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There has recently been something of an uproar over the new book A Troublesome Inheritance by Nicholas Wade, with much of the criticism centering on Wade’s claim that race is a meaningful biological category. This subject is one with which I1 have some personal connection since as a child growing up in South Africa in the 1980s, I was myself categorized very neatly by the Office for Race Classification: 10. A simple pair of digits that conferred on me numerous rights and privileges denied to the majority of the population.

ApartheidPopulationGroups

 Explanation of identity numbers assigned to citizens by the South African government during apartheid.

And yet the system behind those digits was anything but simple. The group to which an individual was assigned could be based on not only their skin color but also their employment, eating and drinking habits, and indeed explicitly social factors as related by Muriel Horrell of the South African Institute of Race Relations: “Should a man who is initially classified white have a number of coloured friends and spend many of his leisure hours in their company, he stands to risk being re-classified as coloured.”

With these memories in mind, I found Wade’s concept of race as a biological category quite confusing, a confusion which only deepened when I discovered that he identifies not the eight races designated by the South African Population Registration Act of 1950, but rather five, none of which was the Griqua! With the full force of modern science on his side2, it seemed unlikely that these disparities represented an error on Wade’s part. And so I was left with a perplexing question: how could it be that the South African apartheid regime — racists par excellence — had failed to institutionalize their racism correctly? How had Wade gotten it right when Hendrik Verwoerd had gone awry?

Eventually I realized that A Troublesome Inheritance itself might contain the answer to this conundrum. Institutions, Wade explains, are genetic: “they grow out of instinctual social behaviors” and “one indication of such a genetic effect is that, if institutions were purely cultural, it should be easy to transfer an institution from one society to another.”3 So perhaps it is Wade’s genetic instincts as a Briton that explain how he has navigated these waters more skillfully than the Dutch-descended Afrikaners who designed the institutions of apartheid.

One might initially be inclined to scoff at such a suggestion or even to find it offensive. However, we must press boldly on in the name of truth and try to explain why this hypothesis might be true. Again, A Troublesome Inheritance comes to our aid. There, Wade discusses the decline in English interest rates between 1400 and 1850. This is the result, we learn, of rich English people producing more children than the poor and thereby genetically propagating those qualities which the rich are so famous for possessing: “less impulsive, more patient, and more willing to save.”4 However this period of time saw not only falling interest rates but also the rise of the British Empire. It was a period when Englishmen not only built steam engines and textile mills, but also trafficked in slaves by the millions and colonized countries whose people lacked their imperial genes. These latter activities, with an obvious appeal to the more racially minded among England’s population, could bring great wealth to those who engaged in them and so perhaps the greater reproductive fitness of England’s economic elite propagated not only patience but a predisposition to racism. This would explain, for example, the ability of John Hanning Speke to sniff out “the best blood of Abyssinia” when distinguishing the Tutsi from their Hutu neighbors.

Some might be tempted to speculate that Wade is himself a racist. While Wade — who freely speculates about billions of human beings — would no doubt support such an activity, those who engage in such speculation should perhaps not judge him too harshly. After all, racism may simply be Wade’s own troublesome inheritance.

Footnotes

1.  In the spirit of authorship designation as discussed in this post, we describe the author contributions as follows: the recollections of South Africa are those of Lior Pachter, who distinctly remembers his classification as “white”. Nicolas Bray conceived and composed the post with input from LP. LP discloses no conflicts of interest. NB discloses being of British ancestry.
2. Perhaps not quite the full force, given the reception his book has received from actual scientists.
3. While this post is satirical, it should be noted for clarity that, improbably, this is an actual quote from Wade’s book.
4. Again, not satire.

Jingyi Jessica Li and Mark D. Biggin

We published a paper titled “System wide analyses have underestimated protein abundances and the importance of transcription in mammals” in PeerJ on Feb 27, 2014 (https://peerj.com/articles/270/). In our paper we use statistical methods to reanalyze the data of several proteomics papers to assess the relative importance that each step in gene expression plays in determining the variance in protein amounts expressed by each gene. Historically transcription was viewed as the dominant step. More recently, though, system wide analyses have claimed that translation plays the dominant role and that differences in mRNA expression between genes explain only 10-40% of the differences in protein levels. We find that when measurement errors in mRNA and protein abundance data is taken into account, transcription again appears to be the dominant step.

Our study was initially motivated by our observation that the system wide label-free mass spectrometry data of 61 housekeeping proteins in Schwanhäusser et al (2011) have lower expression estimates than their corresponding individual protein measurements based on SILAC mass spectrometry or western blot data. The underestimation bias is especially obvious for proteins with expression levels lower than 106 molecules per cell. We therefore corrected this non linear bias to determine how more accurately scaled data impacts the relationship between protein and mRNA abundance data. We found that a two-part spline model fits well on the 61 housing keep protein data and applied this model to correct the system-wide protein abundance estimates in Schwanhäusser et al (2011). After this correction, our corrected protein abundance estimates show a significantly higher correlation with mRNA abundances than do the uncorrected protein data.

We then investigated if other sources of experimental error could further explain the relatively poor correlation between protein and mRNA levels. We employed two strategies that both use Analysis of Variance (ANOVA) to determine the percent of the variation in measured protein expression levels that is due to each of the four steps: transcription, mRNA degradation, translation, and protein degradation, as well as estimating the measurement errors in each step. ANOVA is a classic statistical method developed by RA Fisher in the 1920s. Despite the fact that this is a well-regarded and standard approach in some fields, its usefulness has not been widely appreciated in genomics and proteomics. In our first strategy, we estimated the variances of errors in mRNA and protein abundances using direct experimental measurements provided by control experiments in the Schwanhäusser et al. paper. Plugging these variances into ANOVA, we found that mRNA levels explain at least 56% of the differences in protein abundance for the 4,212 genes detected by Schwänhausser et al (2011). However,  because one major source of error—systematic error of protein measurements—could not be estimated, the true percent contribution of mRNA to protein expression should be higher. We also employed a second, independent strategy to determine the contribution of mRNA levels to protein expression. We show that the variance in translation rates directly measured by ribosome profiling is only 12% of that inferred by Schwanhäusser et al (2011), and that the measured and inferred translation rates correlate poorly. Based on this, our second strategy suggests that mRNA levels explain ∼81% of the variance in protein levels. While the magnitudes of our two estimates vary, they both suggest that transcription plays a more important role than the earlier studies implied and translation a much smaller role.

Finally, we noted that all of the published estimates, as welll as ours given above, only apply to those genes whose mRNA and protein expression was detected. Based on a detailed analysis by Hebenstreit et al. (2012), we estimate that approximately 40% of genes in a given cell within a population express no mRNA. Since there can be no translation in the absence of mRNA, we argue that differences in translation rates can play no role in determining the expression levels for the ∼40% of genes that are non-expressed.

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