 Within this project, we find 16 susceptible genes for lupus for the Chinese population. Use polygenic risk score and machine learning to do the prediction. To do the prediction of the disease for the specific population, because we cannot use the genetic data of the European population to predict a disease in the Chinese population. I know someone's opinion may contradict yours. Where's my friend Alan? It's all about your perspective. Who are we and what is the nature of this reality? What's up everyone? Welcome to Simulation. I'm your host Alan Sokian. We are on site in the beautiful Westlake University in Hangzhou, China. We are now going to be talking about Chinese population genetics. We have Dr. Haofeng Zhang joining us on the show. Thank you so much for coming on our show. Yeah, thank you. I'm so excited. It was great getting a nice tour of your lab and what you guys are working on here. Congrats on all the progress. Those of you who don't know, Haofeng Zhang is principal investigator at Westlake University researching bioinformatic tools for the analysis of large genetic data and complex disease. And you can find the links in the bio. All right, let's start things off with one of our favorite questions we like asking our guests. What are your thoughts on the direction of our world? The direction of our world. Yeah, this question is too hard to answer, I think. As a scientist, what we want to figure out is how humans come from and how we go. I think it's such a big question we would like to work on. Yes. Not only on the nature, but also on the society or the human identity, I think. Yes. I don't have an exact answer. Where did we come from? Where are we going? This question is so big. I love the question. Yeah, I always wonder what are some of the main principles that we can embody to help us make sure that it's going in a positive direction. Right. What do you think maybe one of the key principles is? Maybe nature selection. I do research on complex disease, right? So the development of complex disease can be explained by nature selection. Oh, one kind of nature selection. That's negative selection. Yeah. Yeah. Yeah. Wow. So something that humans can potentially do is try and move further away from negative selection where there may be some of the things that cause suffering or disease or things like this and we can maybe move ourselves more in a direction of flourishing away from the complex disease, which is some of the stuff that you're trying to do is help us heal, help us eradicate things like osteoporosis and these types of things. Yeah. Let's talk about the journey. So who were you as a kid and how did you get interested in genetics and science? You mean who made me feel interested in genetics or science? Yeah. I think it's a long story and you know actually my father, he picked this, he helped me to choose my major when I finished my high school and he expected me to be a doctor and actually I majored in clinical medicine and I finished my MD degree in China and but after my MD degree I didn't actually become a doctor, I chose to do research because at that time I did research on genetics of skin disease like lotus and after my PhD, I continued to do genetics but not on skin disease but on the bone disease, bone health, like osteoporosis. So let's talk about as you find yourself getting more and more interested in you know in clinical medicine and then in population genetics and in helping people with disease. You made some pretty serious discoveries along the way and so the MD was from Binzhou Medical University and the Sandong University, Sandong Province in north of China. And then the PhD was from Anhui Medical University and let's talk about the first discovery so we have several of these discoveries that we're going to talk about. This one was about susceptibility genes for systemic lupus and you did this by a genome-wide study in China. So teach us about how you're finding this. So for example we to find this in order to find the susceptibility genes of lupus we need a group of cases patients and a group of controls health individuals and to compare the allele frequency of these two groups. If a SNP has a very different allele frequency between these two groups, we obsess that this SNP is associated with the disease. And within this project we find 16 susceptible genes for lupus for the Chinese population. Okay so you're given a population-wide genetic database and how many samples were in this one? Maybe a couple? How many samples did you have in this one? You mean this project? The lupus project? I think it's 3,000. Okay so you have 3,000 and so some of them have lupus. Yeah some of them have lupus. Almost a thousand had lupus. Yeah and 2,000 did not. And then what you were doing was you were looking at how did you find the you said 14 genes? 16 genes. 16 genes had the strongest susceptibility to having a SNP in them which is a variant in the genetic code. So an allele, two alleles for one SNP and how did you identify these 16 genes? You know that in the whole journal type, typing data, we have less than a million SNPs right? And we test this one million SNPs one by one, test the association with the disease one by one and find 16. Not exact 16 because the SNPs they have LD that means one SNP, if one SNP exists then another SNP will exist together. That's LD but these 16 SNPs they are independent SNPs. They have no LD with each other. And then you find out these areas of variants that are similar for people with lupus and that people without lupus the other 2,000 don't have the same variants that the ones with lupus do in those 16 genes. Actually it's the frequency difference between cases and controls. Frequency difference. A little frequency difference. Actually in controls they also have the allele but the frequency will be much lower. In cases the frequency will be much higher Interesting. Yeah let's let's let's the SNP. The SNP means the SNP didn't mean that it didn't mean it didn't exist in controls. It did exist in controls but a little the frequency much lower. Interesting yeah okay okay okay so the allele difference the allele frequency difference in the lupus was higher right okay okay and lower in the control that you have lupus okay okay interesting okay and then so then then it was our I'm curious for other discoveries as well is allele frequency difference a pretty good indicator of what a potentially a discovery of a variant in the disease is? Yes that's what we want to say that the allele frequency difference between cases and controls is a pretty good biomarker yeah yeah it's a biomarker yeah one of the top biomarkers is a allele frequency difference right interesting so when you get actually the SNP is the biomark the SNP is the biomarker yes and there's a there's a greater frequency of SNPs in patients with lupus yeah in this case yeah so for all of us that are getting our genome sequenced that we're we want the some of the things that we want to care about is our our allele frequency difference we want to that's that's something that's a big biomarker for okay yeah that's what we want to say let's let's let's uh we we let's we how we choose the SNPs how we choose the SNPs we choose the SNPs with very different uh allele frequency between cases and controls let's we choose these 16 SNPs they have very different allele frequency difference okay yeah so there's 16 specific SNPs that had the greatest allele frequency difference yes okay and then you hypothesized that those were the 16 SNPs that were related to the difference between healthy patients and lupus yes and it was correct like yeah like cool cool okay okay okay so so then it could potentially be that identifying SNPs in genomes and looking at allele frequency differences between healthy populations and the disease populations is one of the biggest ways to identify disease right yeah no okay and was now what were you doing in this next discovery discovering the north south population structure of chinese han population what is tell us about this uh in this project we include uh samples from ten different chinese population uh ten ten chinese population uh provinces provinces uh distribute from north to south we select ten province from south to north and to see the population structure um the population structure uh differences between these ten provinces and we we we discovered that uh there are two dimension distribution of the genetic structure yeah uh let's that means um um in north from uh liao ning that's a province from uh north east from liao ning to sandung them to uh uh hung the zhejiang them to guangdong the geography uh came proxy the genetic uh structure okay so so again you had a sample of yeah how large was the sample of it's six thousand six thousand from these ten uh provinces yes and five were in the north and five were in the south or um i think uh yes um also they have uh six thousand them i think is two thousand from north two thousand from south and also two thousand from middle middle yeah interesting yeah okay and then um when you were analyzing the genetics of those um six thousand what were you looking for to determine the difference between north and south han chinese because we we have already done the whole journal journal typing and this can provide the information of all the genetic information of all these samples and we do we conduct an analysis called principal component analysis this can identify the uh the the genetic component of the populations of the samples a principal component analysis yeah principal component analysis that's pca pca yeah it can identify the the specific genetic differences between yeah because we can use the principal component to uh the the value of the principal component to compare the values of these samples okay okay yeah okay and and the the big um the big difference is that you said there was some sort of a of a distribution between the clear distribution between who was south yeah yeah yes yes if we have a very big reference uh we we have uh ingredients from each uh province uh and the the sample size is big then we can build a this kind of reference yes and a new one come come here we can sequence them and to see which province he's come from yeah yeah yeah and then there was a postdoc that you were doing at McGill university that was in human genetics right and when you were doing that four years you made another discovery in 2012 to discover the susceptible genes for osteoporosis yes okay and this was fracture and thickness of bone in the Caucasian yes okay so explain this one to us as well uh it's uh it's uh it's osteoporosis related to trade a related disease right um the the final outcome of osteoporosis is fracture yeah uh so the just the just tg is the same as what we have done for the skin disease we also the design is also the same uh group of cases and a group of controls and to to see the uh the the uh a little frequency difference and for this project why um this this project uh published in nature because uh we find the rare genetic variant because in in in um the rare genetic variant is hard to find because they are rare you you know in in um for example in 1000 samples maybe the mutation only present in only one individuals so it's very rare it's hard to find so we find the rare genetic variant for osteoporosis yes yeah how out of a thousand samples are you able to find just one of the rare genetic mutations um this just example yeah actually it's not only one maybe several okay yeah because we do the genotyping we do the sequencing and the genotyping we know the the the the the we know the allele of each the uh interviews we know the allele of interviews yeah and then you find and then you know the ones that have osteoporosis and you see which allele variants yeah the rare genetic variant the rare genetic variants associated with osteoporosis yeah and versus the rest of people don't have that right and so then you're able to target that and say that that's probably the right and you maybe look at other people with osteoporosis and see that they have the same right okay right okay okay so then in 2015 these have the these kind did these happen at the same time the 1000 talents program by the chinese government and also the um the uk 10k project which one happened first the uk 10k project first yeah the because the uk 10k project they start from uh they started from 2010 and finished in 2015 so we published this paper in 2015 yeah okay and then in that uk 10k project in 2015 you found the rare variants for bone mineral density yes okay so you were teaching me about this um bones are made of calcium uh bone mineral bone mineral is made of calcium yes and then there's bone marrow inside that we were talking about as well yeah belief has a lot of the stem cells yes okay so the bone mineral density and this is in uh grams per square centimeter yes if it's too low you have higher propensity for osteoporosis yes higher fracture and fracture yeah which leads to fracture yeah bone weakness yes leads to fracture yes um yeah so how did you find out that that this density of of bone mineral was uh that you could figure out based on genetics density of bone minerals uh um yes because yes or no because bone marrow density have a very high habitability uh it's about from uh 15 to 85 percent um 50 to 85 percent yeah yeah of high habitability that means 50 percent to 85 percent of the bone mineral density variation can be determined by genetics yeah yeah but of course it's also influenced by uh by by by the environmental factors yeah so um so the answer is yes for no okay yeah okay so then um you were identifying in this uk 10k project specific is it again a allele variance yes yes for osteoporosis yes bone mineral density being right okay okay became the professor at hanjo normal university for two years in 2015 2017 and what were you doing when you were there um teaching genetics um part of my job is teaching also um research work and research yeah and research your papers have been cited 3400 times that's massive yeah it's a lot of citations yeah that's the total citation of my rotation yeah yeah yeah that's so cool and then um west lake university in march 2017 so you've been here over two years now right and you've been approaching two and a half yeah that's really cool it's now two and a half yeah yeah it's two and a half yeah and so and this has been slow growth process of of acquiring now 10 lab members there's seven phd students working at the lab yes and now this is going to be this deep dive there's so much cool stuff here so bioinformatic tools for the analysis of large genetic data and complex disease so you were teaching me about this you said that right now we have this issue with genetics where most of the whole genome sequences in the world are europeans yes yes and so the the reference that we have of these 30 million variants are are made for analyzing european yes yes so there's this idea of genetics for all right where you can have like you have 1.4 billion people in china another one point almost four in india right and you know so the idea is that can you make a a whole genome sequence reference of these 30 million variants for chinese people yeah and for indian people yes and for all these different countries in the world yeah so it's not just for european people yeah i like i love this so then you had this idea of having the west lake bio bank for chinese right w bbc right so teach us about why you're doing w bbc where you guys are at teach us about it yeah just like you said that most of the genetic data and now generated most of them are uh on the european populations so if we would like to do a prediction of disease we need to um derive the effect from the uh specific populations like if we want to predict disease for chinese population we need to derive the effect from chinese population so uh we that's why we want to do this wet lake bi bank for chinese to um this bi bank can be uh um can can produce a lot of data and to produce a lot of effect which can used to predict the disease i think yes okay so as we have more of these uh bio banks happening for different countries around the world you guys hopefully um going deeper into the chinese bio bank then we can better understand the formation of complex disease and we can help people live healthier yes their lives yeah okay so um where yeah let's talk about where you guys are at with that so um you've had you you're working with universities yes and so universities um are um are the blood samples from universities yes so do you guys take their do you guys take the blood samples to sequence it yourselves or do they sequence it and give you the data how does yeah um we take those samples and we uh sent out to companies they have us to sequence them got it and they return the data to us got it yeah okay so the university the blood from the universities goes to the company sequencing companies which then give you guys the data and then when you guys you have how many total um sequences 4500 plus the 6000 so teach us about the total right it's about 10k right 10 000 yeah yeah and four and a half thousand are whole genome sequenced and then six thousand are whole genome genotype yeah right okay and then now how are you then um studying the genetics of bone mineral density in this sample that you have so you're looking for the allele variants of osteoporosis yes okay yes um so the stress the suggestion is a little different from my previous study um in this study we didn't care about which gene it's associated with the disease we use the whole genome uh data to to do the prediction then we use the uh machine learning method to do this prediction also the uh polygenic risk score yes use polygenic risk score and machine learning to do the prediction okay okay so when you talk about polygenic risk score and machine learning apply to whole genome sequencing what do you find that gives you the understanding of osteoporosis uh in fact this project is still ongoing yes right um i uh just start my uh the laboratory in west lake for two years yes and it takes me a lot of a lot of time to collect all these samples and we just start this project hope hopefully we can uh gate uh work good results to help to predict the uh osteoporosis for the Chinese population yes yes i love the big vision that you have with it and this is one of the things about science and it needing to be better well funded and also have more world-class research places like west lake pop up because then you can have more scientists that are doing these long processes that take time that then it can have better computational resources right and better genetic data bank resources all this type of stuff to help you guys move the pace faster in scientific discovery right okay so one out of every thousand references variants that we have is genetic variants one out of every thousand genetic variants that we have is going to be some sort of a potential formation of maybe a disease maybe yeah it's possible yes okay okay so you can be more susceptible to developing the disease but not develop it right you're right yeah okay okay so i can have um high allele frequency difference and be susceptible to developing the disease but not develop yes so then it's okay to have um in a health some people in the healthy population have high amounts of variants but they don't develop the disease yes okay and then there are other people that yeah because this this is a probability it's a probability yeah this is a probability yeah these people who uh carrier this allele they are most susceptible to the gene to the disease but maybe he he didn't develop develop this disease at the end oh they died before they developed this disease yeah yeah i have let's say an uh an uh allele uh variants that um gives me maybe a 20 probability to develop a disease yeah something like that so it's like one in five it's a little bit lower so i don't have it yeah but you could go the other way and say that oops this fifth person got it the disease because they yeah yeah you can have and you can have different variants probability probabilities yes that's why we would like to do the prediction the prediction also a probability so if if a person who uh have uh at a higher susceptible to the disease then we for example osteoporosis then we can uh do the intervention of these uh patients or individuals yes to um to uh prevent the final outcome yes let's talk about um the interventions because if um i do have the allele variants and a high probability of developing a complex disease um what are some of the best things that are recommended for people right now for like osteoporosis yeah so for the osteoporosis um this uh i think the three three main factors can affect osteoporosis the first one the major one is the genetic fact right we already talked a lot the second one is the uh nutrition the third one is the exercise so um so the genetics now we can we we cannot for for the complex disease um it hard for us to edit it to edit the genetics the the genome so we can uh do uh modify the other two factors the nutrition and the exercise yes yeah so then the behavior changes towards better nutrition and more exercise can help um decrease the probability of developing a osteoporosis and then also down the line it is possible that we can go with something like CRISPR potentially yeah go and make the genetic edit that needs to happen in order to um also fix the disease of the osteoporosis yeah it's uh because this is a complex disease it's a complex trait yeah um it's not a single point yeah it's not a single point yeah uh uh where and can we're talking yeah like dozens yeah points right variants so if we go and change one of that right more than that right if we go and change all of those there's potentially effects five years ten years down the line that we're like oops yeah that could be the problem yes okay yeah right this is why biology simulations are very important because if you can simulate what doing those who knows 30 points of of change are it's forward to make it a healthier individual without the complex disease of osteoporosis can you fast forward 20 years and see if the person yeah yeah okay and can you do that millions of times to make sure after right there okay right yeah yeah right these are some of my favorite things these simulations okay because um hopefully we can get to the point where we can do it better and better um because then people can live healthier and not have to yeah experience complex disease right okay so um also uh at the lab is uh a bunch there's people doing different things here you have you have people looking at the genetic variants in the chinese population you have um a recent study that was finished on alcohol and blood pressure yes which i found to be very interesting um there's a very there's a very specific allele variants yeah for people that can not get drunk fast and people that get drunk too fast yeah which is a very interesting study because then some people will know it's like why do i keep drinking one glass of wine and i'm drunk already yes yeah stuff like that yeah because then you can know that it has to do with your genetics like it's very interesting right and the other ones are like i drink a whole bottle and i'm fine yeah why yeah yeah yeah sometimes we yeah it's cool to know that biology um and even something um like allele variants and this was only at a single point yes it's a single point yeah yes wow and um for for those that um are really interested it's rs 671 yeah rs 671 yes for those that are very interested in that variants that's cool and that was in a a sample of 2349 hanzhainese yes yes if it's a gg you can drink like like if it's a a you're gonna have trouble with right drinking a lot yeah yeah okay for myself i already genotype this my is a g okay so you're middle yeah i'm in the middle but um i cannot drink a lot so yeah and then um and there was also another interesting cultural aspect too with women where women are generally in China not drinking so much so um a lot of them in this study were yeah yeah even yeah if they had gg for drinking a lot they weren't drinking anyway right right yeah and then um another one is doing epidemiology of peak bone mass of chinese obviously so we actually actually we had a we had a good conversation about this before we started too um the number was um the number was quite high it was uh it was 1500 uh total mass of skeletal calcium in grams was 1500 for men between the ages of 20 and 40 yes so so basically that's the peak bone there's the peak bone yeah so so basically when you're a child um your uh total skeletal mass in calcium in grams increases very fast yes until you're like 20 yeah and then it like peaks around that 20 to 40 range like for men it's 1500 grams and for women it's closer to 1200 grams and then after 40 um both men and women start dropping yes and then women have a more significant drop because of menopause yes yes yes i found this to be so so interesting and because you were mentioning this earlier too there's a 50 to 85 percent heritability right of this right um so it's very likely that if your um if your parents have a lower amount of total skeletal calcium mass that you as well will have lower and so then i was asking you about this too is like okay so when you're doing this i mean is it possible that we can find um as you guys are doing this this uh dive into the epidemiology of peak bone mass of chinese is it possible that you can find um specific uh variants um for people that have a higher peak bone mass yeah versus ones that have a lower peak bone yes yes yes yes let's one of our design to in order to find the the genetic variant for the peak bone mass and why we want to do this is um you know most of the drugs treat on osteoporosis nowadays they are the drugs trying to slow down the loss of bone um if we can find a gene or a pathway who uh which is a determinant of the peak bone mass maybe we can find a drug target to increase the bone mass yeah because most of the drugs now they are their target is to uh decrease the bone loss yeah to slow slow down the bone loss yeah yeah interesting so we're in a sense we're fighting the osteoporosis with trying to slow down the uh the bone law the bone density loss yes and we could come up with another solution yeah which is targeting uh more bone density yeah to increase the bone density interesting yeah but we have to find the variants right first right and then we have to target the drug at um those variants yeah and figure out how to increase them right interesting so in a sense there's for only probably for a lot of complex disease there's a couple ways to handle it yes um and many of the ways we handle complex disease right now is like how do you take in uh molecular compound that uh usually affects more than just uh the targeted yeah area and uh it um fights it in a specific way and maybe there's ways that we can take something that um enables us to fight it in a more optimal way right so discovering those pathways to for more optimal combating complex disease yes yeah yeah like that okay so then um also using machine learning to predict the height of chinese men yes so then do you do you have um any like ideas about like these these are complex things like this is a not yeah probably yeah this is single right yeah this is another complex trait yeah um you know because we have this population and this population is at the main age of 19 and we have the height measurement we also have um the weight and other measurement and we would like to build a model to uh predict from genomics to height and if we have this model then new new uh bomb uh comes out if we then if we we uh have their DNA we can predict the height that they are 19 age yeah yeah yeah that's really interesting yeah yeah yeah yeah so yeah and as you get more and more of the um genetic samples you can have a greater greater accuracy right of the height right yeah interesting and height is more likely to be accurate than weight right yeah right weight is so um variable variable about what they eat how often they exercise all this kind of stuff yes but height you're pretty much going to end up being that yeah yeah interesting okay um interesting too that um for probably for those that are you know that are watching that are thinking well um there are likely lots of other scientists um around the world that that watch the program and um especially also you know PhD students etc um uh postdocs whatnot that are just trying to figure out um how do I leverage computational power onto um genetic data sets um and to find unique insights and so you know like you know you guys here if you guys want to you know come and look for opportunities westlake has lots of unique opportunities where they're looking for um people to do PhDs and postdocs yeah and uh be able to join a really powerful yeah of course of course yeah yeah I wonder you know once we leverage more computational power on um on challenges like this what unique insights we'll find like well how many you know variants determine height and where are they uh that type of stuff okay and that's just height that's one thing then there's you know there's all of the other well what about you know this one's really tough but like IQ that's yeah yeah yeah right that's a really interesting right right yes like is it possible to you know to predict something like that uh but that also determines a lot if you're sitting there doing uh like you know too much studying yeah yeah too much uh uh distractive material even if you had a higher propensity for IQ it might have um been lower yeah but uh it could also be the opposite where if you read a lot and if you worked really hard yeah um you could have you end up a little higher too yes yeah yeah this is this is all so interesting to me leveraging computational power on on genetic data yeah and then um okay so also there's um a surname and geography study and an ancestry and geography oh yes yes okay so what are you guys looking at there uh we um get our surname from our father right and we also uh get our uh white chromosome from our father and so we would like to see um um um a person who have the same surname of of me like Zen we I want we want to see how similar of our uh genome is between us yeah if we we have the same surname how what what proportional uh of the genome we share together yeah yes yeah so depending on your last name if it's the same you may have a higher no we cannot say that because uh for example uh uh people with the same name of Zen like me and other people who have a same name of Wang but I have uh more genetic similarity with them yeah yeah because oh these people who uh name of Wang he is my cousin or dancing then we can we may share more uh genetic yeah yeah yeah yeah okay interesting point yeah maybe someone born in your province near you with the different last name you have more in common with than someone with the same last name but from the south part of China yeah from the north part something like that yeah that's the interesting thing we want to do yeah yeah the research yeah yeah and then I was asking you about this too um the uh metagenomics in this in this sense you guys are doing proteomics yes on the 6000 yeah and then what do you guys want to find with doing metagenomics studies on the data researchers would like to link their microbiota to uh different disease also I think is to um um trying to do the intervention yes of all this disease so what we want to do is to link the microbiota to osteoporosis and to to see um because we we have genetic data we have um if we have another other data we can I think we can uh explain more of this disease right yes yes yeah yeah so the more you have these metagenomic data's proteomics microbiota all this type of stuff sequenced yeah you can potentially uh come in with a intervention through the microbiome yeah yeah you can do different interventions yeah yeah interesting to tackle complex disease yes your process and more okay and then what do you think that you know having these bio banks um what will be the big thing like if we have the 30 million reference for the variants um in chinese and then we do that for indian and african and you know your europeans already done south american blah blah blah you just keep going yeah what insights are you most excited about that bringing for chinese and for all these other populations around the world um let's the prediction of the disease to do the prediction of the disease for different for for uh for the specific population yeah if because we cannot use the genetic data of european population to predict uh disease in chinese population yeah yeah how much um overlap is there with um european population genetics with uh chinese is there you know out of these variants out of these 30 million variants yeah how many yeah in fact most of them are overlap most overlap yeah most of them are overlap well i mean um for the calmer variant most of them are overlap for common variants yeah for the rare uh each each population have their own unique rare genetic variant interesting and um for but uh like is osteoporosis rare yes no no no it's not no i mean i mean the i mean the rare genetic variant not the not the rare disease yeah yeah oh rare genetic variants yes most common genetic variants overlap yeah okay and rare genetic variants don't overlap yes what percentage do you think is common versus rare the more bigger of the uh population than the more rare genetic will be found larger populations have have more uh rare genetic and more rare genetic variants yes what would an ideal tool be like let's say 50 years 100 years down the line in the future what would your ideal tool be like for doing the most advanced bioinformatics um you mean the tools developed to do the um to analyze the genetic data to analyze genetic data and then to deal with complex disease yes to augment humans yes make us better yeah what would that tool look like 50 or 100 years down the line uh um yeah because the the genetic field moves fast um i think one thing is the prediction yeah the prediction tools to use a specific population to predict uh disease for that specific population yes so i think this tool can be a good one predicting disease yeah predicting disease but of course we need the reference we need the reference to do so yeah okay okay so it would be maybe something that's all in one that gets the genetic data that does the analysis that predicts the disease that gives recommendations right the whole kind of this okay yeah okay how do you think we can inspire more people around the world to work together if the data can be shared between um um research group or research group within one country or in different country yeah then i think the problem is the data sharing i think yeah yeah i like that answer so much we have a big problem with data silos across the world um across the big tech companies across the big governments across the medical institutions the research institutions etc and so yeah if we can more easily share data to provide unique insights yes yeah people's health into their education into their well-being right you know that's like let's figure out how to maximize doing that soon yeah i like that okay for young people what's an important skill that they need to know as we go into the exponential technology age for young people um young people at at which age young people in general today um i don't know if i can group at the young people um let's say those that are still in the school system yes school high school or yeah sure okay i think this question has different kind of answers in different country or in um with different cultures yeah right um because of education it is very different between like china india and the us right so yeah yeah is there something that you think china india the us and other countries around the world do you think that there's something that young people can learn that makes them just stronger for the future the the the basic thing is i think they should well educated right i think yes mm-hmm yeah i agree yeah i agree mm-hmm it's hard to say what is the best education system yeah we have act sat gaokao we have all these yeah systems but what about social intelligence and what about spiritual intelligence what about solving the sustainable development goals of the united nations mm-hmm so it's hard to exactly say what that is but i agree that the education yeah what do you think is the meaning of life oh so this this is another big question uh we should make our life more happier we should let our parents live better to we should have a happy family mm-hmm to provide a good education to our kids if we were overloaded by our daily life we maybe we have no time to think about the meaning of life right because we don't know the world after we die right so we should live a bad life when we are alive also think about our family of parents and them i think yeah this is exactly why we ask the question because if we don't think about why we're here and what the purpose of this is what my purpose is what is the nature of this reality that we're in then we forget these very first principle yeah questions yeah right right and then we just go and just do whatever right sometimes the economy or sometimes politics or media or whatever determine our life instead of right ourselves yes yeah yeah but in daily life we we always think about this the politics the economics that the also the project I need to finish right let's um if we overload by our the least kind of things maybe we forgot to think about the meaning of life yeah yeah exactly I appreciated how during the conversation we were both a little bit hesitant about the future of genetic engineering because to be to have this seventh generation principle so you have to think about what am I doing right now and how is it going to affect seven generations down the line yeah yeah so that you don't just say do that genetic edit and see what happens hope it's all good no so doing like biological simulations all these types of things to make sure that what we're doing is uh going to be healthy is very important um do you have uh that mentality that you carry with you about genetic engineering you're a little bit more conservative um about it um yes I I think uh scientific society they all I think they all have conserved attitude to this question to this yeah to the genetic editing yeah yeah yeah that I am starting to feel that more and more it's important to have open-mindedness uh and a little bit of liberalness to be able to run the experiments over time yeah and find out okay the healthy ones are are good that so we can start maybe implementing those but to um be very also um cautious about what could be some of the long-term effects of incorrect decisions right um can be very bad yes yes what do you think is the role of love in our world we care about our parents we care about our wife we care about children's yeah love is everywhere yeah do you think that this is a simulation I don't know I I don't know how to answer this question so yeah what do you think is the most beautiful thing in the world love why um because um because love is everywhere and love makes us care about this world care about people's around us yeah yeah with that more love we care more about each other in the world yes yeah huvong thank you so much for coming on to our show thank you thank you it's such a pleasure thank you thank you yeah incredible discoveries thank you thank you yes we're super looking forward to all of the next advancements that you're doing in population genetics and complex disease thanks everyone for tuning in we greatly appreciate it we'd love to hear your thoughts in the comments below on the episode let us know what you're thinking have more conversations with your friends your families co-workers people online about population genetics and about complex disease about all these other topics that we talked about on the show about genetics for all about creating these bio banks in china and in all these other countries around the world making better tools for analyzing these large genetic data sets the different very variants and genetics that we were talking about have more conversations about solving these complex diseases and living more healthy and happy lives also support the artists the entrepreneurs the organizations and leaders around the world that you believe in support them and help them grow you can find all of our links below so you can continue doing cool things like coming on site to beautiful places like wesley university and interviewing some of the great principal investigators here and go and build the future everyone manifest your dreams into the world we love you very much thank you for tuning in and we will see you soon peace that's a wrap good job good job thank you good job okay