Hey folks, we're in the process of learning more about base R so that we can use those skills to enhance our skills using packages and functions from the tidyverse. We are reading in a non-rectangular data type. Specifically, we're reading in a lower triangular distance matrix often called a philip formatted distance matrix. We are using this because we can't read in the distance matrix using something like read TSV or read CSV from the read R package that automatically imports things as tibbles or data frames, right? And so because it's got this odd shape and it's not rectangular, we have to, you know, learn some base R. And that's all good, because learning base R will certainly enhance our skills across the R programming environment. Now in the last episode, we learned about matrices, and we saw how we could kind of eat through a vector and convert that vector into a matrix. We had a couple problems. So problem one is that it is effectively hard coded, if you will, that the matrix we were reading in had data for 10 samples, we read it in using the same chunk of code repeated multiple times. And so if we wanted to read in a matrix that had say 300 samples, I'd have to repeat that code like, I don't know, 290 more times. And that would be really painful. It's what's called not dry, right? And so dry is an acronym for don't repeat yourself. If you will, I guess this is wet code, right? We're repeating the same chunk over and over with subtle differences. So in this episode, I'm going to show you an approach that we can use to dry out our code. And we're going to do that, recognizing patterns in the code and using a for loop. I know you've probably heard that for loops are just the worst possible thing ever in our and you should work to remove all possible for loops. And while that's certainly true in some cases, in this case, it's actually not that big of a problem. So I'm going to show you how we can write a for loop and how we can use that for loop to dry out our code and make it a lot simpler. A for loop has a fairly simple syntax. We're going to say for variable in vector. And then we are going to use curly braces. And the curly braces will define the the functionality that's going to get repeated over all of the values of your vector, right? And so here you might think of this as like your special sauce to be repeated, right? And and so then again, it's going to repeat what's in that, however many times or over all of the values of your vector. Let me go ahead and comment this out because this is kind of a template for what we're going to do. So I could say four x in one to 100, right? So for my variable x in my vector, one to 100, right? So that vector is one, two, three, four all the way up to 100. I'm then going to use those curly braces. Those curly braces are to the right of the P on your keyboard. If you're like me, you probably never use those curly braces except for programming. But curly braces are helpful for defining a body of code, really helpful within a for loop and with functions in our I can then do print x. And so print is going to print the value of x over every element in 100 from one to 100, right? And so now when I run this, I see as the output that it is printing every value of the vector one to 100, right? And so it's printing x and x is iterating between one and 100, right? So I could then modify x and I could say x squared. And again, if I run my for loop again, now I get all of the square values of the values from one to 100, going 149, right? So that's the square of 123. And finally getting down to 10,000, which is the square of 100. So this is a for loop. This is a very simple for loop. And again, what I want to highlight is that the for loop, the four is a function, right? And the argument requires x and some type of vector, right? And so we're taking x as the variable that's used within the body of the function. And we're iterating it or we're looping it over all of the values of the vector, right? So another thing that, you know, perhaps you could do before n in names, right? And perhaps I have a vector of names, right? And I could have c, pat, Mary, Jose, and Doug, right? So I've got that vector of names. I could, I could similarly do print n, right? And so this should then print all of the names in the names vector, right? And so again, names, the vector that we're iterating over could be numerical, it could be character, it could be anything. And what's really cool is that n then takes on each subsequent value of the vector we're giving it and it gives it these values in the order that they are in the vector, we have this read LT matrix dot r script. If you want to get this code and you're following along, be sure to go down below. I have a link in the description to a blog post where you can get the GitHub repository and the code that I'm working with here in this project so you can you can work along with me. And what we're doing is we read in the distances using the scan function. And again, as we saw before, distances is this long vector that has all of the values that were in our mice, simple break, Curtis distance matrix as you see here. So it's clearly not rectangular. And you can kind of see that distances is the values, all these values. And again, that was read and using the scan function, a few episodes back, right? So now we have distances. And the first thing we get out is the number of samples. And we then remove that value from our distances vector. And then in the last episode, we saw how we could make the matrix and a vector for our different samples. And then we saw how we could basically step through this distances vector, populating a matrix in this first step here as we're going through distances, and read in the sample name, and we removed that sample name from the distances vector. Then we came in, and we got the second name and the distance and we took that distance and we plugged it then into the distance matrix matrix, right? And we kept iterating through each row or each sample as we went through. And if we look here, say at samples five, we're reading in the fifth sample name, removing that from the distance matrix. And then the fifth row of the distance matrix are the we're looking at the one through four columns of the fifth row, because again, it's lower triangular and things above the diagonal of the matrix are the same as the values below it, right? And so the first four columns of the fifth row come from the next four values in the distances vector, we then remove those four values from the distances vector to create the new distances vector that's then used for the sixth sample. So we see a bit of a pattern here, right? If I say created a variable I, right, and let's say the value was six, you know, what if I were to write this expression that I have down here in terms of I, what would it look like? Well, let's go ahead and bring this up. And I'm going to make samples I right so that I think is six, right? These ones and minus ones and distance will be the same. Here, I'm going to go the I throw the sixth row. And I'm going to go from one to I minus one, right? So not five, but I minus one, right? And we will then use the same kind of approach to pull those distances out of the distances vector. So we'll do one to I minus one. And then we'll remove the distances or those values from the distances vector that probably wasn't a very well named vector. Oh, well, from one to I minus one, right? So let's go ahead and run everything through this step. And so what we should expect is that if I look at samples, I should have six sample names in here. And sure enough, there they are. And if I look at disk matrix, then I have the first six rows and five columns populated, which sure enough I do. And if I look at distances, I now see I'm ready for the seventh sample, right? And so, you know, a way to think about this might be that well, I could take the same chunk. And I'll just basically copy this down, right? So that's now seven. And then I can do eight. And then nine. And I'll do 10, right? And what you'll notice is that I'm not changing anything in these four lines of code. All I'm changing is the I. So now if I rerun everything, and I look at samples, I now have all 10 sample names. And if I look at disk matrix, I now see the distance matrix that I got before with the previous version of the code. And if I look at distances, I now see I have an empty character vector, which we had before, this is better, but it's still not dry. And what we can see is that we're taking this same chunk of code, repeating it, but for different values of I. So if we take this chunk of code, and we bring it all the way back up, let's see where this pattern starts, right? And so, the first thing we pull out after we get the number of samples is the name, right? And so, I think the pattern really starts here with two, right? The sample two and row two of the distance matrix. So if I did I of two, then, you know, does does everything map here, right? So samples two, right, that maps this one and minus one are the same. And down here at this distance matrix, so this is two comma one, which is what we have right here. And then here as well, one to two minus one or one, right? And so this all holds from two down to 10. So I'm going to go ahead and delete all that code. I'm going to delete this code. So now I have where I'm reading in the vector, I'm reading in the data from the distance matrix, I'm setting things up. And now I'm ready to iterate over all of my samples. So let's go ahead and create our for loop. We'll go ahead and do for I in two colon and samples. And notice that we're using two rather than one, because one is basically the condition up here, right? We could maybe make it go from one to end samples, but that would require a little bit extra coding a little bit more complicated, that we just we just really don't need to worry about. And so then we'll put an open curly brace here, a closing curly brace here. And to make things look nice, I'll go ahead and bump those over a smidge. And let's go ahead and run this and see what we get. Again, we can look at samples. And we get our 10 different sample names. We could look at disk matrix, and get the distance matrix that we've seen now a few times. And we could look at distances. And we see that that's an empty vector. So we've gone from maybe 50 lines of code down to 21. But this is much more maintainable, right? The code is dry. If I had forgotten to make my character is numeric, all I'd have to do is update it here online 19 to add that as dot numeric and we'd be good to go something that I might think about doing actually is go ahead and put in the transpose term, right? So I could do dist matrix one colon i minus one comma i is the same as this thing, right? Because it's mirrored, it's transposed over the diagonal, right? So I added that mirroring here one time, right? I didn't have to repeat it every time I went through each step of the whole process, right? And so again, we can run all this, we see dist matrix. And we now see that we've got a symmetric distance matrix. And again, the only reason to point this out is to demonstrate how easy it is to update the code if you're not repeating yourself, right? If I'm repeating myself multiple times, then I have to add this line of code every time I run that chunk of code. But here in a for loop, I do it once, and we're good to go. The other benefit of making this for loop is that I'm no longer hard coding the 10 samples, the 10 steps through those samples, I'm looping over the number of samples. And so you'll see in here that the number 10 doesn't appear, right? And so what I should be able to do is go to my spray Curtis dot dist, which is our full distance matrix. And I should be able to run this whole thing, read it in and get a 348 by 348 distance matrix. If I do dist matrix, sure enough, I'm only getting the first two rows because it truncates the output, because it's such a big data frame. But I see that I've got a symmetric distance matrix that has 348 columns. And it's telling me it's emitting 346 rows, which with these two that it's printing out, I see now it's got a 348 by 348 distance matrix. So again, that's really slick that we've dried our code out. And in the process, also made it more generalizable so that we can read in any sized lower triangle distance matrix at this point. Again, don't be scared to use for loops. I think we spend a lot of time in our trying to think about how can we avoid for loops, when the goal is to get the right answer. And on data sets this size, we probably don't notice the performance hit. If you write a perhaps poorly written for loop, you notice it more with much bigger data sets. Another trick that you can use to reduce the detrimental effects of using a for loop is to pre define your data, where the data is going to get inserted, right? So if I kept adding another value to samples, without pre defining it, it would be looking for a new place every loop to store the samples data on my computer hard drive. And I realize that seems a little bit esoteric, but again, with these big data sets where it's moving big pieces of data around on your hard drive or in your RAM, more likely it's in the RAM, right? Then things slow down and just kind of bog down. And that's a performance hit that I know our suffers. And anyway, I think we have written though a good for loop here, because we've initialized the data, when we've set things up, and then we're populating an already existing data vector for our sample names and matrix for our distances. Also, we demonstrated the value of having things dry, by going ahead and getting the transpose of the matrix, as we step through each row to populate the column, the corresponding column for each of our samples. So keep practicing with these four loops. Don't be afraid of them. Try to play around with vectors and matrices and all the other base our concepts that we've been working through in recent episodes. And I'll see you next time for another episode of Code Club.


Hello, and welcome to the Contemporary Concerns and Communications Challenges panel. My name is Prabhorna Ganguly. I am the Chief Communications Officer at Nucleus Genomics, a next generation consumer genetic testing and analysis company based in New York City. I am a South Asian woman in my 30s and I'm wearing a gray sweater. Before we begin, a reminder to the audience that you can ask questions through the Q&A chat. Please make a note if the question is for a specific panelist. This panel discussion is not going to be a mean feat because communicating the nuances of social and behavioral genomics is the fundamental intersection between science and society. But I hope the discussion we have will provide much food for thought for all and drive essential decision making within the scientific community. With that, it is my absolute pleasure to introduce our three esteemed panelists. Dr. Rena Bliss is an Associate Professor of Sociology at Rutgers University. She is the award-winning author of Raise Decoded, The Genomic Fight for Social Justice, and Social by Nature, The Promise and Peril of Sociogenomics. Her research explores the personal and societal significance of emerging genetic studies. Dr. Tracy King is a medical officer in the Intellectual and Developmental Disabilities Branch at the Unis Kennedy Shriver National Institute of Child Health and Human Development. She currently oversees the NICHD portfolio on Fragile X Syndrome and works closely with the NICHD-funded Intellectual and Developmental Disabilities Research Centres Program. Dr. Chris Gunter is the Senior Advisor for Genomics Engagement at the National Human Genome Research Institute and the Head of the Engagement Methods Unit in the Social Behavioral Research Branch. The overarching theme to her career has been science communication in and around human genetics and neurodevelopmental conditions. Welcome, everyone. My first question is to all panelists. Can you briefly talk about what brought each of you to be involved in the field of social and behavioral genomics? Let's start with Tracy. Hi. Thanks so much for having me. So to introduce myself, I'm a middle-aged Asian woman with short black hair, and I'm wearing a navy blue sweater. So I actually am, I think, bringing two perspectives today. I am a primary care pediatrician by background, and so I think came at this field with a circle lens, and I, as Purvana said, am currently working at NIH in a branch that focuses on research around intellectual and developmental disabilities, and so am seeing the development of the basic science and applications of those sciences. So it's an interesting perspective. Wonderful. Rina? Hi. I'm Dr. Rina Bliss. And I'm a cisgendered biracial woman of color. I have long black hair and wearing a pink sweater, gold earrings. I'm a sociologist at Rutgers University, and I wrote a book at the dawn of the sociogenomic era called Social by Nature, and I actually started looking into sociogenomics in the early 2000s when I was writing or working on my first book, Race Decoded. Back then, genomics was really just starting to turn towards doing gene environment research, and it was also fighting for minority inclusion in more studies and in the projects, and also many scientists were excited about possibly capturing how social inequality gets under the skin. And what I found, though, was that even with the best of intentions, we were just seeing more and more racialization. So there was racialization of collections, of databases, a lot of publications using the same kind of categories that we use as our targeted and planned enrollment categories, so OMB categories in this country. And so I also saw very little research actually focused on discrimination or racism in our social environments and things like that. So later on, when I was starting to research for Social by Nature, again, this was many years ago now, but I saw things like phenotypes like criminal behaviors, aggression, IQ popping up as being part of this discussion of what's partly genetically driven. And so I also saw at that point very few practitioners in the area were unpacking race or really monitoring inequalities' role in those phenotypes that were popping up. And so my message back then was, but unfortunately still is, that we do need more in-depth environmental analysis, more that tracks inequality and its effects on the body. And this rather than Gene Hunt's drug development for specific racial groups, as we've seen with drugs like Bidale. And as long as the system is racialized, even just saying things like European and leaving it to the public to decide what that means, especially in our publications, we're still going to be seeing racial misinterpretations, all sorts of lies basically, and dangerous if not deadly appropriations, and weaponization, especially against Black Americans and Black, and people of African descent around the world. And I do want to say that in terms of communication, I'm really worried about some of the metaphors that are circulating around. And I want to share my personal story a bit just to bring this home. So I grew up in an immigrant community of color in Los Angeles, where many kids didn't make it through high school. Many kids, many of my closest friends left school in the first year of high school. And many also experienced racism daily from our teachers, our administrators, and just in general from the system, right? I myself almost didn't make it through that first year of high school. And we didn't have parental support or ESL help at home, English as a second language, our scores were low, our outcomes were poor. It's just there was so much behind our scores and numerical outcomes, right, or something we would put into a survey.  So I just really want us to think about what our metaphors are doing for us. And even just those moments where we use a term as seemingly benign as European ancestry, what that does, OK? Chris. Hi, everyone, I'm Chris Country and I am a white cis woman with red hair and bright blue gases and I'm wearing a purple sweater as well. It's an honor to be here with this group so far. It's been amazing today. I got into I'm a human geneticist by training. I did my PhD studying fragile X syndrome, which is what Tracy works on. And then I went into science communication. I worked at a journal that helps set up the preprint server bio archive. I did a lot of communication materials around genetics for a number of different purposes. And then for the last, I guess, 2014 to 2019, I worked at a large autism center at Emory University and helped establish a dissemination and outreach core there. And it was there that I really came more upon the idea that we need to do a much better job of engaging with the communities that we're studying and thinking about the words we're using just as Rena just said. So we'll talk about that more in the panel. But I think that's so important to think about. And then as you've seen, because I've answered a few of the questions today, still doing a lot of work here at NHG or I both looking at looking at genetic literacy, for example, but also working in the field of autism and asking some questions about looking at genetics in that community. Wonderful. My next question is for Rena. Serena, as we have as we've heard throughout the last two panels, there's been a rise of polygenic risk scores as a new tool for understanding disease risks. And it's primate consortium itself is working on building systems for sharing such polygenic score information. And again, as we heard, you know, it's been used to assess social aspects of life such as educational attainment, IQ and a few companies are also starting to use it to select an embryo. So what are your thoughts on using polygenic risk scores in general and the language that's being used around it? And do you think that there are certain groups of populations who could potentially benefit from such scores? I definitely think that all populations could benefit from scores. I don't have a problem with polygenic risk scores or polygenic indices for disease in terms of disease and disease research. I think that's really, really important. But I do want us to fix the systemic racial problem, the problem of racialization in, you know, in terms of the broader field. And I really caution us against, meaning I don't want us to make phenotypes out of social phenomena that are so highly informed by and highly shaped by race and socioeconomic status to the point where, you know, I'm talking about where racism and socioeconomic status is a major confounder. Right. So that's like, you know, aptitude test scores, intelligence test scores, IQ, gang membership, things. I mean, some of the stuff isn't around anymore, maybe, but, you know, things like looking at people's investment patterns and also certain people ending up in certain kind of like lucrative jobs versus ending up, you know, in incarcerated. Things like going into debt, you know, I mean, all of these things are are really just leading us towards a place where we are going to see misuse, misunderstanding, misappropriation. And and so and as I was saying, you know, just a few moments ago, I just want us to be careful not to take a survey response as the full picture of what's going on. Right. So if we can't be sure about that, then maybe it's not a good a good place to apply G was, you know, or to create a polygenic index. Makes sense. Tracy, you work with children and specifically those with intellectual and developmental disabilities. How do you think any of the genomics techniques that we've discussed today actually inform successful treatment and results? Do you think there is an issue of accessibility that the genomics community needs to work on? Thanks so much for the question. So, you know, interestingly, when I think of genomic technologies and thinking about this panel, I was thinking less about the use of PRS or, you know, polygenic indices and more about kind of advanced technologies like whole exome or whole genome sequencing. And I think, you know, the title of today's round table, the promises and pearls is something that really applies to your question. So, you know, the promise of these advanced genomic techniques like whole exome or whole genome sequencing is that they have the potential to give us a lot more information about the biological mechanisms that underlie certain conditions associated with intellectual and developmental disabilities. They can help us identify targets for treatments, especially for symptoms that might be worth treating, you know, such as anxiety or, you know, hypersensitivity to sound or, you know, other things that can really impair functioning. Pharmacogenomics is a field that is really advancing. It's, you know, the idea that, you know, a certain variant might make you more or less likely to respond to a certain medication is certainly a promising area of genetic and genomic technologies. And finally, what I think as more genomic diagnostic technologies are being utilized in, you know, cases like intellectual and developmental disabilities research, it can be a real tool for people in disparate locations who might have a shared genetic diagnosis to find each other and potentially build support systems. On the flip side, I think there are a lot of potential pearls. One is that, you know, the idea of, you know, diagnosis can easily perpetuate the idea that intellectual disability itself is something that needs to be treated or needs to be cured. And, you know, you asked about access, you know, my perspective on the question of access is, I think, really colored by my clinical experience, where, you know, as a former primary care pediatrician, I've seen a lot of the barriers posed by restrictive insurance coverages, long wait times for genetic clinics, the multiple encounters that might be required to take advantage of advanced genetic technologies. And as a clinician, I have to admit that I've sometimes made a conscious decision to invest my energy in a case where a family might have limited financial resources or limited wherewithal to link them with services like, you know, early intervention services or speech therapy or, you know, some such service rather than genetic technologies. I think that certainly contributes to disparities in who is contributing genomic information to databases who ends up having the opportunity to participate in research, who has the ability to take advantage of participation in research once it's offered. And I think all of those are things that we should keep in mind as we're thinking about the problems and perils of genomics. Absolutely. Chris, when it comes to effectively communicating social genomic science to the public, what lessons do you think public institutions can learn from community stakeholders as well as private companies? That great question. That is such a hot topic in my area right now in autism. In fact, there is a large debate going on on Twitter earlier today. I checked because the piece came out in the website Spectrum News, which talked about how some researchers who work on autism are afraid now to say things sometimes in their talks because they're afraid they'll be canceled. So let me talk a little bit about our experience with that. So we often see that, for example, when we talk about using genetics and genomics in autism, a number of researchers will just write in their papers and this can be used for family planning purposes for families and just not maybe even really think about that. But to a number of people who are in the autistic community, that's advocating eugenics, right? So I think we don't even agree necessarily on what the definition of eugenics is in every case here. So in fact, when we published a paper, which I put in the chat earlier in answer to someone on genetic literacy, it worked a little bit with autism and we put the paper out. And sure enough, we had someone on Twitter call us out and say, I think researchers like this don't understand the implications of what they're talking about, even though we didn't talk about family clinic at all. But I thought that might happen. And so we had actually put a statement in our discussion that said we want to explicitly state that all eugenic applications should be important to clinical scientists and biomedical researchers. So I was able to say on Twitter, hey, if you look in the paper, we actually thought about this and that statement is there. So I think more researchers really need to talk to people from their community and again, understand what's going on and understand how something that you might think, oh, everyone should agree with this or this is no big deal. The statement, that's not true, right? It is a big deal. So I think that's really important. And then the second part of your question was about companies. We see an autism, for example, there are already companies that are offering genetic testing for autism, which again will lead to family planning, which is problematic. And also the state of genetics and genomics and autism, as in with some forms of intellectual and developmental disability that Tracy works on is so unclear. There are so few tests that will actually lead to an answer in the first place that I think there's a real concern about over promising with what what some of the companies are saying in their marketing. All right. The next few questions are for all the panelists. So please chime in with any thoughts that you have. Is there a continuous unresolved tension between scientists who want to study certain social and behavioral topics, especially within the genomics context, especially with populations who have been underserved in the past? And what obstacles do we need to overcome to build community trust with scientists, especially where the value proposition for the social genomics topic is slightly unclear? I'll ask Chris to go first. OK, all right. So I think, again, we talked about the sum in the chat earlier, but actually engaging with communities. And so I'll cite another example on the Autism Committee, which is a study called Spectrum 10K, which was proposed in the UK and they proposed to sequence the genomes of 10,000 families or individuals with autism. And a lot of the language that was put out around the story, just a study, excuse me, just caused a lot of angst in the community. And there was so much pushback that the Wellcome Trust, the funder actually paused the study and forced the researchers to go out and do more community engagement before they could come back and restart it. So that's kind of an ongoing tension. So I think doing true community engagement and actually working to earn trust instead of, again, so many scientists. When I talk about science communication, I talk about the deficit model of communication, which is what so many of us use, which has an assumption in it that, oh, when the general public learns what we know about science, they'll act the way we think they should act, which is obviously not true, right? That's based on so many faulty assumptions. So I think scientists need to understand more about that kind of communication theory. I'd like to chime in too. I think that I just was thinking about what Daphne was saying in the prior panel about Indigenous-centered and Indigenous kind of lead research. And I think that if we look back at the genome projects of the 2000s, you see a lot of community engagement and more towards the further upstream in the research process, more towards the warming of sampling protocols and just having different stakeholders, but especially underserved communities or populations get involved and be at the table when decisions were being made before sampling was conducted or before research was conducted on the samples, right? And so I feel like that is something that we might want to advocate for this community as well, to have more of that kind of inclusiveness in the beginning rather than afterwards where you're like, okay, but how did that affect you, right? I also think we've been talking about community trust. And I think we need to remember that the community is not monolithic, right? That there are a lot of different stakeholder communities. They have different interests in kind of the level and type of information, strategies for delivery of that information. They have different concerns about the scientific and the medical community. And they are often contradictory. I think I've been involved in some groups where the people who are most active tend to be very high information consuming and often very highly resource. And they are looking for a specific kind of information and there's specific types of communication that will help build trust with those communities. And there are others that are really looking for different types of information. They're looking, they're more interested in addressing concerns about data privacy or how data might be misused. And those really call for, I think, different communication strategies and different types of engagement. All right. The next question I have is, do you think that there's meaning in providing correlations? This was talked about a little bit during the last panels, but do you think there's meaning in providing correlations between genomics and any given social trait as a curiosity with plenty of nuances and disclaimers included such as FAQs? So for example, studies like intelligence, homosexuality, et cetera, and to give people liberty and information. Reena, do you want to go first? Yeah. I mean, I definitely think, as we've been talking about this whole meeting, that it's essential and it's a first step, but it is just the first step, right? Because it's really great to hear about the researchers that are trying to change curriculum. This is really important. And I wonder if there are some, if we can put our heads together and think of some policy that we could have coming from funding agencies or other entities like the NIH where there's some sort of incentive to, and not just incentive, but kind of a real push to have some kind of public communication aspect of the research. I wonder if that's possible, because I do think that it's going to have to be more than just FAQs, right? Because, and I agree with what some of the other panelists from earlier today were saying about how there will always be racist uptake of genetic research, but at the same time, and that we're not going to cure all of that, but we have to do everything in our power to make sure that we're not complicit with anything like that, right? So everything in our power. And I don't know, we just have to do more basically, more than FAQs. You know, I think I have a slightly different take, and maybe it's my perspective more as a clinician. I think that at least from my point as a clinic, my perspective as a clinician, the contribution of genetics and genomics is so variable. And there's so much variability in phenotypes. There's so much variability in how a particular genomic variant might manifest, that I think it's pretty dangerous for us to start down the path of saying there are correlations because I feel like then, no matter how many caveats you put on and how much back tacking you try to do, I feel like it's still very easy for that to be misinterpreted. I also worry that you all are experts in this field and deeply immersed in kind of genomic literacy and genomic terminology. I'm imagining this kind of wide population of clinicians who you might be relying on to convey this information. And there's also going to be an incredible variation in the way that kind of information is understood by clinicians much less conveyed. So I worry, I think I'm inclined not to talk a lot about whether and how much genomics might predict these other kinds of social periods. That's just me. Yeah, I'm going to add a little bit to Tracy, which is that you specifically mentioned the study on homosexuality. And I think it's hard to say. So just to be spicy here, there wasn't a paper that came out a number of years ago that more recently that looked at sexual behavior and defined that as having one sexual encounter. And this was taken by many people as looking for genes for homosexuality. I would argue with the basic biological premise that having one same sex encounter equals a pre-election or however I've stated to homosexuality, which is not what the researchers said. I believe those researchers, I know them, I believe that they attempted to be very careful about their communication. They had the FAQs, they had a lot of stuff that did not prevent that paper from being misinterpreted in many, many ways, right? And even apps forms that were on the basis of taking that genetic information. So you can look in your own genome and look at, see if you have those markers. So it is a really difficult and thorny question. But when we live in a society where information is great, but I think it's very difficult, as Tracy was saying, to help everyone understand exactly what some of that information, all the caveats that should be taken along with that. And I just want to also add that this is real for me as well. I have a child who is out and transgender and there are states that we cannot visit. There are colleges that she is not applying to in the next few years because she can't go there, right? So the thought of some information being used for discrimination is extremely real in many contexts. And I think that's something that we really have to think more and more about. Absolutely. And I think the topics you brought up here is a really good segue to my next question, which is, I think it's abundantly clear that research within the field of behavioral and social genomics is bound to continue. So not just research, but also influence in private companies and other genomic companies that might use that information for sharing some sort of resources back to the public. So what kind of safeguards or best communication practices do you think are critical that we need to adopt ASAP, to mitigate as much of the negative effects we think might happen? You basically fixed the issue all in one and one question, but I think that is really the fundamental core of what we've been trying to talk about in the panel and in the last few panels. So Grace, Rina, Tracy, one of you wants to try. I'm not sure if this is directly addressing your question for Barnab, but I think that we as academics get easily locked into certain strategies of communication. I've been exchanging texts with a friend during this panel about why aren't we sharing this information through TikTok and Instagram and other venues where people go for information and why aren't we figuring out how to leave our own bubbles and create content and ways of delivering that content that make more sense to the people who don't digest information the way we do. That doesn't answer the whole question, I think, but that is something that I think is lacking in this field. Chris, Rina, do you want to add to that? Yeah, as a person who is on TikTok and puts out multiple videos a day and many of them relate to the topics we're talking about right now, I just can say that while I think that social media is one of the main places that I feel like I'm having an effect on the general public, I think that also it is in the kind of required education where we will have a better chance of making an impact and so I'm talking about like I have small children who just started public school and what are they going to hear? What are they going to learn? I can tell you already that the way that the educators in our community talk about them, it's very deterministic and I feel like they are getting a kind of sense of having some kind of genetic endowment or something and I'm just like this isn't the right direction for them, for us, for my family, for our community, for our society and so if there was something in education starting that young that would be amazing because then teachers would know about it, kids would know about it and not just like me having a conversation with my kids and then talking to my so many followers on TikTok or whatever and I'm saying so I feel like it's always going to come back to you and I think that Daphne and maybe Erin also mentioned this but it's always going to come back to having those policies and those curricular kind of developments in place that are structural and that kind of push back on the structural genetic illiteracy, the structural kind of ignorance and also the structural racism, unfortunately, that is conditioning all of our social environments so we need something that is that structural, that's my take on it. Yeah, I agree and Rena took some of the things I was going to say which is good thing which is that yeah Daphne and Erin talked about the work of for example Brian Donovan which we also recently studied talking about how we need to move genetic literacy from only basic monogenic concepts to start talking about gene bi-environment interaction and polygenic risk scores and that kind of thing so absolutely I think that's something I endorse wholeheartedly and I know Provarna you're trying to to get that at one question and we're kind of answering different questions because that's a hard question to answer right what the safeguards are I mean I think I can only you know I'm not an ethicist that's not my training so I don't want to speak for them but I think there I don't think we might want to be in the business of saying there should be no social and behavioral genomic research I think that's too far and I think there are obviously ethics panels for a reason and there are many ethical standards and I would like to add to those more standards for true community engagement and representing the what the community thinks is important and I think actually Tracy hit on a good example of that earlier and we see this too in our studies when we talk to we and others talk to families they're less concerned about genetics at the time of an autism diagnosis and much much more concerned about getting access to services and access to supports so that's a perfect example of they're not necessarily interested in our social and genomics research right then right that does not mean we should not do it but I do think we should think more about what the community is looking for and how we can use our tools to answer the questions that they need answered wonderful we will now move on to the q&a portion of our session and then actually Chris you hit the name right on the head because one of the questions that was asked by somebody was can you explain what community engagement actually looks like what does it what does this mean in a practical sense for example in the case of the spectrum 10 case study that Dr. Kanra mentioned so can you expand a little bit on what you think just to create give us a tangible idea of what that those two words really mean yeah absolutely and I know that Reena and Trace will talk about this as well but I think ideally it means actually talking to the community before you begin the study and finding out what's important to them and what they would like to do so that's something that Nicholas Sugden is starting in our lab is getting together a panel of neurodivergent community representatives and asking them what what is it that you'd like to know about from things that we study so that would be a great example and then also there are now we're learning about they talked a lot about this as hg including Daphne who had a great session that talked about this as hg last year the human genetics society meeting really we're coalescing around specific methods in community engagement that are becoming more and more widespread so I would say watch the space yeah I totally agree with Chris's points that you know I think we as researchers don't do a good enough job understanding you know what different communities hope to learn from researcher what benefit they hope to gain from research what what their concerns are about research what questions are most important to them and and we need to do better in that space and that's going to help us articulate our questions it's going to help us make our communications more relevant it's going to help our findings be framed in a way that are more meaningful and and like Chris I think there's a lot of exciting work being done in this space and I'm optimistic that it'll get better yeah do you want to add anything to that yeah all right um the next question that we had this is a really interesting one and this was actually a question that was asked a little bit earlier but I think there's deep relevance now which is in a clinical context it seems like many patients are confusing polygenic risk scores or indices and monogenic diseases and apparently certain direct-to-consumer company testing companies aren't always clarifying the difference but with their within their reports so who is responsible for making these clarifications to the general public you know and I think it kind of um goes back to Tracy your your point about training clinicians so I would be really curious to hear your thoughts on that um it's definitely a space where we need to do better you know actually I am aware that I think NHGRI you know has is undertaking a lot of efforts to say you know how do we better train not just the research workforce not just you know kind of clinicians at the top level of training but everybody in you know better understanding basic principles of genetics and genomics better understanding how to communicate about them and I think in some ways much of the responsibility is ours to understand how to you know simplify our language and how to make ourselves you know kind of better um more effective communicators in um in conveying the principles of genomics and what what it does mean and what it doesn't mean yeah my colleague Susan Persky here at NHGRI is doing she works in virtual reality actually and is doing some studies where medical students come in for virtual reality encounters with patients who are obviously made up and they're studying actually the intersection between having polygenic risk scores or not as a clinician in that space if you had that data does that change how you treat the patient and they're also looking to see how race affects that and also looking at body type as well so kind of the intersection of all three of us to try and understand what more we need to understand in that space so and has some kind of counterintuitive findings to what you might expect as far as how polygenic risk scores affect how people are treated so yeah I think absolutely this that's an area where we need a lot more work yeah I think that um then then Sponham um you know has done a great deal of research in this area in terms of race and racial biases and so um yeah I think that clinician training medical education is one place um but there's still this issue of the companies you know companies kind of doing things that the science community isn't really um isn't okay with at all and that sets us back you know a great deal and so that's a regulation issue right so that's something that um is again like more of a very far upstream kind of issue that has to be addressed um it has to be something that we have to think about what are the possible ways that companies will take this research and then turn it into something that is ready readily available to the public as the prior panel was talking about you know and and yet um and that just really promotes genetic determinism and also is um something that kind of intersects with people's um with clinicians biases as well right and um and takes us further into a place that we don't want to be so. And you know Provano you were I think I was thinking mostly about clinical testing you were talking about direct to consumer testing I think one thing that I think about um that I think affects both of those spaces but particularly the direct to consumer testing space is you know our interpretation of genetic results is changing every day you know as we gain new knowledge our understanding of you know what a gene does and what its implications are in terms of you know whether someone might have a certain symptom or certain condition is changing and that kind of movement and that kind of uncertainty I think it's really challenging to convey effectively um but I also think that we've kind of abandoned our responsibility to do that in many cases especially outside of the genetics field and so and and maybe don't I think there's many places where we don't even appreciate that you know what we know about the relationship between a gene and a condition today may be different tomorrow um and and I think that's particularly challenging and direct to consumer testing where you don't necessarily have um you know an ongoing relationship and so you know it might be hard to go back and you know see if the interpretation of you know your information changes over time you're in the direct to consumer testing space for Barna I don't know if you have that's what I was going to say let's make this a discussion for Barna I completely agree with you I think the problem is is really rooted in um lack of courage from company sides I think a lot of companies have taken the easy way out and not recognize the fact that being honest and informative to the public is the best and the most efficient and important way of communicating about health and when you talk about genetic determinism it is because companies don't necessarily ever really say this is a moving target research is is a human endeavor information is constantly improving it is changing there are differences that are being made so I think recognizing that science is is this this field that is never deterministic but at the same time recognizing that there is value that there is value added in the research that there is true engagement that you can have with doctors and clinicians and and get treatment both those things can be held as true I think that's that's really where companies suffer is when they don't believe that those things can be held true and that the public will recognize that as true so that would be my that would be my answer is is more bravery more courage with companies to believe that the general public has the ability to understand some of this information as well as as long as it is said honestly and again I agree with you Tracy completely I think that there has to be constant engagement you know it's a lifelong commitment that companies need to make with people right this is not a one-off situation where you make one trade you buy our thing and that's it right we want we ultimately the goal of I think health care should be there's no sick people right so it has to be a proactive system rather than a reactive system so you know we're working on it we're working on it so I hope I hope we can deliver as much as you know the academic community is working on it as well I think we have time for one last question I think this is a really particularly important one I think which is when working with genomic data and providing information to certain races slash ethnicities for treatment or even genomic interpretation how can we focus on protecting minoritized communities while also helping them I think I'll add one nuances question which is especially when there are groups of people who have been you know who have suffered for a very very long time within the hands of genomics who don't trust the system at all what would what are certain things that we we need to address regarding that I think that what Daphne said is paramount it's to bring representatives from those communities to not just to the table but as as researchers you know into the community because the community is still extremely white and extremely you know it's like a certain kind of demographic that is not going to necessarily be able to achieve getting you know representing all of the many many diverse perspectives that exist in minoritized communities but having at least having minoritized people as a part of the research process is more likely to generate sensitive racially sensitive and you know racially appropriate research and just in general more accurate research and research that serves everybody and not just people of European ancestry and yeah so it's like a labor pipeline issue it's also a you know where the stakeholder conversation begins issued like a timing of including people from the community and you know I was just thinking while we were talking about how with research oftentimes we offer things like amazon gift cards or some kind of or or like a you know a drug or something to kind of or some kind of you know health services or whatever it is genetic education something to incentivize people to be a part of our research to be a part of our studies but I think that we have to think like okay if we want people from minoritized communities to be a part of even just a conversation we need to also treat that as being as important as getting people into the studies themselves right so we have to think like how do you actually enter into a community especially if you don't if you're not a part of it or you've never even had any conversation with anybody from that community um back when I was writing race decoded there was a um a scientist who was just so active proactive he would just go to community centers senior citizens homes you know all kinds of assisted living places and just talk about genetics and and you know at the time he was very pro using OMB categories and and so he was getting that message out to communities of color and he was a person of color and so it's like you know he was really convincing people to buy into using racial categories as opposed to genetic ancestry categories right he was effective um I'm sure he still does this because he's he believes in it you know but like is any is anyone here doing that level of engagement you know so I feel like and it's not on the individual I don't ever want it to be on an individual to like you should be going in and you know you're a PI go in there and but I'm just saying like I think that we have to realize that there are other people who are engaging with and engaging with the nativist populist um white supremacist you know communities as well people who are like yeah I'm I'm going to explain this to you um and that is scary because their message is getting across right but ours is not as much right so we have to figure out how to actually create these um these upstream conversations and participation really like participatory research right wonderful can I sneak in I want to also add I think there needs to be a recognition of past harms as the ASHG statement just did so I think that's really important to get at the question but go ahead Tracy yeah what I was going to say is um I think like we shouldn't think of you know doing better inclusion whether it's better inclusion of um you know kind of racial ethnic minority groups or you know persons with disabilities as you know charity or a service or you know things that we you know should do because of the right thing to do I think we need to understand that it's critical to us doing better science and um that uh you know if we uh don't address you know uh do genetic research that is relevant to persons from um you know racial minority groups where you know we're not doing the best science if we're not uh doing research that applies to everybody that or that we're where we're going to draw conclusions that it applies to everybody but we don't include those groups we're not doing the best science um and uh and that's uh and having I think a lens of doing better science will hopefully you know help drive us to do better in including these groups and asking the right questions wonderful well with those two three extra minutes that we talked I'm so grateful for your for your incredibly insightful insightful discussion and um just thanking all of the panelists during this during these three hours I will give it back to Christopher Donahue to close this up thank you so much thanks for having us thank you for Barna um so this is Christopher Donahue speaking I just want to say uh thank you to our audience uh all of the panelists the moderators the NHGRI and the NIH this is an event that we have been working on for a year and with the ASHG report and the synergistic NHGRI and NIH efforts to promote equity and combat racism and acknowledge historical harms we can be optimistic about there being progress uh so long as it is progress for all and just to know just say I I think um almost all scientists working today in the genetics and genetics community acknowledge eugenics and it's evils and they really do work with intentions but I think it's this panel as this event has made clear that doesn't necessarily mean that they shouldn't guard against the very real possibility that they are close to repeating the mistakes of the past or that especially through vulgar misinterpreting vulgar misinterpretations uh or especially that through vulgar misinterpretations there are there are those that intentionally misuse their findings so I think we've in this event moved well beyond uh from history to science and our communications around social and behavioral genomics and we would do well to have our historical understanding temper our enthusiasm with the hope that we need that together we need not repeat the past while making the present more just more equitable so thanks very much for your attendance today and all of your uh fascinating and pertinent questions and we will certainly uh we've certainly learned a great deal and we certainly will be engaging more with our audiences in this space thank you very much and have a have a good rest of the afternoon or morning or evening wherever you are thanks


powfent y ddechrau'n ddechrau'n teimlo yn y cyfnod, ond mae'n yn ystod ystod yn gweithio fel y gyfnod y Cymru, yw'r cyffredineth yma o'r ddechrau'n ddechrau? Ond, mae'n gwneud fan ymlaen ei ddefnyddio ar y bydd yn gweithio'n gweithio'r gafodd a ddechrau'r ddechrau. Rydym yn ddefnyddio'r cyffredineth cyfnod, mae'n ddechrau'n ddwech i chi'n gweithio yn cymdeithasol sy'n cyfnod o'r gweithio'n ddechrau, gyda'i amddellais bobl yn fwyaf amddellau'r ffaith iawn. Mae wedi cael eu bod hyn yn cymryddiol ei ffrifio'r aud Iechyd, gyda i gael i ddesig arweinio i ddim yn cael ei ffrifio i ddim yn ennill. Dyma sy'n bod hyn yn swyddfa'r cysyllt i'r ddechrau i ddim yn cyhoedd. A ydyntau'r ffwrdd sy'n meddwl fydd yn cael y dyfar, was around similar to to what the club did with phone and people was contacting everybody with a phone call particularly the vulnerable groups and those with underlying medical conditions and initially that was support was as did they need anything that they need shopping we hope we've getting medication from them and there were certainly some cases where we've done some shopping and a little bit of running around for people so that was really important to have that initial contact with our fan base and the community participants you know that led to more wider conversations and quickly we realized that actually you know the projects that we put on are vitally important but probably the most important part of it is the social side of it so people really get to meet new friends at our activities and take part in their activities and obviously that's taken away from them so the opportunity to talk and meet up with those people has been taken away so we developed support groups and we have about 12 or 15 support groups a week that are going across our different projects and different teams where people can conference call in and basically we have a chat and we support each other there's a little bit of an educational part to it as well where we talk about some of their fitness programs or nutrition and sleep patterns and everything that people are going through at this awkward time at the moment so you know that was our initial point of contact was to make some contact with the local community and see as a football club if we can support in any way and I'm sure people across social media may have seen some of their educational packs some of the things you've been doing for the kids as well to talk a little bit about those things you've been setting up yeah I think again we're sitting down and looking at what was needed you could you could quickly see that there was I wouldn't say a bit of fear but a bit of anxiety around parents and having to home school I'm thinking well how are we going to do that I think the schools have been magnificent in some of the work that they've sent out to parents and supported parents with that but there's a real anxiety about what that looks like when you've got children at home all day every day so we felt that we could impact in that area so we've made a number of different educational packs as you would expect they're all around based around the football so football masks and different challenges that we've set up and they've gone down really well and you know we've been able to send them out to lots of families and support them through their home schooling and I believe there's also been some sort of fitness sessions in clubs using zoom to kind of everybody logging in together and trying to make sure they get their exercise up yeah I think so I think obviously fitness and mental health that I mentioned earlier is really really important for us to to make sure that we can support our participants and fan base on so you know you've got to look at these difficult times in different ways and we're all looking at different ways that we can put the sessions on so yeah we've had some zoom fitness sessions which are they put a smile on people's faces you know they're a little bit different but you know obviously getting everyone together doing a little bit of fitness work it's been great to be able to do that but supporting that and backing it up with we know that we work with young children right up to our more older generation so devising fitness programmes to meet everyone's needs and sending them out to them putting them across social media on the website it's been again another important area of our work to engage with the community and as you said before so a lot of that adaptation at the moment people trying to make the best of the situations doing how can people get involved with you and try to as I say make the best from from the difficult situation in at the moment yeah what we tried to do is obviously we haven't got that direct contact as everyone's aware at the moment with these difficult circumstances so it's it's been pushing it out across social media on the website everything all the education packs that we put out there are on there all our details are out there so if there's someone who's not engaged with us at the moment that would like to they can contact us I can put them in touch with any of the support groups that we have going at the moment and they can take part in them so really it's across social media and the website of present and we're past Easter holidays now and the sun's out to kind of greet the spring in and what kind of projects have you got coming up in the next few weeks and times to come yeah i'm not sure that anybody knows quite what days it is really but obviously it's back to school virtual schools so the education packs have been pushed out this morning and we've got lots of parents contacting us and sending that but moving forward if I work with the NHS which is a really important area obviously the NHS the front line staff the social care staff we're all doing a fantastic job and we're really grateful for all the work that they're doing so as a football club looked at how we can maybe work together and and put something out there that might be beneficial to the whole community because you know we've got our women's football team who are obviously on the manager of and we've got three or four players that are still out there working and it's you know it's very prevalent to myself that I think about them on a daily basis that they're out there working particularly within school settings social care settings and at former from pool hospitals so you know we're very proud of the work that they've done so we've been working with the the the NHS in in devising some some daily tips that are going to go out across social media and really putting some information out there for people to link into should they need further support further advice so I mentioned fitness nutrition and sleep and you know it's difficult for people to get regular sleep patterns during this particular period of lockdown so maybe we might be able to give out some daily tips there and for those that are experiencing a bit more difficulty some links to go and get further support so you know we're really grateful for the NHS and the football club to partner with them and push this out over the next few weeks to our local community is something that we're really proud of and just as a final point how are you and how the rest of your staff because of course you're you're dealing with this as well yep obviously the staff are used to being very active there you know they're a hard working team that I've got within community and they do great work and you know I'm proud of the work that they do and obviously they're not out there delivering what they normally do but they've all been fantastic in terms of thinking of these ideas that come up with this it's a real team effort that we've put together to try and keep our engagement going so it's been challenging but it's challenging for everybody and what we want to do is like to say as a football club as a community sports shop is to support our local community


Hello everybody welcome back to another mid-journey version 5 tutorial and this one I'm going to show you how to get the job ID and the seed number it's changed a little bit since version 4 and I've gotten lots of comments of people saying hey how do I get the job and seed number so let me show you first thing I'll do is I'm going to type in settings and hit enter reason why I'm doing that is I want to show you that I'm in MJ version 5 so you know I'm in it the next step here is I've gone ahead and I just created a prompt French bulldog what you want to do is on this square here on these on this uh I guess four images right click on top of it and then what you want to do is you want to either a go to add reaction and click the envelope when you do that it should do it this will get this has given me the job ID and the seed number however sometimes that doesn't work for some strange reason so you can actually right click on it and a second way is you want to go down to this apps option and then you want to go DM results when you click on that it's also going to direct message me the results and here we go job ID seed number so that's how you do it in mid-journey version 5 thanks for watching