Genetic Clusters, Racial Medicine and Fishes
Humans are pattern-seeking animals are are thus prone to detect patterns where none exists. We are also very interested in categorizing things, presumably because it is easier to handle cognitively. Imagine the difficulty we would have if we had to mentally treat each leaf as a separate entity and could not consider them “just a bunch of leaves”! But there is a downside to this as well, because we can be mislead and neglect complicated patterns because our categories are easy and psychological influential. These issues and questions often appear in discussions about human genetic diversity. This is enhanced by the fact that complicated genetic and computational analyses feeds us with visually striking graphs that tickle our imagination, while we do not pay equal attention to the underlying methodology.
However, reality is more complicated. Genetic clusters overemphasize differences, largely ignore similarities and is confounded by low sampling density and geographic distance. Thus, a modern analysis of human genetic variation reveals that it is, with a few exceptions, mostly clinal in nature and that notions of discrete genetic races is not an accurate description.
It is often said that ethnicity is useful in medicine, but this is also more complicated due to confounders such as health disparities, bias, discrimination, healthcare seeking behavior and compliance, as well as socioeconomic status. It turns out that ethnic status is at best a crude proxy for the alleles of a person and sequencing individuals will be much more useful. Finally, a focus on racial medicine has led to misdiagnosis of some diseases, such as sickle-cell anemia, thalassemia and cystic fibrosis.
Recently, Steven Novella wrote a mostly balanced discussion of the issue of human genetic variation and its connections to genetic research and medicine. Novella gets most things right, but the discussion of genetic clustering and racial medicine lacks vital details that show that the evidence either is inconsistent with discrete genetic races or not as supportive as once thought. This led him astray to the faulty conclusion that modern science supports the existence of discrete genetic races.
Are there genetic differences between humans and populations can they sometimes be useful in medicine? Yes, but those genetic differences are not accurately described by discrete genetic races and there are medical downsides and limitations to racial medicine that we also need to consider.
How does genetic clustering work?
To understand why genetic clustering does not support the notion of discrete human races, we need to understand how genetic clustering works and how we can be led astray by not fully appreciating these details.
Genetic clustering works in two steps. The first step involves calculating pairwise distance measures between your samples. This tells you how different any two samples are from each other in your data set. The second step is called (hierarchical) clustering, whereby the samples are put into clusters based on the distance measures. There are other clustering methods besides hierarchical clustering, there are many different ways to calculate distance and there are many different ways to do hierarchical clustering.
In this case, the most important step to understand is the calculation of the distance measure. Two very common methods to do this Euclidean distance and Pearson correlation. To put it simply, you can think of two samples as two points in a graph and the distance measure as the distance between those points. They key take-home message is that this calculation strongly emphasizes differences to the expense of identities or similarities. Large differences are given high distance values, high similarities are given low distance values and identities are given a zero contribution to the genetic distance.
A primer on genetic clustering can be found in D’haeseleer (2005). Although it uses gene expression data as an example, the general principles are universal regardless of what data you are clustering.
Why genetic clustering does not support discrete genetic races
So when you are looking at genetic clusters, you are only looking at differences while similarities have been greatly downplayed and identities have been ignored. This is exactly what we want when we look at gene expression differences between samples in response to some environmental challenge or drug. But if we use it to make claims about the genetic structure of populations, there is a substantial bias for difference built into this method.
This is particularly dangerous when it comes to species where the genetic variation is very low. In humans, for instance, the genetic difference between individuals is very low. If we look at single nucleotide polymorphisms (SNPs), that is, single base variations, the average difference between two individuals is 0.1% (National Human Genome Research Institute, 2016). So on average, 999 out of 1000 SNPs are identical between individuals, and 1 out of 1000 SNPs differ. Genetic clustering would ignore 99.9% of the dataset and focus on the remaining 0.1%. This becomes even more pernicious when modern high-throughput that looks at ~650 000 SNPs and ~400 microsatellites has found that the vast majority of genetic variation (84.7%-95% depending on the study and genetic element) occurs between individuals and within continental groups (Li et al., 2008; Rosenberg et al., 2002).
The other key issue is that genetic clustering analyses on humans typically have a low sampling density and if we are not careful, we can confuse geographical distance for genetic differences. For instance, if we, for instance, sequence a dozen people from Sweden, a dozen people in Somalia and a dozen people in India and do a clustering analysis on that data, we will get clusters. But if we had also sequenced people living in-between these areas in high-resolution, we would see that human genetic variation is mostly continuous and clinal and the illusion of discrete genetic clusters disappear. Here is Serre and Pääbo (2004):
Thus, the evidence does not support the notion of discrete human races and we should think critically about the sampling strategy and computational tools that are used and crucially consider what kind of conclusions we can draw from what data. Humans are pattern-seeking animals, and it is easy to read genetic clusters as discrete genetic races, but the reality is very different.
Although there are some debate between researchers about the details (such as should this or that model for allele correlations be used), even the critics of Serre and Pääbo, namely Rosenberg and colleagues (2005), admit that genetic clusters do not correspond to biological races:
It should be noted that proponents of the idea that genetic clusters demonstrate discrete genetic races typically cite the original Rosenberg et al. (2012) paper that Serre and Pääbo objected to. So even the researchers who did the research disagrees with the race interpretation.
Rosenberg and colleagues (2005) decides to take a more pragmatic approach the genetic clusters. They might not be objectively real and merely an artifact of geographical distance and the methodology of clustering, but could racial classifications still be useful in e. g. medicine? It turns out that this issue is much more complicated than it first seems.
But before we examine that issue, we will take a short detour into fish genetics and evolution.
Something smells fishy…
Novella writes that:
In the exact same way, there is much more genetic variation within Africans, then between Africans an all other human populations. This simply reflects the fact that humans lived in Africa for a long time, evolving extensive genetic diversity, and the population that migrated out of African represents a tiny twig on the African genetic tree. We are all Africans in the exact same way that we are all fish.
Here we should be very careful with phylogenetics. Fishes are not a clade, but a paraphyletic group that excludes a lot of descendants (amphibians, avian and non-avian reptiles, mammals etc.) This means that fishes are just a form group where different organisms have been lumped together because of similarities in appearance and way of life rather than evolutionary history. Thus, fishes has an artificially inflated genetic diversity. It is also a poor analogy because of the high genetic diversity in chordates (since it is a phylum) and the low genetic diversity in humans (because our species recently had a genetic bottleneck ~50k years ago). It should also be pointed out that there is probably more genetic difference within any continent than between continents, not just in Africa.
The core idea behind the fact that most genetic variation occurs within continental groups than between them is that if you want to accurately describe human genetic diversity, you would not typically emphasize the 5-15% of genetic diversity that occur between continental groups, while downplaying the other 85-95% that can be found elsewhere. You would rather say that human genetic variation is largely clinal.
Another common objection is that the ends of a gradient are different even if the changes when moving across the gradient is mostly continuous. While this is true, the rejection of discrete races does not assume that all humans or populations are genetically identical and disproving the latter does not disprove the former. The key idea is that human genetic variation is best represented by mostly clinal variation, not discrete races. There are certainly genetic differences between people, but those differences look nothing like most proponents of discrete races think they do.
The twilight of racial medicine
At this point, some people might concede that discrete genetic races is not an accurate view of human genetic variation, but still suggest that the idea has so much pragmatic merit in e. g. medicine that it can still be justified. After all, is it not the case that differences in mortality between whites and African-Americans account for 260 African-Americans dying prematurely every day on average? Are there not diseases that affect different continental groups? Do they not metabolize medication differently? Are there not gene variations with implications for medicine that are more common in some groups that others?
This turns out to be much more complicated than it first seems for a number of different factors.
First, a big chunk of observed differences can be attributed to disparities in health care, unconscious biases by providers, discrimination and differences in income, education and unemployment. Thus, when we compare raw data from individuals from different ethnic backgrounds, we are not comparing apples with apples, but apples and oranges. Research has found that socioeconomic status is a more important factor than ethnic group for variation in health and health care disparities and discrimination has been linked to health outcomes such as hypertension, all-cause mortality, incidence of asthma, poor mental health, inflammation, coronary artery calcification, obesity, cortisol dysregulation, poor sleep, smoking and other substance use etc. and also with less healthcare seeking and compliance behavior (Williams and Wyatt, 2015). This is, by the way, probably a good reason to stratify medical studies by ethnic background.
Second, some differences are a result of geography of pathogens, rather than continental group per se. While it is true that sickle-cell anemia is more prevalent in Africa, this is mostly the case for regions where malaria is prevalent. If we are not careful with geographical confounders and similar issues (most studies use self-identified ethnic group rather than genetic data), we might detect spurious associations (Weiss and Fullerton, 2005)
Third, differences in allele frequencies are modest most of the time and only a few of them have been found to be related to differential response to medical treatments. For instance, the difference in allele frequency for the nitric oxide synthase G89T4 SNP that is involved in arterial stiffness only differs by ~20 percentage points between African-Americans and whites (Chen, 2004). In this particular case, it means that a special medication for African-Americans will not benefit most of them, and will benefit a minority of whites too.
Fourth, since ethnic group is a very crude proxy of which allele you have, doctors might as well just sequence the version their patient has. This would be much more useful.
Fifth, a lot of the racial medicine discoveries have been either refuted or shown not to be as relevant as first thought. For instance, it was once thought that angiotensin-converting enzyme (ACE) inhibitors were less effective in African-Americans for blood pressure control, before a better study showed that it was effective regardless of ethnic group (Saunders and Gavin, 2003). The medication BiDil has been shown to have a better result for African-Americans with heart failure than a competitor, but the main study did not included people from other groups (Brody and Hunt, 2006), thus making the idea that it is a racial medicine doubtful.
Sixth, racial medicine can lead to misdiagnoses of diseases such as sickle-cell anemia, thalassemia and cystic fibrosis. The latter disease appears to be underdiagnosed in Africa because it is considered a disease that white people have (Yudell et al., 2016). Thus, there are not just pragmatic benefits with racial medicine for disease diagnosis, but also downsides.
In the end, it is not possible to show that ethnic background is completely irrelevant in medicine. But we should keep in mind that it is (1) confounded by socioeconomic status and health care disparities, biases and discrimination, (2) confounded by geographical and methodological considerations, (3) allele frequency differences are most often only modest, (4) self-identified ethnic group is a crude proxy for which allele you have, so if we focus on usefulness, we might as well just sequence the individual, (5) some of the icons of racial medicine have been refuted, (6) racial medicine also have downsides when diagnosing diseases that many people consider strongly associated with ethnicity.
Thus, the utility of ethnic background in medicine is probably non-zero, but it is much cruder than commonly believed and not necessarily connected or relevant to notions of discrete genetic races.
References and further reading
Brody, H., & Hunt, L. M. (2006). BiDil: Assessing a Race-Based Pharmaceutical. The Annals of Family Medicine, 4(6), 556-560.
Chen, W., Srinivasan, S. R., Bond, M. G., Tang, R., Urbina, E. M., Li, S., . . . Berenson, G. S. (2004). Nitric oxide synthase gene polymorphism (G894T) influences arterial stiffness in adults. Am J Hypertens, 17(7), 553-559.
D’Haeseleer, P. (2005). How does gene expression clustering work? Nat Biotech, 23(12), 1499-1501.
Li, J. Z., Absher, D. M., Tang, H., Southwick, A. M., Casto, A. M., Ramachandran, S., . . . Myers, R. M. (2008). Worldwide Human Relationships Inferred from Genome-Wide Patterns of Variation. Science, 319(5866), 1100-1104.
National Human Genome Research Institute. (2016). Frequently Asked Questions About Genetic and Genomic Science. Accessed: 2016-07-23.
Rosenberg, N. A., Pritchard, J. K., Weber, J. L., Cann, H. M., Kidd, K. K., Zhivotovsky, L. A., & Feldman, M. W. (2002). Genetic Structure of Human Populations. Science, 298(5602), 2381-2385.
Rosenberg, N. A., Mahajan, S., Ramachandran, S., Zhao, C., Pritchard, J. K., & Feldman, M. W. (2005). Clines, Clusters, and the Effect of Study Design on the Inference of Human Population Structure. PLoS Genet, 1(6), e70. doi:10.1371/journal.pgen.0010070
Saunders E., Gavin J.R. (2003). Blockade of the renin-angiotensin system in African Americans with hypertension and cardiovascular disease. J Clin Hypertens. 5. 12-7.
Serre, D., & Pääbo, S. (2004). Evidence for Gradients of Human Genetic Diversity Within and Among Continents. Genome Research, 14(9), 1679-1685.
Weiss, K. M., & Fullerton, S. M. (2005). Racing around, getting nowhere. Evolutionary Anthropology: Issues, News, and Reviews, 14(5), 165-169.
Williams D.R., Wyatt R. (2015). Racial Bias in Health Care and Health: Challenges and Opportunities. JAMA. 314(6):555-556.
Yudell, M., Roberts, D., DeSalle, R., & Tishkoff, S. (2016). Taking race out of human genetics. Science, 351(6273), 564-565.
13 thoughts on “Genetic Clusters, Racial Medicine and Fishes”
I have previously referenced an excellent post called Continuous geographic structure is real, “discrete races” aren’t by Nick Matzke over at The Panda’s Thumb. This has more details on clustering and why arguments for the existence of discrete genetic races based on genetic clustering does not work. The references alone makes it worth reading, but it lays out the evidence in a very pedagogic way.
A commenter who seemed very concerned that “the white race” was disappearing or being actively destroyed brought up the concept of haplogroups. However, haplogroups, like genetic clusters, are not “biological races”. This is because haplogroups are based on only the Y chromosome or mitochondrial DNA, which are inherited from father to son or mother to offspring, respectively. This means:
(1) it only traces the ancestry of one of your ancestors, while ignoring all the other hundreds of thousands of ancestors. This is great for tracing migration (because it ignores being having sex that would distort migration patterns), but extremely unsuitable for looking at global patterns of genetic variation (for the very same reason, since it ignores the fact that humans reproduce sexually).
(2) it only looks at a tiny part of the genome. The nuclear (haploid) genome is 3.2 billion base pairs. In contrast, the mtDNA is only about 17 000 base pairs and the Y chromosome is 58 million. So this analysis ignores between 99.9995% to 98.2% of the genome. Any analysis on global patterns on human genetic variation that ignored that much of a dataset is quite limited.
In the end, it boils down to the human tendency to detect patterns. Despite the fact that genetic clusters and haplogroups have no relation to any “biological races”, race realists see what they want to see and then use mischaracterizations of real science to try and back up that nonsense.
This further emphasizes the general conclusion that race realism is pseudoscience. Debunking Denialism has extensive material on the scientific failures of race realism.
The same person returned to deploy the classic “why did white people invent everything” fallacy. Apparently he forgot to consider the Egyptians, Babylonian and Chinese in history, as well as modern day Japan. The general answer is that most high-tech inventions typically require resources and a society that has the necessary systems to promote it and which areas of the world this is true for has changed over time due to a long list of different factors. I address this in additional detail in Mailbag: Modern High-Throughput Genomics Versus Race Realism.
The next claim delivered was “How come we are the richest and they’re poor? Why do they have to come here if there own race and country is so great?”, suggesting that his individual should learn more about history, geography and different systems of government.
Perhaps the most hilarious claim was that “If we had black propel genes, we would be living in mud huts.”, which confuses “genes” with “gene variants”. There are no “black people genes” as almost all humans, barring individuals with loss-of-function mutations, have the same genes. Also, continents only account for a small fraction of the global differences in gene variant frequencies. Furthermore, the mainstream science model of out-of-Africa model for human migration out of Africa shows that white Europeans do, in fact, have gene variants that arose in Africa. No amount of mischaracterization of the science disproves this.
Finally, the commenter suggested that I consult a particular forum user on Stormfront, the largest Internet forum for white supremacists in the world. This suggests that it could be useful for this individual to think about what counts as evidence. Should you really trust everything people tell you on the Internet, or should you consult the original sources?
The same commenter returns, daring me to refute this or that race realist or white supremacist. However, this falls a bit flat since I have already refuted over 100 separate claims by race realists on Debunking Denialism with dozens and dozens of references to the scientific literature. Here are the posts listed at the article library:
Debunking Race Realism and Racism
I have already fulfilled the challenge. Several times over.
I usually use your articles as a resource for rebuttals against the far right wingers I encounter online on some political forums I use. You must know that these people have a disposition for infographs. Now, one of them posted this: https://postimg.org/image/53j6mnyft/.
If you do not want to publish this comment because of the unsavoury content of the infograph, then that’s fine. However, the part that shocked me was the studies on genes and the correlations with the frequency in each respective “races” genome. I would appreciate if at the least, you can debunk those studies (or rather the conclusions drawn) in a post, and if not in a comment.
All of those “arguments” have already been refuted before on the website. In fact, many of these are even addressed in this post, so your comment risks looking like you did not read the post you are commenting on. Regardless, let’s take this blog meme for a spin.
Genetic clustering: race realists abuse clustering algorithms and try to make it appear as if geographical distance is really population structure. It isn’t. The global pattern of human genetic variation is mostly clinal and thus fit extremely poorly with traditional racial categories. Read more in here and here and (references therein).
Self-identification and genetic profiles: modern high-throughput genetic methods are so powerful that they can detect that tiny portion of total human genetic variation that can be attributed to continental group. This, however, does not mean that this is large, important or tell us anything about the global pattern of human genetic variation. Since the race realist conception of “race” looks nothing like the global pattern of human genetic variation, their notion of “race” is a “social construct” (not matching reality). I discussed this in some detail in the comment section of the post by Novella being criticized in this post.
Recent positive selection: most genes that have undergone recent positive selection relates to the immune system, insulin regulation, ethanol metabolism and superficial traits like hair and skin. This was discussed in additional detail here. Race realists cannot realistically deny this, so they instead abuse GO ontology and statistics.
The function of all genes in humans are not completely known (and it is not ethical to do human knockouts), so researchers attempt to infer function by sequence similarity from other species. This is often useful, but has severe limitations. For something to be mapped with a given GO category, all that is required is sequence similarity or that it is somehow related to that function in another species. But these predictions can often be mistaken (and becomes more mistaken as the evolutionary distance between humans and the species it was annotated using) and genes that are related to one function in one species (such as fish or mice) can have another function in humans.
Furthermore, it is also invalid to compare GO terms in the way the blog meme does. This is because GO terms are hierarchical with more general terms being “parents” and more specialized terms being “child” terms. So, for instance, one path in the hierarchy might look like this: biological process > pigmentation > pigmentation during development > regulation of pigmentation during development > positive regulation of pigmentation during development > positive regulation of eye pigmentation. By comparing “pigmentation” (a GO term very high up in the GO hierarchy) with highly specialized GO terms such as “dorsoventral neural tube pattering”, they are comparing apples and oranges. This is because it is more difficult to get a signal from larger categories (since you are combining the specialized GO terms that have the signal with lots and lots of GO terms that have no signal of differential regulation). This why, if you look at the GO bar chart, the top GO terms are often more specialized ones, whereas the more general ones are at the bottom. Since they are on different levels, you cannot actually make the comparison that the blog meme does.
It also appears that the race realists have cropped the image without telling us, since the original paper shows a much, much larger graph that includes most of the factors that I have mentioned above. Thus, this paper actually contradicts the race realist claim that it is nervous system development that accounts for a “big deal of the variation”. This turn out to be false as GO terms that are somehow related to nervous system development are less than a handful of over 70 enriched GO terms. This also has to be put into the context that we already know that there is a tiny genetic variation between continental groups, so when race realists claim that nervous system development that accounts for a “big deal of the variation”, they are only talking about fractions of an already tiny difference. Finally, this kind of study falls prey to the same problem as genetic clustering since it confounds geographical distance with population structure.
Most genetic variation within groups here the race realists continue to straw man the mainstream scientific position. The fact that there is more genetic variation within continental groups than between is not based on Lewontin’s data from the 70s. It is based on modern high-throughput data on 650 000 SNPs and hundreds of STRs. This is discussed in more detail in Fetishizing Richard Lewontin.
IQ socio-economic status is one of the confounders, but there are many more. When you account for these, the IQ gap diminishes. Furthermore, research has shown that the raw IQ gap is narrowing over time and there has been similar IQ gaps between other groups that can be explained by environment and that has largely closed. This is discussed in additional detail in Nisbett et al. (2012). Basically, race realists wants us to take raw correlational data and blindly believe it without taking a serious look at confounders. This is highly ironic since they complain when radical feminists want to do the same with the gender wage gap. Then they are all about confounders. The adoption study they cite is flawed because the “black” group was adopted systematically later (and thus had more influence from their family of origin) and later study that corrected for this found no difference between the two non-white groups. From the review: “Children (both Black and mixed-race) adopted by White families had IQs 13 points higher on average than those adopted by Black families, indicating that there were marked differences in the environments of Black and White families relevant to socialization for IQ; indeed, the differences were large enough to account for virtually the entire Black–White gap in IQ at the time of the study”.
“Subspecies” subspecies are based on arbitrary criteria, typically superficial appearance or arbitrary cut-offs of genetic measures. There is no objective criteria (whatever species you use) like the biological species concept.
Furthermore, it seems that race realists do not read the papers they cite again (a common tendency). The paper in question shows that taking into account more populations sharply reduces global genetic differentiation in humans by almost half:
Oops. The blog meme uses a PCA graph here and that has the same limitation as discussed earlier with geographical distance. In fact, as we save in the above quote, the authors are highly aware of this fact.
Genetic markers linked to IQ: in many complex traits (such as intelligence), it is influence by a large number of SNPs in a large number of genes where each single SNP only account for a tiny portion of IQ heritability and summing over these cannot match the heritability as estimated by twin studies (“missing heritability”). Thus, it is of almost no consequence that some SNPs are more common in some populations than others because it is only a tiny, tiny part of the puzzle and can only account for tiny portions of between-individual differences in IQ. A lot of these findings are also correlational in nature without much experimental support.
This also highlights another case where race realists do not read their own references. The blog meme cites a paper on tau that has been linked to neurodegenerative diseases and speculates on its origins. This has nothing to do with the issues discussed here and it even goes on to talk about the quantum quackery of Roger Penrose where he believes that consciousness is a result of quantum processes in microtubules. But these microtubules look the same in the toe as they do in the brain and are extremely similar between humans, and say, mice. Thus, it is an example of the “old physicist talking nonsense about a field he has no knowledge about”. In an effort to promote their “race” pseudoscience, race realists promote quantum woo about consciousness. Hilarious.
European admixture: so this section presents a graph on the background demographics of participants in a type-2 diabetes study. The study concluded that “genetic ancestry has a significant association with type 2 diabetes above and beyond its association with non-genetic risk factors for type 2 diabetes in African Americans, but no single gene with a major effect is sufficient to explain a large portion of the observed population difference in risk of diabetes.”
Race realists wants to turn this around and say that the small association between education and African Ancestry Median in background demographics demonstrates their position, but that is confounded by type-2 diabetes prevalence. Indeed, the study itself points out that “Diabetic participants tended to have higher BMI, lower education level and lower family income, compared to non-diabetic participants “. Indeed, the supplementary table 1 shows that type-2 diabetes has a massive impact on education than African Ancestry Median. What race realists think is an association between European admixture and educational attainment is really an association between educational attainment and type-2 diabetes (the latter being associated with genetic ancestry).
Race realists again tries to fool us with unadjusted data (slopping taken from background demographics in a completely unrelated paper on type-2 diabetes), hoping that no one actually takes the time to read the paper. Scratch that, they probably did not even read the paper themselves and just copy/pasted it from some other race realist source without applying any critical thinking or skepticism because it superficially seemed to agree with their position.
The lack of association between and European admixture is further discussed in the Nisbett review cited above.
It is troubling that we live in a society where people are more likely to believe shitty blog memes they found in their Facebook filter bubble because it appeals to their own ideology than actually taking the time to read and understanding the science. This might just be a recipe for disaster.
Thank you, I did not believe the info graph but I’m not scientifically literate enough to pick it apart. I appreciate your response, and will use it as a reference.
Could you take a look at this paper on Microcephalic?
That is not a paper, it is a 12 year old blog post.
Intelligence and other cognitive abilities are complex traits. That means that many genes and many environmental factors affect it and each factor only account for a tiny portion of within-group differences.
Anyone who want to point to this or that genetic variant in explaining differences in some cognitive ability within or between populations must show that it explains a considerable portion of the observed variation.
It is not enough, as the blog post author does, to say that there is some gene that may or may affect cognitive ability (outside microcephaly) that occur in different frequencies in different populations. That is known as “sizeless science” and tells us nothing about how importance.
Some mutations in those genes cause microcephaly, but there is really no good evidence that variations in healthy individuals have any affect on cognitive abilities. Not that the second paper is written by lead author J. P. Rusthon, an arch “race realist”. So at this point, “race realists” do not even believe the claim. This paper was published two years after the blog post.
Pretty much all of these “race realist” arguments have been debunked on this website or other websites before. Search and you will find.
What about this paper that found there was a correlation between cortical surface area and microcephaly depending on which sex had which variant? https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2806758/
According to the paper, only two of the 18 SNPs (11%) that they found in their exploratory analysis (table 1) were replicated in their validation set and those were from CDK5RAP2 (not microcephalin!).
Both of these SNPs (rs914592 and rs2297453) are located in nonexonic regions, which mean that they do not change the structure of the resulting protein. This, together with the fact that the main difference is sex-specific while they did not control for obvious confounders such as height (bigger bodies = bigger brain) makes their core claims a bit shaky.
A majority (64%) of their dataset consists of individuals with “severe mental disorders” and they do not report the effect sizes for these association between this and their SNPs, only that it was not statistically significant (but that is sizeless science again). This mean that we do not know how large the impact of this feature of their dataset. The paper also did not mention what proportion of the total variation in cortical area can be explained by these SNPs.
Although we can keep doing this back and forth thing for a long time (you citing a post or paper you found cited on some “race realist” blog or Youtube video and I explaining why the argument doesn’t work), I suggest directing further questions to places like AskScience. That way you can probably get answers faster and with more details from people who have a better command of the literature on a given topic. Although I can spot obvious problems, I am certainly not an expert on all aspects of the questions that come up when dealing with “race realists” because the issues they discuss are so broad and involve everything from molecular biology to economy.
It’s a bit offtopic but I was wondering if you could take an article on gender wage gap from bloomberg (“new gender wage pay gap studies are challenging conventional wisdom”) and not exactly debunk it but shed some light on what kind of questions you ask when you approach this article critically?
(I’ve chosen that article because I used to uncritically buy into wage gape because evil misogyny but, even though I’m now skeptical, I have zero knowledge about economy so it’s a bit harder to unearth relevant questions from under the mountain of wishful thinking and thinking habits. Sorry, I’m stupid.)
The gender wage gap is an extremely socially controversial topic with a ton of ink being spilled debating it. I am reasonably well-read on the subject but if I were to write a post about it I would seriously piss off both extreme radical feminists and anti-feminists. Not sure if it is worth it.
However, I will give you the one-paragraph summary. This neglects a lot of exciting details and oversimplifies the situation a bit, but should be a decent primer:
Many extreme radical feminists are not too keen to take confounders into account, but many anti-feminists falsely believe that it is all due to confounders. In reality, both adjusted and unadjusted wage gaps tell us important (but different) things about economic inequality and none of them is the “one true measurement” on income inequality. There is also a ton of cherry-picking going on, where some extreme radical feminists pick the largest estimates they can find regardless of other issues and some extreme anti-feminists pick smallest estimates they can find regardless of other issues. None of these extremists actually take the time to understand the strength and weaknesses of different measures. Very tiresome.
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