Transcript of Episode 44: Adam Siepel
Grace: [00:00:00] Welcome to The Bioinformatics CRO Podcast. My name is Grace Ratley. I’ll be your host for today’s show, and today I’m joined by Adam Siepel. Adam is chair of the Simon Center for Quantitative Biology and Professor of Biology at Cold Spring Harbor Laboratory. Welcome, Adam.
Adam: [00:00:15] Thank you. It’s great to be here.
Grace: [00:00:17] Yeah, it’s great to have you. So can you tell us a little bit about the research that you’re doing?
Adam: [00:00:21] Yeah. So we do work in a variety of areas in genomics. My laboratory is completely a dry lab. We do only computational work, but we collaborate closely with a number of experimentalists and really try to stay as close as possible to data generation and biological questions. But we do have strong backgrounds in probabilistic modeling, algorithms, machine learning and related areas, and we try to bring those skills to bear on our work. We are interested broadly in questions involving evolutionary genomics, in particular evolution of gene expression. We are interested in demographic reconstruction of human populations involving both humans themselves and human interactions with archaic hominins such as Neanderthals and Denisovans.
[00:01:11] And we’re also interested in natural selection making inferences about the strength of natural selection, which parts of the genome are affected by natural selection, and natural selection on different time scales: ancient natural selection affecting primates and more recent natural selection affecting human populations, for example. And then, I should say also, we’re interested in applications not only in human population genetics, but also in cancer and agriculture and other areas. We’ve also done some recent work on COVID modeling, for example.
Grace: [00:01:43] Yeah. I saw a really interesting paper from your lab on COVID, looking at the influence of daylight savings time on immune response.
Adam: [00:01:53] That’s right. Yeah, it sounds like sort of a crazy connection. But one of my colleagues here, a senior scientist, Rob Martienssen, HHMI investigator, had a hypothesis that there could be an interaction between daylight savings time and seasonal patterns of COVID infection relating to the fact that at certain times of the year, people are most likely to be interacting with other people during their daily commute, exactly at the time of a nadir in immunity, which occurs around sunrise. And he had the observation that daylight savings time changing the clocks prolongs the period of time when the daily commute coincides with sunrise. And so we did some modeling to show that indeed, there seems to be a signal in the public data indicating that there is an effect and that infections could be reduced by eliminating daylight savings time. So that’s one of many reasons to eliminate daylight savings time.
Grace: [00:02:53] Yeah. I’m all for that research as long as I don’t have to wake up an hour earlier than normal.
Adam: [00:02:58] That’s right.
Grace: [00:02:59] Yeah. So can you tell me a little bit about how you get those data sets to predict how human genomes have evolved over time?
Adam: [00:03:09] Well, all of our research is based on data that has been collected by other groups. Much of it collected by Svante Pääbo group at the Max Planck Institute in Leipzig, Germany. And they have over many years now develop techniques for extracting DNA from fossilized bones. The techniques are quite sophisticated because if you’re not careful, it’s very easy to contaminate the ancient hominin DNA with modern human DNA. And so they’ve developed clean rooms and special DNA extraction techniques and special purification techniques. And then post-processing bioinformatics techniques to ensure that the DNA they’re sequencing really represents the ancient remains and not the modern humans who are handling the fossils. But we can’t claim responsibility or credit for any of those works. We’re consumers of the data that they have produced and made publicly available.
Grace: [00:04:10] Right. Of course, I didn’t know if it was some sort of reverse modeling like taking current human DNA to predict what the DNA looks like previously.
Adam: [00:04:19] Well, there’s some of that because what you get is kind of a noisy readout of the the DNA for the ancient remains. And then we have higher quality representations of modern human DNA. And then we try to model the processes that could have given rise to both of those samples. And that does involve sophisticated statistical methods for reconstructing ancestral DNA, as well as explaining the observed samples.
Grace: [00:04:48] Yeah. And so can you tell us a little bit about what you found in that research?
Adam: [00:04:53] Sure. It’s now fairly well known and well accepted based on findings that were developed over the last decade that there has been a genetic interaction between modern humans and Neanderthals. In particular human populations outside of Africa, including Europeans and East Asians show a signal of Neanderthal DNA at something like three percent of their genomes that traces back to Neanderthals. And the best explanation we have for that signal is that there was some sort of interbreeding between modern humans and Neanderthals, probably outside of Africa, after humans had migrated from Africa, something like seventy or eighty thousand years ago. And that signal persisted as these out-of-Africa populations spread across the globe.
[00:05:55] We came in already knowing these findings and already familiar with these findings. And we tried to develop a model that would jointly explain a number of ancient samples and a number of modern samples from around the world. And our goal was to see whether we could both explain this known pattern of Neanderthal-human interaction, but also possibly detect other signals of interest. And what we found interestingly, and this was published in 2016 in a paper in Nature that I jointly co-led with Sergi Castellano, who was then at the Max Planck Institute in Leipzig and has now moved to London. What we found was that there was a signal, surprisingly in the opposite direction of modern human DNA in Neanderthals. And this was something that hadn’t been reported previously. And the signal was quite subtle. And it was quite difficult to convince the community that it even existed.
[00:06:49] But we were able to convince reviewers of our paper, and it has since been supported by a variety of other analyses. And interestingly, this signal is not specific to out-of-Africa populations. It’s shared by Africans as well, and it appears to be much older. We’ve since in more recent work, dated it to somewhere around two hundred fifty thousand years ago. And so that suggests that there was an earlier integration event that left a signature in the opposite direction from modern humans to Neanderthals, and it affected all human populations. So it probably occurred in the ancestor to all modern humans. Furthermore, that’s interesting because it must have predated the migration of humans out of Africa. So it seems like there was a group of early modern humans that migrated out of Africa interacted with Neanderthals, leaving this signature in Neanderthal DNA that we’ve detected.
[00:07:52] And then that group of modern humans either just went extinct or ended up being absorbed back into human populations in Africa before a second migration out of Africa seventy or eighty thousand years ago. So anyway, it suggests not only another interaction between modern humans and Neanderthals, but one that’s much earlier, and it paints a picture of multiple migrations of modern humans out of Africa. And only the more recent cases led to the current populations that we know of today outside of Africa.
Grace: [00:08:28] Yeah, that’s so fascinating. I feel like bioinformatics is already such an interdisciplinary subject. I mean, taking together biology and computer science, and then you take it a whole step further and add anthropology in there. Do you work closely with people in anthropology or studying human history?
Adam: [00:08:48] Yeah, I have not worked directly with anthropologists myself. Although we did have an anthropologist collaborator on the paper in Nature, although we worked more closely with Sergi than with me. But there is a lot of interest across the field at this intersection between genetics and anthropology and Svante Pääbo and David Reich and others have been quite proactive about interacting across fields. I attended a meeting here at Cold Spring Harbor a few years ago that was organized by co-organized by David Reich. That was a group of geneticists and a group of anthropologists together discussing these issues, and it was fascinating. But it’s not something that’s really in the center of my own research.
Grace: [00:09:29] You seems to have a very broad reach with your research. It’s great. Yeah. So you started out doing computational modeling and phylogenetic modeling in HIV. Can you tell us a little bit about that work?
Adam: [00:09:41] Yeah. So this was my first job actually straight out of college. I hadn’t gone to graduate school yet. Through a friend of mine who had been an undergraduate with at Cornell, found out about this opportunity to work at Los Alamos, doing HIV sequence analysis. And it was interesting to me for a number of reasons. I had done an undergraduate degree in agricultural and biological engineering. And so I had been interested for a long time in sort of the intersection between mathematical modeling and questions in biology. But I had never dealt with DNA sequence data before, and I had never dealt with phylogenetics or evolutionary reconstruction. And when I was exposed to those fields, I just found them fascinating. I mean, they resonated with me in a whole variety of different ways. I’ve always been interested in reconstructing the past.
[00:10:28] I’m interested in random processes. I’m interested in computer algorithms. I’m interested in evolution. And so all of these interests sort of came together in this fascinating area of using phylogenetics. And then that work also had an epidemiological component. We were building phylogenetic trees to describe HIV sequences, but then we were making use of them to understand the spread of HIV across the globe because we were seeing different strains emerge in different regions of the world. And then we began to see interactions between these strains and the production of recombinant strains of HIV. So my first scientific paper was actually on an algorithm that I developed, a very simple algorithm, to detect recombinant strains of HIV, which at the time was a kind of a new idea and something that was of great interest in the field.
Adam: [00:11:23] My first experience was very exciting publishing a scientific paper. I think I was 23 years old and was able to publish a paper that established researchers in the HIV field were excited about, and I got to present at meetings and so on. And after that I was hooked. I was hooked on science, I was hooked on computational biology and I was hooked on evolutionary genomics.
Grace: [00:11:45] Do you still keep up with emerging HIV research and things like that?
Adam: [00:11:49] I haven’t participated in HIV research since that time. I moved on to other questions. Although I have to say I got interested again recently in this question of recombination and viruses with the emergence of COVID-19. And reread some of those old papers and including my own old work on detecting recombination in viruses because there was some discussion about the role that recombination might have played in the emergence of SARS‑CoV‑2 in human populations. But that’s my only experience in that area in the last 25 years or so.
Grace: [00:12:30] Yeah. So I guess speaking of the pandemic, from an evolutionary standpoint, do you think we need to worry about new variants and things like that?
Adam: [00:12:41] The basic evolutionary fact is the probability of emergence of a new variant should be proportional to the number of viral replication events, which is going to be proportional to the overall number of cases. And so we need to get the number of cases down. And the best way to do that is through vaccinations. It’s been extremely discouraging to me to have these effective vaccines, more effective than anyone could have hoped, and see people reluctant to use them. So I think we just have to keep hammering on the vaccination efforts. They need to be available across the entire world, not just in rich, first-world countries. We need to push really hard on getting access to them, convincing and incentivizing people to use them.
[00:13:36] Ultimately, I think new variants will emerge. We will develop over time an increasing sort of baseline resistance for most people in the world who will eventually be exposed. And I think the pandemic will eventually reduce itself to a baseline level. But I think the virus will be endemic and we’ll have to adjust to it being part of life. I’m optimistic that with increased baseline resistance, increased vaccination, increased ability to provide new vaccines quickly and efficiently, that we won’t be brought back to our knees by emerging variants. But it’s difficult to say for sure what could happen as new variants emerge.
Grace: [00:14:29] I always like to hear the different perspectives of people in different fields of science on the pandemic. I think the evolutionary take on it is very interesting. You also did a study in bats, on the evolution of bat immunity and things like that.
Adam: [00:14:46] That’s right. Yeah, we’ve gotten very interested in comparative genomics of bats, in part because of their connection with SARS-CoV-2. But for other reasons as well. In fact, our initial work on bats has been funded by our cancer center here at Cold Spring Harbor. Because bats are remarkably resistant to cancer and we’ve been trying through DNA sequencing and comparative analysis to shed light on the genetic underpinnings of both bat specific immune responses, bat specific cancer resistance and longevity of bats. Bats are extremely long-lived mammals for their body size. If you plot body size versus lifespan in mammals, you see a general proportionality. But bats are an outlier. They live much longer than other mammals of similar size, such as mice. Bats can live 35-40 years or more.
[00:15:42] We’ve been doing DNA sequencing and analysis. We have an initial preprint out on our findings. We’ve found some interesting things in both immunity and cancer, in particular a massive contraction of the IFN1 locus. And a strong enrichment among apparently positively selected genes for tumor suppressors and DNA repair genes. And we’re in the process of working closely with experimentalists to begin to test the actual molecular basis of some of these differences between bats and other mammals. And we’re also in the process of applying for grants from the NIH on this topic.
Grace: [00:16:22] That’s so cool. Because I guess I wanted to ask a little bit about the importance of evolutionary biology in the study of cancer, because I wasn’t necessarily sure how those two topics connected. So that’s a really interesting take on comparative genomics and looking at how that immune system has influenced their susceptibility to different cancers. And bats have a reduced immune response. They don’t have a very active immune system, is that correct?
Adam: [00:16:51] Yeah. They seem to be able to tolerate viral infections without having a very powerful immune response. And it’s interesting because when you look at what makes humans sick when they become infected by SARS-CoV-2 or other viruses, it’s often an overly powerful immune response that makes them very sick. And so in some cases, it seems that viruses are killing us, not because our immune response is inadequate, but because it’s too powerful. And one hope is that we can learn something from bats in the way that they’re able to keep from getting sick from these viruses and yet not have an overly powerful immune response that ends up harming them more than the virus itself. Yeah. So that’s one of our interests in this area.
[00:17:44] Of course, it’s also interesting to just understand the dynamics of zoonotic transmission and the way in which bats are harboring viruses and then transmitting them to people. The fact that bats seem to be able to tolerate such high viral loads does seem to be essential to their role as a reservoir for viruses that get transmitted to humans. And so understanding their viral tolerance is also important and interesting in that regard.
Grace: [00:18:16] Yeah. So I mean, evolutionary biology is kind of a pretty popular science topic. So what do you think are some misconceptions that people in the general public have about evolutionary biology?
Adam: [00:18:30] One misconception is understanding the diversity of selective forces that have influenced humans. People tend to think in conventional terms about the strongest humans being the ones that propagate, you know, the ones that are least likely to be killed by predators and that sort of thing. And undoubtedly avoiding predators was a source of selective pressure on humans. But there are many others that I think tend to be underappreciated. One of them is infectious disease. I mean, humans have been enormously shaped by infectious disease. And one of the strongest selection pressures on us is the resistance to infectious disease. The pandemic is helping make this issue more clear. But I think in general, we tend to have forgotten a lot about infectious diseases because they play much less of a role overall in modern life than they have in the past.
[00:19:28] Another really important selective pressure is sexual selection. The choices people make about who they mate with for various reasons. And then there are very strong selective pressures that influence reproduction in a way that humans have no choice over. So, for example, sperm competition individual sperm cells competing with one another to fertilize an egg. So there are many, many levels at which selection acts. And I think when people just think about a caveman dodging a mountain lion or a bear, they’re only getting at a very small sliver of the diversity of selective forces that have influenced human evolution.
Grace: [00:20:14] Yeah, that’s true. There are some really interesting selection events. So after you worked in Los Alamos, where did you head after that?
Adam: [00:20:22] Well, I was working in Los Alamos in the mid 90s. And I had an engineering background and I had a lot of interest in developing computer software. And at the time, I felt that my interests lay more in the software development area than in the scientific research area. And it coincided with a time where there was a lot of opportunity for software development in bioinformatics. A lot of companies were creating bioinformatics groups. A lot of people were developing and either selling or making publicly available bioinformatics software. And so I took a job at a group in Santa Fe, New Mexico, called NCGR, National Center for Genome Resources that was doing a lot of software development. I went there and I worked for about 5 years as a software developer and learned a lot about software development and then kind of came to the conclusion that I wanted to get closer to the science.
[00:21:20] And after many years of putting off going back to graduate school, I decided I really needed to bite the bullet and get my PhD. I was sort of a reluctant academic, I have to say. At the time, I was of the mindset that I could teach myself anything I needed to know. But I finally decided that there was value in getting my PhD and diving back into scientific research. So I left software development, became a full time PhD student and went to Santa Cruz, California, to join David Haussler Laboratory. And from that point on, I have plunged myself into the world of comparative genomics, population genetics, evolutionary modeling and so on.
Grace: [00:22:01] Yeah. And despite that reluctance to going into academic science, you stuck with it after your PhD because you went and became a professor at Cornell and now Cold Spring Harbor. Can you talk a little bit about that decision? How did you change your mind?
Adam: [00:22:16] I actually had not planned to go into academia. I wasn’t sure what I was going to do. But it was an exciting time, the early 2000s for academic computational biology. There were a lot of opportunities emerging, a lot of new departments, new research centers. And in my third year as a PhD student, I had been working with Rasmus Nielsen, who’s now at UC Berkeley, on a book chapter project. He was editing a book and I was writing a chapter with my advisor. And he sent me a job ad at Cornell and I read this job ad and it just sounded like it was written for me. I mean, they were looking for someone who had exactly the sort of expertise I had. And, you know, I had been an undergraduate at Cornell, so I had a lot of affection for the place.
[00:23:07] Coincidentally, I also was considering moving closer to family. My family’s from upstate New York and Cornell is in upstate New York, and I had two small children and we were getting tired of putting them on planes every time we wanted to see family. So I said, what the heck? I’ll apply to this job. I applied and I got the job. So I said, well, you know, I never really planned to be an academic, but this sounds like fun. It sounds like a great opportunity. I love what I’m doing. This is an opportunity to keep doing what I’m doing. And I took the job and never looked back. I’ve really enjoyed academic work since then and have been able to make it work, been able to keep the lab funded and keep publishing papers and keep recruiting students.
[00:23:49] And I think I’ll just keep doing that as long as I can. But it was a different time. I mean, I mentor a lot of my own graduate students and postdocs in their job searches. And I think the job market is much more competitive now than it was then. There was a lot of opportunity in computational biology in the early 2000s, and I benefited from being in the right place at the right time. Sometimes I see the job searches we carry out now, and I wonder if I would have even gotten an interview for some of these jobs.
Grace: [00:24:18] Yeah. Academic science is a very competitive space these days. But there is such a strong need for bioinformaticians and computational biologists. So I mean, there’s a lot of job security in that, but maybe academia is a lot harder.
Adam: [00:24:34] Yeah. I think there are more industry opportunities now than there were at that time. And, you know, the combination of the competitive academic job market and the opportunities in industry means that a lot of young trainees are going into industry, which I think is great. I have a number of recent postdocs from my lab who’ve taken industry jobs and are very happy in them. But, you know, the pendulum tends to swing from one side to another on these things. And I wouldn’t be surprised if in a few years the supply and demand dynamics have changed and things open up in academia again.
Grace: [00:25:08] Certainly. And how have you seen bioinformatics and computational biology as a field evolve in the last few years?
Adam: [00:25:18] One change is just, as I mentioned, a swing toward more activity in research and industry. Another change that I’ve seen in my time in computational biology is just a general shift toward embracing the biology side of the field. They need to ask good biological questions, they need to engage with the data and people not being satisfied with just taking whatever the latest algorithmic or machine learning advances and applying it to a biological data set. I think when I started in the field in the early 2000s, there was a lot of that. There were a lot of people doing computational biology who weren’t that interested in biology and didn’t know that much biology. They were just taking off-the-shelf computational methods and applying them to biological questions in a not very imaginative way.
[00:26:10] And I think over time, people have really realized that in order to do computational biology well, you have to engage with the biology. It’s not enough to just have a computational hammer and look for nails. You have to really think imaginatively about biological questions and how computational methods can be used to address them. And about the interaction between computational methods and experimental methods. About how experimental methods can lead to hypotheses that can be tested computationally and vice versa. Computational methods can generate hypotheses that can be tested experimentally. That feedback between computation and experiment, I think is extremely important and has become more pervasive in the field.
[00:26:54] I think the field is also just bigger and more competitive. Early on, there were really just a handful of people who had this joint background in computer science and biology. And if you were one of those people, then you could sort of write your own job description. It was relatively easy to find a job in the field. Now there are many, many people who have those backgrounds. There are people emerging from PhD programs in bioinformatics and computational biology. There’s a lot more awareness of these questions in biostatistics departments or biophysics departments. It’s just a much more established and competitive academic field.
Grace: [00:27:39] Do you think you would have chosen the same path if you had graduated in bioinformatics today?
Adam: [00:27:46] I really don’t know. I mean, I think I was attracted to the field being so new. And maybe I would feel today that it was too established and I would look for something newer and more niche. But it’s hard for me to say. I also think it’s possible that if I were finishing my PhD now that I would end up in an industry job rather than in an academic job just because of the dynamics of the field at the moment. But it’s always hard to ask these counterfactual questions.
Grace: [00:28:19] True, true. So given the hyper competitive job market for positions and bioinformatics, can you maybe give advice to people who want to enter that field? Like what sorts of skills are most important today?
Adam: [00:28:37] Yeah. I guess, I think it’s true that a graduate from a bioinformatics program who’s interested in this field needs to be fluent in data science and machine learning, basic statistics. But I think that those things are necessary but not sufficient for success in the field. And I think what really will push a person over the edge is also really thinking like a scientist, not just like an engineer. So developing a good taste in problems, developing a nose for questions that can be effectively addressed using computational methods, developing a fluency in the biological technologies and biological questions of interest, the ability to interact closely with experimentalists. I think these are the things that push a person over the edge from being just a data scientist to being a computational biologist who can lead the way in the scientific side of the field.
Grace: [00:29:41] It’s very good advice. So tell me a little bit about you. Like, who is Adam the non-scientists? What do you do outside of research?
Adam: [00:29:49] Well, I have two kids. My daughter just started at the University of Rochester. So I’m adjusting to going from two kids at home to one kid. I live on Long Island in Huntington, New York and live in an old Victorian house and spend a lot of my time fixing that up. And I like to do a lot of cycling and some hiking and spend as much time as I can outdoors. That’s probably a pretty good summary.
Grace: [00:30:16] Yeah. I actually saw in your Twitter that you were planning on heading to Iceland. Did you make it out there?
Adam: Yeah, we did.
Grace: Nice. Yeah, I was just there a couple of weeks ago.
Adam: [00:30:27] Ok. Yeah, we really enjoyed it. We had a fantastic trip. It’s a beautiful place and it felt like the right sort of first trip out of the country after COVID. Relatively safe and controlled.
Grace: [00:30:39] Yeah, that’s excellent. Yeah. Actually, Iceland is a really, probably a very interesting area to study because it’s so isolated and they have a huge dataset. Haven’t they sequenced everybody in Iceland?
Adam: [00:30:52] Yeah. The studies by deCODE have been extremely influential in a variety of different ways, both for association studies and also for studies of rates and patterns of human mutation, which they’re able to trace in great detail, taking advantage of their genealogical databases and pedigrees. So, yeah, it’s been very important in human population genetics. It’s also interesting to look at Iceland from an ecological perspective. I think the largest land mammal was the Arctic Fox in Iceland when Scandinavians arrived and began bringing agricultural animals. So there’s a very short history of large land mammals there. And then there have been interesting events like the introduction of the Icelandic horses and then subsequent genetic isolation, of those horses. And it’s interesting to see the way they have been shaped by the Icelandic landscape and climate, as well as by human selection. But yeah, it’s a fascinating place for questions in evolutionary biology. Certainly.
Grace: [00:31:59] Certainly. And yeah, Iceland horses are really interesting. They had such strict laws that if an Icelandic horse was taken out of Iceland that it couldn’t be brought back into the country. It was just really interesting. And with humans, they have an app. It’s like a dating app where you can check and see if it’s okay to date somebody based on your familial relationship to them.
Adam: [00:32:24] Ah, to see whether you might be related, yeah.
Grace: [00:32:26] Yeah. You put their name in and I think the generally accepted okay line is like fourth cousin or something like that.
Adam: [00:32:33] I see. Well, amazing.
Grace: [00:32:35] Yeah, it’s a really interesting country. Yeah. So as we wrap up the episode, do you have any other any final thoughts on the future of bioinformatics?
Adam: [00:32:46] Well, I guess the future of bioinformatics, I think it’s an open question whether bioinformatics will remain a distinct field. I think that to some degree, the tools of bioinformatics are being absorbed by broader biological sciences. They’re just becoming part of the toolkit of doing biology. And I think in the future, biologists will need to be much more fluent in computational methods and the use of machine learning and the use of powerful computers. And we may not think of it as a distinct field. It may just become part of being trained to do biology. And I think that’s okay. I think often new fields emerge at the interfaces of other fields, and they may or may not remain distinct. They may be absorbed over time, and I think that’s okay. I’m personally very excited to see quantitative methods and computational methods become so central in biology.
[00:33:50] You know, our center at Cold Spring Harbor, it’s called the Science Center for Quantitative Biology. It has begun as kind of a distinct group of investigators doing developing quantitative methods. But increasingly we’re being absorbed by the broader scientific community at Cold Spring Harbor. And the talks when we gather at our annual symposium or some other event to talk about our research. The talks from the quantitative biologists are beginning to involve more experimental biology and more collaboration with experimentalists. And then conversely, the talks by the experimentalists are beginning to incorporate more data analysis and quantitative methods. And I think the logical conclusion of this process is that we probably won’t be a distinct group anymore. We’ll all just be biologists using whatever tools and techniques are available, a combination of experimental and computational tools and techniques. So I guess that’s what I think about the future of the field. It’s dying, and that’s okay.
Grace: [00:34:56] It’s dying, and that’s a good thing. Fantastic. Well, thank you so much for joining me today, Adam. I had a great time listening to your thoughts on evolutionary genetics.
Adam: Yeah, thanks, Grace.
It should be noted that the race and ethnicity data are not normally distributed (Shapiro Test P<0.01 for all).
White P<0.01 | Black P<0.01 | Asian P=0.22 | Indigenous P=0.66 | Hispanic P=0.30 
It should be noted that the race and ethnicity data are not normally distributed (Shapiro Test P<0.01 for all).
White P<0.05 | Black P=0.19 | Asian P=0.26 | Indigenous P=0.70 | Hispanic P<0.01 
