For this last lecture, I'm going to tell you about quantitative genetic analysis of natural variation. So the question here is looking at evolution within a species, so intraspecific evolution, and asking if I take two organisms from outside, and they are phenotypically different, or not necessarily actually, you want to know what's the genetic variation that's causing the phenotypic variation between them. And this is only possible looking really at the DNA level and finding the causative mutation. This is only possible in the last 10 years also because of advances mostly in genomics. And so I will give you some clues at least of how we proceed to do that. So the outline of this morning is this. So I'll give you some background on quantitative genetics. So you'll notice there is quantitative in the term. And this field is actually population and quantitative genetics, our field of biology, which have been quantitative from the start. And then I'll go in the principle of genetic mapping for those of you who don't know anything about this, and how we can identify the nucleotide mutation basis of natural phenotypic variation. And then I'll come back to the C. elegans vulva to show you an example of what we did to try and identify one of these variations in the process that doesn't show at the level of the final phenotype. So first, a quantitative trait, I guess I don't need to define this here. It's just a phenotype, a trait that's measured on a quantitative scale, which is different. So I'm sure you all heard in high school about Mendel's piece and the basics of genetics. So if you think of Mendel's piece, basically they are green or yellow or they are wrinkled and smooth. So it's a discrete trait. Was a quantitative trait would be, for example, the height in humans or any level of activity of a signaling pathway, for example. And so there was a huge controversy at the beginning of the 20th century between Mendelians and what the school that was called biometrician. So Mendel had really wealth and this is just, I mean there might be a little bit of conscious selection but I think that's how we found his basic laws is looking at characters that would segregate like a single trait in his crosses and that were very characters that you can put as discrete traits, not quantitative. So that made it possible for him to find this segregation as single loci. Now another school was, so Gelton was the cousin, the young cousin of Darwin and E and followers started something called biometry, which was a quantitative part of biology. And so from them, so Gelton was interested in human stature. Human height, also in intelligence or this type of thing. But let's keep with human height. The problem here is that first of all in humans you cannot do crosses willingly, I mean the experimenter cannot. And second, this is really a continuously a Gaussian distributed trait more or less. And indeed there is not a single locus. Still to our days the genetics of human height is a total mess. There is no strong, there is not a single variation that causes a large difference in height. Plus it's extremely sensitive to the environment, to what children eat or things like this. So this is a very different type of trait from Mendel's traits. And so the biometricians like a person well done a little later around 1900, so a natural variation so like Mendel's trait has been very abnormal recent mutations. And so real variation has very continuous for existing in population. And following Darwin and this was one of the strong points in Darwin's books is that evolution occurs through a gradual change, a very gradual change. So gradual depends on the scale of time you are taking obviously. But they absolutely refuse the idea that you had this strong mutation that were involved in evolution. On the other hand you had people like Betson and De Vries who are, so you may know that Mendel worked in the 1860s more or less. But his work was not really recognized for 40 years. And it's in 1900 that several people came back and re-found its laws. I think, for example, you go to a race with plants. And for them, again they chose characters which were discreet with a single locus that was explaining the segregation. And so they saw evolution as the fuel of evolution being denouver mutation, not variation standing in the population, and evolution in germs. So there was a really very strong scientific fight in the first decade of the 20th century until a few more quantitatively inclined people realized that as soon as you have a few factors you can turn a discreet factor distribution into a Gaussian distribution. I think for you it will be pretty obvious. But this is the work of these two people here in the 1910s, I think. So just if you take two genes which have two alleles. So an allele is one form of the gene. So we have two genes A and B, and they have two alleles, and usually big A is the dominant. But here they're actually not dominant and possessive. They are the, oh no, they are actually, yes. So you have two loci here, and you have two forms, large A small A, large B small B, and depending on the combination of alleles you have you get a color which is more or less intense. And so from this you already get this type of curve. If you have a population that's equally populated by all these genotypes, you will get a quasi Gaussian distribution already, okay? A second factor that contributes to distributing the phenotypes is the environment. So yesterday I told you that there are several origin of variations. You have a genotype environment, so including the parental genotype and environment. And then you have stochastic variation that basically you cannot control in experiments. And so this experiment by William Johansen at the beginning of the 20th century actually is really the base of quantitative genetics. So what he did here was to take beans with a size distribution, so for some reason the large one on the left and the small one on the right here. And what he did was a selection experiment, a little similar to the butterfly experiment I showed you yesterday. So it took the extreme of the distributions at each generation for a while. So the large ones or the big ones.  So at the beginning you had like this. So if you take this part you're going to displace your distribution a little bit and you take again this, you're going to displace it and so on until there comes a moment where you can take this and you don't displace at all the distribution, okay? So there is, that doesn't seem to be any basically selection here, artificial selection, not natural selection but it's the same process. Cannot improve the trait. You cannot get it larger by just selecting the end of the distribution. And so what's in fact is that he was here in the second phase where he had pure lines so either with a large size or a small size here and he could not select further. So there was no genetic variation he could select on. And the reason for this distribution now is purely environmental or stochastic, okay? So it's from this type of experiment that he actually coined the term himself genotype and phenotype because here to a given genotype you have a range of phenotype. So you really need to consider two different things. You cannot take just one phenotypic class and assign a genotype. So now if you add up the fact that you can have many factors and or several factors and you have also what's called environmental phenotypic variance, you can get distributions, various distributions. So here what's represented is that you take a parent P1 which has a small value and a parent P2 which has a small value in a diploid. So most, the diploid means that the species have two sets of chromosomes like us. So in the F1 generation you're going to have heterozygotes for every chromosome. And then that's where the meiosis that's important takes place. And so what you're looking is the F2 generation and the distribution in the F2 generations. So in a normal Mendelian trait you get I forgot what's recessive and dominant with wrinkled and smooth but you get one fourth of phenotype 1 which is the recessive phenotype and three fourth of phenotype 2 which is the dominant. So here now if you have a quantitative trait instead of having a clear just two classes of phenotype, you can start having a distribution due to environmental variance. So this is a case with a single locus. You just have some, you can have measurement error, you can have environment you don't control or true noise in the population. And so here you have a three quarters here and a quarter here. Now if you continue to complicate the thing, for example, if there are both co-dominants, so the heterozygote has an intermediate phenotype, so now you're getting three distributions. And depending on the strength of the effect of these locus, they're going to be rather far apart. So here you have an allele effect of three and on this scale down you can read. And here if the allele effect is smaller, you're going to have almost a tight distribution. Now if you take a second locus and for example you have all additive effects, so additive effects means that, so if you have locus A with A1 and A2 and locus B with B1 and B2 and the difference between A1 and A2, for example, has an allele effect of two, this means that A1, if you have on the scale A1, A1, A1, A2, A2, A2, and for example the baseline is one, so this is going to be one, three and five, and then you can also add, do the same with B1 and just add the numbers, okay? It's really you, which is not very realistic in biology because if you have variation at two genes very often they are not going, the response is the system is not going to be linear so you're not going to get this. But very often for simplicity in quantitative genetics they assume additivity. That's one of the many assumptions of quantitative genetics that is not very realistic. Anyway, here just for the sake of pedagogy, here we have two loci with additive, so they are both co-dominant and with each other they also add up and so here you really get a quasi-Gotion distribution. The power of mapping is that now, and I'll show you that better later, you can actually, if you actually know the genotype along the chromosomes of all these individuals, you can actually realize that they are distributed differently, yeah. This Gaussian distribution is not due to just environmental noise but is actually due to a genetic basis. Okay, so to come back to history basically, we had in the middle of around 1930s a synthesis, what's called a synthetic theory of evolution, where several fields and especially genetics because in Darwin's time he had no idea of heredity. He actually had weird ideas about heredity. Darwin kind of believed in heredity of a quiet character. But so when the basis of heredity was found, the synthesis was to get natural selection together with the laws of heredity and also the fact that you had no contradiction between this discrete Mendelian heredity and continuous variation. And so came at that time two different fields, one called population genetics, which basically cares about the propagation of different alleles in a population, not caring about the phenotype just using a selection coefficient that would explain how much they are selected or not selected. And so population genetics is really the science of propagation of alleles. And then you have quantitative genetics, which was historically built to help breeding in agriculture. So the history of this science is very strange because of this. So it was a time where they didn't know about DNA and their goal was to perform selection experiments like this, but on a chicken or on corn to improve this. And so one of their main concern was to know how much of their variation they had, for example, was of genetic origin versus environmental origin. And so a lot of it is partitioning the variation that exists in the starting population into genetic versus environmental basis. So here I'm taking a cartoon from a colleague of mine to try to present the different types of genetics here. So what is represented here are genes, the transmission, so really the heredity mechanism, the genetic effects, especially through development, for example, the phenotype and the fitness, which is what's important for the dynamics of natural selection. So if you take Mendelian genetics, so the genetics of single locus, which is often the lab genetics of developmental geneticists, here what you're carrying are the genes and the phenotype, and obviously the transmission of the gene. In population genetics, the phenotype is absolutely relevant. The only thing you attribute is a selection coefficient. So it's coming here, so you have the transmission through generations of genes, allele at different genes and fitness. But then molecular genetics, which is, so the dotted lines here represent the fact that in old Mendelian genetics and in population genetics, you don't identify the loci themselves. So the genetic basis is in firm, but you couldn't a long time ago identify the genes. And neither in population genetics. So it's taking, usually theoretical, or it's taking an average of many genes adding up. So molecular genetics, which appear in the seventies, eighties. Now you can identify the genes, which are the basis of, for example, a mutant phenotype. And this is typically developmental genetics. Quantitative genetics as said cares about fitness, and especially when you're doing artificial selection, the fitness is the artificial fitness that you decide that the milk is good or not good, or enough or not enough. You don't really, so the genes are underlying, but again, there are dotted lines. You don't identify the genes themselves. It's only the overall genetic variation that you're trying to touch. And so you're selecting on the phenotype. And in actual evolutionary quantitative genetics, fitness is the fitness of natural selection. And again, you're looking at the propagation, at how phenotypes will be transmitted to the next generations. Now, what we can do, I think today, is a new synthesis of all this, where we can actually identify four, you know, relevant traits in nature in terms of natural selection. We can identify the fitness effects and their genetic basis, really, at the molecular level. And without forgetting to look, so some part of quantitative genetics, modern quantitative genetics that goes down to the gene actually just goes from genes to phenotype and doesn't really care about genetic effects and development. And I think an interesting synthesis is to actually take everything into account. So the question now is how do we get here to the actual gene that's underlying a phenotypic variation? Do you have questions so far? Sorry? That's a good point. So as I said, the environment is a key factor. And basically, for all this science, especially for breeders, there has been a historical, I mean, environment is noise, basically, for them because they want to improve the genetics. So you're completely right that environment is a little too forgotten in all these pictures. This is a very good point. The second reason now if we, you know, on our part is the environment is more complicated and an environmental effect. So how do you study an environmental effect, for example, for a developmental biologist? And basically, you know, we cannot to dissect the environment, so you can do chemistry, you know, you have a pheromone and you purify the pheromone. But at the end, what we do as a laboratory geneticist is that we end up doing a screen for mutants that don't respond to this cue, for example. So we try to simplify the problem for us by turning it into a genetic problem. Okay. So genetic mapping. So the goal is to identify the variation at the level of DNA that's causing a phenotypic change or part of it. It can be a tiny little part of this change. And so I will go on all this. But the idea is that so you use, so as you know, like an animal or a plant or fungus has different chromosomes. So let's say chromosome one and chromosome two. And you're trying to find along these chromosomes sites where you know that there has been, for example, a transition from A to a C. Or here there is a deletion of a four base pair. Or here there is what's called a microsatellite. So repetition that's at 17 times in one parent and 24 in the other. And so you have molecular markers along the chromosomes that you can use. And you have what's called a genetic map that relates them to each other. Okay. So a genetic map is a map of chromosomes, which are called the linkage group, because they get transmitted at meiosis together with these markers. I come back to it. And then using this recommendation at meiosis, you try to see whether your causative locus in such a cross is segregating together with these markers or not. So just again, high school biology, I think, but so at meiosis. So during reproduction, you have meiosis. So you have when you have a diploid organisms with two pairs of chromosomes here represented in blue and red. So it could be coming from the mother and the father, for example. At meiosis, the two homolog chromosomes that have also duplicated before undergo crossing over. So for example, you have an exchange here where you have this little piece of chromosome from the father that hooks up to the mother chromosome here. And so you end up in this particular case, for example, you have here this chromatic. So this one half of the chromosome that's not recombinant. It's completely blue. This one is completely red. But these two chromatics in the middle are recombinant. So if you have three markers here, A, B, and C, you will see here a recombination between B and C. And so the closer you are on the chromosome, the more likely you will segregate together. And so the matrix that's used, which is the santimorgan, is the proportion of recombinant gametes out of the total number of gametes. So this is the base of establishing a genetic map like this, where you actually, if you take two markers, you can calculate their distance by measuring the proportion of recombinance between them. So for mathematical details, it's actually more complicated because this matrix is not, you cannot add up genetic distances because you may have two crossing overs in the interval and you can also have interference. So when you have a crossing over, you're more likely or less likely to get a second one next to it. So because of this, the genetic distance need actually correction. The other point is that, so the maximum distance that you can detect is 50 santimorgan because it's 50. So if you have two different chromosomes, the probability, imagine that you had a second chromosome here, chromosome two, the probability to reassort the chromosomes at meiosis is one half. And last point on this, so what's the relationship between the genetic map, so the genetic map is in santimorgan. So this may be the map of a species where you say that the zero is here, that's 50 santimorgan, 15, 22, and 35. And so this is a genetic map in santimorgan. And now we have genomes where we have what's called physical map. The physical map is the nucleotide sequence. And so on the physical genome, the matrix is kilobase or megabase. And the relationship between santimorgan and kilobase is not one to one. So here, for example, you have what's called the marine map of C. elegans, where you have the physical position in the x-axis and the santimorgan, the genetic position on the y-axis. And what you can see is that for all chromosomes, you have the same pattern. And this is true for C. elegans, it's not true for other organisms. Every organism has a specific pattern where in the middle here of the chromosome, the slope is much lower, which represents the fact that recombination in this region is low. So in C. elegans, most recombination occurs here on the arms of the chromosome. And now we understand more or less why, because of the mechanics of meiosis. So recombination is this, sorry. Recombination is equivalent to crossing over. You recombine the chromatids. Other questions? Don't hesitate if there is a vocabulary like this, I'm not even aware. Speaking of vocabulary, I put a few definitions here. So we saw a quantitative trait is something called quantitatively. What's often called a complex trait is the fact that it doesn't segregate as a single locus. So for example, a human height is a complex trait. And the one Mendelian trait is used differently depending on the situation. It can be used to denote that it's a discrete trait. It can be used to denote that there is a single locus. And it can also, Mendelian inheritance is also used to denote something that has a normal nuclear genetic pattern of inheritance. So non-Mandelian inheritance is something like mitochondrial or other forms of, for example, DNA methylation or small RNA inheritance. So this is the first question, yes. So again, this depends on the organism. C. elegans is very clear that the genes in the center are genes that are highly conserved across species that don't evolve fast, whereas those on the arms are gene families, which are genes that are proper to C. elegans, that we have no idea what they do, actually. And that evolve fast, that's where transposons accumulate and things like this repeats. Now, what is the evolutionary dynamics behind this is a more complicated issue. You also have, so this is the general pattern, but you can look, you can zoom up here at a given position and it's extremely, it's also very, very local. You have also a fine-grained combination. And this is being studied at the moment and I think very soon we'll understand better what this fine-grained, where does it come from, whether it's always, you know, under selection and there for a reason, I don't think so. The global pattern for sure, I mean, so sorry, I don't want to, the hand-waving arguments is that recommendation is good to make evolution go faster and so having things which evolve fast or that you have to get rid because of deleterious mutations on the arms is supposedly good. That's the hand-waving argument. In C. elegans it's particularly hand-waving knowing that we don't have much out-crossing at all. So just look at the X and Y axis. So the X axis is the physical position, so the genome position, and this is the genetic position. So here, for example, you have this is not the, so yeah, let's say here you have 100 centimorgan and 200 centimorgan. So this reflects the fact that, so if you attack 10 centimorgan here, it means that in a given cross, this was a cross here that was more complicated than just this, they had 10% of recombinant. So you have a direct link between this genetic position and so the fact here that you have a lot of recombinant even though on the X axis, it's not a lot of DNA compared to this means that you had more recombination in this region. No, no, here there is no notion of genes. Genetic position has nothing to do with the notion of genes. It's positioned on a genetic map, so it's the map of recombination. Okay, the word genetic is a gene. No, I think I removed it. It was on this slide. So genetic at the time where people didn't know what DNA was meant that there was a genetic basis in this type of setup, right? And here in terms of recombination, it means that it's the genetic map is the map of transmission through recombination at meiosis. But again, there is no notion of a gene, no molecular gene that we know where you have, let's say at every region and open reading frame and so on. You could have DNA which doesn't do anything, you know, doesn't have any or for anything and you could still have a physical and a genetic map as long as you have meiosis. Okay. So a term, a jargon term that's used a lot is QTL. So for quantitative trait locus, what it means is that it's a genetic locus and ideally now we can identify, sometimes we talk about QTN, quantitative trait nucleotide, we can identify the nucleotide that has changed and affects the phenotype of interest. So quantitative trait locus is a locus that we can, I'll show you later, but that we can map on the genetic map but we don't necessarily know whether there is a single nucleotide variation behind. There might be two different ones, for example, but it's an operational definition in a cross. And another jargon is the notion which is I think an important concept though, is a genetic architecture. So if you take the trait, for example, a human stature in the human population, you can ask what's the genetic architecture? So how many underlying loci, how do they interact with each other? Is it all individual additive contribution? Is there a non-additive effect? And so on. So simple genetic architecture, maybe the Mendel's piece, where you have a single locus. A complex genetic architecture is something like human height. So this is, so here I have a genetic map where I have markers one, two, three, and so on, equivalent to this. And so when we look for QTL, we're looking for place on this genetic map relative to markers that we can genotype that appear to be linked through meiosis. So the variation here appears to be linked through meiosis with the variation in phenotype. And so the principle of detection is very simple in terms of, so you genotype different markers. And so, for example, I have a marker here with two alleles P and J. And you look for those animals or plants that have a genotype PP, those which are heterozygotes and those which are mosaicots. What's the distribution of phenotypes? And so here you may have a typical case. You have some kind of a Gaussian distribution for each of them. But that seems to be an actual relationship between genotype at this marker and the phenotype. Okay. So you make a statistical test. It's very heavy in statistics, this field. And here you're going to find that there is a statistical association between the state of this locus, this marker locus, and the phenotype. The marker itself is not causative. It just means that if this marker is marker one here, it's probably located close to the actual causal locus. Is that clear? So you're using the fact that at meiosis regions of the chromosomes which are close to each other physically are also are going to recombine together to see whether markers that are present naturally and that your genotype on the chromosomes are segregating together with your phenotype. And so in this field, what people use is what's called a lot score for logarithm of the odds based on a likelihood model. So likelihood of a model with a QTL at this position next to the marker versus a null model without the QTL. You mean the distribution is tighter here? That's what you're saying? No, no. So this way happened and that's interesting. But I mean, this is just one example. No, no, it's not the general case. So coming back to the sources of residual variance here. So there are many sources. First of all, this marker is not your causative locus. So you still have recombination between the marker and the causative locus. So this may add a noise here. And therefore was designed interval mapping, which basically takes into account this and you take into account genotype at several markers to account for this. You also have the rest of the genetic background. And so this is taken into account in composite interval mapping, where you actually do a multiple regression on several places in the genome. And finally, you have environment. And the best you can do is to control your environment and your design as much as possible. The problem being that you actually detect the QTL if you have too much environmental noise, you will not. So there are two main ways to proceed experimentally with this, which are often called linkage mapping. It's all based on linkage. But in the jargon, this is called linkage mapping and association studies. So in linkage mapping, what you're doing is that you're doing the crosses yourself in the lab. And I will show you example. So you're using recombination that you produced yourself in the lab. In association studies, so this you cannot do with humans, of course, except with humans, you can use pedigrees of families, which are more or less equivalent to that, except you don't have so many progeny in humans that you can have with model organisms in the lab. The second method is to take use of recombination occurring in the wild. And so that's what human geneticists do a lot in the last 15 years or so, is to use. So we have a lot more in the lab. We can produce a few rounds of recombination. But in natural populations, for example, of humans, you have had historically a lot more recombination that you can have in two or three generations in the lab. So the chromosomes, our two chromosomes of each of us is really a mosaic of different parts of chromosomes that have a history. And because of this, the fineness of recombination in terms of fine on the genetic map is much better. So you get a much sharper signal here. The problem of course here is that so you can control the environment if you're taking the elegance from the wild, bringing them to the lab, and you control the environment. With humans, we cannot control the environment. So association studies have a lot of caveats. And in any case here, the other problem with association studies in humans is that they are very difficult to actually confirm in any way. So it's just based on statistical results. And you cannot check it. If you can cross organisms in the lab, you can check, you can do transgenesis, you can replace the locus, you can actually prove that your locus is causal. No, of course, there is mutation. So in the lab crosses, we neglect a mutation. Okay. Here the mutation, I'll come back to it. So yes, so the problem is you get new mutation. And so the linkage decreases over time with this new mutation. There is a whole history of mutation and recombination. So first lab crosses, so which are often called linkage mapping or traditional QTL mapping. There are many designs. And I will show you a few. The easiest one is just an F2 cross, so the same cross that Mendel was doing. You take two parents, A and B, you cross them. And so the actual recombination occurs here in the F1. And in the F2, you get a mosaic of the parental chromosomes. Okay. The problem here, as I said, we have little recombination events, so you have little resolution on the genetic map. You may have a lot of F2, so you may have a lot of power, though, to detect something. Okay. A design that's often used is a back cross design. And this is true, especially in organisms where having homozygot pieces of chromosomes are deleterious, so where you have inputting depression. It can be difficult to cross F1s and get an even distribution of all possible F2s. So very often the design then is a back cross where you back cross the F1 generation to one of the parents. So here you get at the end about three-quarters blue and one-quarter red aliens. But you still can do a similar statistical association between genotype and phenotype. And finally, especially in organisms like Arabidopsis or silicon that can self easily, what you often do is in the F2, the problem is that you have a lot of combinations with a lot of heterozygotes and mosaics at all the loci. So to simplify what we do is just self them for 10 generations and then obtain at the end what's called a recombinant, because they have recombined, inbred because they have been inbred through generation by selfing lines where you have a mosaic, but it's a homozygous mosaic of the two parental genotypes. So recently things have changed a lot because sequencing has become much easier to sequence the whole genome. So what you can do instead of genotyping marker by marker along the chromosomes is that you can directly sequence the line or the animal, the F2 animal, because it's still expensive usually to sequence 1,000 animals. What you can do is pull designs where for example you take the extremes of the distribution and you sequence them as a pull. And so what you're going to get at the end is you have the blue parent versus the red parent, so you have the frequency of the blue alleles along the chromosomes. If you are on the chromosomes that's absolutely unlinked to the causative mutation, you're going to have about 50% of each of the alleles. Now if you have a chromosome where you have a causal locus, you may have close to 50% here, then it decreases here, for example like this, which means that here you have, this is the percentage of blue. For example, if you got your pull, it's the pull which have a low value of the phenotype. It's probable that the red alleles here is causing a low phenotype. So then this is the way to map the region. And of course the larger the pull is, the better you will, if you get a larger pull, you're going to get more recombinance and so you may actually get a finer interval here. And the best way is what's called extreme mapping, which is basically, instead of you phenotyping all the individuals, you let nature, our lab experiments phenotype them for you. So for example, you can use a selective medium or a pathogen, let's say you're interested in resistance to a pathogen. What you're doing is that on your F2 population, for example, you separate it into one half where you add the pathogen and one half where you don't add it. And after several generations, for example, you can sequence the population with the pathogen or without the pathogen and see again differences in other frequencies. So now the association studies and here we'll go back to this problem of mutation and linkage. So we have this concept called linkage disequilibrium, which is called LD in short. It's also easier to say. If you consider two loci with two alleles, A and B here, linkage equilibrium is when you have the same proportion of all combinations. Linkage disequilibrium is when it's not equal proportions. And the matrix is that, which is basically equivalent to this. So it's the frequency of large A large B minus the frequency of each. So for example, in this representation, I usually have large A with large B and small A with small B. So here I have a very strong linkage disequilibrium. And D here is zero at linkage equilibrium. Okay. There are other metrics which are scaled versions of the same or so this goes between minus one and plus one. This scale to the maximum you can have given the frequencies of your two of your four alleles. And then this is which is basically the squared correlation and the correlation between alleles. Now the interesting part is indeed when you get a new mutation, what happens to its linkage with the background? So let's say you have a chromosome that's, you start with a piece of DNA. You have a population here where you have this and then you have another one which is, you have a whole population of molecules with different genotype which have been evolving for a long time. And then comes a mutation for example here. You have a T here and suddenly comes a mutation here and you transfer this is mutates to a G for example. And so now this chromosome here is going to be linked to this new mutation. And so what you're measuring in this graph is the time it takes for this G to be to be unlinked to another marker or to be less linked to another marker. So it's for linkage disequilibrium to disappear. Because over time you're going to have recommendations with the other chromosomes. So this G for example is very soon going to recombine with this A. And so linkage is going to get smaller over time in the population here. And but it will be tighter with this A for example. Sorry it's my hand waving mathematical way of doing this. And so what you're seeing here is this coefficient of LD. This is a simulation or I guess or an electrical result I don't know where for different recombination rates here. So if you have no recombination it's going to be to stay the same. If you have very strong recombination it's going over generations to decrease very fast. And so this degree of linkage disequilibrium is a biological property of each species basically. And so depending on the species you're talking about you have very different degrees of average linkage disequilibrium in the population. So if you take Drosophila for example the half decay rate is about 100 base pair on the genome in average. In humans it's about a few kb. So this is kind of ideal for mapping at the level of a gene for in human medical genetics. In C elegans because there is little recombination it's on the order of megabase. So this is really something and this is not true for example for normal male female species of close to C elegans where it's very much like Drosophila. You have a lot of mating and recombinations. So this in natural population depending on the size of the populations depending on all sorts of structures and many other parameters you're going to have a short or long range. So the use of association mapping in natural populations is very different in different species. So in Drosophila it's difficult to do. There are advantages and disadvantages on both sides. I'll show you an example in C elegans. We have a five megabase so this is totally unlike association in humans where you get very sharp piece for example. And the advantage in Drosophila is that then you can do association within a genome. But it's not at least until now until world genome sequencing it was not used for much because you would have to sequence the world genome to find your the region. So here it's a plot representing in color the degree of linkage is equilibrium on the genome of a cow actually interesting. So here you have the 29 chromosomes of the cow and as you can expect the diagonal is pretty red so linkage is here is high on the diagonal. But you may also have places in the genome where you have actually linkage in the population between them either because there is a structure in the population so in cows you have that trace of cow and that trace of cow or it can be geographic in other species. And so to have a picture of this in a given population you're doing the association is very important. So to just to come back to comparing the two methods the QTL and through linkage through crosses in the lab and association. So you're not looking also at the same thing biologically because if you choose two parents you're only going to address the variance in these two parents which may be what you're interested in or it may not be was here you're looking at the world population. Here the resolution is low because you have few recombination was here it's much higher at least in some species because there is a lot of recombination in natural population through many generations. The power which is the the ability to detect something of small effect is much better in the lab where you can control the environment and where you can have a lot of progeny. And the other problem is that here you have this structure of populations for example races of cow or structure of human populations and you have to or in humans obviously environmental differences between people. So you have a lot of confounding factors here that you don't have here. A second point before I give an example is this question of power to detect a loci which have only a small effect. Here what is drawn is so this is a system that has been studied a lot in Drosophila which is the number of whistle they have on the body and these are two parts of the flyer body doesn't matter what and so what you're seeing here is the number of QTLs so number of regions of the genome they didn't identify the nucleotide just the region which have been found to be to have a certain strength of effect. So zero is here in the middle and you have negative effects or positive effects it could add up on one side doesn't matter.  So you actually if you think it's really a Gaussian distribution you should imagine that you're losing a lot of of variation genetic variation in the genome that have very small effects that you have trouble detecting yet maybe very important in the wild. So this is a conundrum for evolutionary geneticists is that basically maybe what we can study in the lab is just the gross part of the variation and whether this is the most relevant in nature is not clear. If evolution is really mostly through gradual change we are completely losing this part. If evolution is through de novo mutation with large effects we're going to detect them more easily. So just by the method we're using we're biasing basically the result. Okay now I will give you two examples with C. elegans so which come from my lab so what the first one is not vulva for those who get allergic to the vulva that's a good news. The first one because just what we did in the lab so the first one is an association study that we did to look at sensitivity of C. elegans to a virus that we had found. So I just come back to the mode of reproduction of C. elegans because I missed this in the previous talks. So what we've been talking so far are the amaphrodites which have a vulva and the amaphrodites their body looks like a female of the closely raised species and they make sperm first and then oocytes throughout adult food and so the oocytes are fertilized internally and then they exit through the vulva. You also have males which are X zero so a single X that arise either by non disjunction of the X chromosomes at meiosis or as cross progeny when you have a male and you cross it to an amaphrodite you get half of the cross progeny which is X zero as you would expect and so the male is very different body wise and makes sperm okay. So practically C. elegans reproduces most often by selfing so an X X amaphrodite gives rise mostly to X X amaphrodites and sometimes to a male but when you have a cross the cross progeny is half half. So in this way we can have stocks which are always overzygous but we can also do crosses when we want and so if you look at natural populations so we've been sampling C. elegans in everywhere and what you're seeing here are the six chromosomes of C. elegans and on this axis is a hundred white isolates of C. elegans and the color represents the fact that so look for example at this chromosome that for 90% of this isolates in the center of chromosome five where there is little recombination you have basically all the same genotypes throughout the world okay. So this is very special I mean C. elegans is the extreme in having very little out crossing and this is supposed to come from the fact that if you had a positively selected mutation on this chromosome that is spread throughout the world it took with it basically half of the chromosome okay. So here linkage disequilibrium on this chromosome is enormous it's less so on other chromosomes and here you see clearly that the arms have a lot more recombinations usually you can see. So we isolated many pathogens from the wild on C. elegans and so the one who are of interest here is a virus that we isolated from a C. elegans strain next to Paris and so it's called the RC virus it's an RNA virus so this means that it's the RNA molecules of the viruses that replicate and so when we assay for the presence of the virus we assay the RNA level and just so the experiment we did here was to take 100 isolates that had been genotype as I showed on the colorful slide before so these are the 100 isolates and for each of them what we did was to infect them in the lab with the virus and to look after a couple of generations at the level of the virus in the line and so this is what's the phenotype that's represented on the y-axis on a log scale and so here you have a pretty continuous distribution and so from that we did an association mapping so we used the genotype that our colleague Eric Anderson had found here and for many markers along the chromosomes we asked in this hundred isolates is there a statistical correlation between the level of this phenotype and the genotype and the answer was yes so here you're seeing the six chromosomes and here you have the log of the p-value that you have an association with a threshold that's calculated by permutations and what you see here is that in the middle of chromosome four we have a very wide peak which makes five megabays here and so I'm going not to give any details on the molecular things but from this what we did was to continue taking two parents from the distribution of the hundred isolates isolate this region by crossing one parent many times in the other reduce the interval by recombination and we identified the causal locus here so here it's a case where we can start from an association and because it's a model organisms in the lab we can actually confirm we confirm the the molecular identification by rescuing the the one which was defective now I'm going to give you a second example which is on the vulva and that's now using mapping in the lab exclusively not association so if you remember we have this and again we're going to neglect piece 3p for today it's just looking at the central cells here I told you we have the same fit pattern in the whole family but so also in all white isolates of c elegans and the problem then is to to detect a variation between these white isolates and and so you are on this curve that I showed you yesterday where you have the level of the signal that's inducing the whole system the number of induced cells which is three normally if you vary here we found a plateau here so we think the wild type is here the problem is to see differences between the different wild types we need to perturb the system so one way with we I showed you between different species was to remove the anchor cell and reveal different patterns here now we are within the same species so we can do this and it doesn't reveal a lot a little bit but mostly what we did was to introduce the same mutation in different wild backgrounds of c elegans so you you destroy you basically displace the system either taking a mutant that this place here or up and down you're asking with the same mutation does the background genome of the white isolates have a different effect on the expression of the mutation so to do this we took many mutations that were I mean this was done a few years ago now we could use CRISPR for example to introduce these mutations in different backgrounds but at a time what we did was to take mutants that were obtained in the reference background of c elegans called N2 and just introduce them many times by back crossing them into these backgrounds and then score the phenotype and so here we express the phenotype as the number of induced cells which is three in the wild type and what you see here is that for example for this mutation in the receptor of the signal it has a strong effect in the background in which it was found but it is suppressed in these backgrounds for example a b1 okay so in both cases it's a partially penetrant phenotype but the level of penetrance depends a lot of expressivity here which is the proportion of induced cells depends a lot on the genetic background conversely this mutation which has little effect in this background has a much stronger effect in other backgrounds and interestingly this pathway was not found in the reference background in the screens that were done in c elegans so to come back to a question I had the other day one can ask actually so in this background do I have because this pathway is used many times during development and behavior of the animal is it the fact is it is this mutation actually suppressed in this background for all tissues and so we looked at this and this is a very complicated slide don't look at it but basically the answer is no there is also a tissue specificity of the regulation in the different backgrounds so here you have two different phenotypes and what you're seeing is that the lines are not parallel is the main message so that the penetrance of the mutation in different backgrounds which are the different colors differs a lot so for example here this phenotype is much more penetrant in the blue line than this one was here they are equivalent in the green line and or the orange line so we revealed I mean so in the cartoon way basically we have here these two isolate the reference and two and this other isolate a b1 they have different genotype and these results actually and we could measure this with markers in different level of activity of the rest pathway so this pathway of induction the blue pathway in my slide but because there is buffering of the system it actually doesn't change the phenotype now we could reveal this using a mutation and so now we have these two parents which have a different induction index for the vulva and so now we can ask so what's the molecular basis for this difference what's the genetic basis for this difference so what we did was to take these two lines so these are these parents with the mutation in them so one parent is in orange and the other one in blue plus they have this piece on chromosome two of this mutation and then we did an f2 cross and then as I said we did this selfing to get only homozygous these recombinants in bird lines and then from this we genotyped and we phenotyped we genotyped along each chromosomes we phenotyped the lines and therefore we could obtain so this is a picture let's look just like at the middle row at 25 degrees what you're looking are here is the lot score the so the likelihood of the the logarithm of the odd sorry so the in the likelihood model that there is a qtl the markers that we took are these black triangles and we have two replicates so basically the strongest qtl we get is on chromosome one here so here and it's also so here it's a case where it's a temperature sensitive qtl so we can also map the plasticity so plasticity is the difference in phenotype between the two temperature and we have a large peak here so once you have a peak like this you need it's still a large part of the chromosome so we continued by basically we confirmed that it existed by producing a strain like this which was orange so the n2 background in all chromosomes but then it was a blue background on this piece of chromosome one and so you can confirm like this taking different pieces of chromosome one that it actually has an effect on vulva induction so the orange background here the induction level is around 0.5 here you have two replicates the other parents is very high and here we explain we don't explain 100 percent of the difference between the parents but we explain a large part with just this little piece of the chromosome two now it was because the piece was still a substantial we continued by crossing a line like one of those and trying to to get more recommendations here so this is the typical experiment of recombination in the lab where you're taking two different parents so here it's the only orange and the orange with this little piece and you screen for recombinants that would have a marker here with a blue genotype and one with the orange genotype and so you're screening for recombinants with reverse genotypes so you're screening for animals which have undergone a recombination between your two markers and with this you get a lot of possible combinations and and you can score their phenote so you score their genotype along the chromosomes and for example if you take you know that one it's only blue up here and the genotype the the phenotype is that of N2 so it must be that the the causative nucleotide is not in this piece of the DNA this one for example has a high value of the phenotype so it must be somewhere in between here and so on so if you continue like this you can obtain a small interval and from this interval we had few candidates and so then the approach is to guess which one it is in this case we had a single mutation that caused an amino acid change and we actually confirmed it by several other means that this was the causative mutation again today what we could do is just CRISPR replace this nucleotide by the other one so here we found a causative mutation in a gene doesn't matter really what it is that explains the cryptic variation between these wild isolates in the signaling network underlying C elegans vulva patterning so if we come back to the question we had the other day is this evolution completely new neutral in terms of evolutionary dynamics or did it evolve under selection and so here we used the fact a fact that was at the beginning a bad news but ended up being actually usable is that this mutation turned out to have a reason in the history of laboratory cultivation of this strain not in my lab but much before before any lab started working on it so what we did was to ask okay if is this mutation actually adaptive in the lab environment compare to the other allele and so we looked in the vulva it doesn't change anything because irrespective of the allele you have you have a normal vulva but do you have other phenotypes that can change when this when you vary this allele and yes we found that basically it changes the number of sperm the animal produces and therefore the the brute size of the animal it reduces the brute size but it also thereby reduces the time it takes to reach a reproductive maturity so in C elegans because as I said we first have spermatogenesis and then oogenesis if you make more sperm you have more progeny but if you make less sperm you're actually becoming a mature and laying the first egg earlier so here you have a compromise between producing more progeny and reproducing with a shorter generation time so you can explain expect that there is a trade-off between the two phenomena and that you reach some kind of steady states in a given environmental situation the second thing this allele does is also to increase the rate of production once it has started so the rate of every ovulation so here we have this this is the maximum egg laying rate which which is increased by this allele yeah so finally what we did was to directly compete the allele of N2 so the wild reference allele which is actually not wild and which is the mutated allele with its wild counterpart and what you're seeing here is the proportion of the two alleles along an experiment where we transferred 25 times so that's about a month and you clearly have a decrease of the actual original allele showing that the allele of N2 is actually derived the derived allele is actually adaptive in the lab environment so in conclusion what we have here is a case where we have a genetic variation in this gene which has a cryptic effect on vulva formation so it means it doesn't have a an effect on the final product of on the self-made pattern of the vulva it has an effect on the level of signaling activity of the pathways and therefore on penetrance of a mutation that you introduce but it's difficult to explain so here it could have evolved in a totally neutral fashion with a neutral drift type of dynamics but the fact that it also has a non-cryptic effect an overt effect on other phenotypes which are closely related to fitness spam production and ovulation rate suggests that since we know the environment where this has been evolving it's just a you know one example so we cannot prove it but it suggests that we may have had a positive selection in this new environment for this and that has been driving this evolution in the vulva of course this is not the ideal situation so to actually address this type of question better what we should do is an experimental evolution setup which we can do with the elegance where we we try to put the elegance in a new environment and see whether at that adaptive mutation in these new environments may affect for example the vulva in a neutral fashion when you have directional selection the dynamics of fixation of the allele is much faster than if you have a neutral mutation so you're more likely to fix a maleal if it's under directional selection in another tissue and I just finished by thanking so the people the virus work was done by Tony and the this final work on a cryptic variation was done by Fabia


In this presentation, we will take a look at multiple choice questions related to the statement of cash flows. First question, which is included in the cash flows from financing activities? Support accounting instruction by clicking the link below giving you a free month membership to all of the content on our website broken out by category further broken out by course. Each course then organized in a logical reasonable fashion, making it much more easy to find what you need than can be done on a YouTube page. We also include added resources such as Excel practice problems, PDF files and more like QuickBooks backup files when applicable. So once again, click the link below for a free month membership to our website and all the content on it. A. Interest revenue B. Sale of equipment C. Interest expense D. Payment of principal portion of a note payable E. Purchase of stock in another company Let's go through this again using the process of elimination, which is included in the cash flows from financing activities. A. Interest revenue If we think of the journal entry between interest for interest revenue, we would debit cash and we would credit revenue. Now are either of those going to be income statement accounts? Yeah, the revenue is and therefore it's an operating activity typically we would think it would be in the operating activities. Little confusing because of course interest revenue would indicate that we had a you know we loaned money so we might think it be somewhat like an investment or financing but the interest portion then it's going to be part of our operations and therefore operating activity typically. B says sale of equipment journal entry is going to be that we were going to have a sale of equipment so we're going to debit cash, we're going to credit equipment, debit accumulated depreciation and then have a debit or credit to the gain or loss. So is there something that has to do with the income statement there? Possibly the gain or loss but then we're also dealing with the equipment. We sold equipment so you would think that would be investing activities. So in any case you may think operating or investing it's actually going to be investing but it's not going to be financing you would think. So we're dealing with the sale of an asset here so we invested in the asset we sold the asset therefore investing activity not financing and then C says interest expense journal entry there would be to credit cash and debit interest expense are either of those going to be income statement accounts yeah interest expense is an income statement account and therefore it's probably part of our operating activities and not financing and then D says payment for principal portion of note payable journal entry is going to be credit cash and debit note payable are either of those going to be income statement accounts no because there's not revenue or expenses are either of those going to be dealing with equipment that we purchased or sold or some type of large asset that we purchased or sold no we didn't buy equipment we didn't buy investments we're not selling equipment or investments we're paying back alone so you would think then that that's not operating not investing but financing so in this case this looks like the one D and then E says purchase of stock in another company so if we purchase stock in another company in the debit that journal would typically be we're going to debit investment stock in you know stocks upon these aren't stocks that we're issuing that would be different and that might be financing but in this case we're buying stocks as like as we would just like a normal investor debit an asset and crediting the cash and that sounds like an investing activity a normal type of investment so D looks like the one final answer which is included in cash flows from financing activities D payment of principal portion of a note payable next question the indirect method a separately lists each item of operating cash receipts and cash payments B reconciles net income to net cash provided by operating activities C reports a different amount of cash flows from operations than if the indirect method is used D results in a differently formatted investing activities section and E is not often used who all right let's go through that again the indirect method a separately lists which item of operating cash receipts and cash payments so the indirect method now the indirect method we can compare and contrast when you hear indirect method you want to compare and contrast it to the direct method and this first one we might think what it's separately it's it lists each item of operating cash receipts you know it does list a lot of items either method lists items so maybe we don't really I'm gonna keep that for now B says reconciles net income to net cash provided by operating activities and again we may not totally know what the answer is but we know that the end result is should be cash provided by operating activities so I'm gonna keep that for now C says reports a different amount of cash flows from operations than if the indirect method is used and that's one that that's not going to be true because the two methods should report the same bottom line number just different method to get to the bottom line number so C I don't think is right because we should get to the same results just using different methods D says results in a differently reported investing activities section now when we think of these two methods we want to remember that they only deal with the operating section so that the investing and financing activities will remain the same whether using an indirect or direct method therefore it's not D and E says is not often used and that's not true the the indirect method is probably used more than the than the direct method which is recommended as well so I would think that of the two the indirect method would be used more because it's often required to be used more often so I don't think it's E so it's between a and B the indirect method a separately lists each item of operating cash receipts and cash payments or B reconciles net income to net cash provided by operating activities now of the two be actually sounds exactly like what it does I mean we start with net income and we end with operating you know cash provided by operating activities so B sounds like you know the correct answer here a separately lists each item of operating cash receipts and cash payments that's not really what it does it does list items but it but it lists you know the change in items whereas the direct method lists each item kind of like we had if we had sales on a cash basis and then cost gets sold on a cash basis or in other words cash receipts from customers and cash paid for inventory so a sounds kind of like more like the direct method e sounds more like the indirect method final answer B the indirect method B reconciles net income to net cash provided by operating activities next question when using the indirect method which is subtracted from net income a decrease in income wages payable b decrease depreciation expense c amortization of intangible assets d utilities expense e decrease in inventory here we go again when using the indirect method which is subtracted from net income so we're trying to think of the things that are going to make net income go down when we think about this process we typically you know think about our worksheet and we're usually looking at differences in our worksheet to see what will decrease you know taking those differences and increasing or decreasing net income kind of backing into the cash flows from operations so if we think about each of these we're going to say if there's a decrease in income wages payable now i would i would go through this thought process and think of if we haven't memorized if you can just memorize the rule it's basically if you know if there's a change in assets current assets if the current assets go up we're going to bring down the cash flows if current if there's a current asset that goes down we're going to bring up the cash flows and it's the opposite for liabilities if if liabilities change and liabilities go up then we're going to increase the cash flows and the opposite if it goes down now if you can't remember that i would try to work out just one account which is accounts receivable and then apply the rule to everything else meaning if we look at the change from accounts receivable from the prior period to the current period and the change increased what would that mean that would mean that accounts receivable got debited the journal entry accounts receivable debit and we would credit sales or revenue that would be the journal entry now revenue in this case would be increasing so the question is what are we going to do to net income well if revenue is going up but no cash was received that would be mean that revenue is too high and therefore if if that went up we'd have to decrease if accounts receivable went up with that's why we would have to decrease net income we would be bringing the income account down so that applies to any kind of uh of asset account current asset account and then of course the offset is true if accounts receivable goes down or any kind of current asset account then net income is going to increase now the opposite is going to be true for any liability any current liability so if a liability goes down we're going to increase net income if a liability uh i'm sorry if a liability goes up we're going to increase net income if a liability goes down we're going to decrease net income so if we go through these then we're going to say decrease in in income wages payable, that's a liability. If the liability change goes down, it's gonna be the opposite of what happens here. Net income is gonna go down. So it looks like A is actually the one. V says depreciation expense. Now this is a little bit tricky because you can think of the depreciation expense as the difference in accumulated depreciation. But we also can think of depreciation expense as it's in net income, it's in net income. But there was no cash involved. And what did it do to net income? It decreased net income. And therefore we're gonna have to increase net income to get to a cash basis. So depreciation expense doesn't have anything to do with cash, it brought net income down. So we're gonna have to increase it to make net income on a cash basis. Therefore this would be an increase and not a decrease. C says amortization of intangible asset. Pretty much the same argument for depreciation except this is an intangible. So we would be increasing the net income, not decreasing it. Utilities expense. Utilities isn't something that we're gonna have directly on the indirect method. That would be on the direct method we would be dealing with trying to convert an expense line by line. So it's not really, this isn't really something that we would have on the indirect method. So decrease in inventory. If there's a decrease in inventory, that's gonna be something that's gonna be an asset. So it's gonna be similar to accounts receivable. So if an asset like accounts, similar to accounts receivable inventory went down, we would be increasing net income, not decreasing it. So that wouldn't be it. So we're left with A here. So final answer, when using the indirect method which is subtracted from net income, A decrease in income wages payable. Next question. The first line item in the operating activities section using the indirect method is A, cash and cash equivalent. B, cash received from customers. C, cash provided by operating activities. D, net income. E, gross profit. Let's go through this again using the process of elimination. The first line item in the operating activities section using the indirect method is A, cash and cash equivalents. Now the cash is gonna be at the very bottom of the whole statement of cash flows. That's what we're gonna, that's what we wanna get to at the end. So it's not what we're, what we're typically gonna start with. So it's not A. And you might think that we would start with beginning cash, but really we do the whole thing to calculate the change and then use the beginning cash to get to the ending cash. And then B says cash received from customers. Now you might think that that would be the case. I'll keep that for now. C says cash provided by operating activities. And that's like the whole section that we start off with, but this is within the operating activities. So that doesn't look right. D says net income. And that sounds like it could be it. And E says, I'll keep that for now. E says gross profit. And gross profit is sales minus cost of goods sold. That's a subtotal in the income statement. Probably that's not the same as net income. So it's not that one. So we're left with B and D. We go through this again. The first line item in the operating activities section using the indirect method is either B, cash received from customers, or D, net income. And of these two, the first one cash received from customers is what we would use in the direct method because we would be converting revenue to cash received from customers. The indirect, so it's not that because we're using the indirect method, which would start with net income. The more common method that we would see would mean that we're gonna start with net income and then reverse everything out. So we're gonna start with the net income and then reverse out what we need to get to flows, cash flows from operating activities. So final answer, the first line item in the operating activities section using the indirect method is D, net income.


Wow, it was an experience of a lifetime for some elementary students from the Red Lake Reservation this past weekend. The kids performed in front of hundreds of spectators at the Nicollet Mall. They were asked by the host committee to help bring some culture to the Super Bowl experience. You got to show off your moves and stuff. I'd like to get the kids very involved in other cultures and I also want to make sure that they go out and represent who they are, where they come from, you know, who they are inside as well as the outside and what they can do and they can do anything. Only a select few performers from the school were picked for the trip out of 120, only two dozen were selected. If you enjoyed this segment of Lakeland News, please consider making a tax-deductible contribution to Lakeland PBS.


Live from Las Vegas expecting the signal from the noise. It's theCUBE covering InterConnect 2016. Brought to you by IBM. Now your host, John Furrier and Dave Vellante. Okay, welcome back everyone. We are here live in Las Vegas for IBM InterConnect 2016 exclusive coverage from Silicon Angles, Media's theCUBE, our flagship program. We go out to the events and extract the signal from the noise. I'm John Furrier, the founder of Silicon Angles. I'm my co-host, Dave Vellante, founder of wikibon.com. Our next guest is Adriana Gascoin, who's the CEO and founder of Girls in Tech. Great success in the industry. Has really, from early on, started storytelling, talking about the data around entrepreneurship and success. Welcome to theCUBE. Thank you so much. Great to see you. Great to see you again, yeah. So, obviously, girls who code STEM now, it's STEAM because art's added in there, that buzzword, which I love, I like STEAM better than STEM, that it's science, tech, education, and also arts. So, design, all this is merging together. Engineering, yes. Engineering, all this stuff is now part of it. Tell us what's going on with you guys. What have you been through? Some of the successes you've had, share your story. Yeah, so in the past, I would say two years, we've experienced massive growth and mainly in the emerging markets. So, we've perfected our curriculum in Silicon Valley. We pilot the programs out. So, coding courses, anything from Ruby on Rails to Droomla, Duple, PHP, and then perfect it and test it out with that market and then customize it to the emerging markets to make sure that it is, we have the cultural nuances in mind that they can learn in the best way that they know how. So, we've been expanding a lot in African countries and Latin American countries, some European, Eastern European countries, and it's really exciting to provide these resources, skills, and curriculum to people in places that have very little access, access to the education, access to the role models and access to devices even. So, that is very inspirational to me personally and has sort of fueled the next generation of girls in tech and expanded the opportunities. We've known each other from the web, 1.0s and 2.0s days, and a lot's transpired now with social and now with the connectivity option, certainly outside North America, a huge on-ramp. Talk about that dynamic. What's your observation? What are you seeing? Cause now it's completely democratized. There's some more demand for coding. See the Apple example here at IBM was an absolute surprise for me in terms of validation for IBM, the Swift programming languages, it's super easy to use. It's not like C++, so a huge tsunami. Share some observations about that particular point, the on-ramp of new coders, the diversity angle, some things you've seen, observations, learnings. Yeah, so girls in tech was one of the first, I think, women in tech organizations that existed in Silicon Valley, specifically for the more cutting-edge, I think, participants or women coders. And so it was really exciting to see the growth and demand not only in the United States but around the world. And I feel that now that more women are getting into coding and really experimenting with different coding languages, it's seen as more okay. And it's actually seen as cool. They're getting really excited about it. So you have product marketers and product designers actually learning more about Ruby on Rails because they wanna be able to, one, learn how to do it themselves. And two, they want to understand what their counterparts are working on so they could better understand product development in total. So I think it's sort of this wave of excitement where I can do it too. Hey, it's not as hard as everyone thinks. And so everyone's a part of this coding party, so to speak, and in sharing resources and collaborating and really making. And collaboration is a big part of it. With open source, you're seeing that aspect, that social dynamic big. Has that been a big growth area for the connections and the evangelism, the fusing of these relationships? Yeah, and we see that since we're a global organization. We're the largest women in tech organization in the world. So we see the fusing of collaborations in terms of building new products, sharing curriculum, educating one another, making those connections from one country to the next. We have an exchange program that we do at Girls in Tech where we bring over 20 to 30 female entrepreneurs to experience Silicon Valley. And we're doing that with Nigeria, with Singapore, with Paris, so that they can really understand what goes on, what makes Silicon Valley tick. And then they bring those skills back to their home city country to grow companies there and to really empower people to really focus on socioeconomic development. You're exporting that knowledge in the Valley. How did you get into this?  I'm actually not a coder. I have a product marketing background. Actually, I started at JWT, Consumer Brand Marketing Company. And it was around the time that we met and I was plopped right into the middle of Silicon Valley and thought that this is the most exciting thing I've ever experienced in my life. So working at a startup, you never really look back. It's a flat environment. It's people collaborate. They respect each other. It's a very sort of nimble type of opportunity where you can move the needle and be a part of something bigger and build a family too, essentially. So I really just have enjoyed the experience and realized because I was the one female in the company of 50 people when I was working at Guba, I thought we really needed an organization like Girls in Tech to really make an impact, bring women together. There's a talk and also a share.  One of my daughters, I have two, and she says to me the other day, that I really want to learn how to code. And I'm like, oh, that's awesome.  She really likes it. But she wasn't, I won't say scared, but you can see a little tepid. I'm like, oh, here, so let's, I was walking through it. So the starting point is key. And I felt like, not that she felt like, is it a guy thing? Is it gonna make me different? So that was kind of her vibe, right? So how do we, how do I, as a father, help her? Do I get her involved with Girls in Tech? Is it, how do you get that step, that baby step to get people hooked on the excitement of coding? Yeah, well, I think that you can expose them to organizations like Girls in Tech, take them to workshops or hackathons, or we do iOS building workshops for young girls in middle school and high school, to really expose them to what they can do in creating an iOS app, and then actually getting it into the iTunes store and saying, hey, dad, I really did this. And it's sort of cool when they actually have their own app in the store, right? But there are books out there, there are different blogs. It's as simple as, What's the website, what's the website, URL? GirlsinTech.org, and you can see all the programs that we have. We also have chapter websites. Yeah, there's an event schedule on there as well. But I think it's, even just taking your daughters to the Computer Science Museum or Computer History Museum, to the Exploratorium we have in Embarcadero in San Francisco, exposing them being a part of their exploratory experience in getting to know the STEM fields and getting excited about science. The curiosity's there, right? So nurturing the curiosity, getting them exposed. Is there a language that you've seen just from a pattern standpoint that the girls get interested in first? Is there, I mean, it's kind of a random question, but I mean, is there like a trending, you know? There is, that is actually a good question. Ruby on Rails seems to be very, very popular and not only amongst girls and women in the United States but also around the world. It's a very nimble language. So people are now using it, or I should say companies are now using them a lot, this coding language in their products. So that's why girls and women. And there are a lot of digital natives, a lot of, and we had Tanmay on 12-year-old Coder. We were just talking, he was talking about hyperthreading and he actually had a database problem. He solved the move to Java, oh my God. So the talent is natural. It's not like when we went to school and I was a computer science major. I mean, it was like laying bricks, it was hard work. Heavy work left kind of like thinking. It's work but there's so many resources and organizations and companies like General Assembly and Girls in Tech, we do coding classes and whatnot. I think it's a lot more tangible and easy to access. And because we're in a community where everybody likes to share and collaborate and serve as mentors, I think it's actually a lot more digestible. And people are making it more DIY. So WordPress, for example, making it a lot easier for more layman folk to actually build websites. So you've got what, 25 chapters around the world? 58. 58, yeah. Oh, okay, great, you gotta update the website. Oh, it's not, oh yeah, because that is, we're actually rebuilding all of those websites as we speak. So 58 chapters, so these are sort of self-sufficient chapters. They're sort of, and how does a chapter incubate and get off the ground, how do you see that happen? So we start by, they send me an email, so I've never had to reach out to anybody around the world to start a chapter. If they're really interested in bringing the Girls in Tech model, they will email me or call me or Skype me. And then we have three interviews. And then once we get the green light on everybody, we do background checks and we do reference checks. And then we have an EPK, it's an electronic press kit which basically has all of our curriculum, messaging, guidelines, everything that you need to know in one package and a PDF. That's your product marketing hat coming out. Yeah, yeah, I know. Exactly. Who started that? We try to make it as seamless and easy by keeping everything branded and correct. And then they take that and customize it.  What Silicon Valley does is great for Silicon Valley, but it's not great for every other city around the world. It's not a replica, exactly. It's a little tweak. Exactly. It's a cultural kind of. It's a cultural tweak, cultural nuances. It's customization based on the aptitude, I would say, or the maturity of the tech ecosystem in that city. So we basically allow them to run with it and then we, especially the first six months, we plan the launch party. We have them get calendar events together based on what the need is there. We get the government involved. We get institutions involved. We get incubators, accelerators.  Get the distribution going. Yeah, it's about community. It's about building ecosystems, right? It's like you can't do it alone. We need the help and support from all of these other industry players. And that's the case in Silicon Valley, too. But we just have so much. We have a community oriented, very community driven. That's their charter, is to build that community. Exactly. And grow it, learn, share it back with everyone. Exactly. All right, so what's the most exciting thing that you've seen in your experience? Has there been a aha moment for you personally or has there been a fallout of your chair? Oh my God, I can't believe that person built that product. What's the coolest thing that you've seen come out of the chapters? Well, we did something really cool recently, the Super Bowl hackathon. So with ESPN and with IBM, actually, IBM Blue Mix and Watson teams came out and they supported us. And we had over 175 applicants. We had 14 apps that were actually built and launched to sort of enhance the experience of enthusiasts and football fans during their visit to San Francisco. So it was really neat to see over a three day period, people spent the night, worked really hard, got into teams.  And it was really neat seeing everyone collaborate and build real time. So that's really awesome. We do things, we have a lot of support from the US State Department, especially in our Middle Eastern chapters. I recently went to Jordan to speak and we had three ambassadors come out and actually do speeches in support of girls and tech. So you could see the government really, really emphasizing STEM education for women, not only in the US but around the world. And they're really supporting by funding these programs and also by having these phenomenal women come out and speak to talk about. It's so impressive and so inspiring. Share your vision. As you look forward, obviously you got a lot of work done. Your head's down, you know, moving the needle. What's the vision? Share with the folks out there, your vision. How do you see this thing unfolding? So that's a really good question because if you would have asked me five years ago, I think it would have been a little bit different. Now it's more about building these communities, holding companies accountable because I think everyone wants to be a part of the dialogue and not a part of the solution. There's so many companies, IBM Excluded, because they're a huge partner and advocate of girls and tech that are talking about compensation parity, they're talking about workplace diversity, time off for moms, paid leave for moms, et cetera. And really, they're not doing anything about it. So how do we act? IBM? No, not IBM, but a lot of other companies. A lot of high tech corporations, a lot of the ones that we know. And it's important for us to hold everyone accountable. So those are two of my main agendas, bringing the community together, holding companies and other entities accountable. But most importantly, scaling the organization so we can educate women all over the world in the STEM fields, coding, design, product development, entrepreneurship, entrepreneurship, and really have them start their own companies. I think that'd be really exciting. So for the folks out there that are watching this, hey, you know what, I love it. I want to be part of the solution because you have the haves and have nots, you have the rhetoric and certainly it's trendy to talk about women in tech and whatnot. And then the reality is who's got their feet to the fire. So we're a working example of that. You can see we have no women on our set here, no women anchors, and we have no women coders in our company. So one of the things we get complaints, certainly Grace Hopper was, where are the women? A couple thousand women at the conference, we were all guys. You say it ourselves all the time. So what we want to do is, we're busy, certainly we're building our business, self-funded, a lot of action, but we need help. So where do we go? How do we tap into that telephone? How do we get involved? We would love to. Is there a recruiting program that you guys have, a vehicle for people who really want to be part of the solution? So we do have a recruiting program. Well, first we have our jobs board, which is a global jobs board. So if companies want to list their listing, then they have access to all the talent around the world. We just launched an SVIP program. So it's called the Silicon Valley internship program. Basically through the British government, we give them J1 visas to female software engineers and match make them with startups. So they have the experience of working at the startup, get paid $60,000 a year, and they're there for a year, and these are women top of their class from all over the world. We had 950 applicants this year. That is amazing. 950 applicants that are at the top of their class from all over the world, female software engineers. So we're focusing on that, and we're focusing on working with high-tech companies to provide recruitment strategies. So what are different ways that you can recruit? Rewrite all the job descriptions. Have 50% women, 50% men in your recruitment panel. Find different channels to actually promote your job listings and things like that. So there's various different ways we're working with them to increase the pipeline, get women excited, and once they're in the company, teach these companies how to retain them. How can they stay there? How can they be happy? What are the flexible work environments that you can provide women? I don't agree with Mirsa Meyer in that Yahoo! should have all of the employees working there full-time. That's just not going to make them happy, you know? And so I really feel that we need to have- Flexibilities to keep things. Maybe that was a point. Yeah. Well, they can lay off Tuesday, too. I mean, she has that going on. But this is a good, this is the modern era right now. We are living in a modern era where flexibility on situations like how you work, come in the office on certain days for meetings, but give people the flexibility. That seems to be the top thing. So who's doing it right in your mind? If you look out on Silicon Valley, is there names you can point to in saying, hey, you know, obviously Grace Hopper's doing some great conversations around that. They've got a whole organization around. But what companies? I mean, we talked to Intuit, they're doing amazing stuff, a lot of mentorship, a lot of cool things. Who's got it right in your mind? Well, I think Etsy is doing a really good job of recruiting. They now have 50% of their engineering staff is women. So the ways that I would just mention in terms of finding different ways to test women in tech and coding to get to the end results. So not expecting it to be a cookie cutter approach, but really finding a way to get to the end goal, that's what really matters. So Etsy did a great job of this and now as a result has 50% female engineers. I would say in terms of high tech companies, there's two that really stand out and that's IBM and Cisco. And I've worked with them and it's not just because we're at the IBM Interconnect Conference. Oh, it's true, it's true. But I have worked with them extensively and their teams come out and work hard. So in the hackathons that we've produced with them, they have about 15 of their Watson IBM Bluemix staff come out and they have booths, they have mentors, they engage, they teach lightning talks. So they're very interactive with these people who are hungry to learn. And guess what? Most of these people don't know that much about IBM Watson and Bluemix. So it's a discovery tool, right? And they're like, wow, we can actually do that for the app, you know? So it's really exciting to see that. And a shout out to Meg Swanson, she's awesome. She's a real player. She's just doing the mark. Now she's got a bigger role. Bluemix has been great. IBM is great with Bluemix and tech. So it was at IBM that we, did you want to mention somebody else? Cisco. Oh, you said Cisco, right, right, right, sorry. Yeah, yeah, so I would say another company that obviously does a good job is Google. So they have specific women tech maker type initiatives and they're more focused on female entrepreneurs. So they're Google entrepreneurship type of division. They're the ones that probably teach most, the most of the courses and get more women through the programs. Yeah. Thanks for coming on theCUBE. Really appreciate it. Great to see you. It's been a walk in and all your success. Thank you. I'm so inspired by that. And again, anytime you want to come on theCUBE, we have to take a look at the event. Come down, share. Certainly we have women on Wednesday. So every Wednesday we feature women of the week and guests of the week on theCUBE. So go to SiliconANGLE.tv to check that out. Thanks for coming on theCUBE. More exclusive coverage from IBM Interconnect after this short break.