate degree and postdoc with highly esteemed population geneticists, and took a job at University College in London. There he began working in the nascent field of genetic anthropology—that is, using genes to understand where historical populations lived, where they went, whom they mated with, and perhaps something about their culture. A half-Jew who had spent time in Israel when he was young and impressionable, David wondered if genetics could be used to elucidate some aspects of Jewish history. It could. He became a leader of the group that identified a signature (a set of genetic markers) on the Y chromosome strongly associated with the Cohanim, the ancient Jewish priests of the time of Christ and before, and whose descendants are presumed to carry surnames like Cohen, Cohn, Kahn, Kagan, etc. David and his colleagues also found results that supported the claims of a Bantu tribe (the Lemba) to have Jewish roots. And they used mitochondrial DNA (passed on only by females) to understand how and where Jewish communities were founded.1 Years later his lab found a signature that correlated perfectly with Ashkenazi Jewish ancestry among people who self-identified as Jewish.2

  But David had long since grown bored with the pursuit of human history via genetics, feeling that he had taken the science as far as it could go and wanting to do work that was more clinically relevant. He dug into another burgeoning field, pharmacogenetics: the idea that genes have a lot to do with how well (or how poorly) people respond to medications (see chapters 2, 6, and 10). David reasoned that if we could understand the genetic mediators of responses to drugs, then we couldn’t help but learn something about the diseases those drugs were used to treat.3 Particularly in the areas of epilepsy and infectious disease, his hypothesis has turned out to be correct.4

  He also became an early proponent of genome-wide association studies (GWAS), the approach whereby geneticists collect DNA from thousands of cases with a genetic disease, and thousands of controls without it.5 By typing, say, a million markers across the genome in hundreds or thousands of cases and controls, one could often find markers that were clearly associated with the trait or disease of interest. Indeed, David and his collaborators (including Duke infectious disease specialist Jacques Fellay and sequencing and genotyping czar Kevin Shianna) used this approach to identify genetic markers that determined how well HIV-infected people handled the virus and the amount of time until they would become sick, if ever.6 GWAS quickly became au courant and these studies identified markers associated with scores of diseases. The money continued to flow from NIH to fund them.7

  But because he is honest, because he is often prescient, because he is apt to see the dark side in just about everything, because he is David Goldstein, by 2008, even as his own GWAS were starting to yield compelling results, he was simultaneously telling the world that the emperor had no clothes and chomping on the hand that fed him. With few exceptions, he said, GWAS was a profound disappointment and he was hereby tendering his resignation from the amen chorus. “There is absolutely no question,” he told the New York Times, “that for the whole hope of personalized medicine, the news has been just about as bleak as it could be.”8

  The problem, as geneticists had come to realize, was that we could find variants associated with any given disease, but they didn’t explain very much of the disease and so weren’t very predictive. “For schizophrenia and bipolar disorder, we get almost nothing; for type 2 diabetes, [we’ve found] twenty variants, but they explain only two to three percent of familial clustering, and so on,” said David. If schizophrenia was 80 percent genetic, then why couldn’t anyone find any genes that played a major role in causing it? Geneticists began wondering
where the missing heritability of supposedly genetic diseases could be hiding.9 Unless it could be found, bringing the genome to the clinic in a meaningful way would be difficult, if not impossible.

  That powerful susceptibility genes couldn’t be found also explained why so many of the risk numbers reported by the consumer genomics companies needed to be taken with mammoth grains of salt. 23andMe, for example, typed customers for just two SNPs conferring susceptibility to multiple sclerosis.10 SNPedia, meanwhile, listed twenty-eight of them, all with modest effects.*11 This was the legacy of GWAS: for complex diseases like MS, which are determined by many genes and the environment, any single genetic risk factor was likely to be extraordinarily weak.12 And even if one had multiple genetic risk factors, as I did for MS, those factors interacted in ways that we were still a long way from understanding well enough to make predictions, let alone help guide treatment.

  David is athletic but neither tall nor imposing. He is thin and sometimes socially awkward, almost shy, often looking down with hands in the pockets of his jeans as he makes his way down the hall to a lab meeting, to get a coffee, or to catch a flight to Johannesburg or Taipei. He wears wire-rimmed glasses beneath a mop of thick, unruly hair. His lab is remarkably productive but fairly small given that it oversees an eight-figure annual budget. They are a close-knit bunch, meeting regularly for beer, jogging, meals, trips to the beach, etc. David would not have it any other way. He is free with his money. He rides a motorcycle and smokes cigars. He appreciates a funny joke, a well-told story, a nice bottle of wine, a good song. He enjoys life … as hopeless and futile as it may be.

  David and a few other like-minded people had come to believe that there had to be a better way find the elusive risk factors for inherited traits that GWAS had failed to uncover.13,14 If, as they suspected, these factors were rare, then they would not be found on the “SNP chips” used in GWAS. So how to find them?

  Sequence.15

  As they continued to chase the genetic basis of infectious disease, David and a group of collaborators amassed samples from a cohort of fifty hemophilia patients who had been exposed to HIV-infected blood but remarkably had not gotten infected. Why not? David assumed there must be one or more genetic variants that made them resistant to the virus. Those variants, he reasoned, would be found by sequencing. Kevin’s lab would sequence these fifty people; David’s people would analyze the data (when this book went to press, the hemophilia project was ongoing). In the process, Kevin, an Illumina partisan since buying his lab’s first Genome Analyzer in 2007, would put all of the company’s new toys through their paces.16

  I saw an opportunity.

  “I think you should sequence me,” I said to Kevin. “I am already going to be sequenced and phenotyped out the wazoo.” (I certainly hoped that that was true.) “My information will be completely public, so there will be zero IRB issues. You won’t need to establish a cell line because I’m right down the hall—if you need more blood you know where I live. I am the ultimate control sample … Well, unless of course you’re studying anxiety, metabolic syndrome, or nearsightedness—then I’m a case.”

  “Let me talk to David,” Kevin said. “It’s a question of money.”

  I pressed the “ultimate control” argument and David began to weaken. “Maybe if you were up for having more of your tissues sampled,” he said. “I’ll give some thought to it. If I were you I would try to talk to me about it some time when I’m not sober.”17

  “Done and done,” I said.

  It began with a five-minute blood draw in Clinical Research Coordinator Kristen Linney’s office followed by a routine chemical extraction of DNA from my white blood cells. But after that my sample sat for a while. Why? Because the Institutional Review Board at Duke (upon which I happen to serve) didn’t quite know what to do with me: most IRBs have not made provisions for returning results to research subjects, even lunatics like me who’ve already seen much of that same data and put it on the Internet for the whole world to examine. It’s a situation that IRBs have not ever had to think about—until now. Over the next week the request to have me sequenced and my data returned to me and the world would travel from Kristen’s office to the senior chair of the Duke IRB, to the associate dean of research support services, to the director of Duke’s Center for Bioethics and Humanities, and then back to the associate dean of research support services, then on to the dean of the medical school and the vice dean for research, then back to the IRB chair, back to Kristen, and finally, back to David, Kevin, and me.18

  Once we had institutional approval, I assumed we would be off and running. But around that time, the Shianna lab’s sequencing runs began to fail at an alarming rate: nearly one in two weren’t working. The fluorescent signals emitted by each sequenced base were decaying too rapidly; thus the early cycles could be used from each run but the later ones could not; they were too faint to allow the computer to distinguish one base from another. Within a few days it became clear that it wasn’t the machines, but rather the chemicals. And it was not just the Shianna lab. Genome Analyzers all over the world were failing. Illumina, which was forced to eat millions of dollars, told customers it was no longer shipping reagents until it could isolate the problem.19

  In the meantime, I went back and scrutinized my SNPedia data. Was there anything interesting there that I had missed? I didn’t think so. I was seven times more likely to go bald than most men … duh. I needed only to look in the mirror to confirm that. I had plenty of risk factors for coronary artery disease, type 2 diabetes, stroke, obesity … again, duh. I had had a grandparent on each side die before age sixty from a heart attack. My dad had had a quadruple bypass when he was sixty.

  I opened the SNPedia “Medicines” menu with a bit more optimism: as David and others had shown, pharmacogenomics was one of the most promising places for personal genomics to make a difference. Esther Dyson told me that perhaps the most useful thing she had learned from 23andMe was her family’s sensitivity to the blood thinner warfarin.20 As I witnessed firsthand when my nephew Noam developed deep-vein thrombosis (see chapter 10), warfarin is a tricky customer: not enough of it and you might suffer a debilitating blood clot. Too much and you might bleed to death.21 But unlike warfarin, most of the known drug response markers we knew about did not usually alter one’s sensitivity to a clinically meaningful extent.

  An exception was a series of markers in the ABCB1 gene. ABCB1 is expressed most strongly in tissues that serve either as barriers (the blood-brain barrier, the placenta) or are involved in eliminating waste from the body (kidney, liver, intestines). One can imagine how this gene might impede drug response: by raising a molecular “gate” or by causing the drug to be flushed from the system more rapidly.22 But as a team of German researchers found, some versions of ABCB1 genotypes do just the opposite: they enable drug response.23 Perhaps the gene could also lower a gate or keep a drug in the system for an extended period. In any event, most Caucasians, including me, were significantly less likely to respond to certain antidepressants because of their common ABCB1 alleles. I had been on ten milligrams of Lexapro (escitalopram) since 2008 and, as my wife could tell you, it had drastically changed my life for the better, mostly by reducing my anxiety level and keeping my frequent companion, a deep and abiding sense of impending doom, at bay. Yet my ABCB1 alleles would betray me, no?

  I went back to SNPedia and looked at the four other non-ABCB1 SNPs that had been associated with depression and for which I had been typed. They were a mixed bag. One was associated with a poorer response to clomipramine,24 another antidepressant but of a different chemical class than Lexapro, that is, probably irrelevant to me. Another was associated with major depression in Mexican Americans.25 But 97 percent of Europeans, including me, did not carry the risk allele … okay, next. Another was associated with suicidal thoughts in people taking Lexapro’s predecessor, citalopram (Celexa).26 Despite having the “normal” allele, I admit that in my darkest moments I had had such thoughts.

  The
last “depression SNP,” found in a gene that encoded brain-derived neurotrophic factor, was arguably the most interesting. BDNF is an important gene: it’s expressed like gangbusters in our brains and is crucial to the way our neurons grow and develop. Knock out both copies in mice and they’ll have problems with coordination, balance, hearing, taste, and breathing … and they’ll die soon after birth.27 Knock out just one copy and they’ll have problems learning and remembering.28 Like 28 percent of Europeans, I carry one copy of a variant in BDNF that changes a valine to a methionine at position 66 of the protein. Val66Met, as it’s known in mutation parlance, has been well studied; however, being well studied doesn’t always equate to real understanding. It appears by some accounts to be modestly associated with bipolar disorder,29 though BDNF has been touted as a “depression gene” in humans for many years despite inconsistent evidence and frequent failures to replicate. Some psychiatric geneticists think it’s time to move on.30 But we know that Val66Met has functional consequences. People with the valine allele exhibit higher BDNF activity; they tend to perform better on memory tasks. And the anatomy of their brains is different than people who are Met/Met.31 As I wrote this, pharmacogenomic data were scanty, but some studies suggested that people with lower levels of BDNF responded better to antidepressants.32

  Without knowing my BDNF levels, I knew this was all speculation. But as far as BDNF being a common “depression gene,” David Goldstein was having none of it. “Until fairly recently, psychiatric geneticists would start out believing in a gene because a role for it made such good sense, and then they would go out and find themselves a polymorphism in the gene that showed an association with something: a cognitive trait, an imaging trait, or with some disease of interest. That the association statistics were weak was largely judged acceptable because it all made such ‘good sense.’ Of the many famous genes implicated in this era, none survived into the genome-wide phase when researchers cleaned up their acts and insisted upon consistent standards of evidence. Even career-creating ‘discoveries’ like polymorphisms in BDNF associated with cognitive performance have failed to find support in the much better powered and better controlled genome-wide studies using large samples.” Both he and our colleague Anna Need pointed out, however, that truly rare variants in BDNF might yet turn out to be responsible for some subset of psychiatric illness.33