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    • Hi Victoria, thanks for asking this question! I struggled with test taking and anxiety in school, but my biology teacher took me aside after I failed the midterm and said, "I don't think this score reflects your knowledge"- we then proceeded to have a casual conversation about the topics on the test and afterwards she crossed out the F and wrote A-, saying "this is a score that reflects your understanding of these topics". It was like this massive weight came off of me- I WAS good at this! After that I interned at Children's Hospital Oakland Research Institute in a genetics lab throughout the rest of high school and from there I just kept learning and growing in the field. It is such a perfect marriage of two of my passions- mathematics and human health.

    • How much scientific progress in the modern world is dependent on collaborations between researchers of different specialties such as microbiologists, computer scientists, mathematicians and neurologists?

    • Collaboration is both fundamental and essential to science, and the field of genetics is a great illustration the importance of collaboration. Genetics has been one of the earlier fields in biology to move into 'big data'- this requires enormous amounts of computing expertise. Genetics also requires expert knowledge of molecular systems and pathway biology- e.g. how does a mutation in DNA translate into disruptions in proteins and then lead to disease? In human genetics we also have to understand the physics of how the genome is measured at scale- often via measurements of light and energy emitted in chemical reactions. Chemistry, physics, engineering, computer science, mathematics and statistics, epidemiology, and molecular biology all are integrated into this one amazing science. My background has prepared me with skills in math and biology, but I rely on the expertise of neurologists in my Alzheimer's Disease research, and speech and language pathologists in my research in Developmental Stuttering- success in human genomics is the due to incredibly thoughtful and hardworking experts coming together from across the disciplines.

    • Your research seems particularly broad to me, sometimes focusing on heart disease, diabetes, Alzheimer’s disease, or stuttering. How do you approach such diverse types of diseases?

    • Yes! You are absolutely right, many researchers devote themselves to understanding the treatment or cause of a particular disorder or trait. It's a great observation that my research addresses a diverse range of traits. Right now we have projects on Alzheimer's Disease, cholesterol levels and type 2 diabetes risk in Mexican American and Hispanic populations, developmental stuttering and developmental language disorder. We have a project on apical periodontitis (tooth decay), and even one on looking at HIV susceptibility in rhesus macaque that have been given a vaccine! The common thread in our work is not the disease, but rather it's the math that underlies the search for the causal genetic factors. My lab develops new algorithms and statistical approaches that can be used to find the genes the impact disease risk in different genetic contexts. For example, can we find and use familial relatedness to help us find genes that put people at risk of disease? Can we use genetic samples derived from multiple ancestral populations to help narrow our search for genes? These kinds of questions motivate our work and help us create tools that can be used in my lab and by other geneticists around the world!

    • Is it my imagination or is modern science becoming ever more tech heavy? I see you have a paper using graph databases. Would you consider that math, tech, science or all of the above?

    • Not your imagination at all! That project was especially fun because we were faced with a genetics problem (too much relatedness in my dataset to use statistical methods that assume everyone in an analysis is unrelated), and I got to reach back into my memory of a graph theory course from when I was an undergrad majoring in math: how do you solve the problem of finding the maximum unrelated set in genetics? Mathematicians had already solved the problem of finding maximum sets- all we had to do was know the solution existed and adapt it for genetics! This is why it is so often useful for scientists to be interdisciplinary. The best geneticists I know come from other fields- physicists, anthropologists, and computer scientists often make fabulous geneticists! At the same time, because the scale of data we work with has grown so rapidly, developing resources for the necessary tech has been essential in my field. This trend is becoming more and more widespread- digital imaging of brains and retinas, proteomics, the microbiome, metabolomics- all are driving their respective fields into a space where tech and data analysis are going to be essential. An old mentor of mine used to say that he had enough data on his computer already to spend the rest of his life analyzing... but of course that doesn't mean we won't keep generating more! My field has been developing strategies for this tidal wave of data for more than a decade, and we have made a lot of mistakes on the way! I hope other fields are able to take lessons from genetics as they too become inundated with data and the accompanying tech necessary to handle it.

    • After researching genetic predisposition to type II diabetes, were you able to infer how much of the condition comes from genes as opposed to lifestyle?

    • Great question. The heritability of T2D is typically estimated to be around 40%-60% and the risk of developing T2D ~40% for individuals who have one parent with T2D and ~70% if both parents are affected. T2D affects ~14.3% of the U.S. adult population and the prevalence is projected to at least double over the next two decades. T2D is what we call a 'complex disease trait'- risk of T2D comes from a host of genetic and environmental factors, and to make things even harder, those genetic factors can interact with one another and can interact with the environment as well. So a genetic risk factor in one environmental condition may not have the same effect in another environmental (or genetic) context. Not only that, but it looks like T2D might not just be the result of one system failing... it can result from a number of different pathways being disrupted- all of this makes T2D a poster child for the hard to study diseases. Here is a link to an amazing paper on T2D that likens overall risk to a palette of major pathophysiological processes that contribute to diabetes risk and progression. It really impacted the way I think about the disease and strategies for researching genetic risk factors.

    • What to you think will be the role of AI in science? How much AI do you recommend your students study?

    • In my lab, we use natural language processing and machine learning algorithms to help us interpret medical notes and the electronic health record. It's a growing area and I think will become increasingly relevant due to the size and scope of the text we are ingesting from large electronic health systems. I predict (hope) that pairing these approaches with linked DNA databanks to discover genes that impact human health is going to result in some major advances in personalized/precision health.

    • Yes- I think the space in which I work where I most worry about ethical considerations is in genetic relatedness. When you are studying families, the ethical landscape can grow complex. How do you counsel parents of child with a severe recessive disease, if it turns out the dad is not the father?

      How do you respect the wishes of a mother who has made a decision not to perform genetic testing when her children and their father elect to have it performed (creating a situation where the mother's genetic risk can be inferred).

      As a society are we going to decide that law enforcement should be able to mine genomics data to look for criminals? Or look for the relatives of criminals?

      These are hard questions, and what we are capable of doing in genetics to has somewhat outpaced the ability of society to come to a consensus about what should and should not be ok.

    • It's funny, I fell in love with genetics in high school, but fell out of love with biology a bit in college. I remember calling my mom in tears, freaking out about my 'crazy' decision to major in math instead of biology- I really looked at it as completely divorced from genetics. At the time, I only understood genetics to be a bench science (think test tubes, and beakers), it never occurred to me that I could do both. Yet my math classes were really satisfying in three ways 1) I felt like I was getting to sit around and solve puzzles all day 2) when you prove something in math, it's finished, there's closure, a beautiful contentedness that you never really get in biology 3) it was so incredibly hard for me and the challenge of it just baited me on. It wasn't until I got to graduate school and studied computational/statistical genetics with Dr. Nancy Cox that I suddenly realized that I could apply math in genetics... that was it for me. I had found my passion.

    • I love to cook massive dinners for crowds of friends and throw epic parties, but I don't always like to socialize and mingle. I cooked a 26 course dinner for my friend on her golden birthday, made 75 lbs of homemade sausage for a fourth of July party (and seven kinds of homemade icecream sandwiches!), and for years used to turn my entire house into a massive haunted house with formal dinner for all my friends at Halloween. But, I usually prefer hiding in the kitchen filling pitchers of cocktails and pulling things in and out of the oven to being out in the middle of the party. It's so bad some of my close friends nicknamed me Gatsby!

    • God, I hope so. Alzheimer's is such an awful disease and in part because politicians have decided to prioritize funding for it, researchers are making enormous strides (see for example, This recent paper, for example, is so interesting and exciting:

      In my lab, we are excited about finding genes that have disrupted expression patterns associated with Alzheimer's risk, and then using those genes to find early signatures of Alzheimer's risk in the electronic health record.

      Science communication is a passion for me, so I love your second question. Often, audiences won't read past the title, so if I could encourage the mainstream media to improve one thing, it would be to write accurate and nuanced titles in their coverage of scientific research. This twitter account, for example, really hits home for me:

    • I don't know that buying a direct-to-consumer genetics test like 23andMe or Genos is really an investment in health so much as a fun diversion that can give you rough estimates of where your ancestors came from. The role of complex genomics for shaping clinical decisions or improving health is still very much up for debate (large effect size rare variants on the other hand can have major impacts on health and treatment, but are not well measured by most consumer products). Some geneticists are extremely optimistic about polygenic risk scores, some geneticists are very pessimistic (for example, check out the discussion following this tweet:

      Regardless, it's important to remember that medical decisions should be made under the advisement of a clinical genetics expert based on a CLIA certified genetic test (which to my knowledge, the kind of kits you are referring to are not).

    • I love this- those who cannot remember the past are condemned to repeat it, right? I'm biased because I went to Carleton College, but I think a liberal arts education provides a phenomenal foundation for professions in STEM.

    • I actually wrote a piece on P values recently!

      P-values indicate how incompatible data are with a specified statistical model or hypothesis.

      The American Statistical Association noted, "researchers often wish to turn a P value into a statement about the truth of a null hypothesis, or about the probability that random chance produced the observed data. The P value is neither. It is a statement about data in relation to a specified hypothetical explanation and is not a statement about the explanation itself."

      Their whole piece on P values is excellent and written in a way that I think is readable even to folks without a background in statistics:

    • I am very worried about the long term implications of private genomic data warrehouses like 23andMe and Ancestry. What is a fun novelty today may have very serious implications down the road for privacy and even health. For example, if people are given a 'low risk' profile for diabetes or obesity, it may impact behavior (they could eat more, thinking, "It's ok, I have low risk!"), even though this 'low risk' evaluation has little to no clinical relevance. At the same time, some of these companies will partner with researchers to help inform disease gene discovery, propelling personal/precision genetic medicine, which is potentially a very positive thing. So it's a mixed bag. I think we will learn a lot about data privacy and policy in the next few years as Facebook faces additional regulation- hopefully direct-to-consumer genetics companies can avoid some of these privacy challenges and politicians can be more proactive about putting laws in place that protect individuals and their data.

    • First of all, I'm sorry you and your husband are faced with the challenges of CMT, and I wish you all the best. It looks like the studies he has participated in have followed the trend of human genetics: we started out studying candidate genes, moved to whole exomes when the technology became afforadable, and now even whole genomes can be sequenced at a reasonable price. I'm not a CMT expert, but if you are asking what's next in the trajectory of genomic research, your husband might next be asked to participate in whole genome expression studies that measure RNA. And on the more distant horizon, companies are now claiming to be working on adult human gene editing...

      I hope for your family, that these studies are fruitful, and that translational therapeutics are rapidly forthcoming!

    • My husband is a cyclist and I am a triathlete, and I am much much slower on the bike than he is- we joke that I prefer to do many things poorly rather than any one thing well ;)

      Thank you for your appreciation of r/science! Because so much of scientific research is publicly funded, I believe that researchers have an obligation to do outreach and communicate science to the public- in that endeavor, with >21M subscribed readers, r/science is a major communication resource.

      In practice, only a small portion of the 1500 moderators are active in any given month (less than 5% have more than a handful of moderation actions), and the 'power moderators' are in pretty tight communication on a daily basis. I can't say enough good things about that team of moderators- they are truly amazing in their dedication to promoting high quality scientific communication and discussion.

    • My parents are both very math and science-minded. My dad was a mechanical engineer and my mom developed science curricula for primary school, founded a math and science camp, and has a very scientific approach to how she operates in the world (in everything from the quilts she designs to the courses in robotics and lego-logo she created for elementary school kids). It was really influential having parents that were creators and inventors, and who didn't shy away from challenging math. At the same time, I think my mom is still a little disappointed that I didn't become an opera singer ;)

      I was probably around ten years old, and I'm not sure who said it to me... but you know how it's really common for people for people to demur about math? Saying things like, "Oh, I'm TERRIBLE at math!" almost with a bit of pride in their voice? When I was a kid, someone pointed out how weird that is! How even though math intuition is just as intrinsic to the adult human brain as reading (think about the subconscious calculations your brain is doing just to know when and how hard to start breaking to stop your car at a stop light) no one would ever say, "Oh, I can't READ! I SUCK at READING!"

      It put it in my brain early that the notion that it is somehow cool to suck at math is a weird (dumb) cultural phenomenon; I think I still cringe a little when someone brags about being bad at math.

    • Long reads are going to be awesome for robusting detecting structural variants and copy number polymorphisms! Right now these can be hard to detect with short read sequence data and as a result there is a whole class of variants whose effect on human health we are doing a poor job of detecting.