Finding your passion

People often talk about "finding your passion". I think that phrasing is a little bit over the top; I would put it in milder terms. One of the goals of going to university is to explore your interests. What are you interested in? What do you like spending your time doing?

Your interests can take the form of academic interests, extracurricular interests, social interests, community interests, and so forth. A college can expose you to a broad array of activities.

You want to deliberate over your interests. Talk about them with your friends and family. You can even prepare a short 1-2 minute "elevator talk" about your interests.

Ultimately as you progress through college and explore a wide variety of interests, then you want to start thinking about monetizing your interests or pursuing monetary ends so that you can further your interests. 

Formulate a plan. A key aspect of formulating a plan is figuring out your summers. As you approach graduation, you want to use the summers (especially after junior year) to test out what might happen after you graduate.

So instead of "finding your passion" I would say explore your interests, and do so in a deliberate and thoughtful manner consulting with family and friends.

Genetic epistasis and dominance are nonlinear effects

In my genetics class, I define epistasis as (1) type of gene interaction, and (2) one gene “masks” the phenotypic effect of the other gene. In classical Mendelian genetics, a single gene determines a single trait. Epistasis moves us closer to real world genetics as an example of gene interaction in which two or more genes "determine" (in classical Mendelian sense) a trait. 

But the second part of the definition is opaque: What does it mean for one gene to mask the phenotypic effect of the other? More clarity is provided by the definition of epistasis in an advanced quantitative or human genetics course (Lynch and Walsh, 1998): "Epistasis describes the nonadditivity of effects between loci (genes)". In a linear model of genotypic effects, you sum the phenotypic contribution of each allele for all the genes that contribute to the phenotype.

Epistasis and dominance represent the deviation (residual error) from this linear model. In other words, the nonlinear (nonadditive) interaction between alleles of a single gene is termed dominance, and the nonlinear interaction between alleles of different genes is epistasis. The "masking" part of the epistasis definition refers to the fact that there are fewer phenotypic classes than genotypic classes. In a linear (additive) model, each genotypic class should give rise to a distinct phenotypic class (i.e. the genetic factors sum up to a different value), but with nonlinear interaction some of the genotypic classes will produce the same phenotype. In an analogous fashion, Mendelian dominance gives rise to fewer phenotypic classes than genotypic classes. For example, the heterozygous genotype Aa and the homozygous gentoype AA exhibit the same dominant phenotype given that the A allele is dominant to the a allele. 

I have observed that this "masking" concept does not make a lot of sense to the students, and they tend to use the terms epistasis and gene interaction interchangeably. This is one example in which the more advanced quantitative definition is better and should be used if the students have been taught the concept of a linear (additive) model, which is essentially fitting a line (i.e. $y = ax + b$) to data points. 

The Lynch and Walsh textbook, Genetics and Analysis of Quantitative Traits, nicely explains dominance and epistasis in Chapters 5 and 6. Starting with the Fundamental Law of Genetics (slightly modified version from previous post), we have that the phenotypic value z equals the genotypic value $G$ (contribution of genetic factors) plus the environmental deviation (residual) $E$: $z = G + E$. $G$ in turn is the sum of the effects of the various relevant alleles on the phenotypic value. For example, my genotypic value for systolic blood pressure may be 110 mmHg, and then an unhealthier than average diet may contribute another 20 mmHg for a measured blood pressure of 130 mmHg.

Say there is a gene B that affects blood pressure and this gene has two alleles $B_1$ and $B_2$. We can propose a linear (additive) model in which each copy of allele $B_1$ contributes $\alpha_1$ to the blood pressure (above or below the mean), and each copy of allele $B_2$ contributes $\alpha_2$ to the blood pressure. Then in this example, the genotypic value $G = \mu_G + \alpha_1 N_1 + \alpha_2 N_2$ in which $\mu_G$ is the mean genotypic value in population, $N_1$ represents the number of $B_1$ alleles and $N_2$ represents the number of $B_2$ alleles in the individual. Focusing for the moment on the $B_2$ allele in Figure 1, one can plot our linear model for $G$ versus $N_2$ (black line with slope $\alpha_2$).

Fig 1. The linear dependence of the genotypic value $G$ on the number of alleles. The linear model (i.e. line) produces the estimate $\hat{G}$ (unfilled circles). Unfortunately life being what it is, the observed phenotypic data $G$ (filled circles) do not reside on the line. The deviation $G - \hat{G}$ is referred to as the dominance $\delta$. This plot just happens to depict Mendelian dominance, but other nonlinear relationships can give rise to dominance effects. Adapted from Figure 4.6 of Lynch and Walsh.

Let’s say that $B_2$ is dominant over $B_1$ in the classical Mendelian sense (and causes higher blood pressure). Then the genotypic values of $B_1 B_2$ (1 allele of $B_2$) and $B_2 B_2$ (2 alleles of $B_2$) should be the same (black dots located at genotypic value of dominant phenotype), whereas $B_1 B_1$ possesses a lower value (recessive phenotype). These data points cannot be fit by a line, and the deviation from the linear model is represented by $\delta$ which is referred to as the dominance term. As a reminder, in Mendelian genetics there is a dominant allele (B) and a recessive allele (b). Both the heterozygote Bb and the homozygote BB express the dominant phenotype. The homozygous recessive bb exhibits the recessive phenotype. 

To repeat myself, the residual error (dominance) $\delta = G - \hat{G}$ is the deviation between the observed genotypic value $G$ and the linear estimate $\hat{G}$. Thus, the full one gene equation is $G = \mu_G + (\alpha_1 N_1 + \alpha_2 N_2 + \delta_{12}$). Classical Mendelian dominance can give rise to this deviation, but really any nonlinear interaction between alleles of the same gene can cause nonadditive "dominance" effects.

Now let’s consider the situation in which two genes (A and B) influence the same trait (i.e. an example of gene interaction). We can write this as $G_{ijkl} = \mu_G + (\alpha_i N_i + \alpha_j N_j + \delta_{ij}) + (\alpha_k N_k + \alpha_l N_l + \delta_{kl})$ in which $i$ and $j$ represent the alleles for gene A and $k$ and $l$ represent the alleles for gene B. The total genotypic value is the sum of the contributions from each gene. Visually, one can picture plotting $G$ on the y-axis and the right-hand side of the equation on the x-axis which will result in a line. 

As before, the observed phenotypic data will most likely not reside on the line, and the deviation between the observed data and the estimated genotypic value is denoted $\epsilon_{ijkl}$ which stands for the epistasis. As defined above, epistasis represents the nonadditivity between the two genes. Thus, our final equation for $G$ in the two gene case is:

$G_{ijkl} = \mu_G + (\alpha_i N_i + \alpha_j N_j + \delta_{ij}) + (\alpha_k N_k + \alpha_l N_l + \delta_{kl}) + \epsilon_{ijkl}$

We have the additive terms ($\alpha$'s), dominance terms ($\delta$'s), and epistasis term ($\epsilon$) with $\mu_G$ being the population mean. The genotypic value is the sum of the effects of gene A in the first parenthesis and gene B in the second parenthesis plus the epistasis, which is not accounted for by adding together the individual gene contributions.

TL;DR: The quantitative genetic models describing phenotypes are fundamentally linear models in which nonlinear effects such as epistasis and dominance are “error” terms, and this formulation may predispose researchers to drop them from further analyses.

The Acolyte was cancelled

Apparently, Season 2 of the outstanding Star Wars series "The Acolyte" was cancelled by Disney. In a previous post, I described why I liked Season 1. 

Was the show too "woke"? And by "woke" I mean trying to appeal to an educated and enlightened audience by moving beyond traditional tropes.

The auteur Leslye Headland conjured up a distinctive take on the Star Wars universe that you may like or not like, but it was not cookie-cutter.

The Fundamental Law of Genetics

In the first lecture of my genetics course, I introduce the following as The Fundamental Law of Genetics: 

Phenotype = Genotype + Environment    (1)

The phenotype (observed traits) of an organism depends on genetic factors and environmental factors. One example of a phenotype is blood pressure. Your blood pressure (and whether or not you have high blood pressure) depends on genetic factors (alleles) you inherit from your parents as well as environmental factors (the world we live in influences traits) such as diet and exercise. 

The concept of genotype is central to genetics and is defined as the identity of alleles for genes in an individual i.e. which “version” (allele) for each gene. The gene is the basic unit of heredity, and at a molecular level, encompasses a region of DNA that is transcribed into RNA and then (typically) translated into protein. The DNA sequence of a gene can be altered by mutation to give rise to different versions or alleles which may possess different functional properties (after being transcribed/translated into protein) that influence the phenotype. Your genotype is basically the specification of what allele you have at each gene, e.g. mutant or wild-type allele and if mutant, which mutation(s). 

Humans are diploid and thus carry two sets of genes, each set contributed by one of two parents. Genes are positioned on chromosomes, each a single molecule of DNA, and we receive one set of 23 chromosomes from Mom and one set of 23 chromosomes from Dad. The human genome contains ~20,000 gene pairs, and the complete genotype of an individual is the identity of alleles at all of these genes.

That is a lot of information to process but if you understand the above (i.e. bold terms) then you are well on your way to understanding genetics including human genetics.

At the end of the course, in the human genetics section, I introduce the quantitative version of The Fundamental Law of Human Genetics:

V_P = V_G + V_E    (2)

The variable V represents variance, and the subscript P is phenotype, G is genotype, and E is environment. In other words, phenotypic variance equals genotypic variance plus environmental variance. Genotypic variance is the variation in phenotype caused by differences in genotype among individuals. V_G = 0 in the case of identical twins who possess identical genotypes. Environmental variance is the variation in phenotype caused by differences in environment among individuals. V_E = 0, when two individuals share the exact same environment such as two plants growing next to each other.

Most traits are continuous, taking on a range of values rather than a few discrete possibilities. For example, we can measure the (systolic) blood pressure in a population, and then plot the distribution of values. Most likely, the result will be a Gaussian (normal) distribution whose parameters are the mean (center) and variance (width) of the bell-shaped curve. For a healthy population, the mean systolic blood pressure may be around 120 mmHg with a standard deviation (square root of variance) of roughly 15 mmHg. Thus in this example, the phenotypic variance is 15 X 15 = 225 (mmHg)². According to eq. (2), we should be able to decompose the blood pressure phenotypic variance into genotypic variance and environmental variance (see Figure below).

Figure 25.4(c) from Genetics: From Genes to Genomes. This figure represents a distribution of plant stem lengths, but for the example, the x-axis is systolic blood pressure, and the y-axis is number of people.

In a thought experiment, for the trait of blood pressure in a large population, I can clone myself and send one clone to live with each member of the population fully sharing the environment (e.g. diet) of that person. I can then measure the blood pressure of the clones (all with identical genotypes) after a period of time, and the phenotypic variance among the clones would be V_E (due to environmental differences). Alternatively, each member of the population could all come and live with me and share my environment. After a period of time we could measure the blood pressure of the population and that would be V_G (due to genotypic differences). According to eq. (2), the phenotypic variance of the original population should equal V_G + V_E.

Of course the thought experiment is not practical, but of even greater importance is that eq. (2) is an approximation of the more complete equation:

V_P = V_G + V_GE + V_E        (3)

where V_GE represents the phenotypic variance from Gene-Environment interaction (GxE). Intuitively, V_GE describes how certain genotypes may have a bigger (or smaller) than expected impact on a trait in certain environmental settings. For example, an allele that promotes increased excretion of excess sodium by the kidneys would have a bigger impact reducing blood pressure for a high-salt diet compared to a low-salt diet. Mathematically, V_GE represents the residual from the simple linear model of additive genetic factors plus additive environmental factors in eq. (2). In other words in the thought experiment, most likely V_P would not equal V_G + V_E because V_GE does not equal 0.

Finally, according to the Lynch and Walsh textbook (Genetics and Analysis of Quantitative Traits), V_GE can be decomposed into a genotype-environment interaction term (as described above) and a genotype-environment covariance term which measures how certain genotypes are physically associated with certain environments, i.e. not randomly distributed across environments. For example, immigrants from India tend to be associated with wealthier environments in America.

My next genetics post will be on the concept of heritability. 

Eligibility for women's Olympic events should be based in part on a genetic test

By all accounts Imane Khelif is a woman, but the fact she is a woman is not sufficient to guarantee participation in women's Olympic events. In my opinion, eligibility should also be based in part on a genetic test, namely whether the athlete is XX or XY.

As you know from high school biology, human biological sex is typically determined by the absence or presence of the Y chromosome.  Females possess 2 X chromosomes (XX) and males possess an X chromosome and a Y chromosome (XY). The Y chromosome contains the SRY gene (Wikipedia) that is a master transcriptional regulator responsible for the development of the testes: "SRY initiates testis differentiation by activating male-specific transcription factors that allow these bipotential cells to differentiate and proliferate." The testes in turn produce testosterone which stimulates protein production in muscle cells leading to increased muscle mass especially in combination with intensive training. This is why doping with anabolic steroids (testosterone analogs) is such a big deal in competitive athletics.

In a simple world, based on the genetic test, anyone who is XY would not be allowed to compete in women's Olympic events. But life is more complicated. For example, one can be XY but phenotypically female if the SRY gene has been inactivated by mutation. 

Imane Khelif and Lin Yu-ting were not allowed to compete in the IBA Women’s World Boxing Championships 2023 based on a genetic test. We do not know the results of this test, and so it is impossible to ascertain whether this ruling was justified. Plus, as mentioned above, there are a lot of subtleties with respect to interpreting the genetic test.

Overall, I view this controversy as being vastly overblown by conservative culture warriors, and a red herring compared to the real problem in sports which has been and still is doping. At the highest levels of athletic competition, a tiny sliver of an advantage can be the difference between winning and losing (e.g. 100M men's sprint final), and doping can literally have an oversized impact (e.g. Mark McGwire, Sammy Sosa, and Barry Bonds) which is why athletes continue to dope.

In summary I would say that gender is not based on a genetic test, but eligibility for women's Olympic events should be based in part on a genetic test.

"PopSci should be one of the cornerstones of the scientific enterprise."

In a perspective piece in Science magazine, Agustin Fuentes exhorts the scientific community to produce "better and more PopSci." PopSci, or popular science, is "science presented in a way that is interesting and understandable to people who are not experts." He lays the onus on scientists but I would say that the purveyors of PopSci do not have to be scientists. They can be any member of the scientific community, which includes those who are not professional scientists but engage in some aspect of science whether teaching, research, communication, or engineering.

Science has not fared well in at least certain segments of popular opinion over the last 10 years or so, which surprised me, although in retrospect I probably shouldn't have been surprised. According to Mr. Fuentes, we find ourselves in "a world shaped by a lot of misguided, and often erroneous, information about science." He identifies the problem as the "strong political movement to discredit science and scientists [which] is having a terrifying level of success." To put a name on it, the anti-science movement has been spearheaded by the GOP and other conservative groups. Examples abound from the anti-vaxxers to global warming denialism to the genetic racists. 

Let's be clear, the enemy of science is not the lack of understanding, it is the loss of objectivity. Too often people engage in motivated "reasoning" where the "reasoning" is secondary to supporting some motivated position. The ideal of science is objective reasoning and anyone can do this and be part of the scientific enterprise. Individual scientists are expert in a tiny, infinitesimal sliver of human knowledge, but they are trained to engage in objective reasoning based on factual evidence, and so too are numerous other professions.

I would like to see the scientific community broadened as much as possible and PopSci can contribute significantly to this accessibility.

Agustin closes with the following figurative call to arms:

"With political and legal actors increasingly downplaying and sidelining scientific knowledge, now is not the time for science to be quiet or compliant. It is the time for scientists to speak out, to communicate and participate with and among the public. The alternative is to let the anti-science organizations and movements warp reality and damage society and the planet by injecting more ignorance, antipathy, and malice into public perceptions and actions around issues of scientific relevance. Science must have a larger impact outside of labs, journal pages, and the research industry. For the future of science and society, we need better and more PopSci by scientists."

As mentioned above, I would just slightly modify the final sentence to read: "For the future of science and society, we need better and more PopSci by members of the scientific community which of course includes those who are not professional scientists."

Coaching

 I was watching a coach teach his soccer team. I thought he did three things well:

1. Offer lots of encouragement
2. Give very specific instructions
3. Provide rationale for the instructions

One thing he didn't do so well:

1. Ask for questions

Also at end, it would help to review and fit the lessons in the big picture view of what the team is trying to accomplish.