A boy who did not stop growing ...

The Alpina Non-Fixes Publishing House represents the book of the Australian scientist Edwin Körk “A boy who did not stop growing ... and other stories about genes and people” (translation by Maria Eliferova).

How to give birth to a healthy child? Why does my child have heart problems? Will I have Khorey Huntington, like my father and grandfather? The theme of genetic diseases today becomes ordinary, however, what is medical genetics - many are vaguely. Unlike the therapist accompanying the patient from the cradle to the grave, the geneticist monitors his life before her origin and years after its completion. During his professional activity, Dr. Kurke had a chance to take part in the fate of thousands of people, and this period coincided with the era of an unprecedented breakthrough in genetics.

The experience of a practitioner and a scientist combined with the gift of the narrator allowed the author to write a magnificent overview of the history of medical genetics and evaluate its prospects, clarifying the most difficult issues for real examples for everyone who wants to comprehend the subtlety of this new science.

We offer to read a fragment of the book.

Several times a year I give lectures on genetics to medical students. My task is to give them a general idea of ​​the importance of genetics in medicine, to refresh the fundamental principles in their memory, which they are supposed to have already mastered earlier, and also try to identify the closest prospects for this area of ​​science. If you study medicine today, almost certainly one of your future patients will have to undergo genetic tests, and since you will need to write a referral for testing, receive and analyze its results, it is useful to have an idea of ​​what these results mean. Once this course was read by Anna Turner-she called him “genetics in an hour”, and in the following years, when this duty passed to me, I kept finding that I should tell about some new achievements, while the lecture immediately stretched out and had to reduce part of the material.

Nevertheless, I always find the time to note that all other directions of medicine should be considered only as subsections of genetics. Almost all human ailments , as well as everything that happens to us, not directly related to diseases, is based on genetics.

Take, for example, injuries. You most likely do not think that the ability to get into a car accident or getting in the eye is associated with genetic problems. However, imagine that there is an important genetic risk factor, which largely depends that something similar will happen to you. We are talking about Y Cromosome. At any moment, starting from 12 months of age, when we usually can stand up, go and get into troubles, men have the probability of injury higher than women.

Perhaps you have already guesses why this is happening. The obvious culprit is testosterone, because aggressiveness and impulsiveness are qualities that can bring trouble, and testosterone, as you know, increases both.

This factor may well play a role, but probably not everything is so simple: perhaps there are other ways of influence of the Y Cromosomes on behavior. Of course (with all my desire to put genetics at the forefront of medical science), one should not imagine that the differences in the behavior of men and women are exclusively due to their congenital anatomical and chemical features. The masculinity and behavior of men is influenced not only by genetic, but also by social factors. If you are inspired from birth that you should show courage and experience a love of adventures, no one will be particularly surprised if you really have these qualities. If you are taught that your place is at home and that calm, passive classes are best suited to you, of course, you can rebel, but on average it is likely that such education will affect your life choice and keep from the behavior options that are associated with dangers.

As if in order to confuse even more, there are other genetic factors that affect your chances of getting injury, regardless of whether you have a Y chromosome. Not all men have the same risk of injuries, and there are many patterns of behavior (and risk) common to men and women. Both men and women differ in the degree of impulsiveness; Some men (and women) prefer to sit at home and play computer games, while others are parachuting. These differences in representatives of the same sex are also genetically determined to some extent, but it is not as easy to understand them as to blame all the guilt on one “flawed” chromosome.

In short, the genetics of injuries is complex - there is an interaction between genes and the environment. The ratio of both is variable, and there are times when the environment can completely prevail over heredity. If the most peaceful woman in Baghdad, which is not a peaceful, not prone to adventures, can, by virtue of elementary bad luck, become a victim of an explosion of a mined machine on a market square. Once the most (potentially) reckless and aggressive man in such an environment where men are brought up by opponents of violence, and other opportunities to suffer are limited (note that I could not cite an examples of such conditions, but everything is relative), he can live to an old age without a victim of violence, and die in a dream.

The results of such interaction between genes and the environment are easiest to study at the level of the population, and not an individual. There is about the same difference between them between the climate and the weather: we know that on average men more often become victims of violence, as well as that in the middle summer it is hotter than autumn. However, no one is surprised by the cold summer or warm autumn day; Separate days are like individual people. And just as we cannot predict what the weather will be on a certain day next year, even the most complete knowledge of the child’s genotype will not help us to unequivocally predict which personality will grow out of it and even what he will hurt.

Almost all the ailments of a person at least partially depend on genes. The range extends from diseases that is mainly devoted to this book (when the breakdown of only one gene or disorders at the chromosomal level is enough to cause a hereditary disease), to ordinary diseases like a stroke, which contribute both genes and the environment, and the genetic component is due not to one genome, but by many. This last option includes many different genetic components, each of which slightly changes the probability of a stroke. Some increase his risk, others reduce. There are probably hundreds and even thousands of similar genetic factors that displace the risks of each of the common diseases; For most people, as a rule, each of them individually plays only a slight role. If the genetic version increases or reduces for you the probability of a stroke by 20 %, this is considered a very significant contribution. In the catalog of genes affecting the probability of stroke 1 , 287 different positions are noted, the changes in which most likely have a similar effect, and in most cases this influence is very weak.

The main way to identify the contribution of heredity to common diseases, at least at the moment, is a method of a full-genomic search for associations (Genome-Wide Association Study, Gwas). If you decide to do Gwas 2 , you will need a very large sample of people (from tens to hundreds of thousand 3 ), about which you know anything. For example, you know their indicators such as growth, blood pressure, whether they had a stroke, etc. In each of them you take a sample of DNA and study thousands of areas scattered throughout the genome, which is known to people in people. Then these genetic data are compared with well -known information about people from the sample. The goal is to find a connection between DNA data and the characteristic.

Suppose for some people in a certain position in DNA is C (cytosine), and for others - T (Timin). Having examined the DNA of those who did not have a stroke, we find that 50 % of them have C, while the others have 50 % - T. Then we consider a group of those who suffered a stroke, and it turns out that among these people 60 % in this position costs C, and 40 % - T. That is, people who have suffered a stroke more often than in control group 4 . The next stage is to repeat the study again on the so -called “cohort of reproduction” (new group of people) to prove that the connection established last time is not an error. This is necessary, since at the initial stage of applying the GWAS method there appeared a lot of “sensations”, which later turned out to be a statistical noise that was not related to reality. The statistical bar for the second study is slightly lower than for the first, since we are looking for one specific object, and not consider the whole genome. This means that there are not so many people for the second sample, but the work is still a big one. Suppose that the second time you found something close to the result of the initial study. Congratulations, you opened a stroke risk factor!

However, this hardly helps you much. First of all, it may turn out that this genetic variation itself is not directly related to the risk of stroke. She can be a innocent fellow traveler who simply sits and does not touch anyone somewhere near the other mutation-a true culprit. So, the discovery of such a variation is often just the beginning of a long and tiring search for a real villain. The second problem is that the data obtained, in fact, tell us nothing about a specific person. If half of the population in a certain place of DNA has C and this option only does not increase the risk of stroke, there is no need to especially worry, finding it at home. Data may turn out to be inappropriate to another population: for example, the relationship between the presence of the option with C and stroke is reliable only for Europeans. Unfortunately, we have an excess of research on European material (I say “unfortunately”, since we are desperately lacking similar studies on the material of other populations).

There is a rather high probability that the subject of your search using Gwas is not in this gene. Most often it happens. Sometimes this happens due to the notorious phenomenon of a innocent fellow traveler - there are changes in a significant gene, and c (instead of T) is located next to this mutation. However, most often, when it is possible to establish a causal relationship between this option and the disease that interests us, it turns out that the matter is in managing genes, and not that the genet mutation somehow changes the protein that is encoded by it. The genome is replete with sequences that play an important role in the regulation of activity in the cell nucleus. Usually this is done using signals in the form of RNA - a molecule that is very similar to DNA, but still differing from it. She activates something that suppresses something else, and then, in turn, changes the activity of the gene so that it produces more or less protein ... and all this can be related to the issue that is originally interested in you. We still did not fully understand this signaling network, but, apparently, many coincidences revealed using GWAS are associated with thin equilibrium shifts in a very confusing information web-and this is not what can be found out from the smack.

Ideally, of course, I would like to identify all genetic variations that determine diseases in humans 5 , as well as various signs, such as growth. If we were able to finally understand the genetic factors that determine, for example, whether this person has a heart attack, perhaps we would have new ways to prevent it.

Long before sequencing of the human genome, there was an attempt to find out how the genes determine various diseases and signs. During the notorious dispute, “Nature VS Nurture ) tried to measure the role of“ nature ”in practice. In this area, such a parameter as inheritance is widely used - how much the variability of this attribute in the population is due to genes, not the environment. The name "inheritance" is inspired by the idea that it is a direct measurement of the contribution of "nature" to this equation, but this is not entirely true: in reality, we are talking about variability within the population. For clarity, imagine that you study hair color in two different groups. One group consists exclusively of the Nigerians, the other is a random sample of the Brazilians. All Nigerians have black hair, so there is no variability in the color of the hair that can be measured, and therefore inheritance will be zero. This does not mean that genes do not play roles in determining the color of the hair of the Nigerians - they play very much. At the same time, the Brazilians have all shades - from black to blond. This variability for the most part is explained genetically, so the inheritance will be high.

There are several ways to calculate inheritance. A common and relatively simple method is to compare how the one -eating and multi -tier twins are different. It is assumed that a pair of twins grows in the same environment, up to the conditions of intrauterine development, and that the impact of the external environment on the single -tie and multi -tier twins are the same. Since the one -euthynium twins have all the Genes are common 6 , while there are only half of the common genes on the average of multi -tower, you can expect (usually it happens) that the same -eating twins are more similar to each other than multi -tier, not only externally, but also in such parameters as growth, blood pressure, etc. are quite simple - according to mathematical standards - calculations allow you to measure this The difference within the Gemini group is used to assess the inheritance. The inheritability indicators, regardless of the calculation method, vary from zero (there are no genes to the variability of this population) to one (variability is completely due to genes), and can also be expressed as a percentage.

Assessments of inheritance of various signs are not the same in different studies and for different populations. One of the parameters, the data on which is quite well consistent in a number of studies is growth: in populations where people eat well, growth inheritance is 0.8. Most of the variety of growth is genetically due to. Chinese studies showed lower hereditary growth - about 0.65. This does not cancel the important role of genes, but suggests that the environment in China can play a more significant role than, for example, in the USA. A possible explanation is due to the fact that if the mother was starving, bearing you, and in childhood you also have no time, then you may not reach the growth to which you would have grown under normal conditions. When studying people whose childhood fell on difficult times, there is a stronger influence of the medium, the reducing value of genes.

The use of GWAS became possible in the mid-2000s, and this method began to be widely used around 2007. Very soon, GWAS experts faced a discouraging problem: inheritance was not detected. By 2010, after considerable effort with the help of the GWAS method, only 5 % of the variable of growth was explained, which is very far to 80 %, predicted by the calculations of the inheritability! Over the past ten years, this gap gradually decreased, but at the same time did not go anywhere and received a full explanation. In the most scale at the moment of the growth of growth genetics, the data of almost half a million UK residents were used, who voluntarily provided DNA and detailed personal and medical information in the framework of the Biobank of Great Bilitan project (UK BIOBANK). The group under the leadership of Stephen Xuya (this is another Xu, we will talk about him later) managed to use this storehouse of data to explain 40 % of growth variability - an impressive breakthrough, but we are still far from the exact prediction of the growth of a particular person. The prognostic method they developed made it possible to predict the growth of most people in the group (it included not those who collect the initial data) with an accuracy of several centimeters. It sounds impressive, but the scatter of possible real growth values ​​for each predicted growth value was quite large. Imagine the testimony of a witness in court: “I think that the offender was a growth of 173 cm plus or minus a few centimeters, but perhaps his growth was 158 or 188 cm.”

In addition, Suy and his colleagues convincingly demonstrated that in relation to the growth of people, an increase in the size of the sample or the number of genetic markers under consideration does not increase the accuracy of forecasting. They used this approach as wide as possible. For example, scientists also considered the level of education, which was measured on a six -point scale (the highest point was “the presence of a scientific degree”), and it turned out that they were able to explain only 9 % of the variability of this parameter (however, a larger study has good chances to explain a large share of variability).

So why can even the most complete research on a huge amount of material can explain only part of the desired variability? Two main explanations are put forward on this score. The first assumes that the traditional assessments of the inheritability are significantly overstated: for example, a recent study on the material from Iceland, where genetic data (obtained by full -generation of sequencing) were involved in the calculations of inheritance), revealed much lower values ​​than traditional approaches. For such a sign as growth, a new assessment of the inheritance was not 0.8, but only 0.55. Thus, the Xuya group apparently revealed more than 70 % of what can be found at all. For the body weight index (BMI) 7 values ​​amounted to 0.65 according to the old method and 0.29 in the new one; For the level of education - 0.43 and 0.17, respectively: in both cases, the difference is very significant.

Perhaps even more interesting than the version of the erroneous calculation is an alternative explanation of the lack of inheritance, according to which there are a huge number of non -open genetic factors affecting the signs studied, simply the influence of most of them is so negligible that it is too difficult to get it even in a very large sample of people. This thought is not new - in 2018, it celebrated her century. In 1918, Ronald Fisher, one of the founders of modern statistics, proposed a model of infinitely small quantities (also known as the “infinitezimal model”), according to which variables, such as growth, are controlled by an infinitely large number of genes, each of which has an infinitely small influence on the sign, along, naturally, with the influence of the environment. Fisher did not mean that the genes are actually an infinite set, he proposed only a certain approach to the problem.

1. This catalog (as part of the National Institute of Health of the National Institute of Health) is available at: https://www.ebi.ac.uk/gwas/home.

2. Personally, I do not have such a desire, but I will not judge you strictly if you decide to try.

3. In order to bring together data on such large samples of patients, cooperation of a large number of scientists is often required. Under the article on the inheritance of growth published in the Genetics appendix to Nature magazine in 2014, there were signatures of 445 authors plus mentioning four research groups that were not listed by name. Yes, imagine, I counted.

4. If the ratio is the opposite, this will mean that the option C C is not a risk factor, but on the contrary, protects against a stroke.

5. My book is mainly about people, but the same technology is widely used to other organisms, including agricultural purposes. If you are able to determine the genetic version that increases the integrity of cows, dairy farms will be interested in you. There are many other areas of application of these technologies, from crop production to equestrian sports, and this applies not only to organisms familiar to all. So, for several years I collaborated with Professor Chris Moin from the University of Sydney before he retired. Chris worked in a group that sequenced the genre of crested crocodile, and was engaged in searching for options affecting the growth rate of crocodile cubs, as well as the properties of crocodile skin relevant for manufacturers of this material. Almost every economically significant type of living creatures - from a honey bee to salmon grown on farms - is studied by geneticists in order to find out how to make it more productive and profitable.

6. In theory. In practice, the one -eating twins can have differences in the genetic sequence (which arose after the separation of the embryo: in this case they are usually mosaic) or in some intracellular settings that control the work of the genes. As a rule, the differences are too thin for the difference between the twins to be noticeable, but nevertheless they exist.

7. A measure of the ratio between growth and weight: the value of weight in kilograms should be divided into a height in meters, erected in a square.